TYPE 1 DIABETES AND OBESITY IN CHILDREN
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TYPE 1 DIABETES AND OBESITY IN CHILDREN Focus on inflammation Annemarie Verrijn Stuart
Transcript of TYPE 1 DIABETES AND OBESITY IN CHILDREN
TYPE 1 DIABETES AND OBESITY IN CHILDRENFocus on inflammation
Annemarie Verrijn Stuart
Cover Mariska Bijvanck, www.artidrops.nlPhotography Annemarie Verrijn StuartLayout Renate Siebes, Proefschrift.nuPrinted by Ipskamp Drukkers B.V.ISBN 978-90-393-6058-3
© 2013 Annemarie Verrijn StuartAll rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, photocopying, or otherwise, without the permission of the author, or, when appropriate, of the publishers of the publications.
Address for correspondenceA.A. Verrijn StuartDepartment of Paediatric Endocrinology, KC 03.063.0Wilhelmina Children’s HospitalPO Box 850903508 AB UtrechtThe Netherlands
Type 1 diabetes and obesity in childrenFocus on inflammation
Type 1 diabetes en obesitas in kinderen De rol van inflammatie
(met een samenvatting in het Nederlands)
Proefschrift
ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector magnificus,
prof.dr. G.J. van der Zwaan, ingevolge het besluit van het college voor promoties in het openbaar te verdedigen
op woensdag 4 december 2013 des middags te 12.45 uur
door
Annemarie Agatha Verrijn Stuart
geboren op 20 februari 1970te Naarden
Promotoren: Prof.dr. A.B.J. Prakken Prof.dr. B.O. Roep
Co-promotoren: Dr. W. de Jager Dr. H.S. Schipper
Voor mijn oudersVoor Coen
CONTENTSPart I Introduction
List of abbreviations 11
Chapter 1 Introduction 17
Part II Specific aspects of immune tolerance in type 1 diabetes
Chapter 2 Recognition of heat-shock protein 60 epitopes in children with type 1 diabetes
Diabetes / Metabolism Research and Reviews 2012
65
Chapter 3 CD8 T cell autoreactivity to preproinsulin epitopes with very low human
leucocyte antigen class I binding affinity
Clinical and Experimental Immunology 2012
85
Part III Adipose tissue inflammation & adipokines in type 1 diabetes and obesity
Chapter 4 Altered plasma adipokine levels and in vitro adipocyte differentiation in
paediatric type 1 diabetes
J Clin Endocrinol Metab 2012
105
Chapter 5 Vitamin D deficiency in childhood obesity is associated with high levels of
circulating inflammatory mediators, and low insulin sensitivity
International Journal of Obesity 2013
133
Part IV Inflammatory mediators in pancreatic islet cell transplantation in type 1 diabetes
Chapter 6 Serum cytokines as biomarkers in islet cell transplantation for type 1 diabetes;
a pilot study
157
Part V Discussion and summary
Chapter 7 Discussion 181
Chapter 8 Summary 221
Part VI Addendum
Samenvatting voor niet-ingewijden 227
About the author 233
Introduction
PART I
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Chapter 1
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List of abbreviations
*
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List of abbreviations
*1,25-(OH)2D3 1,25-dihydroxyvitamin D3APC Antigen presenting cellAT Adipose tissueBDNF Brain-derived neurotropic factorBM Basement membraneBMI Body-mass indexBMI-SD Body-mass index standard deviation for age and sexCCL Chemokine (C-C motif) ligandCCL1 I-309CCL2 MCP-1; Monocyte chemoattractant protein-1CCL3 Mip-1α; Macrophage inflammatory protein 1 alpha CCL4 Mip-1β; Macrophage inflammatory protein 1 betaCCL5 RANTES; Regulated on activation, normal T cell expressed and secretedCCL7 MCP-3; Monocyte chemoattractant protein-3CCL11 EotaxinCCL17 TARC; Thymus and Activation Regulated ChemokineCCL18 PARC; Pulmonary and activation-regulated chemokineCCL19 Mip-3β; Macrophage inflammatoryprotein 3betaCCL22 MDC, Human macrophage-derived chemokineCCL27 C-TACK; Cutaneous T-cell attracting chemokineC(X)CR Chemokine (C-(X)-C motif) receptorCXCL Chemokine (C-X-C motif) ligandCXCL5 ENA-78; Epithelial-derived neutrophil-activating peptide 78CXCL8 Interleukin 8CXCL9 MIG; Monokine induced by gamma interferonCXCL10 IP10; Interferon gamma- induced protein 10CXCL13 BLC; B-lymphocyte chemoattractantCXCL8 Interleukin 8 CRP C-reactive proteinDC Dendritic cellECM Extracellular matrixEGF Epidermal growth factorEN-RAGE Extracellular newly indentified RAGE binding proteinFA Fatty acidFABP-4 Fatty acid binding protein 4FAS Fas receptor; Apoptosis antigen 1 (APO-1; APT); CD95; TNFRSF6
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List of abbreviations
*FAS-L Fas Ligand; CD95LGAD Glutamic acid decarboxylaseG-CSF Granulocyte colony-stimulating factorGM-CSF Granulocyte-macrophage colony-stimulating factorHbA1c Glycosylated hemoglobinHC Healthy controlHGF Hepatocyte growth factorHLA Human leucocyte antigenHOMA-IR Homeostasis model assessment of insulin resistance hsCRP High-sensitivity C-reactive proteinHSP Heat shock proteinIDAA1c Insulin-adjusted Hba1c; HbA1c (%) + (4 x insulin dose (U/kg/d)IFN-γ Interferron gammaIGF-1 Insulin-like growth factor 1IL InterleukinIL-1RII IL-1 Receptor IIIL-1RA IL-1 receptor antagonistIL18-BPa IL-18 binding protein a (IFN-gamma inducing factor binding protein) IMT Intima-media thicknessJIA Juvenile idiopathic arthritisKIM-1 Kidney injury molecule-1LIF Leukaemia inhibitory factor LLOD Lower limit of detectionLLOQ Lower limit of quantitationLPS LipopolysachharideM-CSF Macrophage colony-stimulating factorMetS Metabolic syndromeMIA Multiplex immunoassayMIF Macrophage migration inhibitory factorMMP Matrix metalloproteinasesMMTT Mixed meal tolerance testNEFA Non-esterified fatt acid NGF Nerve growth factorNLRP3 NOD-like receptor family, pyrin domain containing 3NO Nitric oxidenPOD Network for Pancreatic Organ Donors with Diabetes
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List of abbreviations
*nTreg Naturally arising regulatory T-cellOPG OsteoprotegerinOPN OsteopontinOSM Oncostatin MPAI-1 Plasminogen activator inhibitor 1PBMC Peripheral blood mononuclear cellsPPI PreproinsulinPPR’s Pattern-recognition receptorsQUICKY Quantitative insulin sensitivity check indexRBP-4 Retinol binding protein 4RA Rheumatoid arthritisSAA-1 Serum amyloid A1SAT Subcutaneous adipose tissuesCD14 Soluble CD14sCD25 Soluble IL-2 ReceptorsCD54 sICAM; Soluble intercellular adhesion moleculesCD106 sVCAM; Soluble vascular cell adhesion molecule sCD163 Soluble haemoglobin scavenger receptorSCF Stem cell factor (kit-ligand, KL, steel factor)sIL-6R Soluble IL-6 receptorsPD-1 Soluble Programmed Death-1sSCF-R Stem cell factor Soluble Receptor; SC-KITT1D Type 1 diabetesT2D Type 2 diabetesTCR T cell receptorTIMP-1 Tissue inhibitor of metalloproteinase 1TLR Toll-like receptorTNF Tumour necrosis factorTNFR Tumour necrosis factor receptorTNF-RI Tumour necrosis factor receptor type 1TNF-RII Tumour necrosis factor receptor type 2TPO ThrombopoietinTreg Regulatory T cellTREM-1 Triggering receptor expressed on myeloid cells 1TSLP Thymic stromal lymphopoietinVAT Visceral adipose tissue
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List of abbreviations
*VD Vitamin D VEGF Vascular endothelial growth factorWAT White adipose tissueXCL-1 Chemokine (C motif) ligand
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List of abbreviations
*
Introduction
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1 Type 1 diabetes and obesity in children
1.1 Type 1 diabetes
Type 1 diabetes mellitus (T1D) is a chronic disease with a severe impact on the daily lives of affected individuals, their families and other caregivers involved. Intriguingly, the incidence of T1D is increasing worldwide. At the same time, age of presentation is decreasing, thus adding to the burden of families of young T1D patients [1, 2]. The International Diabetes Federation (IDF) has estimated that globally 78,000 children develop T1D every year [www.idf.org].
Advanced technologies in insulin delivery and blood glucose monitoring systems have made the treatment of T1D less burdensome. In the future, closed loop systems combining continuous glucose measurement with continuous insulin infusion may further improve the quality of life of T1D patients. For the time being though, daily insulin injections, blood glucose monitoring, lifestyle adjustments and fear of hypoglycaemia all remain as major stressors. Furthermore, the development of advanced technologies does not imply a cure [3]. Disease-associated long-term complications continue to threaten personal development and health prospects of T1D patients.
In T1D, selective destruction of β cells by autoreactive T cells causes insulin deficiency in most patients [4]. At the time of diagnosis however, β cell destruction is usually not complete. Therefore, immune interventions to halt β cell destruction in T1D patients have emerged as a promising novel treatment avenue. The period shortly after clinical onset of T1D constitutes a window of opportunity.
1.2 Obesity
Obesity is another chronic endocrine and inflammatory disorder in children with increasing incidence. Some obese children develop metabolic syndrome and/or type 2 diabetes (T2D). This risk is especially present in individuals with a large abdominal fat mass, also known as central obesity. Metabolic syndrome for adults and children aged ten or older is commonly defined as increased waist circumference plus at least two out of the following four features: hypertension, high plasma triglycerides, low plasma HDL cholesterol, and impaired glucose metabolism (i.e., impaired fasting glucose, impaired glucose tolerance, or T2D) [The international diabetes federation (IDF) consensus worldwide definition of the metabolic syndrome, 2006].
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Importantly, dysfunction of adipose tissue (AT) propagates the development of the metabolic syndrome. AT hypertrophy in obese children drives local inflammation. This leads to the enhanced secretion of AT-derived inflammatory mediators collectively referred to as adipokines. These adipokines mediate systemic low-grade inflammation and insulin resistance, which subsequently promote the development of T2D and cardiovascular disease [5, 6].
Childhood obesity exerts a considerable burden on the psychological and physical health of affected children [7]. Furthermore, the incidence of childhood obesity is increasing [8, 9]. As obese children are likely to become obese adults, the long-term consequences of obesity are of increasing importance to paediatricians [10-13].
The global burden of diabetes has been estimated in 2011: 366 million people had diabetes at the cost of at least USD 465 billion dollars in health care expenditures; 11% of total health care expenditures in adults [www.idf.org]. Cost estimates for T1D in the US amount to an annual 14.5 billion [14]. The costs of T1D and T2D in the Netherlands were conservatively estimated to be at least 814 million euros in 2005 – excluding the costs of diabetes-related complications [Modelling chronic diseases: the diabetes module. Justification of (new) input data. Baan CA, Bos G, Jacobs-van der Bruggen MAM. Bilthoven, RIVM report 260801001/2005; www.rivm.nl]. A recent study aimed to quantify the association between health care costs and level of glycaemic control in T2D; lower levels of glycated haemoglobin were associated with lower diabetes-related health care costs [15].
In summary, T1D, obesity and T2D are linked by their chronic nature and health burden for both the individual as well as society. In addition, inflammation characterizes both diseases. To what extent specific inflammatory features may be similar or different will be discussed below.
1.3 Type 1 and 2 diabetes: the accelerator hypothesis and double diabetes
Some patients present with a form of diabetes with features of both T1D and T2D, which led Wilkin to formulate the so-called accelerator hypothesis. Herein T1D and T2D are hypothesized to be one and the same disease caused by β cell loss, and distinguished only by their rate of β cell loss (rapid versus slow) and the accelerators responsible [16]. Briefly, the hypothesis lines up three accelerators: firstly, a constitutionally high rate of β cell apoptosis; secondly, insulin resistance; and thirdly, autoimmunity. Insulin resistance is often the result of a rising fat mass and will, in combination with low β cell mass, lead to β cell stress,
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hyperglycaemia and glucotoxicity to β cells. Thus, the loop to the first accelerator is closed through interaction between glucose and the apoptosis-signalling β cell Fas receptor. Along with glucotoxicity, lipotoxicity is hypothesized to accelerate β cell death. Excess fat intake leads to liver, muscle and islet triglyceride deposits, which induce low-grade inflammation and also accelerate β cell death. The third accelerator, autoimmunity, develops in a small and genetically well-defined subset of individuals with insulin resistance. In this view, β cells in an insulin-resistant environment are thought to become more susceptible to autoimmune attack, the intensity of which is modulated by the HLA genotype. β cell apoptosis in itself predisposes to autoimmunity as apoptotic cells display autoreactive antigens. These antigens prime tissue-specific cytotoxic T cells and induce autoantibody formation. In its simplest form, the accelerator hypothesis can be reduced to the unfavourable interplay of two phenomena: disturbed β cell apoptosis and insulin resistance [16-19].
The accelerator concept has been challenged, mainly because T1D can occur in individuals without insulin resistance and because evidence is lacking that accelerated β cell apoptosis in normoglycaemic individuals at risk of T2D is mediated through insulin resistance [20-22]. In addition, genetic predisposition profiles in T1D and T2D, if not mutually exclusive, differ substantially [23]. However, it is clear that the old notion of T1D as an entirely autoimmune and of T2D as an entirely metabolic disease is outdated.
A different angle to address the overlapping features in T1D and T2D has been offered by the concept of double diabetes: a combination of T1D with features of insulin resistance and T2D [24]. This model allows for integration of intensive insulin treatment schedules, concomitant weight gain and counter-intuitive insulin resistance in combination with absolute insulin deficiency. Thus, this model raises the question of to what extent inflammatory mediators in longstanding T1D are actually increased and to what extent AT plays a role therein.
2 Inflammation in type 1 diabetes and obesity
T1D and obesity are both characterized by local inflammation. While pancreatic inflammation has the primacy in T1D, obesity is primarily characterized by AT inflammation. Nonetheless, local inflammation does not come alone. Pancreatic inflammation and AT inflammation both have endocrine and metabolic effects. T1D and obesity are thought to be characterized by similar systemic inflammatory processes that ultimately can lead to insulin resistance and cardiovascular disease [25, 26]. These inflammatory features are subject of this thesis, and will be introduced in the following paragraphs.
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2.1 Inflammation and immunoregulatory mechanisms in type 1 diabetes
Inflammation is a key feature of the immunopathogenesis of T1D, and causes onset of disease through β cell destruction. Subsequently, a somewhat more steady state of insulin deficiency in T1D sets in, characterised by chronic, low-grade inflammation. These two phases will be discussed separately below and are introduced in Figure 1.1, a scheme adapted from the original model of G Eisenbarth [27].
Time
Figure 1.1 Scheme depicting ! cell function and inflammation in the development of type 1 diabetesType 1 diabetes as a chronic progressive autoimmune disorder is represented in this scheme, modified from the original model from G Eisenbarth (Eisenbarth GS. Autoimmune β cell insufficiency - diabetes mellitus Type 1. Triangle 1984; 23:111-124). The various stages in β cell destruction are depicted in separate colours with further specification of their genetic, immunological and metabolic markers. During the course of insulitis a state of chronic inflammation develops next to the acute inflammation in the pancreatic islets. This chronic inflammation interacts with insulitis and β cell function, as will be discussed in this thesis. The rate of progress of loss of β cell function is variable, therefore the x-axis is without a specific time scale.
Before disease onset. During childhood,
Disease onset.
Honeymoon period.
Disease progression.
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Normal immune regulation
Healthy individuals have a double line of defence against danger signals threatening the body: the innate and the adaptive immune system. Primarily, the innate system directs the rapid, non-specific response through key players such as granulocytes, natural killer cells and macrophages. These cells can either directly inactivate danger cells or produce inflammatory mediators, which further stimulate the host response. Next, the adaptive immune system comes into play, with B and T lymphocyte activation through recognition of specific antigen components (epitopes), which are presented through antigen-presenting cells (APCs), such as dendritic cells (DCs).
Different T cell subsets have different tasks in this process. On the one hand, CD4+ T cells interact with the innate immune system, engage B cells and amplify the immune response by producing cytokines, or induce tolerance. On the other hand CD8+ T cells directly kill targets after epitope recognition [28, 29]. Next, regulation of the immune system is mediated through regulatory T cells, secreting anti-inflammatory mediators and inducing tolerance. Furthermore, the human equivalent of the major histocompatibility complex (MHC), the human leucocyte antigen (HLA) system, plays a prominent role in the immune system. HLA molecules corresponding to MHC class II molecules (including DQ and DR) participate in extracellular antigen presentation to T lymphocytes, thereby involving T helper cells and subsequently B cells and suppressor T cells. Specific HLA-class II genotypes imply a strong genetic predisposition for the development of autoimmune diseases such as T1D [30]. Next, MHC class I (HLA A, B and C) molecules are involved in cytotoxicity through presentation of intracellular peptides to CD8+ cells [31].
Tolerance: inflammatory control
The master switch in differentiating between self and non-self and thus preventing autoimmune disease by inducing tolerance is located in the thymus. This process is known as central tolerance. Self-peptides are presented in the thymus. Through thymic deletion and negative selection the body is set to remove T cells with a potential to react to these self-peptides. Disturbances of this thymic function, of which AIRE gene mutations which cause monogenetic polyglandular autoimmune disease type 1 (APECED) are the best example, cause T cells with autoimmune potential to survive [32]. Secondly, peripheral tolerance is essential in maintaining tolerance. This is mediated by the immune system in the periphery of the body where regulatory T cells (Tregs) take centre stage. Two major populations of Tregs can be identified: naturally occurring regulatory T cells (nTregs; selected centrally in the thymus) and adaptive Tregs originating from the periphery. The
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latter may produce different cytokines and are classified into different subsets on this basis [33, 34].
When tolerance is induced to an antigen through regulatory CD4+ cells, linked suppression can be induced to another antigen if both antigens are presented by the same APC.
In autoimmune disease, e.g. in pancreatic islets in T1D, self-antigens are detected as danger signals, evoking innate and adaptive cascades of response.
Important players in immune regulation and maintaining the balance between self and non-self are heat shock or stress proteins (HSPs). HSP60-derived peptides can be innate signals for macrophages and DCs. Moreover, they are involved in the adaptive immune system by upregulating Tregs [35-37]. HSPs have the potential to lead to a shift of the immune response from inflammatory to regulatory, as will be further discussed in paragraph 2.4. In this way HSP60 can aid in control of inflammation and immunity [38].
Pancreatic inflammation
In T1D, lymphocytic infiltration of pancreatic islets of Langerhans drives specific destruction of the insulin-producing β cells, so-called insulitis [39]. There are many unsolved questions regarding the initial triggers for insulitis. Both environmental factors and genetic make-up are considered to take part in the susceptibility for T1D [40, 41] (Figure 1.1). Population-based studies in genetically high-risk individuals may shed further light on the initial triggers for autoimmune insulitis [40, 42, 43].
Once evoked, β cell apoptosis comprises a key feature of autoimmune insulitis, with an important role for cytokines secreted by CD4+ T cells. Perhaps more importantly, cytotoxic CD8+ T cells seem to play a pivotal role, as they comprise the dominant cell type in autopsy studies of pancreatic tissue inflammatory infiltrates in T1D patients [44, 45].
! cell function
Pancreatic islets of Langerhans of healthy individuals contain 1–2 billion β cells spread over 106 islets; still, β cells comprise only 2–3% of all cells in healthy pancreatic tissue. In T1D, this β cell mass is targeted by an autoimmune response and decreases over time. As disease onset and glycaemic control are directly influenced by residual β cell mass, knowledge thereof is of great value. A problem in T1D is the impossibility of direct assessment of the (loss of) β cell mass, although recent technical advancement in clinical imaging has shown promise [46]. Mostly, however, β cell function (estimated from either fasting or mixed-meal-, arginine- or glucagon-stimulated C-peptide) has been used as a
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surrogate for β cell mass in observational cohorts as well as intervention trials. Of note, there is an age-related increase in C-peptide levels in children. Therefore, longitudinal studies in paediatric populations should account for this phenomenon [47].
Imaging of ! cell mass
Insight into the inflammatory process in T1D is ever increasing, and a new era with major developments in imaging technology [PET, SPECT, MRI] has started. Pancreatic islet imaging holds promise as a noninvasive and quantitative measure of β cell mass and might further add to the process of unraveling insulitis. Furthermore, imaging is a potentially powerful tool in monitoring therapeutic strategies aimed at restoring or preserving β cell function. In mice, time series imaging after islet transplantation is feasible, which allows researchers to follow graft viability and functionality [48, 49]. Furhermore, β cell imaging might allow to distinguish between β cell mass and β cell function in both observational and intervention studies [46]. Finally, with MRI of magnetic nanoparticles changes in microvasculature in association with insulitis could be visualized [50].
Residual ! cell function
At diagnosis of T1D, the residual β cell mass, based on diurnal glucose and C-peptide profiles and autopsy studies, has, since decades, been estimated at 10–30% [39, 51]. Functional studies that assess insulin secretion after a mixed meal test in new-onset T1D more recently report up to around 50% of residual insulin secretory capacity [52]. After onset of T1D, there is a further destruction of β cells. Importantly, landmark recent studies in pancreatic samples from T1D organ donors (the nPOD initiative, www.jdrfnpod.org) have changed our perceptions of these topics [53]. A surprising finding with potential for therapeutical intervention has been that even decades after onset of T1D there is remaining β cell mass in many individuals, either through β cell survival or functional recovery [54, 55]. Confusingly, this residual β cell mass most often does not appear to be reflected in persistent β cell function. This observation may be explained by the observation that β cells can be nonresponsive to glucose stimuli while they do respond to other stimuli, i.e. the concept of glucose blindness [56]. Even though it is unclear why some individuals show persistence or recovery of β cell mass function whereas most do not, long-term studies on glycaemic control and outcome have underscored the relevance of this phenomenon. Importantly, preservation of β cell function (expressed as stimulated C-peptide > 0.2 pmol/ml) is associated with reduced risk of long-term complications [57, 58].
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Clinical remission
A phenomenon that has fascinated clinicians and researchers with regard to β cell preservation is the clinical remission or honeymoon phase, which occurs in a substantial part of patients after clinical onset of T1D. Starting shortly after initiation of insulin treatment, this phase is marked by a substantial though transient decline in exogenous insulin requirement and concomitant improvement of metabolic control [59]. The underlying mechanism has not been clarified and investigation of the remission phase has been hampered by a variety of definitions used. Various clinical variables have been used as surrogates for β cell function, as longitudinal evaluation of stimulated C-peptide is, for many patients, too burdensome. Frequently encountered descriptive criteria for remission have been based on total insulin need; HbA1c levels; and combinations of these variables, with various cut-off values used (eg insulin < 0.4–0.5 units (U)/kg/d and HbA1c < 6–8%) [60, 61]. In 2009, the concept of insulin-dose-adjusted HbA1c (IDAA1c) was introduced (IDAA1c = HbA1c (%) + (4 x insulin dose (U/kg/d); IDAA1c ≤ 9 defining remission), incorporating both insulin dose and HbA1c and thereby correcting for individuals with a low insulin requirement but questionable metabolic control. Thus, IDAA1c can be seen as a more stringent criterion to define remission. Moreover, among various definitions of partial remission, IDAA1c showed the best correlation with stimulated C-peptide levels [62]. Data on the duration and incidence of the honeymoon phase vary depending on the definitions used. Overall, the picture has emerged of a negative correlation between the occurrence of remission and its duration on the one hand and ketoacidosis and younger age at onset on the other. A low frequency of remission has especially been reported in patients with onset of T1D below 3–5 years of age [61].
There is little insight in mechanisms underlying remission. The prevailing hypothesis is that ongoing hyperglycaemia due to insulin deficiency and insulin resistance in peripheral tissue causes β cell stress and ultimately exhausts β cell function. Insulin treatment after onset of T1D temporarily counteracts this effect, allowing β cells to restore insulin-secreting capacity. Thus remission is thought to be a sign of functional β cell restoration rather than replenishment of β cell mass [63, 64]. Nevertheless, improved understanding of the aetiology of the remission phase may lead to new therapeutic strategies.
Though data are still limited, several studies suggest that immune-regulatory mechanisms play a pivotal role in the remission phase. Firstly, population studies indicate that remitting patients exhibit lower levels of inflammatory cytokines such as interferon-γ (IFN-γ) and
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interleukin (IL)-10, and higher levels of anti-inflammatory cytokines such as IL-1 receptor antagonist (IL-1RA) [65-67]. Secondly, studies in mice show that if autoimmunity and islet inflammation are arrested, functional β cell numbers increase and clinical remission occurs [41, 68]. These studies fuel the hypothesis that the honeymoon period is a ‘window of opportunity’, which shows promise for immune modulatory interventions to increase functional β cell mass in patients with T1D [64, 69].
Chronic inflammation in longstanding, stable type 1 diabetes
Chronic, low-grade, systemic inflammation is present in longstanding, stable and otherwise uncomplicated T1D. Chronic inflammation has as yet been much less investigated than the inflammation at disease onset. Firstly, a study comparing longstanding T1D patients with healthy controls showed increased C-reactive protein (CRP) levels in T1D that was shown to be part of a chronic inflammatory response. Furthermore, higher CRP as a sign of inflammation correlated with markers of endothelial dysfunction (activation). Both these processes are elemental in atherogenesis and development of micro- and macrovascular disease [25]. In children, these observations were confirmed, whereby inflammation was independent of both adiposity and glycaemia [70]. Of note, a study in children at risk for T1D reported elevated CRP concentrations even before clinical onset of disease. However, this might very well primarily mirror insulitis [71]. Nevertheless, a key point here is that determinants of chronic inflammation in T1D have not been extensively studied. Important candidates are firstly factors that are intrinsic to T1D, such as hyperglycaemia, advanced glycation endpoducts, and ongoing autoimmunity. Secondly, extrinsic factors, such as vitamin D deficiency and the gut microbiome and factors that are in part extrinsic and perhaps in part caused by insulin treatment, notably obesity, mediate chronic inflammation [72-76].
2.2 Inflammation and immunoregulatory mechanisms in obesity
Adipose tissue inflammation
Obesity promotes a state of chronic low-grade inflammation, both local and systemic [77, 78]. Chronic inflammation in visceral (or abdominal or upper-body) obesity contributes to insulin resistance and, in susceptible individuals, to β cell failure, and so link obesity to T2D. The inflammatory process is reflected by an increased production of cytokines and pro-inflammatory adipokines by adipose tissue, as well as by a cellular component. Adipose tissue contains, besides mature adipocytes, also immature adipocytes (preadipocytes),
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endothelial cells, fibroblasts, macrophages and other immune cells. The number of adipose tissue macrophages is increased in obesity [79]. This might be caused by adipocyte hypertrophy, possibly together with local hypoxia and adipocyte apoptosis, which in turn generate signals to recruit macrophages [80]. Secondly, hypertrophic adipocytes begin to secrete low levels of TNF-α, which stimulate preadipocytes and endothelial cells to produce CCL2 (monocyte chemotactic protein (MCP)-1), also affecting macrophage infiltration [81]. Thus, local production of chemoattractants that enhance the homing of monocytes to adipose tissue depots can contribute to adipose tissue inflammation. Macrophages in adipose tissue are overrepresented around dead or dying adipocytes, thereby forming so-called crown-like structures [82]. This suggests that adipocyte necrosis may underlie the pro-inflammatory response and macrophage attraction, but at present their concomitant presence represents an association while a direct causal relation remains to be established [83].
Obesity causes type 2 diabetes in some but not all individuals
Insulin resistance in obesity, especially in visceral (or abdominal or upper-body) obesity, leads to an increased demand for insulin, which must be met by an increased insulin production by pancreatic β cells. In some individuals, β cells may at some point no longer be able to meet the high demand, which will lead to impaired glucose tolerance and, subsequently, to T2D. Chronic inflammation in visceral obesity contributes to both insulin resistance and, in susceptible individuals, to β cell failure, and so link obesity to T2D. In contrast, not all fat is bad. It has consistently been shown that approximately 25–30% of obese individuals do not develop insulin resistance or chronic inflammation, and that such individuals are characterized by efficient expandability of the superficial (especially lower body) subcutaneous fat depot, which is likely to limit triglyceride overflow into the visceral and ectopic fat depots [84].
! cell failure
Beta cell failure can result on the one hand from reduced β cell mass due to genetic predisposition and autoimmunity. On the other hand an intrinsic insulin secretion defect in existing β cells can induce β cell failure. Next, prolonged exposure to high levels of glucose and lipids, i.e. glucotoxicity and lipotoxicity, contribute to β cell apoptosis [85-88]. Furthermore, inflammatory cytokines can drive β cell dysfunction [89]. In obesity, the combination of hyperglycaemia, hyperlipidaemia and inflammation exerts hazardous effects on β cells [90]. These effects are mediated by various inflammatory
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mediators, among which IL-1β. This pro-inflammatory cytokine is involved in β cell deterioration in both T1D and T2D [85, 91-93]. In addition, increased concentrations of non-esterified fatty acids (NEFA) were shown to be harmful for β cells and to induce an inflammatory response in pancreatic islets [94, 95]. NEFA induces IL-1β release as well the local production of other IL-1-dependent pro-inflammatory cytokines such as IL-6 and IL-8 [95]. In conclusion, glucotoxicity, lipotoxicity and inflammatory mechanisms are intertwined, and comprise a destructive mix for β cells in obese patients.
Of note, other mechanisms that may explain β cell failure in obesity-associated T2D include ER stress, oxidative stress and amyloid deposition. Most of these mechanisms have also been implicated in inflammation, either because they induce a (local) inflammatory response or because they result from systemic inflammation [96].
Thus, immune dysregulation underlies the demise of β cell mass and function in T1D; thereafter a state of chronic inflammation persists. In obesity local and systemic immune dysregulation are equally important. Inflammatory mediators have an elementary role in immune dysregulation in both diseases and they will be the focus of the next paragraph and this thesis.
2.3 Circulating inflammatory mediators
Various molecules ranging from lipids and proteins to nitrogen oxide are involved in inflammatory signaling. Cytokines are key components in the function, activity and connection of the innate and adaptive immune system. They are pivotal players in progression or regression of a pathological process and may serve as biomarkers to monitor onset and progression of disease as well as to monitor intervention.
In this thesis, we focus on proteins involved in immune signaling, for two different reasons. Firstly, cytokines are hormone-like inflammatory signaling proteins. These are known to play a pivotal role in both the local and systemic inflammation in T1D and obese patients [89, 97, 98]. Secondly, the multiplex immuno assay (MIA) technique allows fast and reliable measurement of many cytokines in small volumes of biological samples [99, 100].
2.3.1 Cytokines Inflammatory signalling proteins that allow cellular communication through cell signalling exist in many different forms. Therefore, cytokines can be classified into functional subgroups such as interleukins, interferons, colony-stimulating factors,
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chemokines and adipokines [89, 101]. However, terms are often used interchangeably. In Table 1.1, cytokines with key roles in the pathophysiology of T1D and obesity are displayed. The role of chemokines and adipokines in T1D and obesity is discussed separately, as they play separate roles in the pathophysiology.
Of note, cytokines comprise both inflammatory and anti-inflammatory molecules such as cytokine-receptor-specific antagonists. Furthermore, some cytokine receptors have soluble forms that function as an agonist. Under normal conditions, pro- and anti-inflammatory cytokines and soluble receptors are balanced. This balance is disturbed by metabolic and inflammatory changes, among others. Hyperglycaemia for instance can acutely increase circulating cytokine concentrations in patients with type 1 diabetes and subjects with an impaired glucose tolerance [102, 103].
2.3.2 Chemotactic cytokines (chemokines)Chemokines are signaling proteins with the ability to induce directed chemotaxis in nearby responsive cells. They can either be pro-inflammatory (induced during an immune response to recruit cells of the immune system to a site of infection) or homeostatic (controlling migration of cells during normal processes of tissue maintenance or development). Chemokines are categorised into four families (C, CC, CX and CXC3) and they have as a distinctive property their redundancy, as some chemokines use the same receptors and one chemokine binds to various receptors (with the exception of IFN-γ-inducible chemokines all interacting exclusively with CXCR3) [101]. Chemokines with important functions in the pathophysiology of T1D and obesity are displayed in Table 1.2.
2.3.3 AdipokinesOver the last decade, AT is increasingly regarded as an endocrine organ rather than merely an inert lipid storage depot [97]. One of the most important reasons to consider AT as an endocrine organ is that AT secretes a multitude of inflammatory signalling proteins with structural and functional resemblance to cytokines, referred to as adipokines. Adipokines regulate multiple aspects of body energy homeostasis, and influence various inflammatory processes. Some adipokines show sexual dimorphism, a difference between males and females from puberty onwards. This sexual dimorphism in adipokines is partly explained by circulating androgen levels and differential fat distribution [97]. In obesity, AT inflammation and insulin resistance are enhanced by the secretion of pro-
30
Chapter 1
1
Tabl
e 1.
1 C
ytok
ines
Cyto
kine
s with
key
role
s in
the
path
ophy
siolo
gy o
f T1D
and
obe
sity.
Des
crib
ed in
the
first
col
umn
are
key
gene
ral f
eatu
res f
or a
spec
ific
cyto
kine
, th
e (p
atho
) phy
siolo
gica
l rol
e w
ith fo
cus o
n ho
w β
cell f
unct
ion
is af
fect
ed a
nd cy
toki
ne-re
late
d dr
ugs w
hen
appl
icab
le. T
he se
cond
colu
mn
focu
ses
per c
ytok
ine
on k
now
ledg
e re
gard
ing
T1D,
T2D
and
obe
sity,
and
inte
rven
tion
stud
ies w
ith re
late
d dr
ugs r
egar
ding
the
prev
ious
ly m
entio
ned
topi
cs.
Cyto
kine
Func
tion
Stud
ies i
n di
abet
es a
nd o
besit
yRe
fere
nces
IL-1
RA
Mem
ber o
f the
IL-1
fam
ily. S
ecre
ted
by im
mun
e ce
lls, e
pith
elia
l ce
lls, a
dipo
cyte
s and
oth
er c
ells.
Bin
ds to
the
IL-1
rece
ptor
Phys
iolo
gica
l rol
e: N
atur
ally
occ
urrin
g co
mpe
titiv
e in
hibi
tor
of IL
-1 si
gnal
ling.
Expe
rimen
tal d
ata:
pro
tect
s isle
ts o
f hig
h fa
t die
t-tr
eate
d m
ice
from
β ce
ll apo
ptos
is, in
duce
s β ce
ll pro
lifer
atio
n, an
d im
prov
es
gluc
ose-
stim
ulat
ed in
sulin
secr
etio
n
Rela
ted
drug
s: An
akin
ra (r
ecom
bina
nt h
uman
IL-1
R an
tago
nist
). Ex
ogen
ous a
dditi
on o
f IL-
1RA
prot
ects
β ce
lls fr
om d
elet
erio
us
effe
cts o
f hig
h gl
ucos
e an
d le
ptin
exp
osur
e
T1D
: Hig
h IL
-1RA
leve
ls a
re a
ssoc
iate
d w
ith b
ette
r β
cell
func
tion.
IL-1
R an
tago
nist
pro
tect
s aga
inst
cel
l dam
age
and
apop
tosis
. Tr
eatm
ent
low
ered
insu
lin r
equi
rem
ent
at T
1D o
nset
in
child
ren;
no
effe
ct in
tria
l in
adul
ts
T2D
and
obe
sity:
Red
uced
β c
ell e
xpre
ssio
n an
d se
rum
leve
ls
in T
2D, w
hile
leve
ls ar
e in
crea
sed
in T
2D-p
rone
indi
vidu
als
IL-1
R a
ntag
onis
t pr
eser
ves
endo
geno
us in
sulin
pro
duct
ion
and
atte
nuat
es in
flam
mat
ion
in T
2D, b
ut n
ot T
1D
Islet
tran
spla
ntat
ion:
stud
y ong
oing
incl
udin
g IL
-1 R
anta
goni
st;
blun
ts c
ytok
ine
stor
m a
fter t
hym
oglo
bulin
indu
ctio
n th
erap
y
[89,
104
-111
]
IL-1
βPr
o-in
flam
mat
ory
cyto
kine
. Pro
duce
d by
isle
t ce
lls, b
lood
m
onoc
ytes
, tiss
ue m
acro
phag
es an
d de
ndrit
ic ce
lls af
ter t
issue
in
sult.
Rol
e in
inna
te im
mun
e re
spon
se. A
mpl
ifies
the
adap
tive
imm
une
resp
onse
Phys
iolo
gica
l rol
e: m
aint
enan
ce o
f β c
ell m
ass a
nd fu
nctio
n.
Path
ophy
siolo
gica
l rol
e: e
leva
ted
leve
ls de
crea
se β
cell f
unct
ion
and
are
β ce
ll pr
o-ap
opto
tic. H
yper
glyc
aem
ia in
duce
s IL
-1β
prod
uctio
n an
d re
leas
e by
pan
crea
tic β
cel
ls.
Fatt
y ac
ids
stim
ulat
e IL
-1β
expr
essio
n
Rela
ted
drug
s: an
ti-IL
-1β
T1D
: Inc
reas
ed in
new
ons
et p
atie
nts;
early
infla
mm
ator
y sig
nal
in T
1D d
evel
opm
ent
Bloc
king
IL-1
β re
spon
se re
stor
es β
cell f
unct
ion
and
amel
iora
tes
hype
rgly
caem
ia
T2D
: Pro
gres
sive
loss
of
β ce
lls o
win
g to
IL-1
- m
edia
ted
infla
mm
atio
n
Inte
rven
tion:
Pot
entia
l the
rapi
es to
dec
reas
e T1
D p
rogr
essio
n ba
sed
on IL
-1β
incl
ude
piog
litaz
one,
IL-1
rece
ptor
ant
agon
ists,
and
agen
ts th
at re
mov
e IL
1β fr
om th
e ci
rcul
atio
n. A
nti-I
L-1β
fa
iled
in T
1D
[89,
106
, 107
, 112
, 11
3]
31
Introduction
1IL
-6
Pro-
infl
amm
ator
y cy
toki
ne.
Prod
uced
by
fibr
obla
sts,
en
doth
elia
l cel
ls, m
onoc
ytes
and
mac
roph
ages
, myo
cyte
s, ad
ipoc
ytes
and
oth
er c
ells
. In
volv
ed in
bot
h sy
stem
ic a
nd
loca
l inf
lam
mat
ion
Phys
iolo
gica
l ro
le:
Invo
lved
in
acut
e ph
ase
resp
onse
. Co
mm
unic
atio
n be
twee
n in
nate
and
ada
ptiv
e im
mun
e sy
stem
. Rel
ease
d by
mus
cle
cells
dur
ing
exer
cise
, and
by
pre-
adip
ocyt
es d
urin
g di
ffere
ntia
tion
Path
ophy
siolo
gica
l rol
e: h
yper
glyc
aem
ia a
nd h
yper
lipid
aem
ia
driv
e IL
-6 p
rodu
ctio
n (e
.g. b
y isl
et c
ells
); IL
-6 th
en p
ropa
gate
s lo
cal a
nd sy
stem
ic in
flam
mat
ion
Rela
ted
drug
s: T
ocili
zum
ab (
reco
mbi
nant
hum
aniz
ed
mon
oclo
nal a
ntib
ody
spec
ific
for t
he h
uman
IL-6
rece
ptor
)
Posit
ive
effe
ct o
n pa
ncre
atic
α c
ells
and
gluc
agon
secr
etio
n
T1D
: Ele
vate
d le
vels
in p
atie
nts w
ith cl
inic
al re
miss
ion.
Impa
ired
insu
lin se
cret
ion
in IL
-6 tr
eate
d isl
ets
Obe
sity a
nd T2
D: P
redi
ctiv
e fa
ctor
for T
2D w
ith e
leva
ted
leve
ls in
obe
sity
and
a de
clin
e w
ith w
eigh
t los
s.
Toci
lizum
ab r
educ
es H
bA1c
leve
ls in
pat
ient
s w
ith T
2D a
nd
rheu
mat
oid
arth
ritis
. How
ever
blo
ckag
e of
IL-6
sig
nalli
ng
appe
ars t
o be
rela
ted
to im
paire
d m
etab
olic
hom
eost
asis
[97,
114
-119
]
TNF-
αPr
o-in
flam
mat
ory
cyto
kine
. Pro
duce
d by
all
imm
une
cells
in
clud
ing
adip
ocyt
es. T
rigge
rs a
re T
LR li
gand
s an
d ot
her
cyto
kine
s.
Phys
iolo
gica
l rol
e: p
oten
t med
iato
r of t
he ac
ute p
hase
resp
onse
an
d se
ptic
shoc
k.
Path
ophy
siol
ogic
al r
ole:
invo
lved
in in
sulin
res
ista
nce
and
met
abol
ic sy
ndro
me.
Indu
ces β
cell d
ysfu
nctio
n an
d ap
opto
sis
thro
ugh
syne
rgy
pote
ntly
with
IL-1
and
IFN
Rela
ted
drug
s: Va
rious
TN
F bl
ocke
rs a
re in
use
, e.g
. eta
nerc
ept
(TN
F re
cept
or-Ig
G f
usio
n pr
otei
n) a
nd in
flixi
mab
(TN
F-α
mon
oclo
nal a
ntib
ody)
T1D
: Eta
nerc
ept i
mpr
oved
β c
ell f
unct
ion
and
low
ered
insu
lin
requ
irem
ent i
n ch
ildre
n
T2D
: Eta
nerc
ept
or i
nflix
imab
in
T2D
pat
ient
s w
ith
RA:
impr
oved
fast
ing
gluc
ose,
HbA
1c a
nd tr
igly
cerid
e va
lues
Isle
t tra
nspl
anta
tion:
stud
y w
ith e
tane
rcep
t ong
oing
TNF
or T
NF
bloc
kade
bot
h pr
otec
t an
d ag
grav
ate
diab
etes
de
velo
pmen
t in
ani
mal
stu
dies
in
a do
se-
and
tim
ing-
de
pend
ent w
ay
[89,
116
, 120
-122
]
Tabl
e 1.
1 co
ntin
ues o
n ne
xt p
age
32
Chapter 1
1Ta
ble
1.1
Con
tinue
d
Cyto
kine
Func
tion
Stud
ies i
n di
abet
es a
nd o
besit
yRe
fere
nces
LIF
Prom
otes
ada
ptiv
e im
mun
e to
lera
nce.
Mem
ber o
f IL-
6 fa
mily
Phys
iolo
gica
l rol
e: c
ontr
ols
panc
reat
ic d
uct c
ell p
rolif
erat
ion
and
repa
ir. P
rom
otes
isle
t cel
l sur
viva
l and
regu
late
s β ce
ll mas
s
Path
ophy
siol
ogic
al r
ole:
mic
e re
ceiv
ing
a hi
gh d
ose
of L
IF
deve
lop
panc
reat
itis
Inte
rven
tion:
In m
ice:
Infla
mm
ator
y im
mun
e re
spon
se c
an b
e re
duce
d by
pro
mot
ing
regu
lato
ry T
cells
thro
ugh
nano
part
icle
s lo
aded
with
LIF
[123
-125
]
MIF
Recr
uits
inna
te a
nd a
dapt
ive
imm
une
cells
to
the
site
of
infla
mm
atio
n, a
mpl
ifies
pro
duct
ion
of p
ro-in
flam
mat
ory
med
iato
rs. P
rodu
ced
by im
mun
e ce
lls, c
ells
of t
he C
NS
and
seve
ral e
ndoc
rine
glan
ds
Phys
iolo
gica
l rol
e: p
arac
rine
and
auto
crin
e ef
fect
s. Re
gula
tor
of in
sulin
secr
etio
n in
phy
siolo
gica
l sta
te; c
o-lo
caliz
es in
β ce
ll gr
anul
es w
ith in
sulin
Expr
essi
on c
an b
e in
duce
d by
glu
coco
rtic
oids
whi
le o
ther
pr
o-in
flam
mat
ory
cyto
kine
s ar
e un
iform
ly s
uppr
esse
d by
gl
ucoc
ortic
oids
Path
ophy
siol
ogic
al r
ole:
aut
ocrin
e pr
o-ap
opto
tic m
olec
ule
thro
ugh
incr
ease
d M
IF se
cret
ion
T1D
: pro
paga
tor o
f aut
oim
mun
e in
flam
mat
ion
or m
odul
ator
of
insu
lin se
cret
ion
T2D
: Inc
reas
ed c
once
ntra
tion
; cau
se o
r ef
fect
unk
now
n (p
hysio
logi
cal a
dapt
atio
n to
incr
easin
g in
sulin
res
istan
ce o
r co
ntrib
utio
n to
the
loss
of i
nsul
in-p
rodu
cing
cells
and
dise
ase
prog
ress
ion)
Isle
t tr
ansp
lan
tati
on:
hig
h M
IF c
once
ntr
atio
ns
pre
tr
ansp
lant
atio
n as
soci
ated
with
sub
sequ
ent
loss
of
graf
t fu
nctio
n.
[126
-128
]
33
Introduction
1Ta
ble
1.2
Che
mok
ines
Chem
otac
tic c
ytok
ines
(che
mok
ines
) with
key
role
s in
the
path
ophy
siolo
gy o
f T1D
and
obe
sity
are
desc
ribed
. Des
crib
ed in
the
first
colu
mn
are
key
gene
ral f
eatu
res
for a
spe
cific
che
mok
ine,
the
(pat
ho) p
hysio
logi
cal r
ole
with
focu
s on
how
β c
ell f
unct
ion
is af
fect
ed a
nd d
rugs
influ
enci
ng th
e ch
emok
ine
func
tion
whe
n ap
plic
able
. The
sec
ond
colu
mn
focu
ses
on k
now
ledg
e re
gard
ing
T1D,
T2D
and
obe
sity,
and
inte
rven
tion
stud
ies
with
re
late
d dr
ugs r
egar
ding
the
prev
ious
ly m
entio
ned
topi
cs.
Chem
okin
eFu
nctio
nSt
udie
s in
diab
etes
and
obe
sity
Refe
renc
es
CCL2
(MCP
-1)
Pro-
infla
mm
ator
y che
mok
ine.
Pro
duce
d m
ainl
y by m
onoc
ytes
, m
acro
phag
es an
d D
C’s.
Anti-
infla
mm
ator
y pro
pert
ies t
hrou
gh
IL-4
pro
duct
ion
Phys
iolo
gica
l rol
e: ch
emot
actic
activ
ity (m
onoc
ytes
, bas
ophi
ls)
Path
ophy
siol
ogic
al r
ole:
Cor
rela
ted
with
insu
lin r
esis
tanc
e an
d in
flam
mat
ion.
Impl
icat
ed in
the
pat
hoge
nesi
s of
man
y di
seas
es (e
.g. R
A, a
ther
oscl
eros
is). H
yper
glyc
aem
ic co
nditi
ons
incr
ease
CCL
2 re
leas
e
T1D
: Ver
y hi
gh le
vels
pro
tect
aga
inst
T1D
; am
ongs
t T1
D
patie
nts i
ncre
ased
leve
ls co
rrel
ate
with
com
plic
atio
ns
T2D
and
obe
sity
: Inc
reas
ed C
CL2
leve
ls; a
ther
oscl
erot
ic r
isk
fact
ors
corr
elat
e w
ith C
CL2.
Hig
her
gene
exp
ress
ion
of C
C ch
emok
ines
and
thei
r rec
epto
rs in
AT
of o
bese
pat
ient
s
Vita
min
D: A
dipo
cyte
CCL
2 pr
oduc
tion
atte
nuat
ed b
y 1, 2
5-O
H
chol
ecal
cife
rol
Islet
tran
spla
ntat
ion:
hig
h do
nor-
deriv
ed C
CL2
asso
ciat
ed w
ith
poor
isle
t allo
graf
t out
com
e
Inte
rven
tion:
Man
y dr
ugs u
sed
for C
CL2
inhi
bitio
n [1
29]
[113
, 129
-13
2]
CCL3
(MIP
-1α)
Pro
-in
flam
mat
ory
chem
okin
e. P
rod
uce
d m
ain
ly b
y m
acro
phag
es, D
Cs a
nd ly
mph
ocyt
es
Phys
iolo
gica
l rol
e: re
crui
ts a
nd a
ctiv
ates
pol
ymor
phon
ucle
ar
leuk
ocyt
es d
urin
g in
fect
ion
and
infla
mm
atio
n, a
nd in
duce
s re
leas
e of
oth
er p
ro-in
flam
mat
ory
cyto
kine
s
Path
ophy
siolo
gica
l rol
e: su
gges
ted
to b
e pr
oduc
ed b
y β
cells
un
der s
tres
s
T1D
: Inc
reas
ed i
n re
mit
ters
; als
o ne
gati
vely
rel
ated
to
C-pe
ptid
e. P
ositi
ve a
ssoc
iatio
n w
ith p
roin
sulin
as β
cel
l str
ess
mar
ker
Obe
sity:
Impa
ired
CCL3
rele
ase
[133
-135
]
Tabl
e 1.
2 co
ntin
ues o
n ne
xt p
age
34
Chapter 1
1
Tabl
e 1.
2 C
ontin
ued
Chem
okin
eFu
nctio
nSt
udie
s in
diab
etes
and
obe
sity
Refe
renc
es
CXCL
10 (I
P10)
“Infla
mm
ator
y” c
hem
okin
e; p
rodu
ced
by m
any
cell
type
s in
m
any
tissu
es in
clud
ing
β ce
lls
Phys
iolo
gica
l rol
e: re
gula
tes i
mm
une
resp
onse
s by
activ
atio
n an
d re
crui
tmen
t of
leu
kocy
tes.
Inf
luen
ce o
n im
mun
ity,
an
giog
enes
is an
d or
gan-
spec
ific
met
asta
ses o
f can
cer
Path
ophy
siol
ogic
al r
ole:
Ele
vate
d C
XCL1
0 le
vels
and
ly
mph
ocyt
e in
filtr
atio
n ex
pres
sing
CXCR
3 in
insu
litic
lesio
ns
T1D
: Det
ecta
ble
/ ele
vate
d in
pre
diab
etic
subj
ects
and
new
ly
diag
nose
d pa
tient
s; p
rodu
ced
by d
istr
esse
d β
cells
. Isl
et
infil
trat
ing
leuk
ocyt
es e
xpre
ss re
cept
or C
XCR3
T2D
: CXC
L10
prod
uctio
n im
pairs
β c
ell f
unct
ion
Inte
rven
tion:
Exp
erim
enta
l mod
els:
inhi
bitio
n of
CXC
L10
hom
ing
to is
lets
pre
vent
s aut
oim
mun
e di
abet
es
[136
-139
]
35
Introduction
1
inflammatory adipokines such as TNF and leptin. Details on key adipokines in T1D and obesity are provided in Table 1.3.
2.4 Inflammatory control
Inflammation is a physiological response with potentially destructive force, which requires tight regulation. Several regulatory mechanisms exist, ranging from anti-inflammatory cytokines to immunosuppressive cells [168]. Regulatory T cells (Tregs; the two major subpopulations being naturally occurring Tregs and adaptive Tregs, as discussed in paragraph 2.1) are key players in the process of discriminating between self and non-self and health and disease [169]. Under normal circumstances, presentation of auto-antigens leads to activation of Tregs, and next to inhibition of proinflammatory islet autoimmunity by secretion of the anti-inflammatory cytokines such as IL-10 and transforming growth factor (TGF)-β [170].
In the pathophysiology of T1D and obesity, regulatory mechanisms also play an important role. In fact, regulatory mechanisms fail to prevent the development of local and systemic inflammation. This occurs either because pro-inflammatory mechanisms prevail, or because regulatory mechanisms are compromised as in T1D. T1D is characterized by loss of self-tolerance, ultimately leading to destruction of β cells. While Treg numbers are not decreased in T1D, their function is impaired and an autoimmune response, as opposed to regulation, prevails [171, 172]. In obesity, in contrast, Treg numbers in AT are significantly decreased, which contributes to the development of local and systemic inflammation [173].
Next to regulatory T cells, other regulatory mechanisms also play a role. The regulatory mechanisms that are subject of this thesis are specifically introduced below.
2.4.1 The role of heat shock proteinsInflammation evokes upregulation of “danger” signals or damage associated molecular pattern (DAMP) molecules. These molecules are of importance in diabetes as well. Examples of DAMP molecules are evolutionary highly conserved, constitutively expressed heat shock or stress proteins (HSPs). HSPs are involved in protein folding and transportation and “chaperone” degradation of damaged proteins. Based on their relative molecular mass, HSPs are classified into six major families (comprising small HSPs, HSP40, HSP60, HSP70, HSP90 and HSP110) [33, 174]. Especially HSP60 (next
36
Chapter 1
1
Tabl
e 1.
3 A
dipo
kine
sAd
ipok
ines
with
key
role
s in
the
path
ophy
siolo
gy o
f T1D
and
obe
sity
are
desc
ribed
. Des
crib
ed in
the
first
col
umn
are
key
gene
ral f
eatu
res
for a
sp
ecifi
c che
mok
ine,
the
(pat
ho)p
hysio
logi
cal r
ole
with
focu
s on
how
β ce
ll fun
ctio
n is
affe
cted
and
dru
gs in
fluen
cing
the
chem
okin
e fu
nctio
n w
hen
appl
icab
le. T
he se
cond
col
umn
focu
ses o
n kn
owle
dge
rega
rdin
g T1
D, T
2D a
nd o
besit
y, a
nd in
terv
entio
n st
udie
s with
rela
ted
drug
s reg
ardi
ng th
e pr
evio
usly
men
tione
d to
pics
.
Adip
okin
eFu
nctio
nSt
udie
s in
diab
etes
and
obe
sity
Refe
renc
es
Adip
onec
tin
Insu
lin-s
ensi
tizin
g, a
nti-a
ther
ogen
ic, a
nti-i
nfla
mm
ator
y an
d an
ti-ap
opto
tic m
edia
tor.
Rece
ptor
s: Ad
ipoR
1 (m
uscl
e; β
cel
ls) a
nd A
dipo
R2
(live
r; β
cells
)
Phys
iolo
gica
l rol
e: P
rom
otes
foo
d in
take
dur
ing
fast
ing.
Pos
itive
re
gula
tor
of p
ancr
eatic
β c
ell f
unct
ion
thro
ugh
prot
ectio
n ag
ains
t ap
opto
sis. S
exua
l dim
orph
ism (h
ighe
r lev
els i
n fe
mal
es)
Path
ophy
siolo
gica
l rol
e: m
etab
olic
ally
unf
avou
rabl
e co
nditi
ons i
nduc
e do
wnr
egul
atio
n of
adi
pone
ctin
rele
ase
T1D
: Hig
h le
vels
in r
ecen
t on
set
T1D
chi
ldre
n; lo
wer
ad
ipon
ectin
dur
ing
clin
ical
rem
issi
on. E
leva
ted
leve
ls
in lo
ngst
andi
ng T
1D; a
ssoc
iatio
n w
ith c
ardi
ovas
cula
r m
orta
lity.
Inve
rse
asso
ciat
ion
with
fast
ing
and
stim
ulat
ed
β ce
ll fu
nctio
n
T2D
and
obe
sity:
dec
reas
ed a
dipo
nect
in le
vels
[89,
98,
105
, 14
0-14
4]
Lept
in
Pro-
infla
mm
ator
y adi
poki
ne, w
hich
incr
ease
s TN
F an
d IL
-6 p
rodu
ctio
n.
Bind
s to
the
lept
in re
cept
or. P
rodu
ced
by a
dipo
se ti
ssue
in p
ropo
rtio
n to
bod
y fa
t mas
s.
Phys
iolo
gica
l rol
e: r
egul
ator
of
body
wei
ght
thro
ugh
decr
easi
ng
appe
tite
and
stim
ulat
ion
of e
nerg
y ex
pend
iture
. Dire
ctly
reg
ulat
es
insu
lin; p
rote
cts
β ce
ll fu
nctio
n. In
crea
ses
TH1-
and
sup
pres
ses
TH2
cyto
kine
s. Se
xual
dim
orph
ism (h
ighe
r lev
els i
n fe
mal
es)
Path
ophy
siolo
gica
l rol
e: A
ggra
vate
s de
velo
pmen
t of T
h1-d
epen
dent
au
toim
mun
e di
seas
e. In
vitr
o, le
ptin
indu
ces
impa
ired
β ce
ll fu
nctio
n an
d β
cell
deat
h
Rela
ted
drug
s: le
ptin
def
icie
ncy
caus
es h
yper
phag
ia, o
besit
y, fa
stin
g hy
perin
sulin
ism
, im
paire
d gl
ucos
e-st
imul
ated
insu
lin r
elea
se a
nd
gluc
ose
into
lera
nce;
all
reve
rsed
by
lept
in tr
eatm
ent i
n m
ice
T1D
: Hig
her B
MI; h
ighe
r lep
tin a
ssoc
iate
d w
ith in
crea
sed
secr
etio
n of
res
idua
l fas
ting
and
stim
ulat
ed C
-pep
tide
and
high
er B
MI,
afte
r adj
ustm
ent f
or s
ex, a
ge, d
iabe
tes
dura
tion,
HbA
1c a
nd fa
stin
g gl
ucos
e le
vels
T2D
and
obe
sity
: Hig
h le
vels
and
lept
in r
esis
tanc
e pr
omot
e in
sulin
res
ista
nce.
Wei
ght
redu
ctio
n lo
wer
s le
ptin
and
aug
men
ts a
dipo
nect
in in
circ
ulat
ion
Inte
rven
tion:
Lep
tin im
prov
es m
etab
olic
dys
func
tion
in
lipod
ystr
ophy
and
con
geni
tal l
eptin
def
icie
ncy
patie
nts.
In T
2D le
ptin
did
not
impr
ove
insu
lin s
ensi
tivity
, bod
y w
eigh
t or c
ircul
atin
g in
flam
mat
ory
mar
kers
[6, 8
9, 9
8,
144-
150]
37
Introduction
1RB
P-4
Se
cret
ed fr
om a
dipo
se ti
ssue
(AT)
, the
live
r and
mac
roph
ages
Phys
iolo
gica
l rol
e: R
etin
ol (v
itam
in A
) tra
nspo
rt fr
om liv
er to
per
iphe
ral
tissu
e. Im
plic
ated
in sy
stem
ic in
sulin
resis
tanc
e.
Path
ophy
siolo
gica
l rol
e: P
refe
rent
ially
pro
duce
d by
visc
eral
AT
in o
besit
y an
d in
sulin
resis
tanc
e; m
arke
r of i
ntra
-abd
omin
al A
T ex
pans
ion.
In v
itro:
tim
e- a
nd d
ose-
depe
nden
t do
wnr
egul
atio
n of
RB
P4 b
y IL
-1β
thro
ugh
IL-1
rece
ptor
sign
allin
g
T2D
and
obe
sity:
ele
vate
d RB
P-4
leve
ls. R
BP-4
regu
late
s gl
ucos
e ho
meo
stas
is in
T2D
mod
els
[6, 9
7, 1
51,
152]
Cath
epsin
S
Prot
ease
; inv
olve
d in
ext
race
llula
r mat
rix (E
CM) d
egra
datio
n. In
duce
d am
ongs
t man
y ce
lls in
adi
pocy
tes a
nd sm
ooth
mus
cle
Phys
iolo
gica
l rol
e: P
rom
otes
adi
poge
nesi
s. I
nvol
ved
in a
ntig
en
pres
enta
tion;
mai
nly
foun
d in
lyso
som
al/e
ndos
omal
com
part
men
ts
of a
ntig
en p
rese
ntin
g ce
lls
Path
ophy
siol
ogic
al r
ole:
exp
ress
ed i
n at
hero
scle
roti
c le
sion
s;
cont
ribut
es to
pla
que
prog
ress
ion
poss
ibly
thro
ugh
elas
tin d
egra
datio
n in
vas
cula
r wal
l.
Obe
sity:
Cat
heps
in S
leve
ls in
crea
sed
Inte
rven
tion:
Inhi
bitio
n ex
pect
ed t
o re
sult
in im
mun
e su
ppre
ssio
n
[153
-156
]
TIM
P-1
Tiss
ue in
hibi
tors
of m
atrix
met
allo
prot
eina
ses (
MM
Ps).
Elem
enta
ry ro
le
in e
xtra
cellu
lar m
atrix
rem
odel
ling
Phys
iolo
gica
l rol
e: in
hibi
ts e
xtra
cellu
lar
enzy
mes
with
pro
teol
ytic
ac
tiviti
es (M
MPs
) par
ticip
atin
g in
cel
lula
r ho
meo
stas
is, a
dapt
atio
n,
and
tissu
e re
mod
ellin
g
Path
ophy
siol
ogic
al r
ole:
inju
rious
and
infla
mm
ator
y in
sults
incr
ease
TI
MP.
Hig
h gl
ucos
e co
ncen
trat
ions
incr
ease
TIM
P-1
expr
essio
n
Obe
sity:
incr
ease
d TI
MP-
1 le
vels
TIM
P-1
is in
volv
ed in
dia
betic
nep
hrop
athy
thr
ough
M
MP-
med
iate
d ef
fect
s on
glo
mer
ular
acc
umul
atio
n of
EC
M a
nd b
asal
mem
bran
e th
icke
ning
. Hyp
ergl
ycae
mia
in
crea
ses
synt
hesis
of e
xtra
cellu
lar
mat
rix c
ompo
nent
s an
d de
crea
ses d
egra
dativ
e pr
oces
ses
[157
-160
]
Tabl
e 1.
3 co
ntin
ues o
n ne
xt p
age
38
Chapter 1
1
Tabl
e 1.
3 C
ontin
ued
Adip
okin
eFu
nctio
nSt
udie
s in
diab
etes
and
obe
sity
Refe
renc
es
Chem
erin
Ac
ts b
oth
pro-
and
ant
i-inf
lam
mat
ory
depe
ndin
g on
env
ironm
ent.
Pro
mo
tes
adip
og
enes
is,
infl
amm
atio
n a
nd
an
gio
gen
esis
. Ch
emoa
ttra
ctan
t fun
ctio
n. M
ainl
y pro
duce
d by
liver
and
whi
te a
dipo
se
tissu
e (W
AT)
Phys
iolo
gica
l rol
e: A
utoc
rine/
para
crin
e ef
fect
s on
adi
pose
tis
sue
deve
lopm
ent
and
func
tion.
End
ocrin
e ef
fect
s on
met
abol
ism
and
im
mun
ity. S
ome
stud
ies p
oint
to g
ende
r- a
nd a
ge-re
late
d di
ffere
nces
(h
ighe
r lev
els i
n fe
mal
es a
nd o
lder
adu
lts)
Path
ophy
siolo
gica
l rol
e: E
leva
ted
in c
hron
ic in
flam
mat
ion
T2D
and
obe
sity:
Ele
vate
d le
vels
in o
besit
y. M
odul
ates
in
sulin
secr
etio
n an
d se
nsiti
vity
. Lev
els c
orre
late
with
BM
I, m
easu
res o
f cen
tral
adi
posit
y an
d m
arke
rs o
f met
abol
ic
synd
rom
e. E
xerc
ise-in
duce
d w
eigh
t los
s dec
reas
es se
rum
le
vels.
[161
, 162
]
SAA-
1 In
flam
mat
ory
acut
e-ph
ase
prot
ein
expr
esse
d in
adi
pose
tiss
ue
Phys
iolo
gica
l ro
le: a
ssoc
iate
d w
ith
syst
emic
inf
lam
mat
ion
and
athe
rosc
lero
sis
Path
ophy
siol
ogic
al r
ole:
indu
ced
infla
mm
ator
y ac
tivity
ass
ocia
ted
with
dev
elop
men
t of
dia
betic
nep
hrop
athy
. Pre
dict
ive
mar
ker
for
card
iova
scul
ar e
vent
s
SAA
and
C-r
eact
ive
prot
ein
(CRP
) ba
selin
e le
vels
pr
edic
t th
e de
velo
pmen
t of
mic
roal
bum
inur
ia a
nd
mac
roal
bum
inur
ia a
s wel
l as c
ardi
ovas
cula
r eve
nts
T2D
and
obe
sity:
incr
ease
d SA
A-1
leve
ls
[132
, 163
, 16
4]
PAI-1
In
hibi
tor
of p
lasm
inog
en a
ctiv
atio
n. P
rodu
ced
by t
he v
ascu
lar
endo
thel
ium
, AT
and
hepa
tocy
tes;
also
pre
sent
in p
late
lets
Phys
iolo
gica
l rol
e: a
ntifi
brin
olyt
ic a
nd p
ro-c
oagu
lant
. Lev
els e
nhan
ced
by o
xida
tive
stre
ss a
nd in
flam
mat
ion.
Impa
irs a
dipo
cyte
diff
eren
tiatio
n an
d in
sulin
resis
tanc
e. D
ecre
ased
by
adip
onec
tin
Path
ophy
siol
ogic
al r
ole:
thr
ombo
lytic
the
rapy
effi
cacy
red
uced
by
elev
ated
PAI. C
ontr
ibut
es to
ath
eros
cler
osis
and
card
iova
scul
ar d
iseas
e.
T2D
and
obe
sity:
- Ele
vate
d pl
asm
a le
vels
in b
lood
and
art
eria
l wal
l.- E
leva
ted
leve
ls in
duce
d by
com
bina
tion
of h
yper
insu
-lin
aem
ia, h
yper
glyc
aem
ia a
nd h
yper
trig
lyce
ridae
mia
Inte
rven
tion:
Elev
ated
leve
ls d
ecre
ase
with
insu
lin s
ecre
tago
gue
and
met
form
in t
reat
men
t. PA
I-1 in
hibi
tors
are
und
er
deve
lopm
ent
[140
, 165
-16
7]
39
Introduction
1
to HSP70 and HSP90) family members have been described as immunodominant molecules.
Innate and adaptive roles for HSP
HSP60 or HSP60 peptides are primarily innate signals for macrophages and DCs. In response to HSP60, monocytes undergo activation and maturation, resulting in the production of pro-inflammatory molecules and factors such as TNF-α, IL-1β, IL-6, RANTES, CCL2, CCL3 and nitric oxide (NO) [175]. Moreover, IL-12, IL-15 as well as MHC and co-stimulatory molecule upregulation is induced.
However, HSP60 also involves the adaptive immune system by upregulating regulatory T cells and involving B cells. In short, HSPs interact with specific T cell receptors as well as with Toll-like receptors (TLRs) to induce pro-and anti-inflammatory effects and thus HSPs have the potential to lead to a shift of the immune response from inflammatory to regulatory. In this way HSP60 can aid in control of inflammation and immunity. HSP60 and HSP60 peptides can have anti-inflammatory effects through TLR2 signalling as well as pro-inflammatory effects through TLR4 signalling and activation of B cells [176, 177].
Thus, T cells can recognize epitopes of self and non-self HSP60 as specific antigens both in health as well as in autoimmune disease HSPs and HSP peptides can be either self or non-self. They can be derived from pathogens during infection or inflammation while at the same time the body may upregulate self-HSPs. Next, so-called cross-recognition can occur between microbial and self-HSPs, in which the same T cell recognizes both HSP peptides; probably, this is facilitated by the high sequence homology between mammalian and microbial HSP. Thus, T cells recognize epitopes of self and non-self HSP60 as specific antigens both in health as well as in autoimmune disease. The pro-inflammatory mediators that can be induced by HSP60 may in turn affect target cells, thereby contributing to the initiation and progression of inflammatory processes such as β cell destruction in T1D or vascular damage in atherothrombosis.
The emergence of the concept that cross-recognition can lead to the induction of self-HSP cross-reactive T cells with a regulatory capacity has led to new insight in (mycobacteria-induced) adjuvant arthritis as well as many other (experimental) disease models, including arthritis, atherosclerosis, allergic encephalomyelitis, and allergic asthma [178-182]. In addition to cross-recognition, the emergence of autoproliferative self-HSP60 reactive T cell lines has been described, with production of IL-10, IL-4 and IFN-γ.
40
Chapter 1
1
An important question regarding HSP 60 concerns its role in the process of maintaining the immune balance. It is thought that the effects of HSP60 on the immune response depend on the concentration, the particular HSP60 epitope (bacterial or self), and the local environment [183].
HSPs and type 1 diabetes
HSP60 in T1D is thought to act in both a pro- and an anti-inflammatory manner. Stressed β cells release HSP60, which, through TLR4, activates macrophages (proceeding to auto-antigen presentation on their cell surface) and T cells (cytotoxic T cells and Th1 T cells) to enhance an inflammatory process. Concomitantly, HSP60, through TLR2, can mediate an anti-inflammatory effect by T cell adhesion and anti-inflammatory cytokine secretion [184, 185].
As HSP60 is a DAMP molecule with obvious regulatory capacities, its therapeutic use has been tested in clinical trials in various diseases such as rheumatoid arthritis and T1D [186]. In T1D, HSP have been the focus of interest since the quest for antigens inducing the autoimmune process. A 65-k Da HSP was identified, whereby T cell reactivity to an antigen cross-reactive with this HSP molecule was found in the NOD mice model of T1D, mediating insulitis. Moreover, modulation of the anti-hsp60 T cell response could arrest autoimmune destruction [187, 188]. Of note, bacterial hsp65 is highly homologous with mammalian hsp60. The 437-460 peptide sequence of mouse hsp60, peptide p277, specifically was identified as an important target. Subsequently significantly elevated responses to the p277 peptide were found in human T1D patients, especially shortly after diagnosis, thus underscoring the role of peptide 277 as a major T cell epitope of human HSP60 [189].
DiaPep277 is a synthetic peptide almost identical to HSP60 peptide p277 with two amino acid modifications to increase stability without changing immunological properties of p277. While HSP can have pro-and anti-inflammatory aspects, DiaPep277 was shown to mediate only anti-inflammatory effects as it does activate TLR2 but, contrary to HSP60, does not activate TLR4 and thereby has only anti-inflammatory effects [176, 187, 190, 191].
In T1D, the first phase III trials with DiaPep have been completed. The rationale is induction of peptide-specific T cells changing the phenotype from pro-inflammatory to anti-inflammatory and producing IL-4 and IL-10. Actual results of the trials have varied in terms of clinical outcome, but the hypothesized immunologic effect has been confirmed by a rise in serum IL-10 and IL-13 and a decrease in IFN-γ production in treated patients compared to a placebo group [176, 184, 192-195]. Recently, results of an extension of a
41
Introduction
1
phase III clinical trial have been announced [www.andromedabio.com]. In patients with residual β cell function after 2 years of treatment, continuation of treatment resulted in improved metabolic control and a higher percentage of patients in clinical remission. Interestingly, results of DiaPep treatment in terms of maintenance of β cell function thus far have been better in adults than in children. In adult phase II trials preserved mean C-peptide levels have been found, while there was no such beneficial effect in children. However, on close examination a possible relation with HLA genotype was found. Further analysis has revealed that adults with low and moderate risk HLA genotypes benefit the most from intervention with DiaPep277 [196]. Currently, 4 trials involving peptide 277 are ongoing (www.ClinicalTrials.gov). There is no data from imaging or biopsy studies on the direct effect of HSP60 treatment on β cell insulitis.
Apart from peptide 277, other HSP60 peptides could also have immunomodulatory effects. However, it is difficult to identify epitopes in individual patients. HLA molecules differ between patients and as antigen presentation by APCs is HLA-specific, the binding of potential epitopes might also differ. Peptides that can bind to multiple allelic variants of the human MHC molecule HLA-DR are called pan-DR epitopes. Thus, HSP peptides with HLA pan-DR binding capacities are interesting candidates for therapy [197]. Identification of such peptides is possible through the use of computer algorithms. In this way, the pan-DR binding peptides described in chapter 2 were generated.
HSPs and type 2 diabetes
HSP60 may also be a factor underlying adipose tissue inflammation and obesity-associated metabolic disorders. While increased proinflammatory adipokines secretion has been reported in adipocytes from obese individuals, it remains unknown what actually triggers this response and a role for HSP60 has been speculated upon. Elevated circulating HSP60 levels have recently been confirmed in T2D and in obese individuals with T2D; furthermore HSP60 levels correlated positively with BMI, blood pressure, leptin and insulin resistance in a recent study [186, 198]. HSP60 was found to be released from adipocytes, with a stimulatory effect on HSP60 release for a TLR4 agonist while, interestingly, a TLR2 agonist did not affect HSP60 release [186]. Moreover, HSP60 could induce release of obesity-related inflammatory mediators (TNF-α, RANTES, CCL2, CCL3, IL-6 and IL-8) by mature adipocytes. Thus, HSP60 could be involved in adipose tissue inflammation by both inducing acute proinflammatory signaling and enhanced release of proinflammatory mediators, which in its turn affects insulin sensitivity. Moreover, HSP60 had endocrine effects on skeletal muscle cells, inducing insulin resistance [186].
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2.4.2 The role of vitamin D Another potential immune regulator in T1D, T2D and obesity is vitamin D. Bioactive 1,25-dihydroxyvitamin D3 (1,25-(OH)2D3), apart from its well-known functions in regulating calcium-phosphate homeostasis and bone metabolism, has many other effects. 1,25-(OH)2D3 is involved in the regulation of over 200 genes, many of which are expressed in the immune system. Through this route 1,25-(OH)2D3 is involved in apoptosis and immune modulation. For bone metabolism, 1-alpha hydroxylase activity, which induces formation of 1,25-(OH)2D3, is under control of parathyroid hormone. However, in the immune system, 1-alpha hydroxylase activity is regulated by immune signals (IFN-γ and TLR agonist) allowing higher intracellular 1,25-(OH)2D3 concentrations for immunomodulatory effects [199]. Moreover, various immune cells express 1-hydroxylase or both 1- and 25-hydroxylase activity to convert precursor hormone to active vitamin D [200].
Vitamin D has a protective role in in the immune system, dampening the potential pathogenic immune response at a cellular level through immunomodulatory effects on both the innate as well as the adaptive immune system. Firstly, the innate response is modulated through inhibition of dendritic cell differentiation, maturation and immunostimulatory capacity, as vitamin D can upregulate IL10, CCL2, CCL18 and CCL22 secretion by dendritic cells and downregulate IL-12, IL-23 and CXCL10 [201, 202]. Next, T cell responses and thereby cytokine levels are affected by vitamin D, with decreased production of IL-2, IFN-γ, IL-17 and IL-22, and increased production of IL-4, thus shifting the balance towards induction of regulatory T cells [202]. Finally, vitamin D has an overall inhibitory effect on the adaptive immune cells through inhibition of T cell proliferation and CD8 T cell-mediated cytotoxicity as well as decreased B cell proliferation, plasma cell differentiation and IgG secretion [200, 203]. Thus, 1,25-(OH)2D3 levels can influence innate and acquired immunity at various stages through activation and differentiation of various immune cells or by influencing susceptibility to viral infections [74].
In view of the above-mentioned physiological role in the immune system, derangements in vitamin D homeostasis might link 1,25-(OH)2D3 to diseases, as is currently studied for cardiovascular disease, various autoimmune diseases including diabetes, and cancer [204, 205].
1,25-(OH)2D3 and type 1 diabetes
In addition to the general effects of Vitamin D on the immune system, vitamin D levels can directly affect pancreatic function. Pancreatic islets express 1,25-(OH)2D3-related
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genes, such as those coding for the vitamin D receptor (VDR) and Vitamin D binding protein (DBP). Vitamin D and T1D are linked on three levels: 1) through polymorphisms in VDR and DBP, which have been related to vitamin D serum levels, T1D autoantibodies and long term complications such as retinopathy [206-209]; 2) through locally produced 1,25-(OH)2D3, which protects β cells against pathogens [205]; and 3) through a direct effect on the immune system, as the VDR is expressed on APCs, activated CD4 and CD8 T lymphocytes and B lymphocytes [210, 211].
There are two additional reasons why vitamin D (deficiency) entered the T1D arena. Firstly, there is a seasonal effect in the onset of T1D, with an increased onset of T1D in winter coinciding with vitamin D trough levels; secondly, incidence of T1D is inversely related to latitude and thus sunshine exposure – the most important factor influencing endogenous vitamin D production [199, 212, 213].
Vitamin D levels appear to influence the risk for development of T1D already in the womb, as maternal vitamin D status has been described to be a risk factor for T1D in offspring [214]. Next, influencing infant vitamin D levels was reported to reduce the risk of development of T1D in at risk individuals [215]. Finally, there are a substantial number of reports on the association of low 25(OH) vitamin D levels and onset of T1D [216-220], although some studies do not confirm these results [221]. However, the question regarding cause or effect between vitamin D levels and T1D remains. Further potential for vitamin D in T1D lies in intervention therapy with vitamin D-treated dendritic cells, as will be addressed in the discussion.
1,25-(OH)2D3 and type 2 diabetes & obesity
There is ample evidence for an association between vitamin D and obesity and T2D [222-224]. A recent study reported 51% of obese children in the US to be vitamin D deficient [223]. One of the mechanisms for vitamin D deficiency in obesity is the increased adipose tissue compartment where vitamin D is stored [222]. Next, glycaemic control in T2D has been observed to deteriorate in winter and spring, seasons usually coinciding with trough levels in Vitamin D [225]. Thirdly, genetic variations in genes involved in vitamin D signalling affected insulin secretion and glucose tolerance in some studies, although not in all [226, 227]. Moreover, 25-(OH)D3 deficiency was associated with insulin resistance in obese children and with obesity and metabolic syndrome in adults [228-231].
A strong link between vitamin D and T2D is systemic inflammation, present in T2D and obesity, as discussed above. The elevated cytokine levels in inflammation may trigger β
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cell apoptosis and thus cause β cell dysfunction, while vitamin D may improve insulin sensitivity and promote β cell survival through influencing cytokine levels [224].
2.4.3 The role of other factors Next to HSPs and vitamin D, other regulatory mechanisms have been described. With regard to this thesis, the role of AT- derived regulatory molecules and gut microbiota specifically will be introduced shortly.
Firstly, AT has emerged over the last decade as an active player in immune regulation, next to its role in glucose and lipid homeostasis [232]. On the one hand adipokines such as resistin, retinol binding protein, and TNF-α have been recognized for their ability to exacerbate inflammation and hyperglycaemia [232]. Of note, while anti-TNF-α therapy improves suppressive Treg function in RA, development of T1D during this treatment of RA patients has been reported [233]. Possibly, based on animal studies, suppression of TNF-α has a therapeutic window for induction of tolerance while at other stages it has an opposite effect [234]. On the other hand, high adiponectin levels in T1D were found to correlate with disease remission [135, 235]. AT-derived factors have even been studied for therapeutic purposes. A recent study in mice with T1D suggested that administration of leptin improves inflammatory and metabolic parameters [236].
Next, the role of brown fat depots gained attraction. Mouse studies have shown that brown fat lipoatrophy induces inflammatory signalling, vascular insulin resistance, and vascular dysfunction through overexpression of cytoadipokines [237]. Further support of the role of brown fat depots was provided by transplantation studies of brown adipose tissue in mice, resulting in marked improvement of glucose homeostasis and reversal of diabetes, accompanied by regeneration of subcutaneous white adipose tissue. It was suggested that brown adipose tissue transplants achieved chronic regulation of glucose through elevation of adipokines and hormones (such as adiponectin, leptin, and IGF-1) and that this regulation correlated with the maintenance of healthy and non-inflamed adipose tissue [238].
Secondly, gut microbiota is increasingly considered as a major player in human metabolism [239]. The composition of gut microbiota is influenced by host characteristics as well as mode of delivery in childbirth. However, it remains stable over time in healthy adults in spite of external influences such as infections or diet [240-242]. The balance between different commensals in gut microbiota determines part of the nutrient uptake, and is essential for an effective immune response against pathogens. Interestingly, gut microbiota can even influence autoimmune responses in distant tissues [242, 243].
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These observations raised hope for faecal transplantation studies to modulate metabolic and inflammatory processes by shifting the balance of gut commensal bacteria. Recently, the therapeutic effects of allogenic lean donor faeces infusion on insulin resistance in male patients with metabolic syndrome were investigated in a randomized controlled trial [244]. Beneficial alterations in glucose metabolism were observed, offering a rationale for the further exploration of therapeutic intervention methods directly affecting gut microbiota-induced metabolism. A trial with faecal transplantation in T1D is ongoing (DIMID1, www.trialregister.nl: NTR3697).
3 Thesis outline
The first part of this thesis focuses on immune (dys)regulation and biomarkers for the immune responses elicited in T1D. In chapter 2, recognition of pan HLA-DR binding HSP-60 epitopes in paediatric new-onset T1D patients is studied and compared to longstanding T1D patients as well as healthy controls. Although HSP60 peptides induced low peptide-specific proliferative responses we detected some, mainly intracellular, peptide-specific cytokine production in T1D patients. Biomarkers for the remission period could not be identified, as differences in peptide-specific cytokine production occurred in parallel with absence of remission but not at disease onset.
In chapter 3, we report our finding that autoreactive CD8 T cells, recognizing preproin-sulin (PPI) peptides binding with very low affinity to HLA molecules, appear to escape thymic selection while numbers of PPI specific CD8 T cells are decreased for peptides with intermediate and high HLA-A2-binding affinity. Therefore, very low affinity binding peptides might play an important role in mediating loss of tolerance and the autoreactive CD8 cells involved might classify as biomarkers for loss of tolerance.
The second part (chapters 4 and 5) focuses on AT inflammation, more specifically on the role of adipokines in T1D and obesity. In chapter 4, we studied the involvement of adipose tissue in the inflammation in new-onset paediatric T1D by assessment of adipokine levels as well as adipocyte differentiation under various conditions. Results were compared to healthy controls (HC) and patients with longstanding T1D. In T1D patients we found increased adipokine levels compared with HC, while some adipokines further increased with longer duration of T1D. Furthermore, plasma factors (but not glucose and free fatty acids) in T1D patients were shown to in vitro influence adipocyte differentiation as well as secretion of adipokines by these adipocytes.
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In chapter 5, the role of vitamin D deficiency with regard to inflammation and insulin sensitivity in obese children is analysed and compared to healthy controls by performing comprehensive profiling of inflammatory mediators. Vitamin D deficiency is shown to be an independent factor influencing systemic inflammation, while vitamin D sufficiency is associated with better insulin sensitivity in obesity.
In chapter 6, we describe an exploratory study to identify prognostic or descriptive biomarkers of disease remission, recurrence and progression in T1D patients transplanted with pancreatic islets. Inflammatory mediators pre- and post-islet transplantation were studied in patients categorized by graft function (good or insufficient engraftment) during the first year after transplantation. Interestingly, inflammatory mediator levels varied more between individual patients than being influenced by transplantation and concomitant immune suppression. Nonetheless, distinct immune correlates could be identified in serum which associated with clinical outcome.
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240. Nieuwdorp M., Vergeer M., Bisoendial R.J. et al. Reconstituted HDL infusion restores endothelial function in patients with type 2 diabetes mellitus. Diabetologia 2008; 51:1081-1084.
241. Crume T.L., Ogden L., West N.A. et al. Association of exposure to diabetes in utero with adiposity and fat distribution in a multiethnic population of youth: the Exploring Perinatal Outcomes among Children (EPOCH) Study. Diabetologia 2011; 54:87-92.
242. Atkinson M.A., Chervonsky A. Does the gut microbiota have a role in type 1 diabetes? Early evidence from humans and animal models of the disease. Diabetologia 2012; 55:2868-2877.
243. Wu H.J., Ivanov I.I., Darce J. et al. Gut-residing segmented filamentous bacteria drive autoimmune arthritis via T helper 17 cells. Immunity 2010; 32:815-827.
244. Vrieze A., van Nood E., Holleman F. et al. Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology 2012, in press.
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Specific aspects of immune tolerance in type 1 diabetes
PART II
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Recognition of heat-shock protein 60 epitopes in children with
type 1 diabetes
A.A. Verrijn Stuart, W. de Jager, M.R. Klein, G. Teklenburg, R. Nuboer, J.J.G. Hoorweg, M.A.M.J. de Vroede, I. de Kruijff, M. Fick, E.J. Schroor,
G.J. van der Vlist, J. Meerding, S. Kamphuis*, A.B.J. Prakken*
* Authors contributed equally
Diabetes Metab Res Rev 2012; 28:527-534
2
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Abstract
BackgroundTreatment with a specific HSP60 epitope in new onset of type 1 diabetes (T1D) patients has been shown to preserve endogenous insulin production. Previously, recognition of pan HLA-DR-binding HSP60 epitopes in various autoimmune diseases was found; this study investigated recognition of these epitopes in newly diagnosed T1D patients and correlated findings to the occurrence of a partial remission.
MethodsPeripheral blood mononuclear cells of 18 children with T1D were prospectively collected at disease onset and a few months after diagnosis. Epitope-specific T cell proliferation and cytokine production (intracellular and in culture supernatants) were measured; results were compared with 31 longstanding T1D patients and ten healthy controls.
ResultsAlthough HSP60 epitope-specific T cell proliferative responses were detected, overall proliferative responses were low. At onset, epitope-specific intracellular IFN-y production was higher in T1D patients compared with healthy controls (p < 0.05). At follow-up, both IL-10 and IFN-γ production were higher in those without a partial remission than in those with a partial remission (both p < 0.05). Also, IL-10 and IFN-γ production were higher compared with onset for patients without a partial remission (both p < 0.01). In supernatants of HSP60 epitope-specific T cell cultures, no substantial differences in cytokine production were found between T1D patients with and without a partial remission, either at onset or a few months after onset. As patient numbers were small, results should be interpreted with caution.
ConclusionsPan-DR-binding HSP60 peptides induced low peptide-specific proliferative responses and peptide-specific production of some, mainly intracellular, cytokines in T1D patients. Recognition did not differ significantly between patient groups and various time points.
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Introduction
Research directed at ameliorating the course of type I diabetes (T1D) has recently focused at β cell replacement therapies on the one hand and at β cell preservation by immunomodulation started shortly after clinical onset of T1D on the other [1-8].
T cells play a key role in autoimmune diseases. In particular, it has been shown that T cells responding to heat shock proteins (HSP), which are highly conserved, ubiquitously expressed proteins, take part in the normal immunoregulatory response and have a potential to dominantly control pro-inflammatory responses and chronic inflammatory disease. HSP can play a dual role, serving either as a danger signal that can start an inflammatory response, or as an inducer of a regulatory T cell response that can dampen inflammation [9-12]. One HSP family member, HSP60, has been shown to play an immunomodulatory role in a number of autoimmune and inflammatory diseases such as rheumatoid and juvenile idiopathic arthritis, atherosclerosis, juvenile dermatomyositis and diabetes [13-16]. Studies on the role of DiaPep277, a specific HSP60-epitope in T1D, have shown that treatment with this epitope can preserve part of the endogenous insulin production, supporting a regulatory role for HSP in T1D. Randomized controlled trials with DiaPep 277 showed preserved endogenous insulin in part of the patients treated with the HSP60 peptide, which underlines the need to explore the role of HSP60 peptides in disease pathophysiology further [1, 5, 6, 17, 18].
Although destruction of β cells is usually advanced at onset of T1D, a substantial part of patients with new onset diabetes develop a transient partial remission (PR) (“honeymoon”) phase, characterised by limited need of insulin with good metabolic control, during the first months of the disease, implying some retained or regained β cell function. Although the underlying mechanism has been speculated upon but is not known, recent data support the hypothesis this PR phase might in part be immunologically driven [19, 20]. Retaining β cell function is of major importance, as it is closely related to a more favourable outcome regarding long-term complications of the disease [21, 22]. Possibly, HSP60-epitopes (can) play a role in this partial remission by inducing dampening of the immune response.
Previously, we identified a number of HSP60 peptide epitopes that can bind multiple allelic variants of the human major histocompatibility complex molecule HLA-DR (pan-DR epitopes). These epitopes were able to induce epitope-specific T cell responses, for instance an anti-inflammatory response in juvenile idiopathic arthritis and rheumatoid
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arthritis, whereas in Crohn’s disease the response showed a pro-inflammatory phenotype [15, 23-25]. In the present study we set out to establish whether the identified epitopes elicit immune responses in peripheral blood mononuclear cells (PBMCs) of patients with T1D and, if so, to determine the profile of the immune response. Therefore, we prospectively investigated recognition of the selected epitopes in a cohort of children with new onset T1D, both at onset and again a few months after diagnosis, when the occurrence or absence of a partial remission was taken into account. The results were compared to patients with longstanding T1D and healthy controls.
Materials and methods
Subjects
Heparinized blood samples were obtained from 18 newly diagnosed T1D patients (nine boys, nine girls, median (range) HbA1c at onset of all individuals: 10.4% (7.5–13.6%)). In addition, 31 randomly selected children with T1D and diabetes duration of more than 1 year (longstanding T1D; 18 boys, 13 girls) as well as ten healthy children (six boys, four girls) were included in this study (Table 2.1). The International Society for Pediatric and Adolescent Diabetes (ISPAD) criteria for diagnosis of T1D were used [26]. The day of diagnosis was defined as the day on which hyperglycaemia was detected and insulin therapy was initiated. All patients were recruited from six community hospitals and one academic hospital.
Table 2.1 Patient characteristics
New onset T1D Longstanding T1D Healthy controls
Partial remission
No partial remission
n 7 11 31 10
Age at onset (years)
Insulin dose (U/kg/d)
HbA1c (%)
Time after onset (days)
8.4 (4.5–9.7)
0.2 (0.0–0.5)
6.9 (6.1–7.5)
80 (40–140)
11.5 (5.8–14.3)
0.8 (0.1–1.2)
8.3 (6.5–10.1)
65 (45–150)
7.2 (2.0–15.1)
1.0 (0.6–1.6)
8.8 (7.1–14.0)
2336 (474–5475)
10.5
NA
NM
NA
Median and (range) are shown. New onset T1D: insulin dose and HbA1c as measured at time point 2 (t = 2; 2–5 months after onset of T1D). New onset T1D, new onset type 1 diabetes patients; longstanding T1D, longstanding type 1 diabetes patients; T1D, type 1 diabetes; NA, not applicable; NM, not measured.
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In patients with newly diagnosed T1D, a blood sample was obtained 1 day (median; range: 0–12 days) after diagnosis and a second sample was drawn at the routinely scheduled outpatient visit 2–5 months after diagnosis (follow-up sample). Seven patients (three boys, four girls) had a partial remission (PR+), defined as both an HbA1c of ≤ 7.5% and an insulin dose of ≤ 0.5 U/kg/d at the moment the second sample was drawn after 2–5 months [27, 28]. Patients not fulfilling these criteria were classified as without a partial remission (PR-) at the second time point.
Blood samples were drawn at random times during the day without a specific fasting episode. Because of the small blood volume that could be drawn in this paediatric population, not all experiments could be performed in all patients. HbA1c was measured at the participating hospitals for routine diagnostics. Written informed consent was obtained from all children and/or their parents. The study was approved by all local medical ethics review boards.
Methods
Selection and synthesis of HSP60 peptides A matrix-based computer algorithm was used to select eight pan-DR binding HSP60 epitopes (p1–p8) as previously described [23, 29]. Via an algorithm, HSP60 peptides were chosen as peptide pairs, each pair consisting of a microbial HSP60 epitope and its human homologue (Table 2.2). All peptides originate from the conserved parts of HSP60. The peptides (> 99% pure) were synthesized by Ansynth Service BV (Roosendaal, Netherlands).
Table 2.2 Characteristics of the peptides
HSP60 peptide Sequence Core epitope
p1
p2
p3
p4
Myc 254–268
Hum 280–294
Myc 216–230 a
Hum 242–256
GEALSTLVVNKIRGT
GEALSTLVLNRLKVG
PYILLVSSKVSTVKD
AYVLLSEKKISSIQS
LSTLVVNKI
LSTLVLNRL
LVSSKVSTVYILLVSSKV
LSEKKISSI
For comparison: p277 (DiaPep277)(1) is human HSP60 peptide 437–460 (VLGGGVALLRVIPALDSLTPANED). Myc, microbial; hum, human. a p3 has 2 core epitopes.
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Proliferation assayPeripheral blood mononuclear cells were isolated by Ficoll (GE Healthcare, Uppsala, Sweden) density gradient centrifugation. After viability was assessed, cells were cultured in RPMI-1640 tissue culture medium, supplemented with 2 mM glutamine, 100 U/ml of penicillin/streptomycin (Invitrogen Corporation, Carlsbad, CA, USA), and 10% (by volume) AB positive heat-inactivated (60 min at 56°C) human serum (Sanquin Blood Bank Utrecht, The Netherlands). T cell proliferation assays were performed by culturing PBMC with medium alone or with 20 µg/ml of the individual HSP60 peptides. All cultures were carried out in triplicate (2 x 105 cells in 200 µ l per well) and in round-bottomed 96-well plates (Nunc, Roskilde, Denmark) for 120 hours at 37°C in 5% CO2 with 100% relative humidity. Plate-bound anti-CD3 (eBioscience, San Diego, CA, USA) was used as positive control. An adenovirus peptide (A5, hexon-associated protein precursor with peptide sequence RQAILTLQTSSSEPR) was used as an irrelevant control [30]. During the last 16 hours of culture 1 µCi (37 kBq) 3H-thymidine (MP Biomedicals, Irvine, CA, USA) was added to each well. Cells were harvested and incorporated radioactivity was measured by liquid scintillation counting and expressed as counts per minute (cpm). The proliferative response was expressed as a stimulation index (SI), calculated as the mean cpm of cells cultured with peptide divided by the mean cpm of cells cultured without peptide. As generally there are low precursor frequencies of epitope (peptide)-specific T cells in PBMCs (usually less than 10 cells per million), a cut-off value of 1.7 was used to define positive proliferative responses to epitopes [30, 31].
Flow cytometryPeripheral blood mononuclear cell cultures were performed as described earlier for 72 hours (2 x 105 cells in 200 µl per well). During the last 5 hours of culture, Golgistop (BD Biosciences, San Jose, CA, USA) was added (0.67 µl/ml final concentration). Cells were washed twice in PBS supplemented with 2% fetal calf serum (FCS) and 0.1% NaN3. Nonspecific staining was limited by incubating the cells with 10% normal mouse serum. Cells were stained with one or more of the following surface marker antibodies CD3, CD4, CD25 and CD127 (all BD Biosciences) according to the manufactures instructions.
For intracellular cytokine analysis, cells were incubated, after surface marker staining, in Cytofix/Cytoperm solution (BD Biosciences) according the manufacturer’s instructions Subsequently aspecific binding was blocked in 10% normal rat serum before cells were
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stained for IL10 and IFN-γ (both BD Biosciences), all stained mononuclear cells were measured with a FACS Calibur flow cytometer. CellQuest software (BD Biosciences) was used for analysis.
Multiplex assay Peripheral blood mononuclear cell cultures were performed as described earlier. After 72 hours, cell culture supernatants were collected and stored at -80°C. Cytokine concentrations (Interleukin 1 receptor antagonist (IL1-Ra), Interleukin (IL)-1α, IL-1β, IL-2, IL-4, IL-6, IL-10, IL-13, IL-17, TNF-α, IFN-γ and CXCL10 (gamma interferon inducible protein; IP10) were measured with the Bio-Plex system in combination with the Bio-Plex Manager software version 6.0 (Bio-Rad Laboratories, Hercules CA, USA), which employs the Luminex xMAP technology as previously described [32]. Fluorescence intensity of the microsphere complex was measured in a final volume of 100 µl of High Performance Elisa (HPE) buffer (Sanquin, Amsterdam, the Netherlands). The antibody pairs were purchased and coupled as previously described [32, 33]. Peptide-specific cytokine production was calculated as the cytokine production of cells cultured with peptide subtracted with the cytokine production of cells cultured without peptide.
Statistical analyses Statistical evaluation was performed using GraphPad Prism software, version 4.02 (La Jolla, CA). Basic descriptive statistics were used to describe the patient populations. Paired data were analysed with the Wilcoxon signed-ranks test; unpaired data were analysed with the Mann-Whitney U test. A p-value less than 0.05 was considered statistically significant.
Results
T cell proliferative responses
Firstly, we compared T cell proliferative responses among patients with newly diagnosed T1D, longstanding T1D patients and healthy controls for each individual HSP60 peptide (p1, p2, p3 and p4) and the irrelevant control peptide (an adenovirus peptide, A5). As no differences between proliferative responses to the individual HSP60 peptides p1, p2, p3 and p4 were detected; the responses were pooled for further analyses. Responses to the indifferent peptide A5 did not differ from responses to the four HSP peptides.
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Various patients showed a positive proliferative response to one of the HSP60 epitopes but in general responses did not exceed the SI threshold of 1.7 (responses per peptide: Supplemental Figure S2.1). We did not find differences in recognition of the epitopes between healthy controls, new onset patients or patients with longstanding disease (p > 0.45 for all comparisons; Figure 2.1A).
Next, we compared HSP60 epitope-specific T cell responses from newly diagnosed patients according to whether they did (PR+) or did not (PR-) develop a PR. At onset,
Figure 2.1 HSP 60 Peptide-specific proliferative responsesThe proliferative responses to the four HSP peptides were pooled as no differences between the individual HSP60 peptides p1, p2, p3 and p4 were detected. (A) Proliferative responses for T1D patients with new onset disease, T1D patients with longstanding disease and healthy controls (all p-values > 0.45). (B) Proliferative responses at onset in patients with new onset who did (PR+) or did not (PR-) have a subsequent partial remission phase (PR) (p < 0.05). (C) Proliferative responses at 2–5 months (t = 2) in patients with new onset who did (PR+) or did not (PR-) have a partial remission phase (p = 0.49). Lines depict mean per group. Onset, new onset T1D patients (n = 18); LD, longstanding T1D patients (n = 31); HC, healthy controls (n = 10); PR+, new onset patients with a partial remission at t = 2 (n = 7); PR-, new onset patients without a partial remission at t = 2 (n = 11).
Onset LD HC0
1
2
3
Stim
ulat
ion
Inde
x
PR+ PR-
Onset
0
1
2
3
Stim
ulat
ion
Inde
x
PR+ PR-
Follow up (t = 2)
0
1
2
3p < 0.05
A
B C
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PR+ patients showed a higher SI than did PR- patients (p < 0.05; Figure 2.1B), but this difference disappeared at follow-up (p = 0.49; Figure 2.1C). A few PR- patients showed a high response to an individual peptide, these patients did not differ in patient characteristics from those PR- patients with lower responses.
Addition of various concentrations of IL-2 to overcome possible anergy [34, 35] did not enhance peptide-specific proliferation (data not shown). Overall, T cell proliferative responses could be detected but appeared to be similar in patients with new onset disease, patients with longstanding disease and healthy controls.
Intracellular IL-10 and IFN-" production
Secondly, intracellular cytokine production was measured to test the quality of the immune response after HSP60 epitope-specific stimulation. Epitope-specific production of IL-10 and IFN-γ was determined by deduction of background production of each specific cytokine in cultures without a peptide (Figure 2.2). The results for the four peptides were pooled per time point as shown in Table 2.3. When newly diagnosed patients were compared to healthy controls, no difference in epitope-specific IL-10 production was found (p = 0.74). However, newly diagnosed patients had more IFN-γ production at onset compared to healthy controls (p < 0.05).
Next, we investigated epitope-specific IL-10 and IFN-γ production at onset between patients with and without a PR. Both IL-10 and IFN-γ production at onset were similar in both patient groups (p = 0.87 for IL-10 and p = 0.41 for IFN-γ). However, at follow-up both IL-10 and IFN-γ production were higher in those without a PR than in those with a PR (p < 0.05 for both) (Table 2.3).
Furthermore, we investigated changes in epitope-specific IL-10 and IFN-γ production over time. IL-10 and IFN-γ production at follow-up were significantly higher compared with onset for patients without a PR (both p < 0.01), but not for patients with a PR. In conclusion, absence of a PR is associated with increased epitope-specific intracellular IL-10 and IFN-γ production (Table 2.3).
Extracellular cytokine production
Finally, we used a multiplex assay to investigate extracellular epitope-specific cytokine production. In a pilot containing 18 randomly selected children with T1D with varying
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disease duration (mean age at onset 5.9 yrs (range 1.1–11.6 yrs); mean disease duration 5.6 yrs (range 0–15.0 yrs) we found peptide-specific induction of 8 cytokines (IL-1α, IL-1β, IL-6, IL-10, IL-17, TNF-α, IFN-γ, and IP10). All data points were digitised to create a cytokine portrait as shown in Supplemental Figure 2.2 to visualise the complete spectrum of induced chemokines and cytokines per individual
In addition, we tested recognition in our follow-up cohort of newly diagnosed patients. Consistent with previous results, we could not detect a specific HSP60 epitope being more
Figure 2.2 Example of FACS staining for IFN-" and IL-10 positive T cellsAll samples were analysed after a 3 day culture without a peptide or with a specific HSP60 epitope. (A) Selection of lymphocytes from the population of peripheral blood mononuclear cells; (B) selection of CD3+ CD4+ T cells from live gate; (C and D) Selection of IFN-γ positive CD3CD4 positive cells after culture without a peptide (C) and after culture with HSP60 epitope p2 (D). Thus, the epitope-specific IFN-γ production was 3.4% for epitope p2 in this patient. (E and F) Selection of IL-10 positive CD3CD4 positive cells after culture without a peptide (E) and after culture with HSP60 epitope p2 (F).
Live gate CD3
CD
4IFN!
CD
4IFN!
CD
4
2.2% 5.6%
A
C
B
D
IL-10
CD
4
IL-10
CD
4
0.6% 0.9%E F
Live gate CD3
CD
4IFN!
CD
4IFN!
CD
4
2.2% 5.6%
A
C
B
D
IL-10
CD
4
IL-10
CD
4
0.6% 0.9%E F
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immunogenic than others; therefore we analysed cytokine production after clustering the results from all four epitope-specific cultures. Values were calculated after (per sample) deduction of the amount of the specific cytokine produced in culture without a peptide (background production). Of the panel of cytokines assessed, IL-4, IL-13, IL-17 and IFN-γ did not reach levels above the lower detection limit in the vast majority of the samples.
Newly diagnosed patients, both at onset of T1D and at follow-up, produced similar amounts of various HSP60 epitope-induced cytokines as healthy controls (data not shown).
When analysing the PR+ and PR- subgroups of patients, we measured higher epitope-specific TNF-α levels at onset in patients who would later experience a PR (PR+) than in
Table 2.3 HSP60 Epitope-specific IL-10 and IFN-" productionPeptide-specific CD3+CD4+ IL-10 or IFN-γ production is analysed as described in Figure 2.2. The results for the four HSP peptides were pooled as no differences in cytokine production in response to the individual HSP60 peptides p1, p2, p3 and p4 were detected. Depicted are median and (interquartile range) for cytokine production in cultures without a peptide (background) and in cultures with peptides p1-p4. For statistical analyses, we used epitope-specific cytokine production, thus after correction for the amount of cytokine produced in cultures without a peptide.
IL-10 IFN-γ
Background Peptides p1–p4 Background Peptides p1–p4
Healthy controls 0.85 (0.70–1.55) 0.89 (0.63–1.42) 3.13 (1.63–6.65) 1.54 (1.20–2.63) d,e
All onset 0.79 (0.31–1.4) 0.55 (0.29–1.29) a 0.80 (0.33–1.32) 0.42 (0.26–1.02) d
Onset PR+ 0.60 (0.23–2.53) 0.58 (0.31–1.29) 0.98 (0.40–2.34) 0.60 (0.27–1.35)
Onset PR- 0.80 (0.32–1.29) 0.42 (0.29–1.29) b 1.14 (0.43–2.53) 0.80 (0.38–1.72) b
All t = 2 0.67 (0.18–0.92) 0.71 (0.25–1.65) a 0.81 (0.43–1.58) 1.35 (0.58–2.32) e
t = 2 PR+ 0.74 (0.32–2.13) 0.75 (0.22–2.21) c 0.75 (0.59–3.46) 0.87 (0.59–3.46) c
t = 2 PR- 0.58 (0.12–0.73) 0.65 (0.25–1.58) b,c 0.97 (0.39–1.56) 1.50 (0.61–2.48) b,c
PR+, patients with a partial remission; PR-, patients without a partial remission; onset, new onset type 1 diabetes (n = 18); PR+, new onset patients with a partial remission at t = 2 (n = 7); PR-, new onset patients without a partial remission at t = 2 (n = 11); HC, healthy controls (n = 6). a Increased IL-10 production in patients at t = 2 compared to onset: p < 0.05. b Increased IL-10 and IFN-γ production at t = 2 compared to onset for patients without a partial remission: p < 0.01 for both IL-10 and IFN-γ. c Increased IL-10 and IFN-γ production at t = 2 in patients without a partial remission compared to patients with a partial remission: p < 0.05 for both IL-10 and IFN-γ. d Increased IFN-γ production in patients at onset compared to healthy controls: p < 0.05. e Increased IFN-γ production in patients at t = 2 compared to healthy controls: p < 0.01.
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those patients who would not go into PR (PR-) (p < 0.05, Table 2.4). At follow-up, IL-6 levels were higher in PR- than in PR+ patients (p < 0.01, Table 2.4). All other cytokines were similar in both groups at both time points (Table 2.4).
Finally, we investigated changes in epitope-specific cytokine levels between onset and follow-up for both groups. In PR- patients, IL-1β (p < 0.05), IL-6 (p < 0.01) and TNF-α (p < 0.001) epitope-specific production increased, whereas other cytokines did not change significantly (Table 2.4). In PR+ patients, epitope-specific IL1-Ra production decreased (p < 0.05), whereas other cytokines did not change significantly (Table 2.4).
Table 2.4 HSP60 Epitope-specific extracellular cytokine productionCytokine expression after peptide stimulation corrected for cytokine expression in cultures without a peptide (pooled data for four peptides, median and interquartile ranges are shown) at onset and at follow-up in patients with or without partial remission. Statistical analyses was based on epitope-specific cytokine production, thus after correction for the amount of cytokines produced in culture without a peptide. Extracellular cytokine production significantly differed for only a few cytokines, though biological ranges are wide.
Onset Follow-up
Protein (pg/ml) Partial remission
No partial remission
Partial remission
No partial remission
IL-1RA
IL-1α
IL-1β
IL-2
IL-6
IL-10
TNF-α
CXCL10/IP-10
14 (-18 / 61) a
0 (-0.1 / 0.4)
0 (-2.1 / 3.2)
0 ( -0.7 / 0.6)
3.1 (-161 / 15)
0 (-0.1 / 0))
0 (-0.5 / 0.9) d
-4.2 (-14 / 1.9)
-15 (-41 / 42)
0 (-0.9 / 0.2)
0 (-4.6 / 2.0) b
0 (0 / 1.4)
-0.6 (-63 / 15) b
0 ( -0.9 / 0.2)
-1.0 (-2.8 / 0.1) b,d
-0.1 (-68 / 20)
-30 (-86 / 67)a
-0.1 (-0.7 / 0.4)
- 0.6 (-17 / 2.1)
-0.4 (-1.8 / 1.5)
-8.9 (-245 / 7.9) c
0 (-1.0 / 0.3)
-0.4 (-1.6 / 0.7)
6.9 (-17 / 21)
-10 (-30 / 14)
0 (-0.5 / 0.2)
0 (-1.2 / 1.7) b
0 (-1.0 / 0.5)
7.8 (-9.7 / 95) b,c
0 (-0.5 / 0.1)
-0.1 (-0.6 / 1.0) b
0.7 (-7.0 / 19)
PR+, patients with a partial remission; PR-, patients without a partial remission; onset, new onset type 1 diabetes (n = 18); PR+, new onset patients with a partial remission at t = 2 (n = 7); PR-, new onset patients without a partial remission at t = 2 (n = 11). a Significant higher levels (p < 0.05) of epitope-specific IL-1RA production at onset compared to follow-up in patients with (PR+) a partial remission. b Significant higher levels of epitope-specific IL-1β (p < 0.05), IL-6 (p < 0.01) TNF-α (p < 0.001) production at follow-up compared to onset in patients without partial remission. c At follow-up: significant lower levels (p < 0.01) of epitope-specific IL-6 production in patients with partial remission compared to patients without a partial remission. d At onset: significant higher levels of epitope-specific TNF-α (p < 0.05) production in patients who will develop a partial remission compared to patients who will not develop a partial remission.
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To summarise, we found extracellular epitope-specific cytokine production to differ for a only few cytokines, and as biological ranges of detected values are wide and we did not correct for multiple testing we conclude there are no substantial differences in epitope-specific cytokine production in patients with and without a partial remission, either at onset or at follow-up.
Discussion
HSP60 has been shown to play an immunomodulatory role in autoimmune diseases, including T1D. Importantly, studies on the role of DiaPep277, a specific HSP60-epitope in T1D, have shown that treatment with this epitope can preserve part of the endogenous insulin production [1, 5, 6, 17, 18, 36]. Previously, we identified pan-DR epitopes which could be shown to induce epitope-specific T cell responses in juvenile idiopathic arthritis and rheumatoid arthritis [15, 23-25]. The aim of the present study, therefore, was to establish whether the identified epitopes elicit immune responses in PBMCs of patients with T1D.
We observed significantly increased (p < 0.05) proliferative responses at onset in patients who would go into transient remission; but as the SI for both groups only occasionally exceeded the predetermined threshold of 1.7, it is debatable whether this is of pathophysiological relevance. As the HSP60 epitopes were specifically selected to be pan-DR binders and to be able to induce T cell recognition irrespective of major histocompatibility complex genotype, the patients in this study were not HLA-typed and recognition was investigated irrespective of HLA type.
The results of this study, with regard to HSP60-specific T cell proliferation, are in agreement with previous studies with whole HSP60 or other peptide epitopes of HSP60 in T1D. Increased (but low) proliferation in response to various HSP peptides could be detected at onset in both adult and paediatric patients [16, 23, 37]. As T cell proliferative responses to auto-antigens including HSP60 and DiaPep277 were previously reported to decline during the first months after diagnosis of T1D [16], it is possible that the absence of recognition in patients with longstanding disease could be due to dampening of the initial inflammatory responses over time. Nevertheless, in the current study, which included samples drawn at onset of disease and a few months later when inflammation would be expected to still play a role, we also could not detect recognition of the epitopes.
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In general, responses to HSP60 peptides are expected to be low since they represent peptides of self origin or with high mimicry to self peptides, making recognition in peripheral blood difficult. Nevertheless, it is remarkable that proliferative responses observed in other autoimmune diseases tended to be higher. Obviously, we used PBMCs, which are known to generally have low frequencies of epitope-specific T cells and which also do not necessarily reflect local conditions in the pancreas [38]. A further explanation for the low proliferative response might be that T cells from the patients are anergic and that additional IL-2 is needed to overcome anergy [34, 35]. However, in our system, adding IL-2 did not significantly improve proliferative responses.
In this study, we observed epitope-specific cytokine production, which overall did not differ substantially between newly diagnosed T1D patients at onset and patients at follow-up. In the present study we could not perform additional analyses a year or more after disease onset to investigate further changes over time. However, we did see some differences in intracellular epitope-specific cytokine production when comparing patients with and without a PR. Absence of transient remission was in our cohort associated with increased intracellular production of epitope-specific IL-10 and IFN-γ (p < 0.05) compared to patients with a PR a few months after onset of T1D. Interestingly, in one of theDiaPep277 vaccination trials, IL-10 production in response to DiaPep277 (before vaccination) was associated with preservation of C-peptide (as a measure of residual β cell function) [6]. In addition, increased IL-10 production at onset compared to IL-10 production during remission was also reported in a study using other auto-antigens (specific IA-2, proinsulin and GAD 65 epitopes) to elicit T cell responses [20]. Our study did not confirm these results for T cell responses to our pan-DR-binding HSP60 epitopes.
It should be noted that, as patient numbers in our study were small, the results for both proliferative responses as well as epitope-specific cytokine production should be interpreted with caution.
Although we were interested mainly in the potential pathophysiological role of HSP60-induced responses, we note that an additional use of such induced responses could be as a biomarker to predict remission, as there is a great need for biomarkers in this era of immunomodulation trials [39]. However, differences observed in our study occurred in parallel with absence of remission (increase of IL-10 and IFN-γ production over time in patients without a PR, p < 0.01) and not before, and thus cannot be used to predict remission at disease onset. In agreement, attempts to identify immunological biomarkers
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by peptide-stimulation to predict remission have as yet not yielded clear positive results [35].
In conclusion, pan-DR binding HSP60 peptides induced low peptide-specific proliferative responses and peptide-specific production of some, mainly intracellular, cytokines in T1D patients, without major differences between patients at various time points.
Acknowledgements
The authors thank the diabetes nurses in all participating hospitals for their support in patient follow up and sample logistics.
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11. Samali A, Orrenius S. Heat shock proteins: regulators of stress response and apoptosis. Cell Stress Chaperones 1998; 3:228-236.
12. van Eden W, Thole JE, van der Zee R, Noordzij A, Van Embden JD, Hensen EJ, Cohen IR. Cloning of the mycobacterial epitope recognized by T lymphocytes in adjuvant arthritis. Nature 1988; 331:171-173.
13. De Graeff-Meeder ER, van der Zee R, Rijkers GT, Schuurman HJ, Kuis W, Bijlsma JW, Zegers BJ, van Eden W. Recognition of human 60 kD heat shock protein by mononuclear cells from patients with juvenile chronic arthritis. Lancet 1991; 337:1368-1372.
14. Bason C, Corrocher R, Lunardi C, Puccetti P, Olivieri O, Girelli D, Navone R, Beri R, Millo E, Margonato A, Martinelli N, Puccetti A. Interaction of antibodies against cytomegalovirus with heat-shock protein 60 in pathogenesis of atherosclerosis. Lancet 2003; 362:1971-1977.
15. Elst EF, Klein M, de Jager W, Kamphuis S, Wedderburn LR, van der Zee R, Albani S, Kuis W, Prakken BJ. Hsp60 in inflamed muscle tissue is the target of regulatory autoreactive T cells in patients with juvenile dermatomyositis. Arthritis Rheum 2008; 58:547-555.
16. Abulafia-Lapid R, Elias D, Raz I, Keren-Zur Y, Atlan H, Cohen IR. T cell proliferative responses of type 1 diabetes patients and healthy individuals to human hsp60 and its peptides. J Autoimmun 1999; 12:121-129.
17. Huurman VA, Decochez K, Mathieu C, Cohen IR, Roep BO. Therapy with the hsp60 peptide DiaPep277 in C-peptide positive type 1 diabetes patients. Diabetes Metab Res Rev 2007; 23:269-275.
18. Raz I, Avron A, Tamir M, Metzger M, Symer L, Eldor R, Cohen IR, Elias D. Treatment of new-onset type 1 diabetes with peptide DiaPep277 is safe and associated with preserved beta-cell function: extension of a randomized, double-blind, phase II trial. Diabetes Metab Res Rev 2007; 23:292-298.
19. Alizadeh BZ, Hanifi-Moghaddam P, Eerligh P, van der Slik AR, Kolb H, Kharagjitsingh AV, Pereira Arias AM, Ronkainen M, Knip M, Bonfanti R, Bonifacio E, Devendra D, Wilkin T, Giphart MJ, Koeleman BP, Nolsoe R, Mandrup PT, Schloot NC, Roep BO. Association of interferon-gamma and interleukin 10 genotypes and serum levels with partial clinical remission in type 1 diabetes. Clin Exp Immunol 2006; 145:480-484.
20. Sanda S, Roep BO, von Herrath M. Islet antigen specific IL-10+ immune responses but not CD4+CD25+FoxP3+ cells at diagnosis predict glycemic control in type 1 diabetes. Clin Immunol 2008; 127:138-143.
21. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The Diabetes Control and Complications Trial Research Group. N Engl J Med 1993; 329:977-986.
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22. Sustained effect of intensive treatment of type 1 diabetes mellitus on development and progression of diabetic nephropathy. the Epidemiology of Diabetes Interventions and Complications (EDIC) study. JAMA 2003; 290:2159-2167.
23. Kamphuis S, Kuis W, de Jager W, Teklenburg G, Massa M, Gordon G, Boerhof M, Rijkers GT, Uiterwaal CS, Otten HG, Sette A, Albani S, Prakken BJ. Tolerogenic immune responses to novel T-cell epitopes from heat-shock protein 60 in juvenile idiopathic arthritis. Lancet 2005; 366:50-56.
24. Puga Yung GL, Fidler M, Albani E, Spermon N, Teklenburg G, Newbury R, Schechter N, van den BT, Prakken B, Billetta R, Dohil R, Albani S. Heat shock protein-derived T-cell epitopes contribute to autoimmune inflammation in pediatric Crohn’s disease. PLoS One 2009; 4:e7714.
25. de Jong H, Lafeber FF, de Jager W, Haverkamp MH, Kuis W, Bijlsma JW, Prakken BJ, Albani S. Pan-DR-binding Hsp60 self epitopes induce an interleukin-10-mediated immune response in rheumatoid arthritis. Arthritis Rheum 2009; 60:1966-1976.
26. Craig ME, Hattersley A, Donaghue K. ISPAD Clinical Practice Consensus Guidelines 2006-2007. Definition, epidemiology and classification. Pediatr Diabetes 2006; 7:343-351.
27. Ortqvist E, Falorni A, Scheynius A, Persson B, Lernmark A. Age governs gender-dependent islet cell autoreactivity and predicts the clinical course in childhood IDDM. Acta Paediatr 1997; 86:1166-1171.
28. Nordwall M, Ludvigsson J. Clinical manifestations and beta cell function in Swedish diabetic children have remained unchanged during the last 25 years. Diabetes Metab Res Rev 2008; 24:472-479.
29. Southwood S, Sidney J, Kondo A, del Guercio MF, Appella E, Hoffman S, Kubo RT, Chesnut RW, Grey HM, Sette A. Several common HLA-DR types share largely overlapping peptide binding repertoires. J Immunol 1998; 160:3363-3373.
30. Haveman LM, Bierings M, Legger E, Klein MR, de Jager W, Otten HG, Albani S, Kuis W, Sette A, Prakken BJ. Novel pan-DR-binding T cell epitopes of adenovirus induce pro-inflammatory cytokines and chemokines in healthy donors. Int Immunol 2006; 18:1521-1529.
31. de Jong H, Berlo SE, Hombrink P, Otten HG, van Eden W, Lafeber FP, Heurkens AH, Bijlsma JW, Glant TT, Prakken BJ. Cartilage proteoglycan aggrecan epitopes induce proinflammatory autoreactive T-cell responses in rheumatoid arthritis and osteoarthritis. Ann Rheum Dis 2010; 69:255-262.
32. de Jager W, Prakken BJ, Bijlsma JW, Kuis W, Rijkers GT. Improved multiplex immunoassay performance in human plasma and synovial fluid following removal of interfering heterophilic antibodies. J Immunol Methods 2005; 300:124-135.
33. de Jager W, te Velthuis H, Prakken BJ, Kuis W, Rijkers GT. Simultaneous detection of 15 human cytokines in a single sample of stimulated peripheral blood mononuclear cells. Clin Diagn Lab Immunol 2003; 10:133-139.
34. Dosch H, Cheung RK, Karges W, Pietropaolo M, Becker DJ. Persistent T cell anergy in human type 1 diabetes. J Immunol 1999; 163:6933-6940.
35. Seyfert-Margolis V, Gisler TD, Asare AL, Wang RS, Dosch HM, Brooks-Worrell B, Eisenbarth GS, Palmer JP, Greenbaum CJ, Gitelman SE, Nepom GT, Bluestone JA, Herold KC. Analysis of T-cell assays to measure autoimmune responses in subjects with type 1 diabetes: results of a blinded controlled study. Diabetes 2006; 55:2588-2594.
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Supplemental information
Supplemental Figure S2.1 HSP 60 proliferative responses per individual peptide Proliferative responses to HSP60 peptide p1, p2, p3 and p4 are shown for T1D patients with new onset disease (Onset), T1D patients with longstanding disease (LD) and healthy controls (HC).
36. van Eden W, van der Zee R, Prakken B. Heat-shock proteins induce T-cell regulation of chronic inflammation. Nat Rev Immunol 2005; 5:318-330.
37. Abulafia-Lapid R, Gillis D, Yosef O, Atlan H, Cohen IR. T cells and autoantibodies to human HSP70 in type 1 diabetes in children. J Autoimmun 2003; 20:313-321.
38. Naik RG, Beckers C, Wentwoord R, Frenken A, Duinkerken G, Brooks-Worrell B, Schloot NC, Palmer JP, Roep BO. Precursor frequencies of T-cells reactive to insulin in recent onset type 1 diabetes mellitus. J Autoimmun 2004; 23:55-61.
39. Roep BO, Peakman M. Surrogate end points in the design of immunotherapy trials: emerging lessons from type 1 diabetes. Nat Rev Immunol 2010; 10:145-152.
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Supplemental Figure S2.2 Digital profile of peptide-induced cytokine production Digital profile of peptide-induced cytokine production of peripheral blood mononuclear cells from a pilot cohort of children with T1D. To determine epitope-specific cytokine production, cytokine production in culture without a peptide was deducted from cytokine production in cultures with a specific peptide. A colour profile was made to show the complete protein profile of each individual patient.
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CD8 T cell autoreactivity to preproinsulin epitopes with very low
human leucocyte antigen class I binding affinity
J.R.F. Abreu, S. Martina, A.A. Verrijn Stuart, Y.E. Fillié, K.L.M.C. Franken, J.W. Drijfhout, B.O. Roep
Clin Exp Immunol 2012; 170:57-65
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Summary
Beta cells presenting islet epitopes are recognized and destroyed by autoreactive CD8 T cells in type 1 diabetes. These islet-specific T cells are believed to react with epitopes binding with high affinity to human leucocye antigen (HLA) expressed on β cells. However, this assumption might be flawed in case of islet autoimmunity. We evaluated T cell recognition of the complete array of preproinsulin (PPI) peptides with regard to HLA binding affinity and T cell recognition. In a comprehensive approach, 203 overlapping 9-10mer PPI peptides were tested for HLA-A2-binding and subjected to binding algorithms. Subsequently, a high-throughput assay was employed to detect PPI-specific T cells in patient blood, in which conditional HLA ligands were destabilized by ultraviolet irradiation and HLA molecules refolded with arrays of PPI peptides, followed by quantum-dot labelling and T cell staining. Analysis of patient blood revealed high frequencies of CD8 T cells recognizing very low HLA binding peptides. Of 28 peptides binding to HLA-A2, a majority was predicted not to bind. Unpredicted peptides bound mainly with low affinities. HLA binding affinity and immunogenicity may not correlate in autoimmunity. Algorithms used to predict high-affinity HLA peptide binders discount the majority of low-affinity HLA binding epitopes. Appreciation that peptides binding HLA with very low affinity can act as targets of autoreactive T cells may help understand loss of tolerance and disease pathogenesis and possibly point to tissue-specific immune intervention targets.
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Introduction
Type 1 diabetes (T1D) is a chronic autoimmune disease in which the insulin-producing β cells are selectively destroyed by autoreactive T cells. It has recently become evident that, in addition to the pathogenic effects mediated by CD4 T cells, cytotoxic CD8 T cells are key players in the β cell destruction process [1-7]. The epitopes recognized by autoreactive CD8 T cells are considered to be derived primarily from β cell proteins [8]. With the appreciation of the pivotal role played by CD8 T cell autoreactivity in the pathogenesis of T1D, efforts to search for their β cell epitopes have increased. Indeed, the discovery of new islet-specific CD8 T cell epitopes could provide new biomarkers to monitor disease progression and responses to therapeutic interventions, and provide novel potential targets for therapy [9]. In attempts to identify novel CD8 T cell epitopes, epitope discovery in T1D has strongly focused on peptides binding with high affinity to human leucocyte antigen (HLA). As peptide binding motifs for numerous HLA-I molecules have been described, prediction algorithms are used commonly and successfully as a screening tool to predict HLA-I binding of T cell epitopes [10-14]. The use of prediction algorithms allows the preselection of peptides with high binding affinity to HLA-I, thought to be the most important for the induction of a T cell response [15], while low-affinity HLA binding peptides are normally disregarded. However, such low-affinity HLA binding peptides might prove relevant in the context of autoimmunity. In mice, weak interactions between proinsulin peptides and major histocompatibility complex (MHC)-II have been proposed to lead to defects in negative selection in the thymus and contribute to autoimmunity by allowing escape of autoreactive T cells from deletion [16, 17]. Serendipitously, one study reported an inverse correlation between the binding affinity of β cell peptides to MHC-I molecules and the corresponding CD8 T cell responses in diabetic patients [18]. Also, in a multi-step discovery approach, we recently uncovered four novel CD8 T cell epitopes derived from preproinsulin (PPI), selected initially for their predicted high HLA binding affinity, but eventually proven to bind with very low to intermediate binding affinities [19]. CD8 T cells recognizing these epitopes could be detected in the peripheral blood of diabetic patients, in spite of their very low HLA binding affinities. These observations underscore the notion that peptides binding with very low affinities to HLA-I may act as important epitopes for autoreactive CD8 T cells.
In this study, we investigated the relation between peptide binding affinity to HLA-A2 and immunogenicity, electing a comprehensive approach in which overlapping peptides
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spanning the entire PPI molecule were investigated, avoiding selection bias. We focused on PPI since there is increasing evidence that this islet autoantigen acts as important target of the immune system in T1D. For instance, the level of insulin expression in the thymus is an important risk factor in T1D. Polymorphisms in the variable number of tandem repeat (VNTR) elements in the human insulin promoter, controlling insulin expression in the thymus, are associated with disease susceptibility [20, 21]. Accordingly, low insulin expression in the thymus associated with high frequencies of insulin-specific CD4 T cells in the peripheral blood [22]. PPI-specific CD8 T cells have also been found in the peripheral blood of diabetic patients [4, 19, 23]. Cloned CD8 T cells specific for PPI15-24 were shown to specifically kill human β cells in vitro [23]. Importantly, this study demonstrated un-orthodox processing of PPI peptide epitopes, independent of proteasomal digestion. This notion implies that epitope repertoire in the context of autoimmune disease is not necessarily restricted by appropriate C-terminal processing by proteasomes.
To address the relation between peptide binding affinity to HLA and immunogenicity, we first tested the HLA-A2 binding capacity of overlapping PPI peptides, spanning the entire molecule. Because HLA-A2, expressed by the majority of T1D patients, is known to preferably accommodate peptides with 9 or 10 amino acid (aa) length, this led to a series of 203 candidate peptides. Strikingly, when testing patient peripheral blood, we observed significantly increased frequencies of CD8 T cells recognizing very low HLA binding PPI peptides, compared to low and intermediate binding peptides. In parallel, we subjected the full PPI aa sequence to commonly used prediction algorithms. While prediction algorithms were capable of predicting binding of peptides for which we confirmed intermediate binding in vitro, this strategy failed to notice peptides binding HLA-A2 with low and very low affinities. Our results indicate that peptides binding with very low affinity to MHC-I can act as epitopes for autoreactive CD8 T cells. Appreciating such peptides in epitope search approaches might disclose islet-specific epitopes with potential as novel diagnostic or therapeutic targets and be useful in the development of biomarkers of disease progression.
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Material and methods
T1D patients
Heparinized blood samples were retrieved from ten paediatric patients with recent-onset T1D (median age 8 years [range 2–14]; median disease duration 0 months [range 0–10]; male: female 6:4). Diagnosis was made according to International Society of Pediatric and Adolescent Diabetes criteria [24]. All patients were positive for at least one islet autoantibody in their serum. Written informed consent was obtained from all children and/or their parents and the study was approved by all involved local medical ethics review boards. Peripheral blood mononuclear cells (PBMC) were isolated by Ficoll-isopaque density gradient centrifugation, frozen and kept in liquid nitrogen until use. HLA-A2 (HLA-A*0201) typing was confirmed by flow cytometry using fluorescein isothiocyanate (FITC)-conjugated anti-HLA-A2 antibodies (BD biosciences, New Jersey, USA).
Peptides
Overlapping 9 and 10aa long PPI peptides and the cytomegalovirus (CMV) peptide were synthesized using solid-phase Fmoc chemistry. All peptides were analysed by reverse-phase high-performance liquid chromatography (RP-HPLC) (purity >85%) and matrix-assisted laser desorption/ionisation-time of flight mass spectrometry (MALDI-TOF) mass spectrometry to confirm the expected mass.
Prediction algorithms
HLA-A2 binding peptides were predicted using the prediction algorithms NetMHC 3.0 and 3.2 ([10], (www.cbs.dtu.dk/services/NetMHC) [10], SYFPEITHI (www.syfpeithi.de/home.htm) [12, 14], BIMAS (http://www-bimas.cit.nih.gov/molbio/hla_bind/) [13] and MOTIFS (available on request) [11].
In vitro peptide-binding studies
Peptide binding to HLA-A2 was performed as described previously [25]. Briefly, 10-fold dilution series of the peptides were made and incubated with 15pmol β 2-microglobulin, 200 fmol recombinant HLA-A2 heavy chain and 100 fmol fluorescein (FL)-labelled indicator peptide. HLA-bound and free FL-labelled peptide were separated by HPLC
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size-exclusion chromatography with fluorescence detection; 50% inhibitory concentration (IC50) values were calculated using nonlinear regression analysis.
Generation of peptide-HLA (pHLA) monomers and quantum-dot (Qdot) labelling
Generation of pHLA monomers was performed as described previously [26]. Briefly, exchange reactions were performed by exposing ultraviolet (UV)-sensitive pHLA monomers [2μM in phosphate-buffered saline (PBS)] to UV light (366-nm UV lamp, Camag, Berlin, Germany) in the presence or absence (negative control) of exchange peptide (200μM) for 60 minutes. Multimeric pHLA complexes were produced by addition of streptavidin-conjugated Qdot-605 and -705 (intermediate, low and very low affinity PPI peptides) or Qdot-585 and -800 (controls) (Invitrogen, Breda, The Netherlands) to achieve a 1:20 streptavidin-Qdot: biotinylated-pHLA ratio.
PBMC staining with Qdot-labelled pHLA multimers
PBMC staining was performed as described previously [7]. Briefly, PBMC (2x106) were stained with pooled pHLA multimers (0.1µg of each specific multimer), according to the peptide binding affinity to HLA-A2 (intermediate, low and very low, see Table 3.1) in PBS/0.5% bovine serum albumin (BSA) for 15 minutes at 37°C. Subsequently, allophycocyanin (APC) labelled anti-CD8, FITC-labelled anti-CD14, -CD20, -CD4, -CD40 and -CD16 (all from BD biosciences) were added for 30 minutes at 4°C. After washing, cells were resuspended in PBS/0.5% BSA containing 7-aminoactinomycin D (7-AAD) (eBioscience, San Diego, CA, USA) to exclude dead cells and analysed using a LSRII (BD Biosciences).
Results
Identification of HLA-A2 binding PPI peptides
To investigate the relation between peptide binding affinity to HLA-I and immunogenicity in T1D, we first determined which PPI peptides could actually bind to HLA-A2 molecules. In an unbiased approach, all 203 overlapping 9-mer and 10-mer PPI peptides were syn-thesized and tested for binding to purified HLA-A2 molecules. A cell-free peptide-HLA
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binding assay was used, in which a mixture of unfolded recombinant HLA heavy chain molecules, β 2-microglobulin and a fluorescent-labelled indicator peptide were allowed to form peptide-HLA complexes [25]. From this assay, the concentration at which the tested peptide reduced by 50% the amount of HLA-bound fluorescent-labelled peptide (IC50 value) was calculated. From 203 PPI peptides, 28 peptides could bind HLA-A2 (Table 3.1). Binding affinities of PPI peptides ranged from intermediate to very low HLA-A2
Table 3.1 In vitro binding affinity of preproinsulin (PPI) peptides to human leucocyte antigen (HLA)-A2
Position a Sequence Length IC50 (nM) HLA-A2 affinity Predicted binder
34-42 HLVEALYLV 9 133,1 Intermediate N3.0,N3.2,S,M15-24 ALWGPDPAAA 10 490,8 Intermediate N3.0,S2-10 ALWMRLLPL 9 748,8 Intermediate N3.0,N3.2,S,B,M15-23 ALWGPDPAA 9 879,8 Intermediate N3.0,N3.2,S,M6-14 RLLPLLALL 9 884,3 Intermediate N3.0,N3.2,S,B,M2-11 ALWMRLLPLL 10 940,3 Intermediate N3.0,N3.2,S,B,M6-15 RLLPLLALLA 10 1296 Intermediate N3.0,N3.2
7-15 LLPLLALLA 9 1372 Intermediate M13-22 LLALWGPDPA 10 1964 Intermediate N3.0,N3.2
1-10 MALWMRLLPL 10 3216 Low N3.0,N3.2,S17-26 WGPDPAAAFV 10 4456 Low22-30 AAAFVNQHL 9 5017 Low7-16 LLPLLALLAL 10 5840 Low N3.0,N3.2,S,B,M101-109 SLYQLENYC 9 5860 Low B,M8-16 LPLLALLAL 9 11835 Low S35-44 LVEALYLVCG 10 13331 Low90-99 GIVEQCCTSI 10 29248 Low S,M10-19 LLALLALWGP 10 29623 Low N3.2
1-9 MALWMRLLP 9 36309 Very low29-38 HLCGSHLVEA 10 40364 Very low N3.2,S9-18 PLLALLALWG 10 43458 Very low4-12 WMRLLPLLA 9 53428 Very low34-43 HLVEALYLVC 10 64329 Very low35-43 LVEALYLVC 9 71249 Very low4-13 WMRLLPLLAL 10 73485 Very low N3.2,S,M76-85 SLQPLALEGS 10 87985 Very low40-49 YLVCGERGFF 10 95026 Very low96-105 CTSICSLYQL 10 97732 Very low M
a Position in the PPI sequence. N3.0, NetMHC 3.0; N3.2, NetMHC 3.2; S, SYFPEITHI; B, BIMAS; M, MOTIFS. IC50, 50% inhibitory concentration.
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binding capacity (Table 3.1). Interestingly, peptides capable of binding to HLA-A2 were mainly located in the signal peptide of PPI (Figure 3.1, upper panels).
Figure 3.1 Predicted and in vitro preproinsulin (PPI) peptide-binding to human leucocyte antigen (HLA)-A2 molecules Overlapping 9-mer and 10-mer peptides were analysed by prediction algorithms and compared to results of the in vitro peptide binding assay. (A) All 9- and (B) 10-mer peptides predicted to bind to HLA-A2 by NetMHC 3.0 (black bars), NetMHC 3.2 (diagonally hatched bars), SYFPEITHI (grey bars), BIMAS (white bars) and MOTIFS (horizontal stripes), and the observed in vitro binding affinity (1/IC50) were plotted against the amino acid sequence of PPI. Boxes on the PPI amino acid sequence indicate the signal peptide, B chain, cleavage site for conversion from proinsulin to insulin, C-peptide, cleavage site and A chain, respectively.
(A)
(B)
MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN
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ted
10-2
10-3
10-4
10-5
10-6
10-2
10-3
10-4
10-5
10-6
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Relation between peptide-HLA binding affinity and T cell recognition
After determining the binding capacity of all 9- and 10-mer PPI peptides to HLA-A2, the relation between peptide-HLA binding affinity and T cell recognition in T1D was investigated. For this, the presence of CD8 T cells recognizing the 28 PPI peptides was examined in PBMC of recent onset T1D patients. In order to detect PPI-specific CD8 T cells, pHLA complexes were generated for the complete panel of 28 HLA-A2 binding PPI peptides. Conventional techniques to produce HLA-I tetramers are very labour-intensive and time-consuming, as for each peptide a separate in vitro HLA refolding reaction is re-quired. We elected to apply an UV-mediated ligand exchange technology that we recently developed and validated, where conditional HLA ligands forming stable complexes with HLA molecules disintegrate upon exposure to UV-light [27]. These empty HLA molecules were then stabilized by binding of the individual 28 PPI peptide ligands, which rescued HLA molecules from disintegration. Multimeric pHLA complexes were generated by labelling refolded pHLA monomers with Qdot fluorochromes. To investigate the relation between HLA binding affinity and T cell recognition, the 28 pHLA multimers were ran-ked based on their PPI peptide IC50 values (Table 3.1) and divided into three groups with intermediate, low and very low HLA-A2 binding peptides (<3,000nM, 3,000–30,000nM and >30,000nM, respectively). Subsequently, we searched for the presence of CD8 T cells recognizing PPI peptides belonging to one of the three groups in PBMC of recent-onset T1D patients. As all pHLA multimers were labelled with the same Qdot combination, any differences in the frequencies of CD8 T cells detected between groups would be attributable to differences in peptide-HLA binding affinity, rather than differences in fluorochrome intensity or sensitivity. For all three groups, CD8 T cells recognizing PPI peptides with intermediate, low and very low HLA binding affinities were detectable. Strikingly, we observed significantly higher frequencies of CD8 T cells recognizing PPI peptides with very low HLA-A2 binding affinity compared to peptides binding HLA-A2 with low and intermediate affinities (p = 0.0017 and p = 0.0013 respectively; Figure 3.2). For reference, the frequencies of autoreactive CD8 T cells in a non-diabetic, HLA-A2 negative blood donor were 0.003 and 0.008% for intermediate, low and very low binding epitopes, res-pectively. The three patients with the highest frequencies of CD8 T cells recognizing PPI peptides with very low affinity were also the oldest in our paediatric cohort, with a median age of 13 years old. Otherwise, no demographic differences between the ten patients were noted. As negative controls, pHLA multimers that were UV-exchanged in the absence of rescuing PPI peptide and Qdot labelled in the same manner as PPI pHLA multimers were used, which showed no background staining. Additionally, patient PBMC were stained
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with CMV-specific pHLA multimers which showed no T cell staining, except for one patient, which may be explained by the young age of the patients in our cohort. The fact that pHLA multimers, loaded with a non-self HLA-A2 peptide, showed no background staining assured the specificity of our reagents.
Accuracy of algorithms in predicting peptide binding to HLA-A2 molecules
To address how accurate algorithms are in predicting binding of peptides that range from very low to high binding affinities to HLA-A2, the complete aa sequence of PPI
Figure 3.2 High frequency of preproinsuline (PPI)-specific CD8 T cells recognizing peptides with very low human leucocyte antigen (HLA)-A2 binding affinity PPI peptide HLA (pHLA) multimers were divided into three groups (intermediate, low and very low HLA-A2 binding affinity). (A) Representative dot plots of CD8 T cells recognizing PPI pHLA multimers of intermediate, low and very low affinity (Qdot -605 and -705) and negative control (Qdot -585 and -800). (B) Peripheral blood mononuclear cells (PBMC) of 10 recent-onset type 1 diabetes patients were screened for the presence of CD8 T cells recognizing the PPI peptides or one cytomegalovirus (CMV) epitope. Statistical analysis was performed using the Mann-Whitney U test.
Intermediate binders Low binders Very low binders Negative control
Qdo
t 605
Qdot 705 Qdot 800Q
dot 5
85
0.025%PPI PPI PPICtrl
0.026% 0.065%0%
0.00
0.05
0.10
0.15
0.20
p = 0.0017
p = 0.0013
Neg ctrl Intermediatebinders
Lowbinders
Very lowbinders
CMV
% A
g-sp
ecifi
c C
D8+ T
cel
ls
(A)
(B)
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was applied to commonly used algorithms to predict peptide binding: NetMHC 3.0, NetMHC 3.2, SYFPEITHI, BIMAS and MOTIFS. We focused on the binding of 9-mer and 10-mer peptides to HLA-A2 molecules. These tools predicted the binding of ten, 12, 14, five and 15 peptides to HLA-A2 when using NetMHC 3.0, NetMHC 3.2, SYF-PEITHI, BIMAS or MOTIFS, respectively (Table 3.2). Next, to determine the accuracy of computer algorithms in predicting peptide binding to HLA-A2, these results were compared to the IC50 results (Figure 3.1). Prediction algorithms were mainly capable of predicting binding of peptides with confirmed intermediate binding affinity to HLA-A2.
Table 3.2 Binding scores of preproinsulin (PPI) peptides to human leucocyte antigen (HLA)-A2, obtained from each prediction algorithm
Position a Sequence NetMHC 3.0 NetMHC 3.2 SYFPEITHI BIMAS MOTIFS
9-mer2–10 ALWMRLLPL 16 13 28 408 546–14 RLLPLLALL 19 14 31 182 587–15 LLPLLALLA > 500 > 500 < 20 < 60 528–16 LPLLALLAL > 500 > 500 20 < 60 < 4415–23 ALWGPDPAA 203 45 22 < 60 5134–42 HLVEALYLV 13 8 27 < 60 6350–58 YTPKTRREA > 500 > 500 < 20 < 60 4453–61 KTRREAEDL > 500 > 500 < 20 < 60 4860–68 DLQVGQVEL > 500 > 500 25 < 60 5491–99 IVEQCCTSI > 500 > 500 < 20 < 60 48101–109 SLYQLENYC > 500 > 500 < 20 87 57
10-mer1–10 MALWMRLLPL 357 104 21 < 60 < 442–11 ALWMRLLPLL 20 68 28 408 644–13 WMRLLPLLAL > 500 234 24 < 60 546–15 RLLPLLALLA 62 61 < 20 < 60 < 447–16 LLPLLALLAL 436 102 28 83 6810–19 LLALLALWGP > 500 453 < 20 < 60 < 4413–22 LLALWGPDPA 273 253 < 20 < 60 < 4415–24 ALWGPDPAAA 150 > 500 22 < 60 < 4429–38 HLCGSHLVEA > 500 475 24 < 60 < 4485–94 SLQKRGIVEQ > 500 > 500 20 < 60 < 4490–99 GIVEQCCTSI > 500 > 500 21 < 60 5896–105 CTSICSLYQL > 500 > 500 < 20 < 60 52
Total predicted binders 10 12 14 5 15a Position in the PPI sequence. Binding thresholds: NetMHC ≤ 500; SYFPEITHI ≥ 20; BIMAS ≥ 60; MOTIFS ≥ 44.
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For peptides binding with low to very low affinity to HLA-A2, prediction algorithms were ineffective and inaccurate in predicting peptide binding (Figure 3.1 and Table 3.1). From a total of 28 HLA binding peptides, prediction algorithms failed to predict more than half of these (Table 3.3). Unpredicted peptides were mainly of low and very low affinity. Using less stringent binding thresholds for the algorithms did not identify the majority of the HLA binding PPI peptides, while the number of false positive predicted peptides increased. Thus, these data show that prediction algorithms should not be used to select low-affinity binders, even though they can be effectively used for prediction of high-affinity HLA-A2-binding peptides.
Discussion
Multiple autoantigens have been described for CD4 and CD8 autoreactive T cells in T1D. These antigenic peptides have generally been demonstrated or assumed to bind MHC molecules with high to intermediate affinities. However, observations in recent years that low-affinity MHC-binding peptides can have highly immunogenic potential led us to speculate that low-affinity epitopes may be equally or even more important in the development of autoimmunity. Here, we addressed this hypothesis in an unbiased manner. Overlapping peptides spanning the whole PPI molecule were tested for HLA-A2 binding and recognition by CD8 T cells from the peripheral blood of T1D patients. We report that CD8 autoreactive T cells from T1D patients preferentially recognize peptides binding with very low affinities to HLA-A2. This may reflect a condition in which peptides
Table 3.3 Accuracy of algorithms in predicting preproinsulin (PPI) peptide binding to human leucocyte antigen (HLA)-A2
Predicted Confirmed binders False positive Unpredicted
NetMHC 3.0 10 10 0 18
NetMHC 3.2 12 12 0 16
SYFPEITHI 14 12 2 16
BIMAS 5 5 0 23
MOTIFS 15 11 4 17
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binding with very low affinity to HLA-A2 are not efficiently presented in the thymus, allowing escape of autoreactive CD8 T cells from negative selection.
During T cell maturation in the thymus, T cells undergo a process of negative selection. Nevertheless, the observation that autoreactive T cells can be detected in peripheral blood of diabetic patients indicates that negative selection is incomplete. High-affinity CD8 T cells have been shown in the case of the islet antigen islet-specific glucose 6 phosphatase (IGRP) to be diabetogenic, while low-affinity IGRP-specific CD8 T cells were shown to be protective [28]. Here, the affinity of the T cell receptor (TCR) for its ligand was explored as a therapeutic opportunity in non-obese diabetic (NOD) mice by specifically deleting the high-affinity, pathogenic, CD8 T cells. In human T1D however, it is undefined whether high-affinity T cells are the most pathogenic. It has been suggested that T cells escaping thymic deletion interact with self-peptide MHC with affinities below the threshold for deletion. This idea is supported by observations that the affinity of TCR for self-peptide MHC is generally lower than that for foreign peptide MHC [29, 30]. In the case of glutamic acid decarboxylase (GAD)-specific CD4 T cells, a diverse range of antigen interactions has been observed, with high-avidity T cells co-existing with low- or moderate-avidity T cells, specific for the same GAD antigen [30-32].
Besides the strength of TCR-MHC interactions, the strength of peptide binding to MHC might influence negative selection in the thymus. While T cell responses in the periphery to non-self peptides appear to favour high HLA binding affinity peptides to elicit a proper immune response [15], autoreactive epitopes in T1D may actually bind with low affinity to HLA-I. Studies in mice have shown that peptides binding with low affinity to MHC-II can be pathogenic [16, 17, 33, 34]. In humans, low-affinity peptides have been observed to be recognized by autoreactive T cells of T1D and multiple sclerosis patients [18, 19, 35-37]. In our study, the frequencies of CD8 T cells recognizing self-antigens with a range of HLA binding affinities was determined. Due to limited amounts of paediatric peripheral blood drawn shortly after diagnosis, PPI peptides were grouped based on their HLA-A2 binding affinity in three peptide pools to test peptide recognition, which precluded determining T cell recognition against each single peptide. Nevertheless, the frequency of CD8 T cells recognizing PPI peptides with very low HLA-A2 binding affinity is significantly higher than T cells recognizing PPI peptides of higher affinities. This implies that such low affinity peptides, regarded commonly as irrelevant in (auto)immunity, may actually prove relevant with regard to potential immunogenicity and pathogenicity in T1D. Future studies to address the pathogenic
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potential of each of these peptides in T1D would be of high interest. Baker and colleagues tested overlapping PPI peptides for granzyme B responses [35]. In this case, peptide pools were made with overlapping PPI peptides, regardless of peptide binding affinity. While combining peptides with high and low binding affinity could lead to competition, they observed none the less that granzyme B responses of T1D patients were directed mainly towards peptide pools containing PPI peptides with low predicted HLA-A2 binding scores. Even though the relation between peptide-binding affinity to HLA and T cell responses was not directly addressed, their conclusion corroborates our current findings and add potential functionality in terms of cytotoxic potential of autoreactive T cells recognizing peptides with low HLA binding affinity. Perhaps more importantly, Baker and colleagues were able to compare their findings in adult-onset T1D patients with non-diabetic age-matched controls, showing a strong disease association of this phenomenon. Since our study was performed in children (mean age at onset 8 years), we were not allowed to draw blood from properly matched healthy children, precluding defining a relationship with disease.
T cells recognizing self-peptides with high affinity are presumably deleted during T cell education in the thymus [38]. However, the fast off-rates of low-affinity (self) peptides and the subsequent reduced epitope presentation in the thymus could lead to T cell escape from negative selection and allow survival of autoreactive T cells. When encountering their self-antigen in high quantities in the periphery, such as preproinsulin in the pancreas, these cells could become activated and promote autoimmunity. Our data indicate that peptides binding with very low affinity to HLA-I may allow escape of CD8 T cells from negative deletion, while T cells specific for higher affinity peptides may be deleted more efficiently and therefore circulate in lower frequencies in peripheral blood.
When testing all overlapping PPI peptides in vitro, we observed that the majority of binding peptides were not predicted to bind HLA-A2 by the algorithms. Notably, the majority of the unpredicted peptides bound HLA-A2 with very low affinity. This may be expected, since prediction algorithms were designed to predict high-affinity HLA binding peptides. Peptides binding with very low affinity to HLA-A2 were recognized none the less recognized by CD8 T cells from T1D patients in significantly increased frequencies, compared to peptides binding with higher affinities. It is unlikely that currently available algorithms will be capable of predicting binding of very low-affinity peptides. Binding experiments may therefore be required to identify this novel category of potential epitopes in autoimmunity.
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Together, our data indicates that peptides binding with very low affinity to HLA molecules allow increased escape of autoreactive CD8 T cells from negative selection. T cells recognizing such epitopes can be found in higher frequencies in the peripheral blood of T1D patients. Our findings open avenues for the discovery of entirely new ranges of epitopes that may hold clues for loss of tolerance and causes of disease and serve as potential targets for tissue-specific immune intervention strategies.
Acknowledgements
We thank Dr. Ton N. Schumacher, Division of Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands, for providing reagents and reviewing the manuscript, and Mr. Robert Cordfunke, Dept. of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands, for the peptide synthesis. This work was supported by the Juvenile Diabetes Research Foundation, the Dutch Diabetes Research Foundation and ZonMw.
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3. Nejentsev S., Howson J.M., Walker N.M. et al. Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A. Nature 2007; 450:887-892.
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5. Serreze D.V., Leiter E.H., Christianson G.J., Greiner D., Roopenian D.C. Major histo-compatibility complex class I-deficient NOD-B2mnull mice are diabetes and insulitis resistant. Diabetes 1994; 43:505-509.
6. Takaki T., Marron M.P., Mathews C.E. et al. HLA-A*0201-restricted T cells from humanized NOD mice recognize autoantigens of potential clinical relevance to type 1 diabetes. J Immunol 2006; 176:3257-3265.
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7. Velthuis J.H., Unger W.W., Abreu J.R. et al. Simultaneous detection of circulating autoreactive CD8+ T cells specific for different islet cell-associated epitopes using combinatorial MHC multimers. Diabetes 2010; 59:1721-1730.
8. Abreu J.R., Roep B.O. Autoreactive CD8 T cells in Type 1 diabetes: implications for pathogenesis, diagnosis, disease progression and therapeutic intervention. Diabetes Management 2011; 1:99-108.
9. Roep B.O., Peakman M. Surrogate end points in the design of immunotherapy trials: emerging lessons from type 1 diabetes. Nat Rev Immunol 2010; 10:145-152.
10. Buus S., Lauemoller S.L., Worning P. et al. Sensitive quantitative predictions of peptide-MHC binding by a ‘Query by Committee’ artificial neural network approach. Tissue Antigens 2003; 62:378-384.
11. D’Amaro J., Houbiers J.G., Drijfhout J.W. et al. A computer program for predicting possible cytotoxic T lymphocyte epitopes based on HLA class I peptide-binding motifs. Hum Immunol 1995; 43:13-18.
12. Dick T.P., Stevanovic S., Keilholz W. et al. The making of the dominant MHC class I ligand SYFPEITHI. Eur J Immunol 1998; 28:2478-2486.
13. Parker K.C., Bednarek M.A., Coligan J.E. Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol 1994; 152:163-175.
14. Rammensee H., Bachmann J., Emmerich N.P. , Bachor O.A. , Stevanovic S . SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 1999; 50:213-219.
15. Chen W., Khilko S., Fecondo J., Margulies D.H., McCluskey J. Determinant selection of major histocompatibility complex class I-restricted antigenic peptides is explained by class I-peptide affinity and is strongly influenced by nondominant anchor residues. J Exp Med 1994; 180:1471-1483.
16. Levisetti M.G., Lewis D.M., Suri A., Unanue E.R. Weak proinsulin peptide-major histocompatibility complexes are targeted in autoimmune diabetes in mice. Diabetes 2008; 57:1852-1860.
17. Levisetti M.G., Suri A., Petzold S.J., Unanue E.R. The insulin-specific T cells of nonobese diabetic mice recognize a weak MHC-binding segment in more than one form. J Immunol 2007; 178:6051-6057.
18. Ouyang Q., Standifer N.E., Qin H. et al. Recognition of HLA class I-restricted beta-cell epitopes in type 1 diabetes. Diabetes 2006; 55:3068-3074.
19. Unger W.W., Velthuis J., Abreu J.R. et al. Discovery of low-affinity preproinsulin epitopes and detection of autoreactive CD8 T cells using combinatorial MHC multimers. J Autoimmun 2011; 37:151-159.
20. Pugliese A., Zeller M., Fernandez A., Jr. et al. The insulin gene is transcribed in the human thymus and transcription levels correlated with allelic variation at the INS VNTR-IDDM2 susceptibility locus for type 1 diabetes. Nat Genet 1997; 15:293-297.
21. Vafiadis P., Bennett S.T., Todd J.A. et al. Insulin expression in human thymus is modulated by INS VNTR alleles at the IDDM2 locus. Nat Genet 1997; 15:289-292.
22. Durinovic-Bello I., Wu R.P., Gersuk V.H., Sanda S., Shilling H.G., Nepom G.T. Insulin gene VNTR genotype associates with frequency and phenotype of the autoimmune response to proinsulin. Genes Immun 2010; 11:188-193.
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23. Skowera A., Ellis R.J., Varela-Calvino R. et al. CTLs are targeted to kill beta cells in patients with type 1 diabetes through recognition of a glucose-regulated preproinsulin epitope. J Clin Invest 2008; 118:3390-3402.
24. Craig M.E., Hattersley A., Donaghue K. ISPAD Clinical Practice Consensus Guide-lines 2006-2007. Definition, epidemiology and classification. Pediatr Diabetes 2006; 7:343-351.
25. Tan T.L., Geluk A., Toebes M., Ottenhoff T.H., Drijfhout J.W. A novel, highly efficient peptide-HLA class I binding assay using unfolded heavy chain molecules: identification of HIV-1 derived peptides that bind to HLA-A*0201 and HLA-A*0301. J Immunol Methods 1997; 205:201-209.
26. Hadrup S.R., Bakker A.H., Shu C.J. et al. Parallel detection of antigen-specific T cell responses by multidimensional encoding of MHC multimers. Nat Methods 2009; 6:520-526.
27. Toebes M., Coccoris M., Bins A. et al. Design and use of conditional MHC class I ligands. Nat Med 2006; 12:246-251.
28. Han B., Serra P., Amrani A. et al. Prevention of diabetes by manipulation of anti-IGRP autoimmunity: high efficiency of a low-affinity peptide. Nat Med 2005; 11:645-652.
29. Deng L., Mariuzza R.A. Recognition of self-peptide-MHC complexes by autoimmune T cell receptors. Trends Biochem Sci 2007; 32:500-508.
30. Gebe J.A., Falk B.A., Rock K.A. et al. Low-avidity recognition by CD4+ T cells directed to self-antigens. Eur J Immunol 2003; 33:1409-1417.
31. Mallone R., Kochik S.A., Reijonen H. et al. Functional avidity directs T cell fate in autoreactive CD4+ T cells. Blood 2005; 106:2798-2805.
32. Reijonen H., Mallone R., Heninger A.K. et al. GAD65-specific CD4+ T cells with high antigen avidity are prevalent in peripheral blood of patients with type 1 diabetes. Diabetes 2004; 53:1987-1994.
33. Anderton S.M., Radu C.G., Lowrey P.A., Ward E.S., Wraith D.C. Negative selection during the peripheral immune response to antigen. J Exp Med 2001; 193:1-11.
34. Liu G.Y., Fairchild P.J., Smith R.M., Prowle J.R., Kioussis D., Wraith D.C. Low avidity recognition of self-antigen by T cells permits escape from central tolerance. Immunity 1995; 3:407-415.
35. Baker C., Petrich de Marquesini L.G., Bishop A.J., Hedges A.J., Dayan C.M., Wong F.S. Human CD8 responses to a complete epitope set from preproinsulin: implications for approaches to epitope discovery. J Clin Immunol 2008; 28:350-360.
36. Muraro P.A., Vergelli M., Kalbus M. et al. Immunodominance of a low-affinity major histocompatibility complex-binding myelin basic protein epitope (residues 111-129) in HLA-DR4 (B1*0401) subjects is associated with a restricted T cell receptor repertoire. J Clin Invest 1997; 100:339-349.
37. Standifer N.E., Ouyang Q., Panagioto-poulos C. et al. Identification of Novel HLA-A*0201-restricted epitopes in recent-onset type 1 diabetic subjects and antibody-positive relatives. Diabetes 2006; 55:3061-3067.
38. Klein L., Hinterberger M., Wirnsberger G., Kyewski B. Antigen presentation in the thymus for positive selection and central tolerance induction. Nat Rev Immunol 2009; 9:833-844.
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Adipose tissue inflammation & adipokines in type 1 diabetes and obesity
PART III
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Altered plasma adipokine levelsand in vitro adipocyte differentiation
in paediatric type 1 diabetes
A.A. Verrijn Stuart*, H.S . Schipper*, I. Tasdelen, D.A. Egan, A.B.J. Prakken, E. Kalkhoven**, W. de Jager
*,** Both authors contributed equally
J Clin Endocrinol Metab 2012; 97:463-472
4
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Abstract
ContextType 1 diabetes (T1D) is considered a proinflammatory condition. Adipose tissue involvement seems evident because adiponectin levels correlate with disease remission and administration of leptin suppresses the low-grade systemic inflammation in mice with T1D. Whether adipose tissue involvement in T1D already occurs at a young age is yet unknown.
ObjectiveThe aim was to explore the extent of adipokine alterations in paediatric T1D and gain more insight into the mechanisms underlying the involvement of adipose tissue.
Design and participantsFirstly, plasma adipokine profiling (24 adipokines) of 20 children with onset T1D, 20 children with longstanding T1D, and 17 healthy controls was performed using a recently developed and validated multiplex immunoassay. Secondly, the effects of diabetic plasma factors on preadipocyte proliferation and differentiation were studied in vitro.
ResultsIn children with onset and longstanding T1D, plasma adipokine profiling showed increased levels of various adipokines acting at the crossroads of adipose tissue function and inflammation, including CCL2/monocyte chemoattractant protein-1 and the novel adipokines cathepsin S, chemerin, and tissue inhibitor of metalloproteinase-1 (p < 0.05). Furthermore, onset and longstanding diabetic plasma significantly induced preadipocyte proliferation and adipocyte differentiation in vitro (p < 0.05). Two candidate plasma factors, glucose and the saturated fatty acid palmitic acid, did not affect proliferation or adipocyte differentiation in vitro but were found to increase CCL2 (monocyte chemoattractant protein-1) secretion by adipocytes.
ConclusionsThe adipogenic effects of diabetic plasma in vitro and the altered adipokine levels in vivo suggest adipose tissue involvement in the low-grade inflammation associated with T1D, already in paediatric patients.
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Introduction
Type 1 diabetes is increasingly considered to be a proinflammatory disease [1]]. Compelling evidence for this view comes from the observation that circulating levels of proinflammatory proteins such as TNF-α, C-reactive protein, IL-1β, and IL-6 are enhanced and that cytokine secretion by peripheral blood mononuclear cells is increased in type 1 diabetes [2-6].
In type 2 diabetes, low-grade systemic inflammation reflects adipose tissue inflammatory processes. Adipocyte hypertrophy and insulin resistance are known to boost the production of inflammatory proteins by adipose tissue, thereby establishing adipose tissue as a key mediator between type 2 diabetes, cardiovascular disease, and inflammation [7, 8].
In contrast to type 2 diabetes, adipose tissue involvement in the low-grade systemic inflammation observed in type 1 diabetes has been investigated far less. Yet there are strong indications for adipose tissue involvement. From a theoretical point of view, type 1 diabetes accompanying features like glucose dysregulation and dyslipidaemia [9] may well impact adipose tissue function and inflammatory processes [8, 10]. Indeed, alterations in circulating levels of the adipose tissue-secreted cytokines (adipokines) leptin and adiponectin in adult and paediatric type 1 diabetes indicate adipose tissue involvement in the low-grade systemic Inflammation observed [11]. Interestingly, adiponectin levels were found to specifically correlate with disease remission [12, 13]. Furthermore, recent studies in mice suggest that administration of adipose tissue-secreted factors in type 1 diabetes can even improve inflammatory and metabolic parameters; leptin therapy was found to reduce low-grade systemic inflammation and improve the metabolic balance [14]. Although findings in mice may not easily translate to humans [15], these studies emphasize the emergence of adipose tissue function and adipokines in type 1 diabetes as an important new field of research.
To explore the extent of adipokine alterations in paediatric type 1 diabetes and to gain more mechanistic insight in the involvement of adipose tissue, we employed two novel approaches. Firstly, we compared adipokine profiles in plasma of children at onset of type 1 diabetes, longstanding paediatric type 1 diabetes patients, and healthy controls (HC). For this, a recently developed and validated multiplex adipokine immunoassay [16] was used to analyse 24 different adipokines. Secondly, to investigate whether changes in adipocyte differentiation may account for the altered adipokine levels, we conducted in
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vitro adipocyte differentiation assays with patient and control plasma pools. In addition, we studied whether the effects of diabetic plasma on adipocyte differentiation and adipokine production could be reproduced by varying two plasma factors associated with type 1 diabetes - glucose and free fatty acids.
Subjects
Heparinised daytime blood samples were obtained from 20 children with onset type 1 diabetes mellitus, 20 children with longstanding type 1 diabetes (> 1 yr after onset), and 17 HC (Table 4.1). Using the International Society for Paediatric and Adolescent Diabetes (ISPAD) criteria for diagnosis of type 1 diabetes mellitus, the day of diagnosis (“onset”) was defined as the day on which hyperglycaemia was detected [17]. For onset type 1 diabetes patients, a blood sample was obtained shortly after diagnosis (median, 1 d; range, 0–12 d).
Nonfasting blood samples were drawn at the participating hospitals at random times of the day together with samples for routine diagnostics, including glycosylated hemoglobin (HbA1c) as a glycaemic state indicator. Body mass index (BMI) was calculated as weight in kilograms/height in meters2, and SD values were calculated based on Dutch reference values [18]. Written informed consent was obtained from all children and/or their parents. The study was approved by all local medical ethics review boards (METC 01/020-K). The study design was retrospective and cross-sectional. Researchers were blinded for the patient groups when performing the laboratory measurements described below.
Methods
Patient plasma samplesHeparinised blood samples were centrifuged at 150 X g for 10 min, after which plasma was aliquoted and stored at -80°C until analysis. Before analysis, plasma samples were centrifuged on a 0.22-µm nylon membrane (Spin-X column; Corning Life Sciences, Lowell, MA) to remove cellular debris. To prevent interference from heterophilic antibodies, Ig were removed by 1-h incubation on protein L-coated plates (Pierce, Rockford, IL), as described previously [19]. Lipid levels were measured using routine diagnostics.
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Adipokines and adipocyte differentiation in paediatric T1D
4
Tabl
e 4.
1 P
atie
nt c
hara
cter
istic
s
Ons
et T
1DLo
ngst
andi
ng T
1DH
ealth
y co
ntro
ls
Fem
ale
Mal
eFe
mal
eM
ale
Fem
ale
Mal
e
N10
1010
1010
7
Age
(yr)
13.0
(7.8
–16.
4)13
.4 (1
0.0–
15.4
)15
.2 (1
2.0–
18.8
)14
.1 (9
.4–1
7.9)
12.9
(7.9
–16.
5)9.
5 (6
.5–1
2.9)
Age
at o
nset
T1D
(yr)
13.0
(7.8
–16.
4)13
.4 (1
0.0–
15.4
)9.
7 (3
.0–1
3)8.
3 (1
.7–1
4.8)
NA
NA
Dur
atio
n T1
D (y
r)0
05.
4 (3
.1–1
1)5.
9 (1
.7–1
2.8)
NA
NA
HbA
1c (%
)11
.8 (8
.2–1
7.3)
10.7
(8.5
–13.
8)9.
0 (7
.4–1
4)8.
7 (6
.9–1
1.2)
NA
NA
BMI (
SD-s
core
)-0
.7 (-
2.9–
0.5)
-0.6
(-2.
2–1.
2)0.
8 (-0
.8–2
.6)
0.6
(-1.2
–2.2
)-0
.2 (-
1.3–
1.8)
0.4
(-0.4
–+1.
7)
Tota
l cho
lest
erol
(mm
ol/L
)4.
2 (3
.3–5
.4)
4.3
(3.4
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110
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Adipocyte differentiation and plasma stimulationThe human preadipocyte SGBS cell line was cultured and differentiated as described previously [20]. In short, preadipocytes were grown to confluence. Differentiation was initiated with a 5-d pulse of dexamethasone, rosiglitazone, 3-isobutyl-1-methylxanthine, T3, biotin, and D-panthotenic acid in DMEM/F12 medium (Invitrogen, Carlsbad, CA) and continued with 4 d of T3, biotin, and D-panthotenic acid stimulation only.
At d 7, pooled patient or control plasma (five random patients per plasma pool) was added to the cell supernatants to a final concentration of 20% for 24 h. At d 8, cells were washed with PBS once and incubated with fresh differentiation medium. At d 9, supernatants were harvested and stored at -80°C until the adipokine multiplex immunoassay analysis. Cells were fixed with 4% paraformaldehyde for 30 min and stored in PBS.
Free fatty acidsBefore incubation with the adipocytes, oleic acid and palmitic acid were dissolved in potassium hydroxide in 100% ethanol, and complexed with BSA at 37°C for 1 h in a fatty acid:BSA 3:1 molar ratio, as described [21].
Effects of hyperglycaemia and free fatty acid on adipocyte differentiationHuman preadipocytes were cultured and differentiated as described above. At d 7, cells were exposed to different concentrations of glucose (low glucose, 1 g/liter; normal glucose, 4.5 g/liter; or high glucose dose, 20 g/liter) or free fatty acids (vehicle, KOH in 100% ethanol; oleic acid, 250 µM; or palmitic acid, 250 µM) for 24 h. Please note that 1 g/liter glucose represents a low concentration of glucose in in vitro adipocyte cultures [16, 20], whereas being a relatively normal dose in vivo (5.7 mmol/liter). At d 8, cells were washed once with PBS and incubated with fresh differentiation media. At d 9, supernatants were harvested and stored at -80°C until the adipokine multiplex immunoassay analysis. Cells were fixed with 4% paraformaldehyde for 30 min and stored in PBS.
Quantification of adipocyte differentiationFixed cells were either stained with Oil-Red-O as described previously [20] or stained with Nile Red and 4’ ,6-diamidino-2-phenylindole for automated fluorescence microscopy (Cellomic ArrayScan, VTI HCS Reader; Thermo Scientific, Rockford, IL) to quantify adipocyte differentiation. This was carried out using the Cellomics Target Activation Bioapplication package. Essentially, this application identifies the 4’ ,6-diamidino-2-
111
Adipokines and adipocyte differentiation in paediatric T1D
4
phenylindole-stained nuclei. An x pixel mask was created around the nucleus, and the intensity of Nile Red staining within this mask was reported. The percentage of responders was based on a cutoff of y for the average pixel intensity/total intensity of Nile Red within the mask. Details on the algorithm settings are given in the Supplemental Data.
Adipokine multiplex immunoassayAdipokine levels were measured with the Bio-Plex system in combination with Bio-Plex Manager software version 5.0 (Bio-Rad Laboratories, Hercules CA), as described recently [16, 19]. Adipocyte supernatants were measured undiluted. Plasmas were measured undiluted for most of the adipokines. For measurement of plasma adipokines naturally occurring in very high concentrations [i.e. tissue inhibitor of metalloproteinase-1 (TIMP-1), chemerin, plasminogen activator inhibitor-1 (PAI-1), adiponectin, adipsin (complement factor D), serum amyloid A (SAA-1), retinol binding protein 4 (RBP-4), cathepsin S, thrombopoietin, and leptin], patient and control plasmas were diluted 100 times. For measurement of osteopontin (OPN), all plasma samples were diluted five times.
Statistical analysesStatistical evaluation was performed using GraphPad Prism software, version 4.02 (GraphPad Software, La Jolla, CA) and SPSS 15.0 for Windows (SPSS, Inc., Chicago, IL). Basic descriptive statistics were used to describe the patient populations. For the adipokine levels and lipids, paired data were analysed with the Wilcoxon signed-ranks test; unpaired data were analysed with the Mann-Whitney U test. For the adipocyte differentiation, differences in the number of differentiated adipocytes per microscopic field between groups were analysed with a one-way ANOVA and Tukey’s multiple comparison post hoc test. A p-value < 0.05 was considered statistically significant.
Results
Plasma adipokine levels
We compared plasma adipokine profiles of HC with newly diagnosed and longstanding type 1 diabetes patients, as shown in Figure 4.1 and Table 4.2. Plasma concentrations of several adipokines were significantly altered between type 1 diabetes patients and HC.
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In both onset and longstanding type 1 diabetes patients, adiponectin, leptin, RBP-4, cathepsin S, TIMP-1, chemerin, SAA-1, and PAI-1 levels were significantly increased compared with HC. Interestingly, leptin, SAA-1, and PAI-1 levels were higher in long-standing compared with newly diagnosed patients. CCL2 (monocyte chemoattractant protein-1) levels were higher in patients with longstanding disease compared with newly diagnosed patients and HC, and macrophage inhibitory factor (MIF) levels were signifi-cantly enhanced in patients with longstanding disease compared with HC only. Finally, OPN showed increased levels in patients at onset of disease compared with longstanding diabetes patients.
Differences in adipokine profiles between the three groups could not be attributed to differences in age or BMI-SD (linear regression analysis, data for BMI-SD shown in Supplemental Table S4.1). No significant differences between the study populations were found for any of the other adipokines studied (IL-1RA, IL-1β, IL-6, IL-10, TNF-α, interferon-γ, MIF, CXCL8 (IL-8), CXCL10 (gamma interferon inducible protein 10 (IP10)), granulocyte-macrophage colony stimulating factor (GM-CSF), resistin, throm-bopoietin, and adipsin).
Figure 4.1 Adipokine and cytokine levels in plasma Plasma levels of CCL2, chemerin, RBP-4, leptin, cathepsin S, adiponectin, and PAI-1 in HC, patients with new onset type 1 diabetes (onset), and patients with longstanding type 1 diabetes (LD) are shown. Lines depict mean value per group. * p < 0.05.
* * *
***
*
CCL2
(pg/
ml)
HC Onset LD0
50
100
150
Chem
erin
(ng/
ml)
0
200
400
600
HC Onset LD
RBP-
4 (
g/m
l)
0
100
200
300
HC Onset LD
ng/m
l)
0
100
200
300
HC Onset LD HC Onset LD HC Onset LD HC Onset LD
Cath
epsi
n S
(ng/
ml)
0
75
150
225
g/m
l)0
5
10
15
PAI-1
(g/
ml)
0
3
6
9
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Adipokines and adipocyte differentiation in paediatric T1D
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In conclusion, both onset and longstanding paediatric type 1 diabetes patients showed extensive alterations in plasma adipokine levels compared with HC. Apart from increased free fatty acid levels in type 1 diabetes patients at onset compared with longstanding disease (p = 0.04), these alterations were not accompanied by plasma lipid alterations (Table 4.1).
Table 4.2 Adipokine and cytokine levels in plasma
Units Healthy controls Onset Longstanding disease
IL-1RA aa pg/ml 45 (6.0–421) 46 (2.3–417) 42 (6.0–114)IL-1β pg/ml 6.3 (0.4–73) 2.7 (0.4–22) 7.2 (0.4–87)IL-6aa pg/ml 17 (6.0–161) 6.7 (0.4–26) 16 (2.0–192)IL-10 pg/ml 39 (0.1–407) 13 (0.1–95) 28 (0.9–312)TNF-α aa pg/ml 4.7 (0.5–33) 2.9 (0.1–3.0) 3.9 (1.0–23)IFN-γ aa pg/ml 7.4 (6.0–25) 6.0 (6.0) 9.7 (4.5–81)MIF ng/ml 1.3 (0.0–3.1) a 1.5 (0.1–3.4) 2.5 (0.6–11) a
CCL2 pg/ml 18 (1.0–45) a 27 (4.4–65) b 45 (13–100) a,b
CCL3 aa pg/ml 91 (61–280) a,c 61 (61) c 60 (54–97) a
CXCL8 (IL-8) pg/ml 28 (0.4–160) 14 (0.3–103) 21(0.4–99)CXCL10 pg/ml 371 (100–2333) 359 (108–2210) 281 (125–712)GM-CSF aa pg/ml 36 (34–36) 35 (5.5–63) 35 (0.7–125)OPN ng/ml 83 (16–253) 71 (18–288) b 44 (15–77) b
Resistin ng/ml 190 (58–452) 213 (111–624) 178 (75–359)Thrombopoietin μg/ml 4.0 (1.0–7.6) 5.4 (1.1–6.4) 6.0 (5.2–7.1)Adipsin ng/ml 79 (3–183) 111 (12–154) 126 (107–152)Chemerin ng/ml 98 (13–256) a,c 220 (118–326) c 255 (126–452) a
SAA-1 μg/ml 10 (3.3–18) a,c 16 (7.0–18) b,c 18 (18) a,b
RBP-4 μg/ml 81 (31–161) a,c 180 (133–245) c 184 (154–213) a
Leptin ng/ml 52 (22–96) a,c 90 (22–118) b,c 112 (80–220) a,b
TIMP-1 ng/ml 202(51–356) a,c 389 (261–482) c 389 (265–495) a
Cathepsin S ng/ml 86 (52–120) a,c 114 (25–160) c 127 (114–152) a
Adiponectin μg/ml 2.7 (0.7–8.3) a,c 6.4 (2.5–19) c 5.7 (2.5–9.6) a
PAI-1 μg/ml 1.8 (266–3.6) a,c 3.0 (31–6.4) b,c 4.0 (2.7–5.8) a,b
Plasma adipokine and cytokine levels are shown in HC, patients with new-onset type 1 diabetes, and patients with longstanding type 1 diabetes. Data are displayed as mean (range). IFN-γ, interferon-γ; GM-CSF, granulocyte-macrophage colony stimulating factor; CXCL10, γ interferon inducible protein 10 (IP10); CCL3, macrophage inflammatory protein 1α. aa ≥75% of all values below detection limit. a p < 0.05 between longstanding type 1 diabetes and HC. b p < 0.05 between onset type 1 diabetes and longstanding disease. c p < 0.05 between onset type 1 diabetes and HC.
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Diabetic plasma induces adipocyte differentiation in vitro
The differences in plasma adipokine levels observed between type 1 diabetes patients and HC (Figure 4.1 and Table 4.2) might be related to plasma effects on the adipocytes in type 1 diabetes patients. To investigate this possibility, we examined the effects of dia-betic plasma versus control plasma on adipocyte differentiation. Pooled diabetic plasma induced adipocyte differentiation, as assessed by Oil-Red-O staining, whereas pooled HC plasma had no effect (Figure 4.2, A-C). To quantify these differences, pooled plasmas were added in 12-fold, and differentiation was assessed by Nile Red staining and quantified by automated microscopy. Onset as well as longstanding diabetic pooled plasma signifi-cantly induced the number of differentiated adipocytes per microscopic field within 24 h, compared with control plasma and the nonplasma control (Figure 4.2D). In addition, onset and longstanding diabetic plasma significantly induced preadipocyte proliferation compared with HC plasma and the nonplasma control (Figure 4.2E). Thus, plasma of type 1 diabetes patients induced adipocyte differentiation and preadipocyte proliferation, and this effect was already detectable at the onset of type 1 diabetes.
Plasma-induced adipokine secretion by adipocytes
To test whether the differences in plasma adipokine levels between diabetic patients and controls could be attributed to plasma effects on adipocytes, we performed multi-plex adipokine analysis on plasma-conditioned adipocytes. The levels of adiponectin, CCL2, and TNF-α secreted by adipocytes upon incubation with diabetic plasma were significantly higher compared with control plasma treatment, reflecting the differences in adiponectin and CCL2 levels observed in plasma (Figure 4.3 and Supplemental Table S4.2). The trend for higher RBP-4 release of longstanding diabetic plasma-conditioned adipocytes and higher cathepsin S release of onset diabetic plasma-conditioned adipocytes also reflected the differences in plasma RBP-4 and cathepsin S levels. Yet MIF levels were higher in control plasma-conditioned adipocytes compared with onset diabetic plasma, and no differences in the adipocyte release of leptin, chemerin, SAA-1, and PAI-1 were observed, whereas these adipokines did show higher levels in diabetic compared with control plasma (Figure 4.3 and Supplemental Table S4.2).
Taken together, our findings indicate that diabetic plasma can induce alterations in adi-pocyte function (proliferation, differentiation, adipokine profile), which may contribute to the proinflammatory condition in type 1 diabetes.
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Figure 4.2 Diabetic plasma induces adipocyte differentiation in vitro (A-C) Oil-Red-O staining of adipocyte differentiation without additional plasma (A) and with addition of pooled plasma of HC (B) or paediatric type 1 diabetes patients (C). Images were made with the 50x and 200x zoom objective, respectively. (D-E) Differentiated number of adipocytes per microscopic field for various pooled plasmas (D), together with the total number of cells per microscopic field for these plasmas (E). * p < 0.05.
A No Plasma B Control Plasma C DM Plasma
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
No Plasma
Control
Plasma
0
25
50
75
100
125
150
D
E
*
*
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Plasma
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Glucose and free fatty acids alter adipokine secretion in vitro
Both the glucose dysregulation and dyslipidaemia associated with type 1 diabetes may affect adipose tissue function. Therefore, we studied whether the effects of diabetic plasma on adipocyte differentiation and adipokine production could be reproduced by incubation with glucose or lipids. For lipids, we focused on free fatty acids because this was the only lipid class that was significantly induced in the (onset) patient group (Table 4.1). Similar to the plasma incubations described above, adipocytes were incubated for 24 h with various glucose doses (1, 4.5, or 20 g/liter), the unsaturated fatty acid oleic acid (250 µM), the saturated fatty acid palmitic acid (250 µM), or the fatty acid vehicle. Next, cell proliferation, adipocyte differentiation, and adipokine secretion were assessed (Figure 4.4). We selected seven adipokines that were found altered in diabetic plasma compared with controls (adiponectin, CCL2, RBP-4, cathepsin S, chemerin, leptin, and PAI-1). In
Figure 4.3 Adipokine and cytokine levels in adipocyte culture supernatants Levels of CCL2, chemerin, RBP-4, leptin, cathepsin S, adiponectin, and PAI-1 in supernatants of adipocyte cultures after incubation with pooled plasma of HC, patients with new onset type 1 diabetes (onset), and patients with longstanding type 1 diabetes (LD). Data represent mean values ± SD. * p < 0.05.
HC Onset LD0
100
200
300
400
500CC
L2 (p
g/m
l)
HC Onset LD0
1
2
3
4
5
Chem
erin
(ng/
ml)
HC Onset LD0
200
400
600
800
1000
RBP4
(ng/
ml)
HC Onset LD0
200
400
600
800
1000
Lep
n (p
g/m
l)
HC Onset LD0
40
80
120
160
200
Cath
epsin
S (p
g/m
l)
HC Onset LD0
20
40
60
80
100
Adip
onec
n (n
g/m
l)
HC Onset LD0
200
400
600
800
1000
PAI-1
(ng/
ml)
*
*
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Adipokines and adipocyte differentiation in paediatric T1D
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contrast to type 1 diabetic plasma (Figure 4.2), glucose or free fatty acid treatment did not affect cell proliferation or adipocyte differentiation (Figure 4.4). High glucose levels and palmitic acid did, however, increase CCL2 secretion by adipocytes (Figure 4.4). In addition, high glucose levels specifically reduced RBP-4 secretion. These results indicate that type 1 diabetic plasma can alter adipokine secretion by adipocytes, either directly (e.g. glucose, palmitic acid) or indirectly through its effects on adipogenesis (plasma).
Discussion
Adipose tissue-secreted factors in type 1 diabetes have emerged as a promising new field of research and may comprise potential targets for intervention [14]. Here, we report altered plasma adipokine profiles in children with onset and longstanding type 1 diabetes versus HC, making use of a recently developed and validated multiplex immunoassay for 24 adipokines [16]. To the best of our knowledge, this is the first study exploring such a wide range of adipokines in paediatric type 1 diabetes. Our in vitro studies indicate that plasma factors in paediatric type 1 diabetes can affect adipokine profiles, either directly (e.g. glucose, palmitic acid) or indirectly through their effects on differentiation (plasma), providing a possible explanation for the altered adipokine profiles observed in vivo.
We identified three novel adipokines that are altered in children with type 1 diabetes. Firstly, whereas cathepsin S has recently been implicated in immune regulation in mouse models for type 1 diabetes [22] and plays a key role in various adipose tissue-mediated inflammatory and atherogenic processes [23, 24], this is the first time that cathepsin S levels in type 1 diabetes patients have been determined and found to be increased. Chemerin, a second adipokine at the crossroads of adipose tissue and inflammation [25] that has not been analysed in type 1 diabetes patients so far, was significantly increased in both onset and longstanding type 1 diabetes children compared with HC. Thirdly, elevated levels of TIMP-1 have been reported in adults with type 1 diabetes before [26, 27] but are now also observed in children with type 1 diabetes. TIMP-1 is produced by adipocytes [16], its levels are elevated in obesity [27], and it has been considered to be part of an antiinflammatory pathway counteracting β cell death [28]. As for the inflammatory proteins CCL2, MIF, and PAI-1, which are known to be elevated in adult type 1 diabetes patients [11, 13, 29, 30], this study now also found elevated levels in paediatric type 1 diabetes patients. Furthermore, we confirmed elevated adiponectin levels in paediatric type 1 diabetes patients. In contrast, RBP-4 and SAA-1 levels were found to be higher
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Figure 4.4 Glucose and the saturated fatty acid palmitic acid alter adipokine secretion in vitro (A) Oil-Red-O staining of adipocytes incubated for 24 h with low glucose (1 g/liter), normal glucose (4.5 g/liter), or high glucose doses (20 g/liter). Images were made with a 50x and 200x zoom objective, respectively. (B) Oil-Red-O staining of adipocytes incubated with vehicle (KOH in 100% ethanol), unsaturated oleic acid (250 µM), or saturated palmitic acid (250 µM). (C-D) Quantification of the cell proliferation and adipocyte differentiation upon 24-h incubation with the various glucose doses and fatty acids. (E-F) Measurement of adipokines in the supernatants of the adipocytes after 24-h incubation with the various glucose concentrations and fatty acids. Data represent mean values ± SD. * p < 0.05.
Normal glucoseLow glucose High glucose
A
Oleic AcidVehicle
B
Ad ip o n ec n
0
100
200
300
400
ng/m
l
02 55 07 5
1 0 01 2 51 5 0
C
0
1
2
3
4
pg/m
l
Cathepsin S
200
400
600
0
Chemerin
pg/m
l
0
25
50
75
100
pg/m
l
ng/m
l
0
50
100
150PAI-1
E
*CCL2
0
50
100
pg/m
l
150 *RBP-4
0
100
200
300
400
ng/m
l
500 *
Low glucose (1g/L)Normal glucose (4.5g/L)High glucose (20g/L)
Ad ip o n ec n
0
50
100
150
200
ng/m
l
*
02 55 07 5
1 0 01 2 51 5 0
Vehicle
Oleic acid
D
0
25
50
75100
125 *
pg/m
l
CCL2 RBP-4
0
100
200
300
400
ng/m
l
500
0
1
2
3
4
pg/m
l
Cathepsin S
F
200
400
600
0
Chemerin
pg/m
l
0
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50
75
100
pg/m
l
ng/m
l
0
50
100
150PAI-1
Oleic acid (250ђM)
Low gl
ucose
Normal g
lucose
High gl
ucose
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Adipokines and adipocyte differentiation in paediatric T1D
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in children with type 1 diabetes than in HC, whereas in adult type 1 diabetes patients unaltered or even lowered levels have been reported [31, 32].
Although the metabolic disturbances in patients with onset type 1 diabetes may be expected to result in an extra derailment of adipokine levels, we found children with onset and longstanding type 1 diabetes to display comparable adipokine profiles. We considered various explanations. Firstly, the adipokine profiles may be comparable because they are more severely affected in longstanding type 1 diabetes patients than expected. This may be explained by the accelerator hypothesis, which assumes a continuum between β cell function and insulin resistance [33]. Accordingly, the manifest β cell dysfunction in onset patients, followed by insulin treatment, may lead to weight gain [34], insulin resistance, and further derailment of the adipokine levels in longstanding patients. We performed correlation studies of BMI and HbA1c with adipokines (Supplemental Figure S4.1, A and B), and also of BMI with HbA1c (Supplemental Figure S4.1C). The positive correlation between BMI-SD and HbA1c in the longstanding patient group (p = 0.03) suggests that weight gain may indeed worsen the phenotype in longstanding patients. However, due to the relatively small group size, no definite conclusions can be drawn on this point. A second explanation may be the variation between onset patients. Although we strictly followed the ISPAD criteria for onset patients [17], the variation in days after diagnosis (0–12 d) may have dampened the effects of the metabolic disturbances seen directly after diagnosis. Furthermore, whereas blood withdrawal was nonfasting and at random times during the day in all patient and control groups, standardized morning and fasting blood withdrawal might have reduced the variation and would have helped to study the effects of individual variables on the adipokine levels. Finally, the lack of data on residual β cell function together with the retrospective and cross-sectional study design prevents drawing conclusions on the role of weight gain and insulin resistance in accelerating the onset of type 1 diabetes.
Nevertheless, the multitude of altered adipokines in children with type 1 diabetes strongly suggests adipose tissue involvement in the low-grade systemic inflammation observed in type 1 diabetes, and our in vitro experiments with diabetic plasma provide further support for the involvement of adipose tissue. Glucose and free fatty acids are candidate factors to explain the marked adipogenic effects of type 1 diabetic plasma. Gogitidze et al. [30] recently reported glucose dysregulation effects on adipokine secretion, and lipids have long been known for their adipogenic properties [10, 35]. In our model system, neither glucose nor the saturated fatty acid palmitic acid affected proliferation or differentiation, indicating that (combinations of) other plasma factors are required for these effects.
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Although the effects of glucose and palmitic acid on the CCL2 secretion by adipocytes suggest that these diabetic plasma factors can directly contribute to the adipokine secretion by adipocytes, the decreased adiponectin release upon glucose and palmitic acid incubation once more indicates that (combinations of) other plasma factors are required to explain the in vivo adipokine levels and the in vitro adipogenic effects of diabetic plasma. Unfortunately, limited amounts of plasma available (i.e. ethical restrictions with respect to blood withdrawal in children) precluded fractionation experiments to identify the adipogenic plasma factors. Therefore, further experiments in adult type 1 diabetes patients are needed to identify the diabetic plasma factors specifically responsible for the observed effects on adipogenesis and adipokine profiles.
The adipogenic effects of diabetic plasma raise three interesting hypotheses. Firstly, enhanced preadipocyte proliferation may partly account for the increased levels of circulating inflammatory cytokines in type 1 diabetes. Preadipocytes are known as potent producers of CCL2, and to a lesser extent TNF-α [16, 36]. In accordance with the proliferative effect of diabetic plasma on preadipocytes, we observed enhanced TNF-α and CCL2 release by the (pre)adipocytes (Figure 4.2 and Supplemental Table S4.2) and increased plasma CCL2 levels in the diabetic children (Figure 4.1 and Table 4.2). Secondly, enhanced adipocyte differentiation may also contribute to the altered adipokine profiles in diabetic patients. Both adiponectin and RBP-4 are, for example, produced by differentiated adipocytes [16, 36]. In accordance with the diabetic plasma-induced adipocyte differentiation, we observed enhanced adiponectin and RBP-4 release by the (pre)adipocytes (Figure 4.3 and Supplemental Table S4.2), and increased plasma RBP-4 and adiponectin levels in the diabetic children (Table 4.2). Concurrently, it is important to note that adipocyte differentiation models do not suffice to study the diabetic plasma-induced secretion of, for example, chemerin, SAA-1, and PAI-1. In vivo, these adipokines are mainly secreted by adipose tissue stromal vascular cells [36-38]. The heterogeneity of the stromal vascular fraction, i.e. varying compilations of stem cells, preadipocytes, leukocytes, and endothelial cells, prevented us from studying the effects of diabetic plasma on this fraction. Thirdly, it is interesting to consider a general implication of increased adipogenesis for diabetic children. Childhood type 1 diabetes is associated with a significant increase in BMI within 3–6 months [34]. Adipocyte number is known to be a major determinant for fat cell mass, BMI, and supposedly insulin resistance at a later age [39, 40]. Increased adipogenesis in diabetic children may thus contribute to a higher fat cell mass and insulin resistance later in life.
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Adipokines and adipocyte differentiation in paediatric T1D
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In conclusion, our data fuel the hypothesis that the assumed continuum between type 1 and type 2 diabetes is partly explained by the adipose tissue involvement in both diseases. As recently emphasized [40], studying adipose tissue involvement may be key to developing novel targets for prevention and treatment.
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8. Guilherme, A., Virbasius, J.V., Puri, V., Czech, M.P. Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes. Nat Rev Mol Cell Biol 2008; 9:367-377.
9. Maahs, D.M., Ogden, L.G., Dabelea, D., Snell-Bergeon, J.K., Daniels, S.R., Hamman, R.F., Rewers, M. Association of glycaemia with lipids in adults with type 1 diabetes: modification by dyslipidaemia medication. Diabetologia 2010; 53:2518-2525.
10. Hotamisligil, G.S. Endoplasmic reticulum stress and the inflammatory basis of metabolic disease. Cell 2010; 140:900-917.
11. Huerta, M.G. Adiponectin and leptin: potential tools in the differential diagnosis of pediatric diabetes? Rev Endocr Metab Disord 2006; 7:187-196.
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12. Kaas, A., Pfleger, C., Hansen, L., Buschard, K., Schloot, N.C., Roep, B.O., Mortensen, H.B. Associat ion of adiponect in, interleukin (IL)-1ra, inducible protein 10, IL-6 and number of islet autoantibodies with progression patterns of type 1 diabetes the first year after diagnosis. Clin Exp Immunol 2010; 161:444-452.
13. Pfleger, C., Mortensen, H.B., Hansen, L., Herder, C., Roep, B.O., Hoey, H., Aanstoot, H.J., Kocova, M., Schloot, N.C. Association of IL-1ra and adiponectin with C-peptide and remission in patients with type 1 diabetes. Diabetes 2008; 57:929-937.
14. Wang, M.Y., Chen, L., Clark, G.O., Lee, Y., Stevens, R.D., Ilkayeva, O.R., Wenner, B.R., Bain, J.R., Charron, M.J., Newgard, C.B. et al. Leptin therapy in insulin-deficient type I diabetes. Proc Natl Acad Sci USA 2010; 107:4813-4819.
15. Myers, M.G., Kahn, C.R., Accili, D. Leptin therapy for type 1 diabetes gains traction. Nat Med 2010; 16:380.
16. Schipper, H.S., de Jager, W., van Dijk, M.E., Meerding, J., Zelissen, P.M., Adan, R.A., Prakken, B.J., Kalkhoven, E. A multiplex immunoassay for human adipokine profiling. Clin Chem 2010; 56:1320-1328.
17. Craig, M.E., Hattersley, A., Donaghue, K. IS-PAD Clinical Practice Consensus Guidelines 2006-2007. Definition, epidemiology and classification. Pediatr Diabetes 2006; 7:343-351.
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20. Wabitsch, M., Brenner, R.E., Melzner, I., Braun, M., Moller, P., Heinze, E., Debatin, K.M., Hauner, H. Characterization of a human preadipocyte cell strain with high capacity for adipose differentiation. Int J Obes Relat Metab Disord 2001; 25:8-15.
21. De Vogel-van den Bosch, H.M., de Wit, N.J., Hooiveld, G.J., Vermeulen, H., van der Veen, J.N., Houten, S.M., Kuipers, F., Muller, M., van der Meer, R. A cholesterol-free, high-fat diet suppresses gene expression of cholesterol transporters in murine small intestine. Am J Physiol Gastrointest Liver Physiol 2008; 294:G1171-G1180.
22. Hsing, L.C., Kirk, E.A., McMillen, T.S., Hsiao, S.H., Caldwell, M., Houston, B., Rudensky, A.Y., LeBoeuf, R.C. Roles for cathepsins S, L, and B in insulitis and diabetes in the NOD mouse. J Autoimmun 2010; 34:96-104.
23. Jobs, E., Riserus, U., Ingelsson, E., Helmersson, J., Nerpin, E., Jobs, M., Sundstrom, J., Lind, L., Larsson, A., Basu, S. et al. Serum cathepsin S is associated with serum C-reactive protein and interleukin-6 independently of obesity in elderly men. J Clin Endocrinol Metab 2010; 95:4460-4464.
24. Taleb, S., Clement, K. Emerging role of cathepsin S in obesity and its associated diseases. Clin Chem Lab Med 2007; 45:328-332.
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26. Maxwell, P.R., Timms, P.M., Chandran, S., Gordon, D. Peripheral blood level alterations of TIMP-1, MMP-2 and MMP-9 in patients with type 1 diabetes. Diabet Med 2001; 18:777-780.
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27. Maury, E., Brichard, S.M., Pataky, Z., Carpentier, A., Golay, A., Bobbioni-Harsch, E. Effect of obesity on growth-related oncogene factor-alpha, thrombopoietin, and tissue inhibitor metalloproteinase-1 serum levels. Obesity 2010; 18:1503-1509.
28. Kang, S., Park, E.J., Joe, Y., Seo, E., Park, M.K., Seo, S.Y., Chung, H.Y., Yoo, Y.H., Kim, D.K., Lee, H.J. Systemic delivery of TNF-related apoptosis-inducing ligand (TRAIL) elevates levels of tissue inhibitor of metalloproteinase-1 (TIMP-1) and prevents type 1 diabetes in nonobese diabetic mice. Endocrinology 2010; 151:5638-5646.
29. Zineh, I., Beitelshees, A.L., Silverstein, J.H., Haller, M.J. Serum monocyte chemoattractant protein-1 concentrations associate with diabetes status but not arterial stiffness in children with type 1 diabetes. Diabetes Care 2009; 32:465-467.
30. Gogitidze, J.N., Hedrington, M.S., Briscoe, V.J., Tate, D.B., Ertl, A.C., Davis, S.N. Effects of acute hypoglycemia on inflammatory and pro-atherothrombotic biomarkers in individuals with type 1 diabetes and healthy individuals. Diabetes Care 2010; 33:1529-1535.
31. Jourdan, M., Jaleel, A., Karakelides, H., Ford, G.C., Kahn, B.B., Nair, K.S. Impact of type 1 diabetes and insulin treatment on plasma levels and fractional synthesis rate of retinol-binding protein 4. J Clin Endocrinol Metab 2009; 94:5125-5130.
32. Basu, S., Larsson, A., Vessby, J., Vessby, B., Berne, C. Type 1 diabetes is associated with increased cyclooxygenase- and cytokine-mediated inflammation. Diabetes Care 2005; 28:1371-1375.
33. Wilkin, T.J. Changing perspectives in diabetes: their impact on its classification. Diabetologia 2007; 50:1587-1592.
34. Luczynski, W., Szypowska, A., Glowinska-Olszewska, B., Bossowski, A. Overweight, obesity and features of metabolic syndrome in children with diabetes treated with insulin pump therapy. Eur J Pediatr 2010; 170:891-898.
35. Iyer, A., Fairlie, D.P., Prins, J.B., Hammock, B.D., Brown, L. Inflammatory lipid mediators in adipocyte function and obesity. Nat Rev Endocrinol 2010; 6:71-82.
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Supplemental information
Settings for automated fluorescence microscopy (Thermo Scientific Cellomic ArrayScan; VTI HCS Reader) to quantify adipocyte differentiation using Cellomics Target Activation Bioapplication software.
Image AcquisitionObjective 10x
Camera Name ORCA-ER;1.00Acquisition Camera Mode Standard (1024x1024;2x2)AutoFocus Camera Mode AutoFocus (1024x1024;4x4)
AutoFocus Field Interval 0
AutoFocus ParametersFine Focus Step Size 17.6
Fine Focus Plane Count 9Coarse Focus Step Size 70.4
Coarse Focus Plane Count 9Smart Focus Plane Count 21
Use Extended Range Focusing FalseApply Backlash Correction False
AutoFocus Method STANDARDUse Relaxed Pass/Fail Criteria False
Focus Edge Threshhold 0Focus Adjustment 0
Focus Score Min Ratio 0.2Focus Score Mid Ratio 0.4Focus Score Max Ratio 0.5
Focus Exposure Time for AutoExpose (seconds)
0.1
Scan LimitsMax Fields for Well 20
Min Objects for Well 500Max Sparse Fields for Well No Limit
Min Objects for Field N/AMax Sparse Wells for Plate N/A
Channel 1: NucleiDye XF93 - Hoechst
Apply Illumination Correction FalseApply Background Correction True
Gain 25Use Apotome False
Z Offset 0.00Exposure Parameters
Method FixedExposure Time (seconds) 0.02074
Object IdentificationMethod FixedThreshold
Value 70Object Selection Parameter Min Max
ObjectAreaCh1 100 550ObjectShapeP2ACh1 1 10ObjectShapeLWRCh1 1 10
ObjectAvgIntenCh1 0 4095ObjectVarIntenCh1 0 32767
ObjectTotalIntenCh1 0 10000000000Display Options
Composite Color (Hex) #0000FFRejectedObject #FF7F00
MaskCh2 #00FF00
Channel 2: nile redDye XF93 - TRITC
Apply Illumination Correction FalseApply Background Correction True
Gain 25Use Apotome False
Z Offset 0.00Exposure Parameters
Method FixedExposure Time (seconds) 0.069027
Object IdentificationMethod None
Value 0Object Selection Parameter Min Max
AvgIntenCh2 50 4095TotalIntenCh2 9500 10000000000
Display OptionsComposite Color (Hex) #80FF80
SelectedObject #0000FFRejectedObject #FF7F00
MaskCh2 #00FF00
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AssayAssay Algorithm TargetActivation.
V3Assay Version 6.1 (Locally
Installed Version: 6.1.0.3018)
Focus Channel 1#Channels 2
Assay ParametersUseReferenceWells 0
MinRefAvgObjectCountPerField 2UseMicrometers 0
PixelSize 1.29Type_1_EventDefinition 0.926Type_2_EventDefinition 0Type_3_EventDefinition 0
ObjectTypeCh1 0BackgroundCorrectionCh1 10
ObjectSmoothFactorCh1 5ObjectSegmentationCh1 5.1RejectBorderObjectsCh1 1
ObjectAreaCh1LevelHigh 150ObjectAreaCh1LevelLow 70
ObjectAreaCh1LevelHigh_CC 1ObjectAreaCh1LevelLow_CC 1
ObjectShapeP2ACh1LevelHigh 1.26ObjectShapeP2ACh1LevelLow 1.1
ObjectShapeP2ACh1LevelHigh_CC 1ObjectShapeP2ACh1LevelLow_CC 1
ObjectShapeLWRCh1LevelHigh 1.67ObjectShapeLWRCh1LevelLow 1.19
ObjectShapeLWRCh1LevelHigh_CC 1ObjectShapeLWRCh1LevelLow_CC 1
ObjectTotalIntenCh1LevelHigh 75000ObjectTotalIntenCh1LevelLow 27300
ObjectTotalIntenCh1LevelHigh_CC 1ObjectTotalIntenCh1LevelLow_CC 1
ObjectAvgIntenCh1LevelHigh 630ObjectAvgIntenCh1LevelLow 305
ObjectAvgIntenCh1LevelHigh_CC 1ObjectAvgIntenCh1LevelLow_CC 1
ObjectVarIntenCh1LevelHigh 330ObjectVarIntenCh1LevelLow 55
ObjectVarIntenCh1LevelHigh_CC 1ObjectVarIntenCh1LevelLow_CC 1
BackgroundCorrectionCh2 25
Assay ParametersMaskModifierCh2 7
TotalIntenCh2LevelHigh 12100TotalIntenCh2LevelLow 0
TotalIntenCh2LevelHigh_CC 1TotalIntenCh2LevelLow_CC 1
AvgIntenCh2LevelHigh 125AvgIntenCh2LevelLow 0
AvgIntenCh2LevelHigh_CC 1AvgIntenCh2LevelLow_CC 1
VarIntenCh2LevelHigh 60VarIntenCh2LevelLow 0
VarIntenCh2LevelHigh_CC 1VarIntenCh2LevelLow_CC 1
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Well Feature ExtentsFeature Name Lower Extent Upper Extent
CV_ObjectShapeP2ACh1 0 100%RESPONDER_ObjectShapeP2ACh1 0 100
SE_ObjectShapeLWRCh1 0 100CV_ObjectShapeLWRCh1 0 100
%RESPONDER_ObjectShapeLWRCh1 0 100SE_ObjectTotalIntenCh1 0 100CV_ObjectTotalIntenCh1 0 100
SE_ObjectAvgIntenCh1 0 100CV_ObjectAvgIntenCh1 0 100
SE_ObjectVarIntenCh1 0 100CV_ObjectVarIntenCh1 0 100
%RESPONDER_ObjectVarIntenCh1 0 100SE_TotalIntenCh2 0 100CV_TotalIntenCh2 0 100
SE_AvgIntenCh2 0 100CV_AvgIntenCh2 0 100
SE_VarIntenCh2 0 100CV_VarIntenCh2 0 100
%RESPONDER_VarIntenCh2 0 100ValidObjectCount* 0 100
SelectedObjectCount 20 750SD_VarIntenCh2 0 10000000
MEAN_ObjectTotalIntenCh1 0 10000000000SD_ObjectTotalIntenCh1 0 10000000000
MEAN_TotalIntenCh2 0 10000000000SD_TotalIntenCh2 0 10000000000
%RESPONDER_TotalIntenCh2 0 100EventType1ObjectCount 0 100
%EventType1Objects 5 15EventType2ObjectCount 0 100
%EventType2Objects 0 100EventType3ObjectCount 0 100
%EventType3Objects 0 100%RESPONDER_ObjectTotalIntenCh1 0 100%RESPONDER_ObjectAvgIntenCh1 0 100
%SelectedObjects 0 100ValidFieldCount 0 111
SelectedObjectCountPerValidField 0 89.9708044982699MEAN_ObjectAreaCh1 0 10000000
SD_ObjectAreaCh1 0 1048576MEAN_ObjectShapeP2ACh1 0 100
SD_ObjectShapeP2ACh1 0 100MEAN_ObjectShapeLWRCh1 0 100
SD_ObjectShapeLWRCh1 0 100MEAN_ObjectAvgIntenCh1 0 4095
SD_ObjectAvgIntenCh1 0 4095MEAN_ObjectVarIntenCh1 0 10000000
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Well Feature ExtentsFeature Name Lower Extent Upper Extent
SD_ObjectVarIntenCh1 0 10000000MEAN_AvgIntenCh2 0 4095
SD_AvgIntenCh2 0 4095%RESPONDER_AvgIntenCh2 0 100
MEAN_VarIntenCh2 0 10000000SE_ObjectAreaCh1 0 100CV_ObjectAreaCh1 0 100
%RESPONDER_ObjectAreaCh1 0 100SE_ObjectShapeP2ACh1 0 100
* Indicates the default well feature for the Assay Protocol. ** Indicates feature extents are dependent upon system reference well settings.
Selected Cell Features to StoreTargetActivationV3Cell:AvgIntenCh2TargetActivationV3Cell:AvgIntenCh2StatusTargetActivationV3Cell:Cell#TargetActivationV3Cell:EventType1StatusTargetActivationV3Cell:EventType2StatusTargetActivationV3Cell:EventType3StatusTargetActivationV3Cell:EventTypeProfileTargetActivationV3Cell:HeightTargetActivationV3Cell:LeftTargetActivationV3Cell:ObjectAreaCh1TargetActivationV3Cell:ObjectAreaCh1StatusTargetActivationV3Cell:ObjectAvgIntenCh1TargetActivationV3Cell:ObjectAvgIntenCh1StatusTargetActivationV3Cell:ObjectShapeLWRCh1TargetActivationV3Cell:ObjectShapeLWRCh1StatusTargetActivationV3Cell:ObjectShapeP2ACh1TargetActivationV3Cell:ObjectShapeP2ACh1StatusTargetActivationV3Cell:ObjectTotalIntenCh1TargetActivationV3Cell:ObjectTotalIntenCh1StatusTargetActivationV3Cell:ObjectVarIntenCh1TargetActivationV3Cell:ObjectVarIntenCh1StatusTargetActivationV3Cell:TopTargetActivationV3Cell:TotalIntenCh2TargetActivationV3Cell:TotalIntenCh2StatusTargetActivationV3Cell:VarIntenCh2TargetActivationV3Cell:VarIntenCh2StatusTargetActivationV3Cell:WidthTargetActivationV3Cell:XCentroidTargetActivationV3Cell:YCentroid
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Selected Well Features to StoreStatusTargetActivationV3Well:%EventType1ObjectsTargetActivationV3Well:%EventType2ObjectsTargetActivationV3Well:%EventType3ObjectsTargetActivationV3Well:%RESPONDER_AvgIntenCh2TargetActivationV3Well:%RESPONDER_ObjectAreaCh1TargetActivationV3Well:%RESPONDER_ObjectAvgIntenCh1TargetActivationV3Well:%RESPONDER_ObjectShapeLWRCh1TargetActivationV3Well:%RESPONDER_ObjectShapeP2ACh1TargetActivationV3Well:%RESPONDER_ObjectTotalIntenCh1TargetActivationV3Well:%RESPONDER_ObjectVarIntenCh1TargetActivationV3Well:%RESPONDER_TotalIntenCh2TargetActivationV3Well:%RESPONDER_VarIntenCh2TargetActivationV3Well:%SelectedObjectsTargetActivationV3Well:CV_AvgIntenCh2TargetActivationV3Well:CV_ObjectAreaCh1TargetActivationV3Well:CV_ObjectAvgIntenCh1TargetActivationV3Well:CV_ObjectShapeLWRCh1TargetActivationV3Well:CV_ObjectShapeP2ACh1TargetActivationV3Well:CV_ObjectTotalIntenCh1TargetActivationV3Well:CV_ObjectVarIntenCh1TargetActivationV3Well:CV_TotalIntenCh2TargetActivationV3Well:CV_VarIntenCh2TargetActivationV3Well:EventType1ObjectCountTargetActivationV3Well:EventType2ObjectCountTargetActivationV3Well:EventType3ObjectCountTargetActivationV3Well:MEAN_AvgIntenCh2TargetActivationV3Well:MEAN_ObjectAreaCh1TargetActivationV3Well:MEAN_ObjectAvgIntenCh1TargetActivationV3Well:MEAN_ObjectShapeLWRCh1TargetActivationV3Well:MEAN_ObjectShapeP2ACh1TargetActivationV3Well:MEAN_ObjectTotalIntenCh1TargetActivationV3Well:MEAN_ObjectVarIntenCh1TargetActivationV3Well:MEAN_TotalIntenCh2TargetActivationV3Well:MEAN_VarIntenCh2TargetActivationV3Well:SD_AvgIntenCh2TargetActivationV3Well:SD_ObjectAreaCh1TargetActivationV3Well:SD_ObjectAvgIntenCh1TargetActivationV3Well:SD_ObjectShapeLWRCh1TargetActivationV3Well:SD_ObjectShapeP2ACh1TargetActivationV3Well:SD_ObjectTotalIntenCh1TargetActivationV3Well:SD_ObjectVarIntenCh1TargetActivationV3Well:SD_TotalIntenCh2TargetActivationV3Well:SD_VarIntenCh2TargetActivationV3Well:SE_AvgIntenCh2TargetActivationV3Well:SE_ObjectAreaCh1TargetActivationV3Well:SE_ObjectAvgIntenCh1TargetActivationV3Well:SE_ObjectShapeLWRCh1
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Selected Well Features to StoreTargetActivationV3Well:SE_ObjectShapeP2ACh1TargetActivationV3Well:SE_ObjectTotalIntenCh1TargetActivationV3Well:SE_ObjectVarIntenCh1TargetActivationV3Well:SE_TotalIntenCh2TargetActivationV3Well:SE_VarIntenCh2TargetActivationV3Well:SelectedObjectCountTargetActivationV3Well:SelectedObjectCountPerValidFieldTargetActivationV3Well:ValidFieldCountTargetActivationV3Well:ValidObjectCount
Supplemental Table S4.1 Univariate logistic regression analysis for Hba1c and BMIDepicted are Pearson correlation coefficients (r) and p-values per adipokine.
Hba1c BMI
Onset LD HC Onset LD HC
IL-1RA -0.19 / 0.43 -0.11 / 0.69 NA 0.30 / 0.21 0.16 / 0.51 -0.20 / 0.10IL-1β 0.12 / 0.63 -0.04 / 0.72 NA -0.03 / 0.91 0.18 / 0.46 0.21 / 0.42IL-6 -0.21 / 0.39 -0.05 / 0.76 NA 0.30 / 0.21 0.13 / 0.57 0.21 / 0.42IL-10 0.00 / 0.99 -0.06 / 0.68 NA 0.11 / 0.65 0.15 / 0.51 0.24 / 0.36TNF-α 0.27 / 0.26 -0.02 / 0.83 NA -0.22 / 0.35 0.15 / 0.53 0.24 / 0.35IFN-γ -0.14 / 0.56 -0.03 / 0.82 NA 0.33 / 0.15 0.15 / 0.52 0.07 / 0.79MIF -0.49 / 0.03 0.34 / 0.60 NA 0.15 / 0.76 -0.33 / 0.09 0.09 / 0.46CCL2 0.16 / 0.52 0.03 / 0.49 NA -0.33 / 0.15 -0.10 / 0.68 -0.01 / 0.97CCL3 NA 0.01 / 0.95 NA NA 0.08 / 0.74 0.16 / 0.55CXCL8 (IL-8) -0.28 / 0.24 0.02 / 0.85 NA 0.27 / 0.26 0.12 / 0.61 -0.05 / 0.86CXCL10 -0.14 / 0.58 0.14 / 0.42 NA 0.12 / 0.62 0.30 / 0.20 0.37 / 0.14GM-CSF -0.48 / 0.04 -0.07 / 0.79 NA 0.07 / 0.76 -0.38 / 0.09 -0.19 / 0.47OPN -0.27 / 0.27 0.18 / 0.74 NA 0.35 / 0.54 0.02 / 0.15 0.08 / 0.72Resistin -0.41 / 0.08 -0.23 / 0.63 NA 0.27 / 0.26 -0.08 / 0.73 0.03 / 0.92Thrombopoietin 0.32 / 0.18 0.03 / 0.10 NA 0.05 / 0.84 0.20 / 0.40 0.09 / 0.73Adipsin 0.32 / 0.18 -0.04 / 0.30 NA -0.01 / 0.98 0.10 / 0.68 0.13 / 0.62Chemerin -0.05 / 0.83 0.06 / 0.17 NA -0.27 / 0.25 0.32 / 0.17 0.49 / 0.05SAA-1 0.41 / 0.08 NA NA -0.19 / 0.42 NA 0.09 / 0.74RBP-4 0.26 / 0.28 0.38 / 0.02 NA -0.41 / 0.07 0.08 / 0.73 -0.23 / 0.38Leptin 0.45 / 0.05 0.11 / 0.01 NA 0.00 / 0.99 0.51 / 0.02 0.29 / 0.26TIMP-1 -0.18 / 0.46 0.28 / 0.04 NA -0.22 / 0.35 0.41 / 0.07 -0.17 / 0.53Cathepsin 0.11 / 0.64 -0.03 / 0.95 NA 0.32 / 0.17 -0.16 / 0.51 1.00 / 0.62Adiponectin -0.15 / 0.55 0.25 / 0.12 NA -0.34 / 0.14 0.29 / 0.22 -0.19 / 0.48PAI-1 0.15 / 0.84 -0.23 / 0.98 NA 0.30 / 0.57 -0.11 / 0.63 0.24 / 0.44
Onset, onset type 1 diabetes; LD, longstanding type 1 diabetes; HC, healthy control; NA, not available or cannot be determined.
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Supplemental Table S4.2 Adipokine and cytokine levels in adipocyte culture supernatantsCytokine and adipokine levels in adipocyte cultures after incubation with pooled plasma of healthy controls, patients with new onset type 1 diabetes and patients with longstanding type 1 diabetes. Data are mean values (range).
Units Healthy controls Onset Longstanding disease
IL-1RA pg/ml 23 (21–25) 23 (21–25) 22 (21–25)IL-1β pg/ml 4.6 (3.6–6.6) 4.0 (3.9–4.1) 3.8 (3.3–4.1)IL-6 pg/ml 55 (35–77) 67 (64–73) 70 (41–92)IL-10 pg/ml 20 (19–20) 21 (19–22) 19 (16–21)TNF-α pg/ml 5.3 (4.5–5.7) a 6.2 (6.0–6.5) a 5.6 (4.5–6.0)MIF pg/ml 1192 (1052–1497) a 876 (734–1029) a 920 (826–1125 )CCL2 pg/ml 185 (137–251) a,b 367 (304–426) a 410 (385–412) b
CCL3 pg/ml 620 (348–793) 608 (542–668) 557 (493–655)CXCL8 (IL-8) pg/ml 1034 (737–1289) 866 (799–928) 785 (689–958)CXCL10 pg/ml 18 (16–20) 16 (14–17) 15 (12–17)GM-CSF pg/ml 33 (30–35) 32 (31–32) 31 (29–34)OPN pg/ml 545 (507–570) 567 (556–579) 540 (485–592)Resistin pg/ml 1192 (1052–1497) 876 (734–1029) 920 (826–1125)Thrombopoietin ng/ml 7.3 (6.9–7.4) 7.3 (7.2–7.3) 7.1 (6.6–7.6)Adipsin ng/ml 331 (306–352) 342 (331–352) 338 (304–360)Chemerin ng/ml 3.2 (3.1–3.4) 3.3 (3.2–3.3) 3.4 (3.2–3.7)SAA-1 ng/ml 31 (28–34) 32 (31–33) 31 (27–35)RBP-4 ng/ml 718 (634–823) 778 (745–796) 881 (788–963)Leptin ng/ml 0.7 (0.6–0.7) 0.7 (0.7) 0.7 (0.6–0.7)Cathepsin S pg/ml 102 (92–107) 108 (106–111) 102 (89–114)Adiponectin ng/ml 46 (28–59) a 66 (65–68) a 73 (58–88)PAI-1 ng/ml 549 (380–714) 365 (292–380) 403 (338–546)
Not included in table: IFN-γ, all values below detection limit; TIMP-1, all values above detection limit. a p < 0.05 between onset type 1 diabetes and healthy controls. b p <0.05 between longstanding type 1 diabetes and healthy controls.
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l)
0 4 8 12 16 20HbA1c (%)
0
40
80
120
160
200
Cath
epsi
n S
(ng/
ml)
0 4 8 12 16 20HbA1c (%)
0
4
8
12
16
20
Adip
one
g/m
l)
0 4 8 12 16 20HbA1c (%)
0
2
4
6
8
10
PAI-1
(g/
ml)
B
OnsetLD
-4 -2 0 2 4BMI SD
0
4
8
12
16
20
HbA1
c (%
)
C
OnsetLD
* LD p < 0.05
*
* LD p < 0.05 *
* LD p < 0.05*
* LD p < 0.05
*
Supplemental Figure S4.1 Correlation of BMI and HbA1c with plasma adipokine levels (A-B) Plasma levels of CCL2, chemerin, RBP-4, leptin, cathepsin S, adiponectin and PAI-1 in healthy controls (HC), patients with new onset type 1 diabetes (onset) and patients with longstanding type 1 diabetes (LD) are shown in relation to BMI-SD and HbA1c. * p < 0.05. (C) Correlation between BMI-SD and HbA1c for all type 1 diabetes patients (both onset and long standing type 1 diabetes).
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Vitamin D deficiency in childhood obesity is associated with high levels
of circulating inflammatory mediators, and low insulin sensitivity
M. Reyman*, A.A. Verrijn Stuart*, M. van Summeren, M. Rakhshandehroo, R. Nuboer, F.K. de Boer, H.J. van den Ham, E. Kalkhoven,
A.B.J. Prakken, H.S. Schipper
* Authors contributed equally
International Journal of Obesity 2013; doi:10.1038/ijo.2013.75
5
134
Chapter 5
5
Abstract
HypothesisChildhood obesity is accompanied by low-grade systemic inflammation, which contributes to the development of insulin resistance and cardiovascular complications later in life. As vitamin D exhibits profound immunomodulatory functions and vitamin D deficiency is highly prevalent in childhood obesity, we hypothesized that vitamin D deficiency in childhood obesity coincides with enhanced systemic inflammation and reduced insulin sensitivity.
MethodsIn a cross-sectional study of 64 obese and 32 healthy children aged 6–16 years, comprehensive profiling of 32 circulating inflammatory mediators was performed, together with assessment of 25-hydroxyvitamin D (25(OH)D) levels and measures for insulin sensitivity.
ResultsSevere vitamin D insufficiency, which is further referred to as vitamin D deficiency, was defined as a 25(OH)D level ≤ 37.5 nmol/l, and highly prevalent in obese (56%) versus healthy control children (16%). Throughout the study, 25(OH)D deficient children were compared with the other children, including 25(OH)D insufficient (37.5 to 50 nmol/l) and 25(OH)D sufficient children (≥ 50 nmol/l). Firstly, 25(OH)D deficient obese children showed a lower insulin sensitivity than other obese children, as measured by a lower quantitative insulin sensitivity check index (QUICKI). Secondly, the association between 25(OH)D deficiency and insulin resistance in childhood obesity was confirmed with multiple regression analysis. Thirdly, 25(OH)D deficient obese children showed higher levels of the inflammatory mediators cathepsin S, chemerin and soluble vascular adhesion molecule (sVCAM), compared with the other obese children. Finally, hierarchical cluster analysis revealed an over-representation of 25(OH)D deficiency in obese children expressing inflammatory mediator clusters with high levels of cathepsin S, sVCAM and chemerin.
Conclusion25(OH)D deficiency in childhood obesity was associated with enhanced systemic inflammation and reduced insulin sensitivity. The high cathepsin S and sVCAM levels may reflect activation of a pro-inflammatory, pro-diabetic and atherogenic pathway, which could be inhibited by vitamin D supplementation.
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Vitamin D deficiency in childhood obesity
5
Introduction
Vitamin D is a key nutrient involved in various physiological processes next to bone metabolism [1]. Vitamin D versatility is illustrated by the widespread expression of the vitamin D receptor and the molecular machinery to convert 25-hydroxyvitamin D (25(OH)D), which is the primary circulating form of vitamin D, to the biologically active 1,25-dihydroxyvitamin D [1, 2]. In accordance with its role as a key nutrient, insufficient levels of 25(OH)D (< 50 nmol/l) have been associated with various diseases, including inflammatory bowel disease, infectious diseases, and cardiovascular disorders [1-3]. Recently, an association between 25(OH)D insufficiency and decreased insulin sensitivity in obese children was added to the list [4]. The association between 25(OH)D insufficiency and the development of type 2 diabetes (T2D) was underscored in a large and prospective population-based cohort study in adults [5].
As a higher body mass index (BMI) leads to lower 25(OH)D levels, possibly via sequestration of vitamin D in adipose tissue [6], 25(OH)D insufficiency is endemic in childhood obesity [4]. In order to investigate the consequences of severe 25(OH)D insufficiency, which is further referred to as 25(OH)D deficiency, 25(OH)D deficiency was defined as a 25(OH)D level ≤ 37.5nmol/l, in accordance with previous studies [7-10]. Strikingly, 51% of obese US children show a 25(OH)D deficiency, compared with 9% of all US children [8].
25(OH)D deficiency may affect glucose homeostasis in several ways. Firstly, as vitamin D has a key role in calcium metabolism and insulin secretion is a calcium-dependent process, it was hypothesized that 25(OH)D deficiency hampers insulin secretion by β cells in a calcium-dependent manner [11]. Secondly, 25(OH)D deficiency was associated with decreased peripheral insulin action, either via reduced insulin receptor expression or via impaired signaling downstream of the insulin receptor [11]. Thirdly, vitamin D is well known for its immune modulatory functions [12]. Considering the deleterious effects of systemic inflammation on insulin resistance in obesity [13], it was proposed that 25(OH)D deficiency aggravates insulin resistance in obesity through enhanced systemic inflammation [11, 14].
Here, we focused on the latter hypothesis. In a cross-sectional study of 64 obese and 32 age- and gender-matched healthy control children, we analysed circulating levels of 25(OH)D and 32 systemic inflammatory mediators, together with measures for insulin sensitivity. To our knowledge, this is the first study performing comprehensive profiling of
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5
inflammatory mediators in relation to 25(OH)D status, showing that 25(OH)D deficiency in childhood obesity is accompanied by both reduced insulin sensitivity and enhanced systemic inflammation.
Methods
Subjects
Childhood obesity was defined as a BMI > 2.5 SD of the mean BMI for age and gender (BMI-SD), in accordance with the international definition of childhood obesity as a BMI of > 30 kg/m2 projected to 18 years of age [13, 15, 16]. In a cross-sectional study, 64 obese children and 32 age and gender-matched healthy controls with a BMI-SD < 2.5, aged 6–16 years were included at the paediatric outpatient department of the Meander Medical Center, Amersfoort, The Netherlands. Exclusion criteria were inflammatory or infectious conditions, endocrine disorders, growth abnormalities, and intoxications. 25(OH)D deficiency was defined as a 25(OH)D level ≤ 37.5 nmol/l (≤ 15 ng/ml), 25(OH)D insufficiency was defined as a 25(OH)D level between 37.5 nmol/l and 50 nmol/l (15-20 ng/ml) and a 25(OH)D level ≥ 50 nmol/l was classified as sufficient, in accordance with previous studies and international guidelines [3, 7-10, 17]. One 25(OH)D deficient and one sufficient obese patient reported the use of oral insulin sensitizing drugs (metformin). None of the children reported the use of 25(OH)D supplementation. Written informed consent was obtained from all children and their parents. The study was approved by the institutional medical ethical review board (METC 09/217K).
Clinical parameters
For bioelectrical impedance measurements (total body fat percentage), a foot-hand bio-impedance analyser was used, in accordance with the manufacturer’s instructions (Analyzer Model BIA 101; Akern Srl, Florence, Italy). Skin tone was categorized in light, mid color and dark [18]. Waist circumference SD for age and gender were calculated with Cole’s LMS method and data of the Dutch national growth study [16, 19].
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Vitamin D deficiency in childhood obesity
5
Blood samples
Blood samples were taken in sodium-heparin tubes upon overnight fasting. 25(OH)D levels were measured with an Elecsys Vitamin D Total assay which measures both 25(OH)D2 and 25(OH)D3 (Roche Diagnostics, Mannheim, Germany) [20]. Undercarboxylated and carboxylated osteocalcin levels were determined using ELISA kits (Takara MK111 and MK118, Takara Bio USA, Madison, WI, USA). Total osteocalcin levels represent the sum of carboxylated and undercarboxylated osteocalcin. Routine laboratory testing included fasting glucose, fasting insulin, triglycerides, high and low-density lipoprotein cholesterol (HDL and LDL) and high sensitivity C-reactive protein levels (hsCRP). The quantitative insulin sensitivity check index (QUICKI) and homeostasis model assessment of insulin resistance (HOMA-IR) were calculated as described earlier [21]. Circulating inflammatory mediators were measured in plasma using a recently developed and validated multiplex immunoassay [22].
Statistical analyses
Throughout the article, 25(OH)D deficient obese children (n = 36) were compared with (in)sufficient obese children (n = 28) to study the effect of very low 25(OH)D levels in obese children. Next, (in)sufficient obese children were compared with (in)sufficient healthy controls (n = 27), to study the effect of obesity. Please note that the five deficient healthy control children were excluded from further analyses, as this group was considered too small to compare with the deficient obese children (Figure 5.1).
As most clinical parameters and inflammatory mediators showed a non-parametric distribution, Mann-Whitney U tests, or the Fisher’s exact test if applicable, were used to assess differences between groups. Benjamini and Hochberg’s False Discovery Rate correction was used to correct p-values for multiple testing [13]. Multiple linear regression analysis was performed to examine the relation between QUICKI and 25(OH)D status, correcting for BMI-SD, age, gender, skin tone and undercarboxylated osteocalcin. Similarly, the relation between inflammatory mediators and 25(OH)D status was assessed, correcting for BMI-SD, age and gender.
Statistical analyses were performed with SPSS 15.0 for Windows (SPSS, Chicago, IL, USA) and R, a free software environment for statistical computing and graphics [23]. Non-supervised hierarchical cluster analysis of the inflammatory mediator profiles was executed as recently described [13], using the Pvclust package [24].
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Figure 5.1 25(OH)D deficiency in obese children (A) 25(OH)D levels of obese children (n = 64) and healthy controls (n = 32), showing significantly lower 25(OH)D levels in obese children. Severe 25(OH)D insufficiency, referred to as 25(OH)D deficiency, is defined as a 25(OH)D level ≤ 37.5 nmol/l, in accordance with previous studies and international guidelines. Throughout the article, 25(OH)D deficient children are compared with a combined group of 25(OH)D insufficient (25(OH)D 37.5–50 nmol/l) and 25(OH)D sufficient children (25(OH)D ≥ 50 nmol/l). (B) 25(OH)D levels of deficient obese children (group I, n = 36), (in)sufficient obese children (group II, n = 28), and (in)sufficient healthy controls (group III, n = 27). Throughout the article, groups were compared as follows: 1) deficient obese children versus (in)sufficient obese children, to study the effect of very low 25(OH)D levels in obese children, and 2) (in)insufficient obese children versus (in)sufficient healthy controls, to study the effect of obesity. Please note that the five deficient healthy control children were excluded from further analyses, as this group was considered too small to compare with the deficient obese children. (C) Age and gender distribution of the three groups. For panel A-C lines represent medians.
(I) Obese
Obesen = 64 n = 32
0
50
100
150
25(O
H) D
(m
l/l)
A
0
50
100
150
25(O
H) D
(m
l/l)
B
(II) Obese
* p < 0.0001
0
5
10
15
20C
Obese (II)
Obese (I) (III)
Obesen = 36
Obesen = 28 n = 27
Male
Female
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Vitamin D deficiency in childhood obesity
5
Results
25(OH)D deficiency is associated with lower insulin sensitivity
25(OH)D deficiency showed a 56% prevalence in obese children, compared with 16% in healthy children (Table 5.1, Figure 5.1a). The 25(OH)D status of obese children seemed relevant, as deficient obese children (i.e. 25(OH)D deficient, n = 36) showed a lower insulin sensitivity than (in)sufficient obese children (i.e. 25(OH)D (in)sufficient, n = 28), as measured by a lower quantitative insulin sensitivity check index (QUICKI) and a higher HOMA-IR (Table 5.1, Figure 5.1b), which are surrogate markers for insulin sensitivity [21]. Differences in insulin sensitivity could not be explained by clinical parameters such as age, gender, BMI-SD, waist circumference SD (a measure for visceral adiposity [25]), fat percentage, lipid profile and season of inclusion, which were all comparable between deficient and (in)sufficient obese children (Table 5.1, Figure 5.1c).
Interestingly, deficient obese children showed significantly higher levels of undercar-boxylated osteocalcin and total osteocalcin levels than (in)sufficient obese children (Table 5.1). As undercarboxylated osteocalcin appears to be part of an adaptive process to counter glucose intolerance [26], differences in undercarboxylated osteocalcin levels may mask differences in insulin sensitivity. Furthermore, deficient obese children showed more mid-colored skin tones than (in)sufficient obese children (Table 5.1), which may also influence 25(OH)D levels and/or insulin sensitivity [4]. Therefore, multiple regression analysis in all children was performed, to study the association between 25(OH)D deficiency and insulin sensitivity, independent of possible confounders such as undercarboxylated osteocalcin and skin tone, next to BMI-SD, age, and gender (Supplemental Table S5.1). Importantly, multiple linear regression analysis underscored the association between 25(OH)D deficiency and insulin resistance: QUICKI depended on 25(OH)D status even when adjusting for BMI-SD, age, gender, undercarboxylated osteocalcin, and skin tone (p = 0.045, β = 0.188, R2 = 0.555, Supplemental Table S5.1). Next to 25(OH)D status, only BMI-SD and age were significantly associated with QUICKI (p < 0.001 and p = 0.030 respectively, Supplemental Table S5.1).
In conclusion, 25(OH)D deficiency was highly prevalent in obese Dutch children, and associated with reduced insulin sensitivity. Although (in)sufficient obese children (n = 28) showed lower insulin sensitivity, lower HDL levels, and higher hsCRP levels than (in)sufficient healthy children (n = 27) (Table 5.1), deficient obese children (n = 36) showed
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Table 5.1 Clinical characteristicsClinical characteristics of the obese children, divided in a 25(OH)D deficient (25(OH)D ≤ 37.5 nmol/l) and a combined insufficient (25(OH)D 37.5–50 nmol/l) and sufficient group (25(OH)D ≥ 50 nmol/l), and of the (in)sufficient healthy controls. Data for the groups are presented as median (interquartile ranges), unless indicated otherwise.
Obese Healthy controls
25(OH)D deficient 25(OH)D (in)suficient 25(OH)D (in)sufficient
Number of children (%) 36 (56%) 28 (44%) 27 (84%)
Age (year, mean ± SD) 12.6 (± 2.6) 12.3 (± 3.0) 11.1 (± 3.2)
Gender (% female) 61.1 50.0 51.9
BMI-SD 3.40 (3.10–3.94) 3.39 (3.09–3.76) † 0.68 (-0.60–1.27) †
Waist circumference (SD) 2.58 (2.38–2.91) 2.46 (2.14–2.89) † 0.02 (-0.76–0.81) †
Fat percentage (%) 40.4 (38.0–42.8) 39.1 (36.3–41.5) † 27.8 (23.4–32.6) †
25(OH)D (nmol/l) 27.5 (20.0–33.5) + 46.0 (41.3–61.3) +,† 65.0 (52.0–75.0) †
Total osteocalcin (ng/ml) 48.5 (39.7–60.2) * 40.9 (31.9–52.5) * 47.9 (36.6–56.4)
Undercarboxylated osteocalcin (ng/ml) 19.5 (11.8–28.4) * 15.4 (9.39–19.5) * 16.5 (9.95–23.5)
Fasting glucose (mmol/l) 5.30 (5.10–5.60) 5.20 (5.00–5.40) † 4.90 (4.60–5.10) †
Fasting insulin (mU/l) 15.0 (11.0–26.0) * 11.0 (5.00–18.0) *,† 2.00 (2.00–9.00) †
QUICKI 0.32 (0.29–0.33) * 0.33 (0.31–0.37) *,† 0.43 (0.34–0.44) †
HOMA-IR 3.56 (2.44–6.44) * 2.64 (1.20–4.32) *,† 0.51 (0.44–2.00) †
Triglycerides (mmol/l) 1.05 (0.70–1.45) 0.80 (0.60–1.40) 0.70 (0.50–0.90)
HDL cholesterol (mmol/l) 1.20 (1.10–1.38) 1.20 (1.00–1.40) † 1.50(1.30–1.70) †
LDL cholesterol (mmol/l) 2.50 (2.05–3.18) 2.50 (2.00–3.30) 2.40 (2.20–2.60)
hsCRP (mg/l) 2.32 (1.14–3.52) 1.59 (0.70–2.73) † 0.19 (0.15–0.68) †
Skin tone (%)Light Mid color Dark
34.3
48.6*
17.1
57.1 †
17.9 *
25.0 #
92.6 †
7.40.0 #
Season of inclusion (%)SpringSummerFallWinter
22.219.413.944.4
46.4 †
21.47.1
25.0
11.1 †
48.122.218.5
BMI-SD, body mass index, standard deviation for age and gender; HDL, high-density lipoprotein; HOMA-IR, homeostasis model assessment of insulin resistance; hsCRP, high-sensitivity C-reactive protein; LDL, low-density lipoprotein; QUICKI, quantitative insulin sensitivity check index; 25(OH)D, 25-hydroxyvitamin D. * p < 0.05, + p < 0.01 (between both obese groups). # p < 0.05, † p < 0.01 (between (in)sufficient obese and healthy control groups).
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5
even lower insulin sensitivity (Table 5.1), independent of other relevant measures such as BMI-SD, age, gender, undercarboxylated osteocalcin and skin tone (Supplemental Table S5.1).
25(OH)D deficiency is associated with enhanced levels of circulating inflammatory mediators
Immune modulatory effects of vitamin D may partly explain the association between 25(OH)D deficiency and insulin resistance in childhood obesity [11, 12, 14]. To assess the effects of 25(OH)D deficiency on systemic inflammation in childhood obesity, 32 circulating inflammatory mediators were measured. The set of inflammatory mediators was established in earlier studies, and comprises multiple adipokines and cytokines acting at the crossroads of metabolism and inflammation [6, 22]. Deficient obese children exhibited higher plasma levels of cathepsin S, chemerin, retinol-binding protein 4 (RBP-4) and soluble vascular adhesion molecule (sVCAM) than (in)sufficient obese children (Table 5.2, Supplemental Table S5.2 and Supplemental Figure S5.1). After correcting for multiple testing, differences in cathepsin S, RBP-4 and sVCAM remained significant, while chemerin showed a trend (p = 0.09).
To study the effect of obesity independent of 25(OH)D deficiency, differences in circulating inflammatory mediators between the (in)sufficient obese and healthy children were assessed as well. In (in)sufficient obese children, higher levels of interleukin-18 (IL-18), hepatic growth factor (HGF), leptin, epidermal growth factor (EGF) and tumour necrosis factor receptor 2 (TNF-R2) were observed, and lower levels of plasminogen activator inhibitor 1 (PAI-1) (Table 5.2). After correction for multiple testing, differences in leptin, EGF, TNF-R2 and PAI-1 remained significant, while IL-18 and HGF showed a trend (p = 0.07 and 0.05 respectively). Taken together, the effect of 25(OH)D deficiency on circulating inflammatory mediators in childhood obesity seemed different from the effect of obesity itself. Although obesity was associated with altered levels of leptin, EGF, TNF-R2, PAI-1, IL-18 and HGF, in accordance with a recent study [13], 25(OH)D deficiency in childhood obesity coincided with higher levels of cathepsin S, chemerin, RBP-4 and sVCAM.
For cytokines IL-6, IL-10 and TNF-α; adipokines adiponectin, adipsin, fatty acid-binding protein 4 (FABP-4), macrophage migration inhibitory factor (MIF), CCL2 (monocyte chemotactic protein 1; MCP-1), omentin, resistin, tissue inhibitory of metalloproteinase
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Tabl
e 5.
2 C
ircul
atin
g in
flam
mat
ory
med
iato
rsLe
vels
of c
ircul
atin
g in
flam
mat
ory
med
iato
rs s
igni
fican
tly d
iffer
ing
betw
een
grou
ps a
re d
ispla
yed
as m
edia
n (in
terq
uart
ile ra
nges
). Pl
ease
not
e th
at a
n ov
ervi
ew o
f all
stud
ied
infla
mm
ator
y m
edia
tors
is sh
own
in S
uppl
emen
tal T
able
S5.
2. In
flam
mat
ory
med
iato
rs a
re ca
tego
rized
as c
ytok
ines
, ad
ipok
ines
and
othe
r med
iato
rs, t
o in
crea
se su
rvey
abili
ty. M
ultip
le lin
ear r
egre
ssio
n an
alys
is of
the
infla
mm
ator
y med
iato
rs w
ith 2
5(O
H)D
stat
us w
as
perf
orm
ed to
con
trol
for B
MI-S
D, a
ge a
nd g
ende
r, as
pos
sible
con
foun
ders
. Sho
wn
are
the
stan
dard
ized
β a
nd R
2 for t
he in
flam
mat
ory
med
iato
rs.
Obe
seH
ealth
y co
ntro
ls Al
l gro
ups
25(O
H)D
def
icie
nt25
(OH
)D (i
n)su
ffici
ent
25(O
H)D
(in)
suffi
cien
tM
ultip
le li
near
regr
essio
n
Uni
tsβ
R2
Cyto
kine
sIL
-18
pg/m
l38
5 (3
16–4
79)
339
(295
–482
) #26
2 (2
15–3
55) #
-0.0
570.
074
Adip
okin
esCa
thep
sin S
ng/m
l62
.5 (5
6.2–
72.2
) +56
.2 (4
8.7–
61.3
) +57
.7 (4
6.3–
60.6
)-0
.341
0.14
1 ++
Chem
erin
μg/m
l3.
13 (2
.74–
3.47
) *2.
87 (2
.50–
3.11
) *2.
80(2
.48–
3.00
)-0
.229
0.07
9H
GF
pg/m
l37
5 (2
42–5
30)
354
(262
–534
) #28
2 (2
21–3
40) #
-0.0
640.
033
Lept
inng
/ml
309
(239
–486
)25
2 (1
84–4
24) †
130
(111
–159
) †-0
.112
0.38
1PA
I-1μg
/ml
160
(136
–183
)14
1 (1
17–1
75) #
177
(136
–194
) #-0
.173
0.03
5RB
P-4
aμg
/ml
166
(149
–175
) *14
8 (1
37–1
58) *
153
(140
–184
)-0
.175
0.12
8
Oth
er EGF
bpg
/ml
89.5
(39.
7–11
8)85
.0 (3
9.0–
117)
†46
.1 (2
0.3–
66.9
) †-0
.112
0.05
2sV
CAM
μg/m
l5.
29 (5
.02–
5.55
) +4.
98 (4
.64–
5.14
) +4.
97 (4
.67–
5.42
)-0
.342
0.27
3 ++
TNF-
R2ng
/ml
2.97
(2.6
3–3.
40)
2.88
(2.4
9–3.
18) †
2.42
(1.9
8–2.
85) †
-0.0
940.
045
BMI-S
D, b
ody
mas
s ind
ex, s
tand
ard
devi
atio
n fo
r age
and
gen
der;
EGF,
epid
erm
al g
row
th fa
ctor
; HG
F, he
patic
gro
wth
fact
or; IL
-18,
inte
rleuk
in-1
8; PA
I-1, p
lasm
inog
en
activ
ator
inhi
bito
r 1; R
BP-4
, ret
inol
-bin
ding
pro
tein
4; s
VCAM
, sol
uble
vasc
ular
adhe
sion
mol
ecul
e; TN
F-R2
, tum
or n
ecro
sis fa
ctor
rece
ptor
2; 2
5(O
H)D
, 25-
hydr
oxyv
itam
in
D. * p
< 0
.05,
+ p <
0.0
1 (b
etw
een
both
obe
se g
roup
s). # p
< 0
.05
and
† p <
0.0
1 (b
etw
een
(in)s
uffic
ient
obe
se a
nd h
ealth
y co
ntro
l gro
ups)
. **
p <
0.05
, ++ p
< 0
.01
(all
thre
e gr
oups
, mul
tiple
line
ar re
gres
sion
anal
ysis)
. Med
iato
rs fo
r whi
ch v
alue
s wer
e m
issin
g, m
ostly
due
to u
ndet
ecta
ble
leve
ls, a
re in
dica
ted
as fo
llow
s: a
19%
miss
ing
valu
es, b
2%
miss
ing
valu
es.
143
Vitamin D deficiency in childhood obesity
5
1 (TIMP-1) and thrombopoietin; and other inflammatory mediators chemokine (C-X-C motif) ligand-8 (CXCL8), extracellular newly identified RAGE binding protein (EN-RAGE), CXCL10 (IFN-γ-induced protein 1; IP-10), macrophage colony-stimulating factor (M-CSF), CCL3 (macrophage inflammatory protein 1α; MIP-1α), CCL4 (macrophage inflammatory protein 1β; MIP-1β), soluble CD14, soluble intercellular adhesion molecule (sICAM), TNF receptor 1 (TNF-R1) and vascular endothelial growth factor (VEGF) no differences between 25(OH)D deficient and (in)sufficient obese patients nor between the (in)sufficient obese and healthy children were found (Supplemental Table S5.2).
Multiple linear regression analysis in all children supported the 25(OH)D dependency of cathepsin S, chemerin and sVCAM: cathepsin S and sVCAM depended on 25(OH)D status, even when adjusted for BMI-SD, age and gender, while chemerin levels showed a trend towards 25(OH)D dependency (p = 0.08; Table 5.2). RBP-4 levels did not depend on 25(OH)D status after adjustment for the aforementioned potential confounders (p = 0.160). In conclusion, 25(OH)D deficiency in childhood obesity was associated with enhanced levels of circulating inflammatory mediators, specifically cathepsin S, chemerin and sVCAM.
Clustering of inflammatory mediators distinguishes 25(OH)D deficient obese children
As an alternative approach to study the association between 25(OH)D deficiency and inflammation, the inflammatory mediator profiles of obese children were submitted to hierarchical cluster analysis. Interestingly, cathepsin S and sVCAM, the inflammatory mediators most strongly associated with 25(OH)D deficiency (see above), showed a bootstrapping probability of 65%, which is indicative for robust clustering (Figure 5.2a) [13]. They comprised an inflammatory mediator cluster together with TIMP-1, leptin, chemerin, resistin, sICAM and PAI-1, which distinguished two clusters of obese children (Figure 5.2b). Cluster I represented a mixed group, including both deficient and (in)sufficient children. Cluster II contained predominantly deficient obese children (p = 0.008). Taken together, cluster analysis of the inflammatory mediators underscored the link between 25(OH)D deficiency and inflammation. In obese children expressing inflammatory mediator clusters with high levels of cathepsin S, sVCAM, and chemerin, 25(OH)D deficiency was overrepresented.
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Figure 5.2 Cluster analysis of inflammatory mediators (A) Bootstrap analysis of circulating inflammatory mediators, to identify clusters of related inflammatory mediators. Shown are bootstrap probabilities (%) for clusters of inflammatory mediators. Of note, inflammatory mediators with > 10% missing values (for example, RBP-4, 10 values above upper detection limit) interfered with the cluster analysis and were excluded from the analysis. Grey boxes mark inflammatory mediators that cluster together with a bootstrap probability > 50%, indicative for robust clustering. (B) Hierarchical cluster analysis of the inflammatory mediator profiles of all obese patients, to identify clusters of related patients. Displayed are log-transformed inflammatory mediator levels in a heat-map. White boxes represent missing values. Hierarchical cluster analysis showed 2 clusters of patients: a mixed cluster (I) and an inflammatory cluster with patients exhibiting high levels of inflammatory mediators among which cathepsin S, sVCAM and chemerin (II). Cluster II showed an overrepresentation of 25(OH)D deficient obese children (p = 0.008). Please note that the bootstrap analysis of inflammatory mediators displayed in panel (A) is also part of panel (B). In fact, the order of the inflammatory mediators in panel (B) is determined by the bootstrap analysis displayed in panel (A).
Cluster IMixed
Cluster II
(p = 0.008)
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Discussion
The high prevalence of 25(OH)D deficiency (56%) observed in this Dutch cohort of obese children equals the reported 51% prevalence in obese US children [8]. Moreover, this study confirmed the reported association between 25(OH)D deficiency and insulin resistance in childhood obesity [4, 27, 28]. Notably, other groups observed no association between 25(OH)D levels and insulin resistance in childhood obesity, possibly due to higher 25(OH)D levels in the patient groups, or dominant effects of adiposity parameters in the regression analysis [29, 30]. The novelty of our present study is its focus on the immune modulatory role of vitamin D, which may partly explain the relation between 25(OH)D deficiency and insulin resistance in childhood obesity [11, 12, 14]. 25(OH)D deficiency in obese children coincided with enhanced systemic inflammation, independent of BMI-SD, age and gender, factors which are known to affect inflammation and metabolic outcome [13, 25]. The systemic inflammation was specifically reflected by increased levels of circulating cathepsin S, chemerin and sVCAM, which all have been associated with insulin resistance in several patient groups [31-33]. Clustering of the inflammatory mediator profiles confirmed the link between 25(OH)D deficiency and systemic inflammation, as 25(OH)D deficiency was overrepresented in obese children with explicitly inflammatory profiles. In conclusion, our results fuel the hypothesis that 25(OH)D deficiency lowers insulin sensitivity in obese children through enhanced systemic inflammation.
Interestingly, the association between 25(OH)D deficiency and high cathepsin S and sVCAM levels is supported by recent fundamental studies. Vitamin D was shown to induce the expression of cystatin D, which is a high-affinity inhibitor of cathepsin S [34, 35]. Accordingly, 25(OH)D deficiency may enhance cathepsin S activity in obese children through reduced expression of cystatin D. In animal models and human studies, enhanced cathepsin S activity was implicated in the development of diabetes and atherosclerosis, and high levels of cathepsin S were associated with an increased mortality risk [36, 37]. Thus, high cathepsin S levels in 25(OH)D deficient obese children may reflect activation of a pro-inflammatory, pro-diabetic and atherogenic pathway, which could be inhibited by vitamin D supplementation. Similarly, vitamin D was shown to attenuate the expression of sVCAM by human endothelial cells [38]. sVCAM is highly expressed in atherosclerotic plaques, has a key role in mononuclear cell adhesion, and contributes to the progress of atherosclerotic lesions [39, 40]. Thus, the high sVCAM levels may reflect a second pro-inflammatory and atherogenic pathway that could be inhibited by vitamin D supplementation. Of note, mechanisms underlying the association between
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25(OH)D deficiency and high chemerin levels are currently unknown, and require further investigation.
Notably, this study does not exclude a calcium-dependent effect of 25(OH)D deficiency on insulin receptor expression or signaling, as these parameters were not investigated. Furthermore, because the number of 25(OH)D deficient healthy controls was too small to include in subsequent analyses (n = 5, Figure 5.1a), this study specifically focused on the impact of 25(OH)D deficiency for obese children. Whether 25(OH)D deficiency is also associated with enhanced systemic inflammation in non-obese children requires further investigation. Next, food intake as well as outdoor activity and exercise levels, which all can influence 25(OH)D levels [8], were not registered during this study. Finally, because of its cross-sectional design, a causal relationship between enhanced systemic inflammation and insulin resistance could not yet be established. Prospective studies are needed to that end. As a proof-of-concept study though, our data provide novel insights in the pathophysiological mechanisms that may link 25(OH)D deficiency to insulin resistance. Moreover, this study underscores the consequences of 25(OH)D deficiency in childhood obesity, which seem to exceed mere effects on calcium metabolism and bone growth [2, 7].
Finally, this study is of value for the assessment of vitamin D supplementation studies in obese children. In analogy with a recent study in adults with T2D, which showed reduced expression of inflammatory markers such as RBP-4 and enhanced insulin sensitivity upon vitamin D supplementation [14], supplementation studies in obese children are currently ongoing (for example, NCT01386736, NCT00994396, NCT00858247, NCT01217840, www.clinicaltrials.gov). These prospective studies could further establish the relationship between 25(OH)D deficiency, systemic inflammation and insulin resistance. More importantly, vitamin D supplementation may provide a novel avenue for the prevention of T2D and atherosclerosis in obese children in the near future [41].
Acknowledgements
This study was supported by grants from the Wilhelmina Children’s Hospital Research Fund and the University Medical Center Utrecht Vascular Prevention Project. We thank the secretaries of the paediatric outpatient department and the paediatricians of the Meander Medical Center in Amersfoort for their help with subject recruitment. We also would like to thank Professor van Buuren for supplying waist circumference data of the Dutch Growth Study.
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Supplemental information
Supplemental Table S5.1 Multiple linear regression analysisModel: Insulin sensitivity (QUICKI) = β0 + β1 * 25(OH)D status + β2 * BMI-SD + β3 * Age + β4 * Gender + β5 * undercarboxylated osteocalcin + β6 * Skin tone
Independent variable Standardized β Significance (p-value)
25(OH)D status 0.188 0.045
BMI-SD -0.591 <0.001
Age -0.171 0.030
Gender -0.129 0.101
Undercarboxylated osteocalcin 0.051 0.530
Skin tone 0.072 0.397
Multiple linear regression analysis of the insulin sensitivity check index (QUICKI) with the independent variables 25(OH)D status, BMI-SD, age, and gender, and the potential confounders undercarboxylated osteocalcin and skin tone. As shown, QUICKI depends signi( cantly on 25(OH)D status, BMI-SD and age, but not on gender nor the potential confounders undercarboxylated osteocalcin and skin tone. R2 of the model: 0.555.
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Supp
lem
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5.2
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152
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Supp
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ere
miss
ing,
mos
tly d
ue to
und
etec
tabl
e le
vels,
are
indi
cate
d as
follo
ws:
a 1.0
–3.0
% m
issin
g va
lues
, b 3.1
–10.
0% m
issin
g va
lues
, c 10.
1–20
.0%
miss
ing
valu
es, d >
20.0
% m
issin
g va
lues
.
153
Vitamin D deficiency in childhood obesity
5
Supplemental Figure S5.1 Circulating inflammatory mediators Levels of 5 inflammatory mediators in the three groups. Four of these inflammatory mediators (cathepsin S, chemerin, RBP-4 and sVCAM) showed significantly higher levels in the 25(OH)D deficient obese group, as compared to the 25(OH)D (in)sufficient obese children, but no differences were found between (in)sufficient obese and healthy control children. Leptin levels, on the contrary, were significantly lower in the 25(OH)D (in)sufficient healthy control children, but showed no significant differences between the two obese groups. Lines represent medians. For RBP-4, 10 values were above detection limit, and have been set to the upper detection limit.
Cathepsin S0
50
100 *p = 0.005
ng/m
l
*p = 0.040
Chemerin0
2.5
5.0
g/m
l
*p < 0.0001
0
500
1000
ng/m
l
*p = 0.005
sVCAM0
4
8
g/m
l
*p = 0.011
RBP-40
100
200
g/m
l
Obese
Obese
154
Chapter 5
5
Inflammatory mediators in pancreatic islet cell transplantation in type 1 diabetes
PART IV
156
Chapter 1
1
Serum cytokines as biomarkers in islet cell transplantation for type 1 diabetes
A.A. Verrijn Stuart*, C.R. van der Torren*, D.H. Lee, H.J. van den Ham, J. Meerding, U. van de Velde, D. Pipeleers, B. Keymeulen, P. Gillard, W. de Jager, B.O. Roep
* Authors contributed equally
Manuscript in preparation
6
158
Chapter 6
6
Abstract
Islet cell transplantation holds a potential cure for type 1 diabetes (T1D), but many islet recipients do not reach long lasting insulin independence. In this exploratory study we investigated whether serum factors associate with islet transplantation in relation with clinical outcome.
Thirteen islet transplantion patients were selected from our cohort receiving standardized grafts and immune suppressive therapy on basis of good graft function (reaching insulin independence) or insufficient engraftment (insulin requiring). Patients reaching insulin independence were divided in those with continued (> 12 months) versus transient (< 6 months) insulin independence. A panel of 94 cytokines, adipokines and other serum proteins was measured in sera taken before and at 1 year after transplantation using a validated multiplex immunoassay platform.
Ninety serum proteins were detectable in concentrations varying markedly among patients at either time point. Thirteen markers changed significantly after transplantation, while another seven markers changed in a clinical subpopulation only. All other markers remained unaffected in spite of transplantation and generalized immunosuppression. Good graft function could be distinguished from insufficient outcome by IFN-α, LIF, SCF and IL-1RII before and after transplantation, by IL-16, CCL3, BDNF and M-CSF only before and by IL-22, IL-33, KIM-1, S100A12 and sCD14 after transplantation. Three other cytokines (Leptin, Cathepsin L and S100A12) associated with loss of temporary graft function before or after transplantation.
Distinct immune correlates and cytokine signatures could be identified in serum that predict or associate with clinical outcome. These serum markers may help guiding patient selection and choice of immunotherapy, or act as novel drug targets in islet transplantation.
159
Serum cytokines as biomarkers in islet cell transplantation for T1D
6
Introduction
Pancreatic islet cell transplantation can cure T1D, since it has the potential to achieve normoglycaemia with independence of exogenous insulin and near-physiological β cell function [1-4]. Beta cell replacement therapy has been shown to decrease HbA1c and the risk of long-term complications, although at the cost of continuous immune suppression [2, 4-8]. Even without complete and longlasting insulin independence, islet transplantation has been shown to have a beneficial impact on both the long term outcome in terms of complications as well as on quality of life in patients selected on the basis of hypoglycaemia unawareness [4, 9].
A key limitation of islet transplantation is loss of insulin independence in most patients over time. Several factors contribute to poor outcome. Firstly, insufficient graft size or quality impairs initial graft function [3] and may lead to β cell exhaustion over time. Secondly, inflammatory and immune reactions, such as instant blood-mediated immune reaction, recurrent autoimmunity or allograft rejection can lead to graft destruction [10, 11]. Thirdly, immunosuppressive drugs can affect engraftment and β cell function or cause insulin resistance [12]. Knowledge of processes that affect graft function has expanded over the last years, including insight in markers that may help to predict recurrent autoimmunity [13-15]. Still, more detailed understanding and further improved prediction of the processes affecting graft survival, in order to guide patient selection and immunosuppression, would be a major step forward [16].
Some insight has been gained in the immunological determinants of islet graft function in humans. The main focus has been on islet specific adaptive immune responses [15, 17-23]. Allograft-specific cytokine profiles in vitro have been associated with clinical outcome pointing to IL-10 as a correlate for low alloreactivity and good graft function [21]. Circulating serum proteins, including cytokines and chemokines, have only sparsely been investigated. Pfleger et al. suggested that levels of certain cytokines (IL-10, IL-13, IL-18, and MIF) were associated with islet graft outcome in patients receiving a kidney and islet transplant [24].
With the emergence of multiplex immunoassay technology, it has become possible to simultaneously investigate a broad spectrum of serum markers in minimal amounts of serum. Our platform has been recently validated to measure a panel of cytokines, adhesion molecules, adipokines, growth factors and various other mediators [25-27].
We designed an explorative study to identify whether the serum secretome is affected by transplantation and to investigate whether potential biomarkers that correlate with
160
Chapter 6
6
outcome of islet transplantation can be identified that may act as targets of intervention therapy or guide patient selection.
Methods
Graft recipients
Thirteen consecutive patients, transplanted with standardized islet cell grafts between January 2002 and April 2004, were selected out of a previously described cohort of 21 T1D patients [15]. Patients were selected on basis of clinical outcome to cover the spectrum from good engraftment (n = 9, patients reached insulin independence) to insufficient engraftment (n = 4, patients remained insulin dependent during the first year after transplantation). Three out of nine patients reaching insulin independence had early loss of graft function (< 6 months of insulin independence). All recipients were non-uraemic and C-peptide negative, had large individual variation of fasted glycaemia (coefficient of variation of pre-breakfast glycaemia > 25%), and had one or more signs of diabetic complications (hypoglycaemic unawareness, microalbuminuria or retinopathy). Patient characteristics are depicted in Table 6.1. Informed consent had been obtained from all candidate recipients for the islet transplant study protocol [3], including clinical and immunological follow-up.
Islet cell grafts were transplanted in the portal vein under anti-thymocyte globulin (ATG, Fresenius Hemocare, WA, USA) induction therapy and maintenance immune suppression with mycophenolate mofetil and tacrolimus [3]. Maintenance immune suppression was continued throughout the first year irrespective of graft function.
Blood samples were taken before transplantation and 1 year (median 53 weeks; range 47–60) after transplantation in serum tubes containing silicate granulate. Serum was aliquotted and stored at -80°C until analysis.
Serum mediators
Measurement of 94 serum markers was performed using a recently developed and validated multiplex immunoassay based on xMAP technology (Luminex Austin TX USA) [25, 27]. An overview of the markers and data is depicted in Table 6.2. For the measurement of biomarkers naturally occurring in high concentrations, samples were
161
Serum cytokines as biomarkers in islet cell transplantation for T1D
6
diluted either 1:100 (Adipsin, Cathepsin S, Cathepsin L, Chemerin, Leptin, PAI-1, CCL18, RBP-4, Resistin, SAA-1, TIMP-1, TPO, sCD14, sICAM and sVCAM) or 1:1000 (Adiponectin). Acquistion was performed with a Biorad FlexMap3D system using Xponent 4.2 software. Serum values were calculated using Bio-Plex Manager 6.1.1.
Statistical analyses
Descriptive statistics are reported as median (range). For analysis, data was normalised by log transformation and out-of-range values above detection limit were set to maximum of standard curve; values below the minimum of standard curve that could not be reliably extrapolated were set to the lowest detected value. Student’s t-test (paired if applicable) with Welch correction for variance was used for numerical values to ensure quantitative effect analysis, realizing that the data set is too small to ensure normal distribution. Fisher’s exact test was used for binominal data.
For analysis of changes over time, samples continuously out-of-range were excluded. All statistics as well as figure plotting was performed with R v3.0.0, aided by packages Stats,
Table 6.1 Patient characteristicsClinical characteristics of islet cell transplantation patients reaching continued (> 6 months), or temporary (< 6 months) insulin independence or not achieving (n = 4) insulin independence. Data refer to moment of first islet cell transplantation, unless stated otherwise.
Reaching Insulin Independence (II) Insulin Requiring (IR) p-value
Continued (C) Temporary (T) II-IR / C-T
N 6 3 4Gender (M-F) F, M, F, F, M, M M, M, M M, F, M, M 1.0 / 0.46Age (yr) 41, 49, 48, 42, 55, 52 44, 39, 39 35, 34, 52, 31 0.22 / 0.04Duration of T1D 14, 20, 24, 27, 38, 39 33, 26, 33 33, 12, 32, 20 0.52 / 0.46BMI (kg/m2) 26, 26, 25, 17, 26, 28 23, 22, 27 24, 25, 26, 23 0.97 / 0.79HbA1c (%) pre Tx 8.1, 7.2, 7.2, 7.0, 6.5, 7.0 4.0*, 6.9, 7.3 8.3, 7.3, 8.0, 7.6 0.05 / 0.83HbA1c (%) 1 yr post Tx 6.1, 6.6, 4.0, 6.6, 6.0, 6.2 4.1*, 6.9, 5.3 6.5, 7.8, 5.9, 7.7 0.12 / 0.86Creatinine (μmol/L) pre Tx 86, 84, 72, 88, 80, 80 101, 99, 80 101, 77, 87, 108 0.37 / 0.20Time to II (wk) 7, 14, 28, 23, 17, 23 12, 27, 34 NA NA / 0.48Loss of II (wk after Tx) NA 43, 32, 37 NA NA / NANumber of transplants 1, 1, 2, 2, 2, 2 1, 2, 1 2, 1, 2, 1 0.93 / 0.46β cell mass (10ˆ6/kg BW) 4, 2, 4, 9, 5, 6 4, 4, 6 5, 3, 4, 4 0.16 / 0.54
Tx, islet transplantation; NA, not applicable; BW, body weight. P-values represent Student’s t-test for numerical and Fisher’s exact test for binominal data. *Unreliable measurement, excluded from statistical analysis.
162
Chapter 6
6
Tabl
e 6.
2 O
verv
iew
of s
erum
mar
ker l
evel
s Se
rum
mar
ker l
evel
s in
islet
cell t
rans
plan
tatio
n pa
tient
s pre
- and
1 ye
ar p
ost-
tran
spla
ntat
ion
are
depi
cted
per
gro
up. V
alue
s are
dep
icte
d as
med
ian
(rang
e) in
pg/
ml,
exce
pt w
hen
indi
cate
d: n
g/m
lA , μg/
mlB o
r mg/
mlC . O
ut-o
f-ran
ge le
vels
are
depi
cted
as z
ero
or m
axim
um o
f mea
sure
men
t ran
ge,
indi
cate
d w
ith (# ).
Cyto
kine
s with
> 2
0% m
issin
g va
lues
are
indi
cate
d w
ith (§ ).
Reac
hing
Insu
lin In
depe
nden
ceIn
sulin
Req
uirin
g
Cont
inue
d In
sulin
Inde
pend
ence
Tem
pora
ry In
sulin
Inde
pend
ence
Pre
Post
Pre
Post
Pre
Post
Cyto
kine
s
IL-1
α2.
51 (1
.83–
27.9
)2.
59 (1
.89–
10.1
)2.
9 (2
.39–
3.57
)2.
77 (2
.51–
5.77
)2.
84 (1
.69–
3.75
)2.
17 (1
.91–
3.7)
IL-1
β1.
81 (0
.95–
1.99
)1.
76 (1
.14–
2.36
)1.
98 (1
.84–
42)
4 (2
.02–
2070
# )1.
52 (1
.31–
4.01
)1.
86 (1
.21–
2.19
)IL
-1Ra
§0
(0–1
9.9)
0 (0
–0)
0 (0
–148
)0
(0–5
40)
0 (0
–0)
0 (0
–0)
IL-2
§0
(0–7
.28)
0 (0
–0)
0 (0
–0)
0 (0
–2.1
1)0
(0–5
.46)
0 (0
–0)
IL-3
§0
(0–8
.77)
0 (0
–12.
4)0
(0–2
8.8)
0 (0
–0)
1.54
(0–6
9.6)
0 (0
–3.0
7)IL
-4
0.94
(0.5
1–1.
2)0.
935
(0.5
7–1.
24)
1.05
(0.8
2–1.
07)
0.98
(0.7
8–1.
38)
0.99
5 (0
.67–
1.11
)0.
785
(0.6
5–1.
14)
IL-5
§1.
38 (0
–3.3
8)1.
48 (0
–2.6
4)1.
38 (0
–14.
6)4.
43 (0
–47.
1)3.
77 (0
–32.
6)0.
32 (0
–24.
1)IL
-6§
0 (0
–0)
0 (0
–0)
0 (0
–116
0)79
.7 (0
–268
0)4.
86 (0
–51.
8)0
(0–0
)IL
-7§
0 (0
–0.6
3)0.
825
(0–1
.21)
0 (0
–0)
0 (0
–0.9
)0.
015
(0–0
.55)
0.12
5 (0
–1.5
4)IL
-941
.4 (3
.05–
77.4
)19
.4 (4
.68–
62.7
)25
.5 (1
5–69
6)52
.5 (1
3.3–
1600
)20
.4 (2
.49–
26.5
)12
.2 (3
.33–
21.8
)IL
-10
20.9
(0–3
7.6)
20.9
(0–4
5.6)
43.4
(34.
7–20
90)
181
(35.
3–87
20)
88.7
(0–8
36)
28.4
(4.0
6–74
4)IL
-11
4.2
(0–9
.9)
1.36
(0.0
1–10
.6)
2.91
(1.4
7–5.
2)1.
58 (1
.16–
4.22
)0.
855
(0–2
.26)
0.47
5 (0
.14–
1.7)
IL-1
2 11
.8 (5
.7–5
6.4)
9.84
(6.7
7–15
.3)
9.06
(8.8
8–15
.6)
9.67
(7.8
3–25
.8)
12.4
(6.0
8–21
.4)
8.93
(6.7
2–13
.3)
IL-1
3 10
(3.3
7–15
.6)
12.4
(4.4
6–14
.5)
10.6
(6.8
7–12
3)18
.8 (8
.9–4
45)
11.8
(5.1
4–22
.1)
11.4
(5.3
9–12
.8)
IL-1
59.
09 (5
.15–
21.4
)8.
07 (5
.71–
13.9
)7.
58 (6
.64–
39.4
)11
(5.7
1–14
.6)
6.22
(5.4
–8.6
8)8.
62 (7
.46–
10.4
)IL
-16
198
(41.
6–66
8)82
(48.
9–26
5)12
4 (8
5–26
0)79
.3 (6
8.2–
84.7
)63
.4 (4
9–10
2)62
.6 (4
9.6–
71.2
)IL
-17§
0.13
(0–0
.68)
0.01
(0–0
.2)
0.25
(0–4
.01)
0.08
(0–8
.67)
0.02
(0–0
.23)
0.06
(0–0
.15)
IL-1
817
.9 (4
.54–
80.3
)14
.7 (4
.39–
36.2
)23
.8 (1
1.2–
31.8
)16
.7 (9
.63–
52.1
)20
(4.4
4–34
.7)
11.5
(6.3
3–41
.1)
IL-2
1A1.
73 (0
.616
–1.9
8)1.
78 (0
.667
–2.6
2)1.
98 (1
.7–4
5.7)
4.73
(1.7
–246
)1.
62 (1
–3.2
6)1.
73 (1
.08–
3.61
)
163
Serum cytokines as biomarkers in islet cell transplantation for T1D
6
IL-2
2§51
.8 (0
–66.
6)19
.9 (0
–77.
4)0
(0–7
.87)
13 (0
–13.
2)17
.1 (0
–39.
3)0
(0–0
.36)
IL-2
3A1.
21 (0
.367
–3.6
1)0.
724
(0.4
21–1
.11)
1.16
(0.4
61–3
5.1)
1.24
(0.4
45–9
.37)
0.67
(0.3
42–0
.976
)0.
528
(0.4
5–0.
64)
IL-2
513
8 (0
–103
00)
160
(0–3
990)
319
(251
–147
0)23
6 (2
31–4
310)
199
(28.
5–45
7)12
8 (1
1.3–
569)
IL-2
750
5 (2
91–2
110)
425
(279
–114
0)43
8 (2
08–5
71)
306
(219
–398
)27
7 (2
0.6–
549)
172
(38.
2–27
7)IL
-33
7.7
(3.4
1–27
.1)
6.49
(3.4
1–18
.5)
3.76
(2.5
1–8.
63)
3.27
(2.7
6–10
.9)
4.7
(1.7
3–9.
39)
2.71
(1.7
6–3.
37)
IFN
α§12
.7 (0
–20.
4)8.
3 (0
–13.
3)7.
68 (5
.68–
45.5
)9.
56 (2
.28–
79.4
)1.
03 (0
–8.2
7)0
(0–1
.94)
IFN
β 18
3 (1
18–6
73)
192
(105
–286
)23
8 (1
24–1
730)
404
(147
–795
0)26
7 (6
6–57
1)18
4 (7
1.5–
244)
IFN
γ§0
(0–3
06)
0 (0
–104
)64
(0–1
790)
336
(0–1
0000
)19
5 (0
–202
)43
.2 (0
–150
)LI
F22
.7 (0
–39.
5)2.
66 (0
–29.
2)11
.8 (6
.14–
19.1
)4.
15 (0
.88–
5.6)
0 (0
–0.9
4)0
(0–0
)M
IFA
5.83
(0.8
25–1
0.2)
7.74
(2.1
8–13
.4)
4.01
(3.2
2–7.
11)
1.36
(0.9
63–9
.58)
8.35
(0.7
25–9
.44)
2.74
(2.3
8–4.
2)O
SM0
(0–0
)0
(0–0
)0
(0–0
)0
(0–0
)0
(0–0
)0
(0–0
)TN
Fα§
5.67
(0–2
7.6)
2.68
(0–1
5.6)
0 (0
–202
)14
.2 (0
–80.
5)1.
8 (0
–13)
7.74
(0–1
2.3)
TNFβ
§0
(0–7
6)0
(0–2
4.9)
0 (0
–82.
9)0
(0–6
8.9)
0 (0
–0)
0 (0
–0)
TSLP
0.07
5 (0
–0.0
9)0.
1 (0
–0.2
4)0.
19 (0
.04–
40.3
)1.
2 (0
.08–
307)
0.06
5 (0
.03–
1.14
)0.
14 (0
.01–
0.45
)
Chem
okin
esCC
L1§
0 (0
–0)
0 (0
–0)
0 (0
–0)
0 (0
–0)
0 (0
–0)
0 (0
–0)
CCL2
83.3
(41.
7–15
3)11
8 (3
7.6–
162)
56.1
(27.
5–63
.4)
68 (4
5.2–
80.1
)50
.2 (3
5.2–
68.4
)73
.5 (6
1.7–
78)
CCL3
121
(94.
2–14
7)10
9 (9
9–12
8)13
0 (1
13–1
54)
125
(108
–152
)10
1 (9
9–12
0)11
1 (1
00–1
18)
CCL4
209
(107
–258
)20
0 (9
5.6–
323)
282
(171
–282
)22
0 (1
92–2
44)
113
(54.
3–26
0)12
0 (4
8.1–
251)
CCL7
11.7
(8.1
8–16
.1)
13.2
(9.5
–21.
8)16
.3 (7
.96–
324)
27.2
(7.5
–139
)13
.8 (6
.57–
19.1
)17
.5 (1
4–22
.2)
CCL1
152
.2 (1
3.7–
97)
42.2
(11.
8–11
2)65
.8 (4
3.8–
69.5
)71
.6 (5
0.9–
102)
16.3
(6.4
6–44
.3)
27.9
(10.
2–32
.6)
CCL1
719
2 (5
9.6–
395)
182
(58–
459)
138
(107
–152
)19
1 (8
1.3–
213)
136
(32.
6–23
8)13
3 (6
0.3–
235)
CCL1
8A59
0 (8
2.6–
7570
)22
7 (4
5–29
500)
3150
(84.
4–39
00)
848
(270
–390
0)22
50 (5
3.8–
4570
00)
1960
(12.
4–38
300)
CCL1
95.
79 (1
.51–
14.2
)3.
62 (1
.14–
7.49
)5
(1.5
7–9.
87)
2.59
(1.5
9–44
.2)
3.34
(0.9
5–4.
35)
1.52
(0.8
3–3.
48)
CCL2
247
1 (2
38–8
36)
345
(201
–896
)43
2 (2
85–4
74)
257
(218
–380
)44
8 (1
74–6
76)
365
(216
–491
)CC
L27§
0 (0
–0)
0 (0
–0)
0 (0
–0)
0 (0
–0)
0 (0
–0)
0 (0
–0)
CXCL
539
0 (6
2.3–
732)
198
(112
–612
)11
9 (1
01–7
85)
129
(95.
9–24
0)52
.6 (1
.87–
260)
53.4
(5.8
4–16
7)CX
CL8
231
(71.
7–11
50)
252
(89.
3–33
7)26
6 (2
18–1
830)
302
(158
–927
0)15
8 (7
1.1–
356)
187
(76.
7–30
0)
Tabl
e 6.
2 co
ntin
ues o
n ne
xt p
age
164
Chapter 6
6
Tabl
e 6.
2 Co
ntin
ued
Reac
hing
Insu
lin In
depe
nden
ceIn
sulin
Req
uirin
g
Cont
inue
d In
sulin
Inde
pend
ence
Tem
pora
ry In
sulin
Inde
pend
ence
Pre
Post
Pre
Post
Pre
Post
CXCL
950
.3 (3
1.3–
240)
49 (2
4.5–
172)
39.1
(29.
3–41
.3)
76.4
(25.
3–10
5)41
.8 (1
6.8–
72.1
)29
.4 (2
5.5–
73.3
)CX
CL10
72.6
(43–
233)
85.7
(22.
5–23
6)95
(68.
2–26
3)17
0 (6
8–10
40)
86.2
(40.
9–13
7)93
.1 (4
1–12
5)CX
CL13
16.6
(10.
9–21
7)14
.5 (1
0.2–
249)
24.1
(4.7
4–16
2)25
.8 (6
.18–
667)
15.8
(13–
33.5
)23
.8 (1
3.6–
50.8
)XC
L-1
10.2
(6.4
7–12
3)11
.7 (8
.91–
51.5
)11
.2 (1
0.9–
11.5
)12
.5 (1
2.4–
16.9
)13
.9 (7
.16–
17.6
)11
.4 (7
.16–
16.6
)
Adip
okin
esAd
ipon
ectin
C0.
991
(0.6
99–1
.23)
1.05
(0.8
07–1
.17)
0.85
7 (0
.617
–0.9
06)
0.93
8 (0
.915
–1.0
6)0.
951
(0.8
72–1
.09)
1.05
(1.0
2–1.
07)
Adip
sinA
102
(26.
5–22
6)12
0 (2
8.9–
159)
64.6
(63.
8–70
.9)
103
(88.
8–13
0)61
.8 (2
2.6–
162)
73.4
(34.
5–10
2)Ca
thep
sin B
A15
.3 (1
.76–
20.2
)17
.5 (2
.16–
23.1
)20
.4 (1
0.5–
26)
13.3
(8.7
4–16
.7)
9.64
(0.9
45–1
8.5)
8.91
(2.1
5–22
.6)
Cath
epsin
LA
14.1
(7.7
4–17
.7)
9.77
(4.9
–18.
3)6.
78 (4
.62–
7.95
)5.
54 (5
.3–1
7)10
.4 (6
.75–
12.3
)7.
03 (5
.53–
8.26
)Ca
thep
sin S
A30
9 (2
59–3
47)
279
(225
–362
)22
7 (1
91–2
58)
269
(231
–316
)30
2 (2
88–3
35)
251
(203
–284
)Ch
emer
inB
1.31
(0.9
74–1
.58)
1.43
(0.8
97–1
.82)
0.76
8 (0
.467
–0.9
6)1.
3 (1
.03–
1.39
)1.
39 (1
.03–
2.09
)1.
43 (1
.23–
2.09
)Le
ptin
B0.
496
(0.3
45–1
.66)
0.33
6 (0
.141
–1.2
3)0.
198
(0.1
42–0
.258
)0.
189
(0.1
2–0.
2)0.
519
(0.2
46–1
.05)
0.28
8 (0
.162
–1.6
7)O
men
tin0.
01 (0
–0.0
1)0.
01 (0
–0.0
1)0.
01 (0
.01–
0.01
)0.
01 (0
–0.0
1)0.
01 (0
–0.0
1)0
(0–0
.01)
PAI-1
B4.
02 (3
.49–
5.17
)3.
43 (2
.97–
4.53
)3.
71 (2
.99–
3.84
)3.
86 (2
.73–
5.17
)3.
62 (3
.42–
5.41
)3.
52 (3
.2–3
.97)
RBP-
4C0.
389
(0.3
26–0
.571
)0.
325
(0.3
14–0
.352
)0.
393
(0.3
22–0
.479
)0.
317
(0.2
88–0
.576
)0.
312
(0.2
64–0
.417
)0.
342
(0.3
12–0
.396
)Re
sistin
B1.
11 (0
.855
–1.3
)1.
21 (0
.973
–1.3
8)1.
33 (1
.03–
1.33
)1.
26 (1
.21–
1.51
)1.
23 (0
.895
–1.3
5)1.
09 (1
.08–
1.2)
SAA-
1A10
3 (0
–226
)13
1 (2
9.4–
235)
148
(0–3
02)
194
(19.
5–47
2)19
4 (1
34–2
35)
162
(42.
8–20
6)TI
MP-
1A38
1 (3
11–4
82)
369
(300
–419
)32
3 (3
11–3
63)
317
(309
–431
)40
2 (3
28–4
17)
359
(354
–373
)Tr
ombo
poie
tinB
1.46
(1.2
3–3.
65)
2.09
(1.2
6–3.
95)
1.1
(0.5
9–1.
11)
1.98
(0.7
55–2
.87)
1.16
(0.3
48–2
.1)
0.98
6 (0
.327
–1.6
8)
Gro
wth
fact
ors
BDN
F25
7 (1
04–7
03)
148
(105
–209
)30
8 (7
1–41
3)11
6 (6
2.6–
171)
108
(59.
6–14
3)99
.6 (7
5.9–
144)
EGF
58.7
(28.
9–19
9)56
.7 (4
.92–
93.6
)44
.4 (1
6.4–
71.2
)25
.3 (1
9.6–
37.2
)69
.7 (2
2.6–
80.2
)44
.1 (1
4–74
.6)
165
Serum cytokines as biomarkers in islet cell transplantation for T1D
6
G-C
SFA
58.7
(30.
9–73
)50
.3 (2
8–83
.9)
66 (4
5.5–
72.3
)64
(43.
5–67
.1)
50.6
(40.
3–14
0)48
.5 (4
1.3–
127)
GM
-CSF
3.42
(0–4
.85)
1.85
(0–4
.01)
0 (0
–91.
5)9.
41 (0
–372
)6.
15 (0
–14.
5)1.
21 (0
–4.5
3)H
GFA
1.36
(0.5
07–3
.53)
1.38
(0.5
11–2
.44)
0.94
9 (0
.767
–1.5
6)1.
08 (0
.604
–4.0
3)1.
13 (0
.122
–1.5
1)0.
933
(0.2
24–1
.08)
M-C
SF37
.1 (2
1.7–
54.9
)30
.5 (2
4.2–
40.5
)35
.8 (3
1.6–
44.3
)31
.2 (2
9.8–
31.5
)27
.3 (2
4.3–
32)
28.1
(25.
2–29
.1)
NG
F0
(0–0
)0
(0–0
)0
(0–0
)0
(0–0
)0
(0–0
)0
(0–0
)SC
F15
.3 (4
.77–
35.6
)16
.4 (4
.77–
18.5
)8.
53 (7
.87–
13)
10.4
(9.0
6–10
.5)
6.52
(4.7
7–7.
37)
5.46
(4.4
8–10
.9)
sICA
MA
68.6
(43.
8–11
0)73
(51.
4–87
.3)
77.5
(75.
2–19
5)89
.5 (7
4.4–
349)
104
(85.
6–12
4)83
.1 (7
2.9–
104)
sVCA
MB
0.78
7 (0
.399
–1.1
4)0.
517
(0.3
3–0.
686)
0.51
9 (0
.327
–0.8
04)
0.65
5 (0
.577
–1.2
7)0.
614
(0.4
83–0
.805
)0.
52 (0
.436
–0.6
1)VE
GFA
1.41
(1.1
3–2.
03)
1.26
(0.5
43–2
.77)
1.7
(0.8
41–3
.56)
1.96
(0.5
74–2
.88)
1.11
(0.3
85–1
.33)
1.02
(0.8
17–1
.36)
Oth
er
FAS
595
(305
–182
0)68
5 (5
50–1
490)
586
(369
–647
)77
0 (6
73–8
14)
415
(54.
3–81
1)40
8 (8
3.5–
1100
)FA
S-L
6.82
(4.5
7–10
.7)
6.3
(5.8
4–8.
66)
6.83
(5.2
9–7.
52)
7.2
(5.5
5–8.
53)
5.65
(4.8
1–7.
94)
7.12
(6.0
6–8.
53)
Gra
nzym
e B
39.8
(21.
8–53
.3)
43.1
(23.
4–56
.8)
50.6
(37.
3–59
.3)
48.2
(36.
9–11
0)41
.5 (2
5.9–
44)
33.3
(28.
8–53
.7)
IL-1
RIIA
6.35
(2.1
2–7.
33)
4.98
(2.2
9–9.
17)
5.41
(5.4
–6.1
6)5.
49 (4
.58–
7.46
)1.
54 (1
.22–
3.49
)2.
11 (1
.14–
3.37
)IL
-18B
PA15
7 (6
6–23
5)15
8 (8
2.1–
279)
209
(146
–100
0)22
3 (1
60–5
310)
114
(66.
2–14
9)10
2 (8
0.8–
271)
KIM
_119
.9 (5
.92–
37.1
)45
.6 (2
0.1–
161)
20.4
(7–3
9.7)
23.3
(8.8
2–30
.4)
12.4
(6.5
7–54
.1)
12.9
(5.0
4–17
.1)
MM
P-8
87.4
(81.
1–47
3)86
.5 (7
3.3–
98.9
)83
.5 (6
9.6–
84.4
)80
.8 (6
4.9–
85.3
)81
.1 (7
4.6–
98)
91.5
(74.
5–10
6)O
PG64
8 (4
11–8
37)
595
(525
–784
)44
1 (2
99–6
57)
512
(328
–524
)39
5 (1
03–6
88)
392
(227
–516
)O
PNA
2.23
(0.8
61–3
.38)
2.61
(1.1
5–4.
26)
2.84
(1.4
5–5.
22)
3.37
(3.3
5–3.
41)
1.62
(0–3
.32)
1.56
(0–5
.46)
S100
A12A
178
(97–
504)
103
(35.
7–30
3)56
3 (1
51–5
66)
273
(226
–449
)14
6 (3
7.6–
187)
37.3
(25.
4–13
6)sC
D14
A90
.9 (6
8.5–
1010
0)96
.8 (6
4.2–
156)
78.7
(47.
1–84
.1)
82 (6
0.7–
85.1
)94
.4 (7
8.4–
1010
0)67
(65.
1–68
.2)
sCD
2563
3 (2
94–7
85)
662
(347
–760
)76
7 (6
19–8
13)
700
(655
–148
0)63
2 (4
14–7
15)
519
(485
–833
)sC
D16
3A14
.9 (2
.6–1
8.1)
12.3
(2.0
6–30
.2)
14.8
(13.
3–15
.3)
17 (1
1.2–
44.5
)8.
09 (4
.48–
16.3
)7.
82 (6
.51–
14.4
)sI
L-6R
A1.
98 (0
.793
–2.2
6)1.
75 (0
.947
–2.6
8)1.
99 (1
.51–
2.18
)1.
68 (1
.36–
1.95
)1.
62 (0
.354
–1.8
4)1.
39 (0
.581
–2.4
7)sP
D-1
250
(112
–170
0)17
2 (5
1.8–
910)
164
(107
–263
)12
8 (8
4.2–
158)
188
(3.3
–376
)64
.6 (1
0.1–
247)
sSCF
-RA
40.5
(11–
40.5
# )38
.9 (1
2.1–
40.5
# )34
.9 (3
4.1–
40.5
# )40
.5 (2
5.8–
46.2
)18
.4 (2
.4–2
8)19
.8 (5
.76–
46.8
)TN
F-RI
A3.
06 (1
.08–
4.46
)3.
86 (1
.58–
4.1)
1.71
(1.5
5–3.
37)
2.24
(0.7
42–4
.1)
2.59
(0.8
33–5
.66)
2.93
(1.6
6–5.
25)
TNF-
RIIA
1.09
(0.6
54–2
.08)
1.32
(1.0
3–2.
58)
1.2
(1.1
7–2.
07)
1.69
(1.4
7–1.
7)1.
12 (0
.694
–1.8
1)1.
04 (0
.725
–3.0
1)TR
EM-1
11.8
(3.8
9–32
.6)
7 (5
.15–
14)
9.43
(6.3
–12.
9)8.
62 (6
.19–
11.2
)5.
96 (5
.15–
9.08
)8.
36 (5
.5–1
0.6)
166
Chapter 6
6
Plotrix, Graphics and Heatmap.plus [R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: http://www.R-project.org/]. P-values of < 0.05 were considered significant.
Results
Patient characteristics
Patients were categorised by graft function during the first year after transplantation; nine out of 13 patients reached insulin independence (good engraftment) while four patients remained insulin requiring (insufficient engraftment). These groups did not differ regarding clinical and graft characteristics except for HbA1c, which tended to be lower before transplantation in patients reaching insulin independence (p = 0.052). HbA1c
improved in all patients with good engraftment and in two of four patients with insufficient engraftment. Pre-breakfast glucose variability improved in all patients, decreasing from 42.6 ± 4.5% and 46.8 ± 3.0% to 11.7 ± 3.7% and 27.0 ± 5.7% for patients with good and insufficient engraftment, respectively (mean ± SD). Of nine insulin independent patients, three showed gradual loss of graft function during the first year after transplantation while six remained insulin independent (Table 6.1).
Serum markers
A total of 94 serum markers were measured (Table 6.2), of which four (OSM, CCL1, CCL27 and NGF) were undetectable in all patient samples and one (Omentin) did not show any variability. Therefore these analytes were excluded from further analysis. Furthermore, 16 other markers showed more than 20% of values out of measurement range; these values were substituted as described in the methods section. Out-of-range values were evenly distributed between both groups, except for leukaemia inhibitory factor (LIF), which was mainly detected in patients with good engraftment (Table 6.2).
Impact of transplantation and immunosuppression on serum marker levels
Serum marker levels pre- and post-transplantation were studied irrespective of graft function. Notably, serum protein levels varied widely among individuals while levels of
167
Serum cytokines as biomarkers in islet cell transplantation for T1D
6
individual patients, comparing pre- and post-transplantation samples, remained markedly stable in spite of transplantation and immunosuppression (inter-patient variability was larger than intra-patient variability for all analytes). Nevertheless, 13 markers changed significantly with intervention: IL-7, CCL2, Adiponectin, Chemerin and FAS increased, while IL-11, IL-16, IL-23, LIF, CCL22, Leptin, BDNF and S100A12 decreased during the first year after transplantation (Figure 6.1A).
Taking graft function into account while analysing the effect of intervention, another three markers (IL-13, TSLP and KIM-1) were identified in patients with good engraftment and another four markers (IL-15, IL-22, cathepsin S and sICAM) in patients with insufficient engraftment. IL-13, TSLP and KIM-1 increased after transplantation in patients reaching insulin independence (Figure 6.1B); while IL-15 increased and IL-22, Cathepsin S and sICAM decreased in insulin requiring patients (Figure 6.1C).
Identifying biomarkers of islet transplantation outcome
Firstly, serum marker levels before transplantation were compared between individuals with good and insufficient engraftment. Eight markers (IL-16, IFN-α, LIF, CCL3, BDNF, M-CSF, SCF and sIL-1RII) were higher in individuals with good engraftment, while no markers were lower (Figure 6.2A). In addition, we investigated whether serum marker levels before transplantation in patients with good engraftment differed between patients with and without gradual loss of graft function during the first year. Leptin and Cathepsin L were higher in individuals with continued insulin independence (Figure 6.2B).
Next, we analysed whether serum levels one year after transplantation reflect graft function. Patients with good engraftment had higher levels of IL-22, IL-33, IFN-α, LIF, SCF, IL-1RII, KIM-1, S100A12 and sCD14, than patients with insufficient engraftment (Figure 6.2C). Patients with continued insulin independence had lower S100A12 than patients with transient insulin independence after initial good graft function (Figure 6.2D).
In total 13 serum markers were identified that correlated with reaching insulin inde-pendence after islet transplantation, of which four (IFN-α, LIF, SCF, IL-1RII) correlated with insulin independence before as well as one year after transplantation (Figure 6.3).
168
Chapter 6
6
,/í� ,/í��
10
,/�
50100
200
500
,/�
0.05
0.2
0.51.0
0.5
2.0
1020
5.0
p = 0.0014
p = 0.0019
p = 0.027 p = 0.035
&&/�160
4060
100
&&/�� Chemerin
0.5
1.0
2.0
1.5
200
400
600800
p = 0.0028
p = 0.0038
p = 0.035
Leptin
200
500
1000 p = 0.025
Adiponectin FAS
2000
50100
500
6���$��
0.7
1.0
1.2
0.80.9
50200
500
100
p = 0.013 p = 0.049 p = 0.016
BDNF
100
200
500
p = 0.046
0.2
0.5
2.0
5.0
1.0
A
.,0B�
510
2050
100
TSLP
0.01
1.0
100
0.10
10
Pre Post Pre Post Pre Post Pre Post
,/�
0.5
2.0
1050
205.0
,/�
510
1520
3040
sICAM
50100
200300
Cathepsin S
200
250
350
300
LIF
0.5
2.0
5.0
2010
p = 0.0034
p = 0.036 p = 0.028
p = 0.027
p = 0.0075
p = 0.028
p = 0.0014
,/�
510
50200
20500
p = 0.035
B
C
Figure 6.1 Serum markers changing by transplantation and immunosuppression Serum marker levels that significantly change from pre islet cell transplantation to one year post transplantation are depicted (Student’s t-test, p < 0.05): for all patients (A, black), for patients with good engraftment (B, green) and for patients with insufficient engraftment (C, orange). Serum levels, depicted on the y-axis, are in pg/ml for: IL-7, IL-11, IL-13, IL-15, IL-16, IL-22, LIF, TSLP, CCL2, FAS, BDNF, KIM-1; in ng/ml for: IL-23, CCL22, Cathepsin S, sICAM, S100A12; in ug/ml for: Chemerin, Leptin; in mg/ml for: Adiponectin. Samples out-of-range at both time points were excluded from statistical analysis.
169
Serum cytokines as biomarkers in islet cell transplantation for T1D
6
Figure 6.2 Serum marker levels before or after islet transplantation that could differ-entiate between good and poor graft function Heatmap representation of significantly different serum markers between patients with good graft function (green) and patients with poor graft function (orange) before (A & B) or 1 year after (C & D) transplantation (Student’s t-test, p < 0.05). In the group of patients with good graft function, those with continued insulin independence (green) and temporary insulin independence (grey) were subdivided (B & D). Heatmap gradient represents min to max (cyan – black – red) normalised serum titers.
1894
5
C
1848
3
2016
6
2019
9
1932
1
1366
1
1530
4
1725
8
KIM_1
sCD14
LIF
SCF
IFNa
S100A12
2060
3
1247
3
1552
0
1407
0
S100A12
D
1948
9
CCL3
IFNa
BDNF
SCF
LIF
A
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Discussion
Upon intraportal islet infusion, islet cells are in direct contact with serum proteins. Although many of these proteins are related to immune responses and several have been described to affect β cell survival directly, they have been poorly studied in islet transplantation [28, 29]. Therefore, we investigated whether serum markers relate to clinical outcome and describe the effect of islet transplantation on these markers.
Thirteen patients were selected from a cohort treated under a stringent protocol with a standardised graft, thereby minimizing the chance that technical alterations affect outcome [3, 15]. Before and after transplantation we tested a large serum marker panel in a multiplex immunoassay. We reasoned that this unbiased approach would allow study of changes in immune and endocrine homeostasis due to islet transplantation and immunosuppression, which would enable us to identify biomarkers of islet graft survival that may help understand and improve transplantation protocols or patient selection for individualised care.
With regard to changes in the immune homeostasis following allograft transplantation; despite immunosuppression in all patients and major improvement of blood glucose homeostasis in most patients; we observed most serum markers to be stable within patients the first year after transplantation, while concentrations between patients varied
Figure 6.3 Overlap of serum markers correlating with outcome of islet transplantation Venn diagram depicts serum markers that predict or correlate with clinical outcome (good or poor graft function) 1 year after islet transplantation.
Before After
BDNFCCL3IL-16M-CSF
IFNaIL-1RIILIFSCF
IL-22IL-33KIM-1S100A12sCD14
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more widely. Of course, this observation does not indicate that the current immune suppressive standard has no effect on investigated analytes with potential clinical or therapeutic value, as local effects in the lesions may not be reflected in serum or may only be temporary. Observations on CCL2 may underline differences between local and systemic effects. CCL2 release by human islets prepared for transplantation has previously been shown to be positively associated with release of other pro-inflammatory mediators (IL-6, CXCL-8); inflammatory markers in serum immediately after transplantation and transplantation outcome [29, 30]. Furthermore, high islet CCL2 release was associated with higher parameters of inflammation in recipients. We observed that systemic pre-transplantation CCL2 levels did not predict graft function; however our finding that CCL2 levels increase during the first year after transplantation matches previous observations on the role of CCL2.
Next, the role of glucose on serum marker stability is of interest. Increased serum glucose levels are known to affect several inflammatory proteins in the circulation [31, 32]. On the other hand, increased pro inflammatory cytokine levels such as IL-1β, TNF-α and IFN-γ have in vitro been shown to decrease glucose oxidation, activity of glycolytic enzymes and inhibit insulin secretion, thus creating a vicious loop in diabetic patients [33]. In this cohort, HbA1c levels tended to be lower before transplantation in patients reaching insulin independence, reflecting lower glucose levels and thereby possibly affecting serum marker levels. However, a major influence of glucose on measured markers seems unlikely, since few markers changed significantly while glucose control improved in all patients and HbA1c levels did not differ with clinical outcome one year after transplantation.
The second aim of this study was to identify possible biomarkers of islet graft survival. We identified 13 serum markers that correlated with insulin independence in the first year after islet transplantation, of which eight were predictive of outcome before transplantation. Serum markers that predict outcome represented various categories of inflammatory mediators; mostly growth and differentiation factors (LIF, BDNF, M-CSF and SCF), which may promote graft survival and mediators of innate immunity (IL-16, IFN-α, CCL3, and IL-1RII) that may stimulate regulatory responses or prevent recruitment of harmful cells by masking the local cytokine gradient.
Of the factors identified by this study that correlate with outcome, LIF is an interesting cytokine that has not been extensively investigated. Yet, LIF has previously been identified to regulate β cell mass and promote islet cell survival and allograft tolerance via regulatory T cell induction [34-37]. In a recent study, targeted LIF delivery in combination with
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nano-PEG-encapsulation of the islet graft was shown to give superior graft survival in vivo [38]. Our finding that LIF was predominantly present in patients with good engraftment is in concordance with previous results and underlines the role of LIF as possible mediator influencing β cell mass.
At 1 year after transplantation, several growth and differentiation factors retained their discriminative capacity (LIF, IFN-α, SCF, IL-1RII) (Figure 6.3). Moreover, IL-22 (member of the IL-10 family); IL-33 (member of the IL-1 family) and molecules of the innate immune system (S100A12, sCD14 and kidney injury molecule 1 (KIM-1)) distinguished patients with good and poor graft function. A number of these markers associated with outcome of graft function will be discussed more in detail. Firstly, KIM-1 is a molecule of which urinary levels are associated with kidney dysfunction and albuminuria [39]. KIM-1 levels in blood of rodents reflected development of diabetes and renal impairment [40]. Whether serum KIM-1 levels also correlate with nephropathy in T1D patients is not known to us. In kidney transplantation, serum KIM-1 levels were powerful predictors of acute rejection [41]. In contrast, we found serum KIM-1 levels 1 year after transplantation to be higher in patients with good outcome. Secondly, IL-33 is able to activate cells of both the innate and adaptive immune system. In addition, depending on the disease, IL-33 can either promote the resolution of inflammation or drive disease pathology [42]. Furthermore, cytotoxic T cells express high levels of the IL-33 receptor ST2 and elevated levels of decoy receptor soluble ST2 associate with T2D [43, 44]. In this study, higher IL-33 levels were associated with retained graft function in T1D. Thirdly, IL-22 is thought to play a role in coordinating adaptive and innate immune responses. IL-22 has both proinflammatory and tissue-protective properties depending on the context in which it is expressed. Th17 cells, specifically implicated in autoimmune disease including T1D, can secrete IL-22 and IL-17A has been identified as a factor promoting the pro-inflammatory effect of IL-22 [45-48]. In our study, IL-22 levels post transplant were higher in patients with good engraftment, suggesting a tissue-protective role for IL-22. Possibly, this association with an immunoprotective rather than a pro-inflammatory role is influenced by the immunosuppressive regimen.
Several of the markers measured in this study have previously been described as predictors of islet cell transplantation outcome after in vitro culture of islets (CCL2 and CXCL8) or after stimulation of immune cells (IL-10) [21, 30, 49]. None of these markers correlated with outcome of islet cell transplantation in this study. Possibly, local production of these factors is not sufficient to considerably alter circulating levels. Furthermore, increased
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levels of CXCL8 have been reported shortly after islet infusion. It is therefore conceivable that any change in production of these factors is temporary and has returned to normal one year after transplantation.
Previously, IL-13 and IL-18 were described to be prospectively associated with subsequent loss of graft function in islet cell after kidney transplantation after correction for suggested confounders [24]. In addition, elevated MIF concentrations were associated with subsequent graft loss in simultaneous islet cell and kidney transplantation, while IL-10 was lower in patients losing graft function [24]. In our study, in the context of islet cell transplantation without kidney transplantation, we could not confirm a predictive role for IL-18 or MIF, nor did we confirm IL-10 to correlate with engraftment. In addition, although within the group of patients with good engraftment an increase in IL-13 during the fist year correlated with retained graft function, IL-13 did not differentiate between good and poor engraftment overall.
In conclusion, we have identified several serum markers that may give insight in the fate of islet grafts. In addition, these markers can be further investigated for their potential as biomarkers to guide individualised therapy. These observations warrant further studies to validate these markers and their possible role in predicting long-term graft survival.
References 1. Shapiro A.M., Lakey J.R., Ryan E.A. et
al. Islet transplantation in seven patients with type 1 diabetes mellitus using a glucocorticoid-free immunosuppressive regimen. N Engl J Med 2000; 343:230-238.
2. Ryan E.A., Paty B.W., Senior P.A. et al. Five-year follow-up after clinical islet transplantation. Diabetes 2005; 54:2060-2069.
3. Keymeulen B., Gillard P., Mathieu C. et al. Correlation between beta cell mass and glycemic control in type 1 diabetic recipients of islet cell graft. Proc Natl Acad Sci U S A 2006; 103:17444-17449.
4. Barton F.B., Rickels M.R., Alejandro R. et al. Improvement in outcomes of clinical islet transplantation: 1999-2010. Diabetes Care 2012; 35:1436-1445.
5. Fiorina P., Venturini M., Folli F. et al. Natural history of kidney graft survival, hypertrophy, and vascular function in end-stage renal disease type 1 diabetic kidney-transplanted patients: beneficial impact of pancreas and successful islet cotransplantation. Diabetes Care 2005; 28:1303-1310.
6. Fiorina P., Gremizzi C., Maffi P. et al. Islet transplantation is associated with an improvement of cardiovascular function in type 1 diabetic kidney transplant patients. Diabetes Care 2005; 28:1358-1365.
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7. Thompson D.M., Begg I.S., Harris C. et al. Reduced progression of diabetic retinopathy after islet cell transplantation compared with intensive medical therapy. Transplantation 2008; 85:1400-1405.
8. Warnock G.L., Thompson D.M., Meloche R.M. et al. A multi-year analysis of islet transplantation compared with intensive medical therapy on progression of compli-cations in type 1 diabetes. Transplantation 2008; 86:1762-1766.
9. Poggioli R., Faradji R.N., Ponte G. et al. Quality of life after islet transplantation. Am J Transplant 2006; 6:371-378.
10. Bennet W., Groth C.G., Larsson R., Nilsson B., Korsgren O. Isolated human islets trig-ger an instant blood mediated inflamma-tory reaction: implications for intraportal islet transplantation as a treatment for patients with type 1 diabetes. Ups J Med Sci 2000; 105:125-133.
11. Van der Linde P., Roep B.O. T-cell assays to determine disease activity and clinical efficacy of immune therapy in type 1 diabetes. Am J Ther 2005; 12:573-579.
12. Nanji S.A., Shapiro A.M. Islet transplan-tation in patients with diabetes mellitus: choice of immunosuppression. BioDrugs 2004; 18:315-328.
13. Vendrame F., Pileggi A., Laughlin E. et al. Recurrence of type 1 diabetes after simul-taneous pancreas-kidney transplantation, despite immunosuppression, is associ-ated with autoantibodies and pathogenic autoreactive CD4 T-cells. Diabetes 2010; 59:947-957.
14. Pinkse G.G., Tysma O.H., Bergen C.A. et al. Autoreactive CD8 T cells associ-ated with beta cell destruction in type 1 diabetes. Proc Natl Acad Sci U S A 2005; 102:18425-18430.
15. Huurman V.A., Hilbrands R., Pinkse G.G. et al. Cellular islet autoimmunity associ-ates with clinical outcome of islet cell transplantation. PLoS One 2008; 3:e2435.
16. Harlan D.M., Kenyon N.S., Korsgren O., Roep B.O. Current advances and travails in islet transplantation. Diabetes 2009; 58:2175-2184.
17. Roep B.O. T-cell responses to autoantigens in IDDM. The search for the Holy Grail. Diabetes 1996; 45:1147-1156.
18. Roep B.O., Stobbe I., Duinkerken G. et al. Auto- and alloimmune reactivity to human islet allografts transplanted into type 1 diabetic patients. Diabetes 1999; 48:484-490.
19. Velthuis J.H., Unger W.W., van der Slik A.R. et al. Accumulation of autoreactive effector T cells and allo-specific regulatory T cells in the pancreas allograft of a type 1 diabetic recipient. Diabetologia 2009; 52:494-503.
20. Hilbrands R., Huurman V.A., Gillard P. et al. Differences in baseline lymphocyte counts and autoreactivity are associated with differences in outcome of islet cell transplantation in type 1 diabetic patients. Diabetes 2009; 58:2267-2276.
21. Huurman V.A., Velthuis J.H., Hilbrands R. et al. Allograft-specific cytokine profiles associate with clinical outcome after islet cell transplantation. Am J Transplant 2009; 9:382-388.
22. Roelen D.L., Huurman V.A., Hilbrands R. et al. Relevance of cytotoxic alloreactivity under different immunosuppressive regi-mens in clinical islet cell transplantation. Clin Exp Immunol 2009; 156:141-148.
23. Piemonti L., Everly M.J., Maffi P. et al. Al-loantibody and autoantibody monitoring predicts islet transplantation outcome in human type 1 diabetes. Diabetes 2013; 62:1656-1664.
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24. Pfleger C., Schloot N.C., Brendel M.D. et al. Circulating cytokines are associated with human islet graft function in type 1 diabetes. Clin Immunol 2011; 138:154-161.
25. Schipper H.S., de Jager W., van Dijk M.E. et al. A multiplex immunoassay for human adipokine profiling. Clin Chem 2010; 56:1320-1328.
26. de Jager W., Bourcier K., Rijkers G.T., Prakken B.J., Seyfert-Margolis V. Prerequi-sites for cytokine measurements in clinical trials with multiplex immunoassays. BMC Immunol 2009; 10:52.
27. de Jager W., Prakken B.J., Bijlsma J.W., Kuis W., Rijkers G.T. Improved multiplex immunoassay performance in human plasma and synovial fluid following removal of interfering heterophilic antibodies. J Immunol Methods 2005; 300:124-135.
28. Eizirik D.L., Colli M.L., Ortis F. The role of inflammation in insulitis and beta-cell loss in type 1 diabetes. Nat Rev Endocrinol 2009; 5:219-226.
29. Melzi R., Mercalli A., Sordi V. et al. Role of CCL2/MCP-1 in islet transplantation. Cell Transplant 2010; 19:1031-1046.
30. Piemonti L., Leone B.E., Nano R. et al. Human pancreatic islets produce and secrete MCP-1/CCL2: relevance in human islet transplantation. Diabetes 2002; 51:55-65.
31. Festa A., D’Agostino R., Jr., Tracy R.P., Haffner S.M. Elevated levels of acute-phase proteins and plasminogen activa-tor inhibitor-1 predict the development of type 2 diabetes: the insulin resistance atherosclerosis study. Diabetes 2002; 51:1131-1137.
32. Spranger J., Kroke A., Mohlig M. et al. Inflammatory cytokines and the risk to develop type 2 diabetes: results of the prospective population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study. Diabetes 2003; 52:812-817.
33. Kiely A., McClenaghan N.H., Flatt P.R., Newsholme P. Pro-inflammatory cytokines increase glucose, alanine and triacylglycerol utilization but inhibit insulin secretion in a clonal pancreatic beta-cell line. J Endocrinol 2007; 195:113-123.
34. Baeyens L., De B.S., Lardon J., Mfopou J.K., Rooman I., Bouwens L. In vitro generation of insulin-producing beta cells from adult exocrine pancreatic cells. Diabetologia 2005; 48:49-57.
35. Baeyens L., Bonne S., German M.S., Ravassard P., Heimberg H., Bouwens L. Ngn3 expression during postnatal in vitro beta cell neogenesis induced by the JAK/STAT pathway. Cell Death Differ 2006; 13:1892-1899.
36. Koblas T., Leontovyc I., Zacharovova K. et al. Activation of the Jak/Stat signalling pathway by leukaemia inhibitory factor stimulates trans-differentiation of human non-endocrine pancreatic cells into insulin-producing cells. Folia Biol (Praha) 2012; 58:98-105.
37. Gao W., Thompson L., Zhou Q. et al. Treg versus Th17 lymphocyte lineages are cross-regulated by LIF versus IL-6. Cell Cycle 2009; 8:1444-1450.
38. Dong H., Fahmy T.M., Metcalfe S.M. et al. Immuno-isolation of pancreatic islet allografts using pegylated nanotherapy leads to long-term normoglycemia in full MHC mismatch recipient mice. PLoS One 2012; 7:e50265.
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39. Conway B.R., Manoharan D., Manoharan D. et al. Measuring urinary tubular biomarkers in type 2 diabetes does not add prognostic value beyond established risk factors. Kidney Int 2012; 82:812-818.
40. Alter M.L., Kretschmer A., Von Websky K. et al. Early urinary and plasma biomarkers for experimental diabetic nephropathy. Clin Lab 2012; 58:659-671.
41. Jin Z.K., Tian P.X., Wang X.Z. et al. Kidney injury molecule-1 and osteopontin: new markers for prediction of early kidney transplant rejection. Mol Immunol 2013; 54:457-464.
42. Miller A.M. Role of IL-33 in inflammation and disease. J Inflamm (Lond) 2011; 8:22.
43. Yang Q., Li G., Zhu Y. et al. IL-33 synergizes with TCR and IL-12 signaling to promote the effector function of CD8+ T cells. Eur J Immunol 2011; 41:3351-3360.
44. Miller A.M., Purves D., McConnachie A. et al. Soluble ST2 associates with diabetes but not established cardiovascular risk factors: a new inflammatory pathway of relevance to diabetes? PLoS One 2012; 7:e47830.
45. Harrington L.E., Hatton R.D., Mangan P.R. et al. Interleukin 17-producing CD4+ effector T cells develop via a lineage distinct from the T helper type 1 and 2 lineages. Nat Immunol 2005; 6:1123-1132.
46. Stockinger B., Veldhoen M. Differentiation and function of Th17 T cells. Curr Opin Immunol 2007; 19:281-286.
47. Sonnenberg G.F., Nair M.G., Kirn T.J., Zaph C., Fouser L.A., Artis D. Pathological versus protective functions of IL-22 in airway inflammation are regulated by IL-17A. J Exp Med 2010; 207:1293-1305.
48. Honkanen J., Nieminen J.K., Gao R. et al. IL-17 immunity in human type 1 diabetes. J Immunol 2010; 185:1959-1967.
49. Citro A., Cantarelli E., Maffi P. et al. CXCR1/2 inhibition enhances pancreatic islet survival after transplantation. J Clin Invest 2012; 122:3647-3651.
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Discussion and summary
PART V
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1 Introduction
Endocrinology is one of the oldest specialties in medicine. Since Claudius Galenus described the thyroid, pineal and pituitary gland around 170 A.D., insight in human endocrinology has increased over the ages [1]. The relevance of inflammatory processes for endocrine homeostasis was already recognized in the 19th century, fuelled by the discovery of anti-thyroid antibodies [2]. The concept of type 1 diabetes (T1D) as an autoimmune disorder, with a pre-diabetic period of variable duration, was first proposed in 1958 by Willy Gepts, while Georg Eisenbarth in 1986 added 3 stages (II-IV) to the model of development of T1D: I) genetic susceptibility; II) triggering, III) active immunity, IV) progressive loss of glucose-stimulated insulin secretion, V) overt diabetes and VI) complete β cell destruction [3,4] . Over the last few decades, the pivotal role of inflammatory mediators in the pathophysiology of T1D and obesity has emerged, and has marked a novel era in diabetes research [5, 6].
Inflammation, β cell function and adipose tissue function are closely related, as illustrated by the various studies presented in this thesis. Here, the role of inflammatory mediators in the pathophysiology of T1D and obesity is discussed, together with their potential as biomarkers for disease progression.
2 Immune dysregulation in type 1 diabetes
Before specific aspects of immune tolerance in T1D, as investigated in this thesis, are addressed (paragraph 2.2.1: heat shock proteins and paragraph 2.2.2: T cell epitopes with very low HLA class I binding affinity), some general concepts related to the above mentioned stages in T1D disease development and progression will be discussed. Next, to bridge “acute” insulitis and chronic low-grade systemic inflammation in T1D, the role of adipose tissue (AT) will be explored in paragraph 2.2.3
2.1 Disease progression
The early phases in the development of T1D are characterized by the loss of immune tolerance. Although the mechanisms underlying loss of tolerance have not been entirely unravelled, a wide range of factors appear to contribute. Firstly, there is a predominance of specific human leucocyte antigen (HLA) genotypes in T1D patients while other genotypes appear to be protective; this strongly supports a role for genetic susceptibility
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factors [7-10]. Of note, genetic predisposition is strongest in early childhood onset T1D; this susceptibility is matched by faster decline in endogenous insulin production compared to adult onset T1D patients. Thus, among T1D patients different susceptibility profiles are present [11, 12]. Genetic predisposition based on HLA susceptibility alleles is of importance in a variety of autoimmune disorders. Notably, celiac disease and T1D share the same HLA II susceptibility alleles while juvenile arthritis (JIA) and Hashimoto’s thyroiditis share a partly different set of HLA II susceptibility alleles [10, 13]. Loss of tolerance is one of the topics of this thesis and will be further addressed in paragraph 2.2.2. Secondly, environmental factors interact with genetic susceptibility. Interestingly, T1D shows a lower incidence close to the equator and increasing incidence at more moderate climate zones (grossly matching distribution of less and more developed countries) [14]. One explanation for this phenomenon is provided by the ‘hygiene hypothesis’, which states that a lack of exposure to infectious agents prevents the development of a stable immune balance predisposing individuals to autoimmune disorders. Repeated exposure to microbes provides antigenic stimulation and thereby activation of lymphocytes while an environment with little microbial exposure induces relative antigenic deprivation and allows for lower lymphocyte numbers. The ensuing relative lymphopenia can be compensated through homeostatic proliferation, whereby autoreactive T cells are expanded, and autoimmune disease may develop [15, 16]. Next, dietary factors such as bovine milk protein and gluten are investigated as possible environmental factors predisposing to T1D. In young infants, these ‘foreign’ food proteins may propagate inflammatory immune responses, which in turn predispose to the development of autoimmune disorders such as T1D [17, 18]. In addition, viral pathogens such as enteroviruses may evoke inflammatory responses that promote loss of tolerance and subsequent development of T1D. The seasonality of enterovirus infections appears to coincide with high levels of circulating T1D autoantibodies. Intriguingly, enterovirus infections are up to ten times more common in T1D patients than in healthy controls or siblings, which supports a reciprocal relationship between T1D and enterovirus susceptibility [19]. Finally, vitamin D promotes immune tolerance, and vitamin D deficiency is associated with enhanced risk of developing T1D [20, 21]. Recent studies indicate that environmental factors overtake the importance of genetic predisposition in the development of T1D. This is illustrated by the fact that, although high-risk HLA genotypes are becoming less frequent, the incidence of T1D among moderate- and low-risk HLA genotypes is increasing [22].
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The next phase in the development of T1D, after loss of tolerance, is characterized by the invasion of islet autoreactive T cells that promote the destruction of pancreatic β cells. CXCL10 expression by insulitic β cells is involved in chemoattraction of CXCR3+ autoreactive T cells [23]. The autoreactive T cells comprise both CD4+ and CD8+ T cells. Autoreactive CD4+ T cells release an array of cytokines and chemokines, including IL-1α and IL-1β, which fuel the immune response. Furthermore, the release of IFN-γ by CD4+ T cells induces hyperexpression of HLA-class I molecules on the β cell surface, which exposes β cells to killing by cytotoxic CD8+ T cells [24-26]. Priming of the autoreactive CD8+ cells occurs by APCs with CD4+ T cell help, predominantly in local lymph nodes [27]. HLA-I molecules, and particularly HLA-A2 molecules expressed by 60–70% of human T1D patients, present a variety of antigenic peptides that are elemental in priming of the autoreactive CD8+ T cells [28]. Hyperglycaemia, which is one of hallmarks of de novo T1D, accelerates β cell destruction by promoting β cell epitope display [29]. β cell killing ultimately occurs through CD8+ T cell contact-dependent degranulation. The pore-forming perforin paves the way and secures intracellular access for granzymes, which induce cell death through caspase-dependent and -independent routes. Other routes involve cytokines or interaction with TNF-family-related death receptors (FAS-L and TNFRs). Of note, CD20+ B cells appear to play a minor role [30-32].
Investigating the autoimmune process in T1D is complex as pancreatic tissue is rarely available. As a surrogate for studying pancreatic tissue, circulating inflammatory mediators and autoreactive T cells have been the focus of investigation [33-35]. High levels of circulating IL-17 and interferon (IFN)-γ levels were shown to reflect pancreatic inflammation in T1D patients, while healthy controls exhibited higher levels of circulating IL-10 [36, 37]. In this thesis, circulating inflammatory mediators in new-onset and longstanding T1D patients were extensively investigated. Below, the potential of these inflammatory mediators as biomarkers for disease and disease progression is discussed. An important area of debate is to what extent peripheral blood biomarkers accurately reflect tissue processes, as autoreactive CD8+ T cells in pancreatic tissue far outnumber those in peripheral blood. Nevertheless, systemic inflammatory markers such as circulating autoreactive T cells correlate with insulitis [38-40].
Pan-DR binding heat-shock protein 60 epitopes in type 1 diabetes
In the early 90s, the discovery of autoantibodies and autoimmune T cells had far-stretching implications. One of the acting antigens was unmasked as heat shock protein (HSP)60 [41-43]. HSP60, the mammalian homologue of mycobacterial hsp65, has thereafter been
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shown to play an immunomodulatory role in animal models of several autoimmune diseases, including T1D [16]. HSP60 can evoke both pro- and anti-inflammatory immune responses. So-called cross-reactive T cells, recognizing both mycobacterial hsp65 antigens as well as a β cell self-antigen were shown to induce insulitis in mice. On the other hand, modulation of the anti-hsp60 T cell response through vaccination with hsp60 antigen in soluble form could arrest autoimmune destruction [41, 44]. Though initially heavily debated, pro-inflammatory responses by HSP60 were shown to be mediated through toll-like receptor (TLR) 4 while anti-inflammatory responses were mediated through TLR2 stimulation [45, 46]. Stressed pancreatic β cells release HSP60, which may enhance an inflammatory process through TLR4 proficient macrophages and T cells [45, 46].
In T1D patients with recent onset of disease, T cell proliferation to a specific HSP60 epitope, peptide p277, was significantly elevated compared to healthy controls [47]. This finding underlined the role of HSP60 as a possible antigen involved in human T1D immune pathogenesis. In line with this finding is the fact that p277 binds to an MHC class II molecule (HLA-DQ 0302) which is known to confer T1D susceptibility [48]. DiaPep277, which is peptide 277 in a stabilized form without altered immunogenicity, has been used for therapeutic intervention trials. Treatment with DiaPep 277 preserved part of the endogenous insulin production in T1D patients [49-54].
Other HSP60 peptides have been implicated in immunomodulation as well [55]. Especially the therapeutic potential of pan-DR HSP60 peptides, which bind multiple allelic variants of the human HLA-DR, was subject of intense investigations. In juvenile idiopathic arthritis a number of pan-DR HSP60 peptides were identified that induced tolerogenic T cells [56].
Thus, in chapter 2, immune responses elicited by these recently identified pan-DR binding HSP60 peptides were studied for their tolerogenic capacity in paediatric T1D patients and healthy controls. The HSP60 peptides induced low peptide-specific proliferative responses in PBMCs in T1D, contrary to previous findings in juvenile idiopathic arthritis (JIA) and rheumatoid arthritis (RA) [56-59]. Nevertheless, some intracellular peptide-specific cytokine production was detected in T1D patients. In our study, differences in peptide-specific cytokine production could not predict which children with onset T1D maintained part of their β cell function (in this study defined as a remission period) [60].
In conclusion, pan-DR binding HSP60 peptides induced lower immune responses in PBMCs of T1D patients than previous studies showed for JIA or RA patients. Furthermore,
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cytokine production induced by pan-DR HSP60 peptides was not correlated with β cell preservation in T1D patients. Thus, in T1D, pan-DR binding HSP60 peptides may hold less of a promise for immune intervention than HLA-specific HSP60 epitopes such as peptide 277 do.
Additional trials in T1D with peptide 277 show promising preliminary results [www.andromedabio.com]. In addition, therapeutic options for HSP are being expanded towards dendritic cell-targeting vaccines [61].
CD8 T cell epitopes with very low HLA class I binding affinity
Cytotoxic CD8 T cells are key players in the β cell destruction in T1D. Autoreactive cytotoxic CD8 T cells that survived thymic deletion promote autoimmunity when they encounter their self-antigen, such as pre-proinsulin (PPI) in the pancreas. The epitopes recognized by autoreactive CD8 T cells appear to be derived primarily from β cell proteins [62]. CD8 T cell epitope studies have mostly focused on peptides with high-affinity binding to HLA. However, this focus on high-affinity binding may have induced an observation bias as, in fact, autoreactive CD8 T cells with a low affinity for β cell peptides may have a better chance of escaping thymic deletion than high-affinity CD8 T cells [63, 64]. In this thesis, the relation between peptide binding affinity to HLA-A2 and immunogenicity was further investigated. The results indicate that peptides binding with very low affinity to HLA-I allow escape of CD8 T cells from negative deletion, while T cells specific for higher-affinity peptides may be deleted more efficiently and thus circulate in lower frequencies in peripheral blood. Therefore, low-affinity binding peptides might play a more important role than anticipated based on their binding profile only [65].
Of note, HSP60 peptides have also been implicated in the CD8 T cell response. Recently, CD8+ T cells specific for HSP peptides binding to HLA-E (an MHC-1 molecule) with intermediate avidity have been identified in humans. These peptides appear to maintain peripheral self-tolerance by discriminating self from non-self [66].
An inflammatory role for adipose tissue in T1D
T1D as an autoimmune disease is a generally accepted concept. T1D as a disease with low-grade systemic inflammation, and AT as a contributor thereof, has been far less explored. However, there are strong indications for AT involvement in T1D. For example, recent studies in mice suggest that administration of the adipokine leptin improves metabolic homeostasis and thereby reduces low-grade systemic inflammation [154]. Furthermore,
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alterations in circulating levels of adipokines in adult and paediatric T1D indicate AT involvement in the low-grade systemic inflammation observed [67].
The study described in chapter four was initiated to explore the extent of adipokine alterations in paediatric T1D and to gain more mechanistic insight in the involvement of AT.
Several adipokines acting at the crossroads of AT function and inflammation, including CCL2 and the novel adipokines cathepsin S, chemerin and TIMP-1, showed higher circulating levels in children with T1D than healthy controls. In addition, our study supports the hypothesis that adipose tissue contributes to the metabolic and immune dysregulation in children with T1D by showing that diabetic plasma factors affect pre-adipocyte proliferation and differentiation in vitro [68].
The adipogenic effects of diabetic plasma factors may be important considering the clinical observation that both children and adults with T1D often suffer from weight gain. For example, in the DCCT/EDIC study, 25% of patients in the intensively treated group had weight gain (BMI) from approximately 24 to 31 kg/m2 [69]. Furthermore, among T1D patients the prevalence of obesity has risen over the last 20 years [70]. This weight gain has important implications, as obesity leads to the development of AT inflammation and insulin resistance. Weight gain is associated with higher blood pressure and triglycerides, lower (protective) HDL-cholesterol, higher insulin requirement and a more atherogenic lipid profile: all associated with increased cardiovascular risk. In a cohort of Dutch children with T1D, almost 40% of children (median age 12, median diabetes duration 5 years) were either overweight or obese. Furthermore, being overweight or obese correlated with a higher prevalence of systolic hypertension [71]. In addition, a large case-control study in adolescents showed higher inflammatory mediator levels in T1D patients compared to healthy controls. In addition, higher BMI resulted in higher inflammatory mediator levels without further effect of T1D status [72]. A combination of T1D and predisposition to T2D, so-called ‘double diabetes’, thus lies in wait for many children with T1D [73]. Large prospective studies such as EURODIAB and the EDC cohort have pointed out that T1D patients with a T2D family history had an increased risk of both macrovascular and microvascular complications [74, 75]. Further elucidation of the diabetic plasma factors contributing to enhanced pre-adipocyte proliferation and differentiation may help to reduce weight gain of children with T1D, as adipocyte number is a major determinant of fat cell mass and BMI [76]. It remains to be elucidated how AT inflammation affects long term outcome in T1D. Improved weight control may aid in prevention of long-term complications of ‘double diabetes’ in the future.
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3 Immune dysregulation in obesity
3.1 Adipose tissue inflammation
As discussed in the introduction, obesity leads to low-grade local and systemic chronic inflammation. Visceral fat is generally considered the ‘bad’ fat depot. Adipocytes within the visceral fat depot release more non-esterified fatty acids (NEFA) than superficial subcutaneous abdominal adipocytes [77]. Furthermore, visceral adipose tissue fat is characterized by higher secretion of pro-inflammatory cytokines such as tumour necrosis factor (TNF)-α and IL-6 and lower secretion of adiponectin, the anti-inflammatory adipokine, as compared to abdominal subcutaneous fat [78, 79]. However, not all fat is bad. It has consistently been shown that approximately 25–30% of obese individuals do not develop insulin resistance or chronic inflammation, and that such individuals are characterized by efficient expandability of the superficial (especially lower body) subcutaneous fat depot, which is likely to limit triglyceride overflow into the visceral and ectopic fat depots [80].
Cellular players
On a cellular level, a growing body of evidence suggests that not only the innate, but also components of the adaptive immune system contribute to AT dysfunction. Human adipocytes and pre-adipocytes possess the full machinery to prime inflammation and attract T cells independently of macrophages [81]. Importantly, AT-resident T cells show a bias towards antigen-specific (clonotypic) T cell receptor repertoires, suggesting that local antigens drive selection and expansion of AT-resident T cells [82]. Functionally, B and T cells infiltrated in AT of obese patients contribute to inflammatory macrophage differentiation [83].
Mouse studies have shed further light on cellular interactions in inflamed AT. T cells derived from AT of obese mice produced more IFN-γ than those from control mice and hampered preadipocyte-to-adipocyte differentiation [84, 85]. The number of IFN-γ producing T cells is increased in obese adipose tissue. Moreover, IFN-γ deficient obese mice showed improved glucose tolerance, partly mediated via altered cytokine expression [84]. Next, regulatory T cell (Treg) numbers were found to be reduced in abdominal fat of obese mice compared to lean mice, resulting in impaired immune balance with predominance of IFN-γ secreting Th1 cells, which displace Tregs and IL-4- and IL-13-producing Th2 cells [86]. Importantly, restoring the balance, with increase of Th2 and
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Tregs, was shown to be beneficial as it reduces AT inflammation and insulin resistance; anti-CD3 had the potential to restore this balance in mice [82, 87]. Notably, the anti-inflammatory master switch in adipocyte differentiation, PPAR-γ, was recently identified as a potent inducer of visceral AT-resident Tregs [86].
3.2 Inflammatory control
Gut microbiome
The interest in gut microbiota as a key regulator of the immune system is expanding. The gut microbiome may partly underlie the inflammatory response that is observed in insulin resistance and T2D. Obese humans and rodents exhibit higher concentrations of gut-derived endotoxins than their non–obese counterparts. These endotoxins can potentially trigger toll-like receptors (TLR)s in AT or on pancreatic β cells, thus contributing to both insulin resistance and β cell failure [88]. A recent study in humans underscores the potential of the gut microbiome, as transfer of intestinal microbiota from lean donors to recipients with metabolic syndrome altered the microbial population with associated improved insulin sensitivity [89].
Interestingly, evidence suggests that the gut microbiome indeed affects the development of T1D. Firstly, mouse studies point to an interaction between intestinal microbes and the innate immune system, irrespective of TLR2 and TLR4, as a factor influencing T1D predisposition [90]. Secondly, the increased incidence of T1D in humans during the last decades has recently been linked to differences in the intestinal bacterial flora [91]. Next to altered intestinal microbiota composition, increased intestinal permeability has been shown to be present [90, 92-94]. Possibly, bacteria enter the pancreatic ductal system, subsequently trigger β cell destruction and thereby induce T1D. During this process, loss of tolerance to gastrointestinal commensals allows microbiota-specific T cells to be activated, affecting β cell function [95].
These observations on the correlation between the gut microbiome and T1D might help to put in further perspective the observations regarding the protective role of breastfeeding and the “window of opportunity” for solid food introduction [96, 97]. Thus, the gut microbiome presents new options for both primary prevention as well as intervention studies; in this light the results of the recently initated study on faecal transplantation in T1D are awaited [26].
Of note, the gut microbiome may also influence immune responses to HSP. In paediatric Crohn’s disease, specific pan HLA-DR binding HSP60-derived peptides induced
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proinflammatory responses specifically in parts of the intestine with inflamed intestinal mucosa but not in unaffected intestinal tissue of the same patient [58]. In addition, HSP60 was detected in epithelial cells in Crohn’s disease and ulcerative colitis but not in normal controls [98]. Increased intestinal permeability has been described for both these inflammatory bowel diseases and, in addition, intestinal microbiota composition is altered (30–50% reduced) [93].
Vitamin D
Vitamin D is not merely an important hormone for bone mineralization; it has a second important function as immunological mediator, through activation of the nuclear vitamin D receptor. Vitamin D is immunoprotective by dampening the potential pathogenic immune response at a cellular level; this is mediated by immunomodulatory effects on both the innate as well as the adaptive immune system. Therefore, derangements in vitamin D homeostasis may contribute to immune-mediated disease. In addition to general effects on the immune system, vitamin D levels can directly affect pancreatic function through local production of 1,25-(OH)2D3. Moreover, 25-(OH)D3 deficiency has been associated with insulin resistance in obese children and with obesity and metabolic syndrome in adults, and it has been proposed that 25(OH)D deficiency may aggravate insulin resistance in obesity through enhanced systemic inflammation [99-106].
In chapter 5, the prevalence of vitamin D deficiency in a cohort of Dutch obese children amounted to an alarming 56% [107]. Vitamin D deficiency was shown to be associated with systemic inflammation, independent of BMI-SD and other possible confounders known to affect inflammation. Circulating inflammatory mediators reflected systemic inflammation by increased levels of circulating cathepsin S, chemerin and soluble vascular cell adhesion molecule (sVCAM). The importance of vitamin D was underscored by the observation that vitamin D sufficiency in obesity was associated with better insulin sensitivity [107].
Vitamin D deficiency is endemic. In the US, 6 million children are estimated to be vitamin D deficient, while the prevalence of vitamin D deficiency is over 50% in obese children [108]. Of children under 12, 20% and 67% are estimated to be vitamin D deficient or insufficient respectively (with higher percentages in black and Hispanic children) [109]. In Europe, the HELENA study reported vitamin D status to be equally troubling, with a deficiency in over 40% of adolescents and an inverse relationship – although not significant – with BMI [110]. Data on the prevalence of vitamin D deficiency is affected by
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a variety of definitions. Nonetheless, there is overwhelming evidence that the prevalence is alarmingly high in both developing as well as developed countries. One explanation for the emergence of vitamin D deficiency is the increased storage compartment, i.e. AT in obesity. Dietary aspects, as well as television and computer time diminishing outdoor activity, and decreased sunshine exposure are others. Recently, advice on vitamin D supplementation in the Netherlands has been extended: in all dark-skin individuals as well as in all individuals with little sunshine exposure daily additional vitamin D supplementation is advised [Gezondheidsraad. Evaluatie van de voedingsnormen voor vitamine D. Den Haag: Gezondheidsraad, 2012; publicatienr. 2012/15. ISBN 978-90-5549-931-1].
In T1D, the effect of maternal vitamin D supplementation during pregnancy has been investigated as well as vitamin D supplementation in high risk individuals. Surprisingly, longitudinal data on actual vitamin D levels in these groups is scarce [111]. However, a case-control study in the US underscored the importance of vitamin D, as low serum levels were associated with higher risk of T1D while sufficient levels were protective (3.5-fold lower risk); this study did not include information on the genetic make-up [112]. In new onset T1D patients, lower levels of vitamin D compared to healthy controls were reported [113, 114]. In Australia, serum vitamin D levels were lower in children with T1D than in controls [115].
One research focus is how polymorphisms in the vitamin D receptor affect the risk of T1D in correlation with vitamin D levels and other environmental factors [116, 117]. Possibly, a strong genetic predisposition to T1D (e.g. HLA-mediated) cannot be countered by vitamin D supplementation. In genetically low risk individuals on the contrary, vitamin D receptor polymorphisms may importantly contribute to decreased vitamin D action and the development of T1D, thus allowing for intervention.
As discussed, vitamin D deficiency is endemic in obese children. Two US studies reported an inverse association between vitamin D levels and fasting glucose in adolescents (after adjustment for confounders including BMI) [118, 119]. A recent study in schoolchildren aged 9–14 years addressed cardiometabolic risk factors in childhood obesity, combined with vitamin D deficiency. Almost half of the children were overweight or obese while vitamin D deficiency (measured in late winter) was found in an alarming 75% of children. No correlations were found, yet the unexpectedly high prevalence of vitamin D deficiency did not allow for optimal comparison [120].
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The inflammatory mediators in our study found to be specifically related to vitamin D deficiency in obesity, e.g. cathepsin S, chemerin and sVCAM, match previous results, where these mediators were associated with insulin resistance [107, 121, 122].
In view of the above–described associations, the question emerges whether optimizing vitamin D levels might ameliorate insulin sensitivity and AT inflammation. This question was addressed in obese adolescents in a 6-month randomized placebo-controlled trial studying the effect of vitamin D supplementation on insulin sensitivity and inflammation. Increased vitamin D levels did not affect BMI or inflammatory markers (IL-6, TNF-α, CRP). However, insulin sensitivity increased while the leptin to adiponectin ratio decreased [123].
Currently, 14 supplementation studies in obese children are ongoing (www.clinicaltrials.gov). Thus, further elucidation of relationship between 25(OH)D deficiency, systemic inflammation and insulin resistance may be expected. In the near future, vitamin D supplementation in obese children may help to partly suppress inflammation and improve insulin resistance.
4 Immune intervention in T1D
Studies aimed at reduction of the incidence and burden of T1D have addressed all stages in the development of T1D, from primary prevention through secondary prevention in high-risk individuals to tertiary prevention or intervention studies targeting preservation of the residual β cell mass after clinical onset of the disease. Next, β cell replacement through pancreas or islet cell transplantation has been studied and, finally, autologous stem cell transplantation (Figure 7.1). Here, a selection of these studies is discussed in line with the topics discussed in this thesis.
Prevention studies in T1D
Primary prevention studies, either population-wide or in genetically high-risk individuals, aim at preventing loss of tolerance and subsequent development of autoimmunity. One potential trigger initiating autoimmunity is early exposure to cow’s milk in infants. While breast feeding might have a protective effect, early cow’s milk introduction might either induce cross-reactivity between human and bovine insulin or affect the immune system through increased gut permeability early in life [17]. Some primary preventions studies such as “Trial To Reduce Insulin Dependent Diabetes Mellitus In The Genetically At Risk”
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(TRIGR), which studies the (putative protective) effect of breast milk as an environmental factor in genetically at risk individuals, are still ongoing. Other dietary interventions, such as a gluten-free diet based on the concept of an immature intestinal barrier function, did not show an effect [124, 125]. However, a recent study suggests that, in high-risk individuals, there might be an optimal time-window for solid food introduction, with an additive protective role for breast milk during this period [97].
Time
?
anti-CD3
Figure 7.1 Scheme depicting the effect of various intervention strategies on ! cell function in type 1 diabetes This model is based on the scheme of loss of β cell function and inflammation in type 1 diabetes. Firstly, interventions to preserve β cell function address different stages of disease progression, or might be applicable to various stages. Next, while some therapies aim at re-installing tolerance and in some cases even preventing disease, others mainly address influencing the balance between insulitis, β cell function and inflammation with the aim of preserving some more of the β cell function to ameliorate disease. The black line represents progression of T1D without intervention. Red and green curves depict how pro- and anti-inflammatory events /interventions might influence β cell function. A rightward / upward shift of the curve represents a state whereby β cell function is preserved somewhat longer and thus later onset of overt disease. A leftward / downward shift of the curve represents a state whereby accelerated β cell failure will lead to earlier presentation of T1D symptoms. Double diabetes, as discussed in this thesis, is hypothesized to induce a leftward shift, while anti-inflammatory interventions such as vitamin D supplementation or drugs targeting pro-inflammatory cytokines aim to induce a rightward shift. Finally, a number of therapies target replacement or regeneration of β cell function by various forms of transplantation schemes. Anakinra, human IL-1 receptor antagonist.
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Currently, prevention studies have shifted their focus from primary prevention influencing the environment to secondary prevention targeting the immune process. Unfortunately, secondary prevention studies targeting the immune system derailment by oral or intranasal insulin treatment have been reported to be less effective than hoped for [126, 127]. In addition, the role of vitamin D in primary and secondary prevention has been investigated (as well as the effect of intervention with additional vitamin D after onset). Several studies have suggested that low 25(OH)D3 intake or low 25(OH)D3 levels are associated with increased risk of T1D, although some have not confirmed these results. However, longitudinal studies are needed to establish the potential of 25(OH)D in T1D prevention [112, 115, 128-131]. Recently, a number of excellent reviews focusing on primary and secondary T1D prevention trials have been published [18, 132-134].
Intervention studies in T1D
Studies on interventions after clinical onset of T1D have targeted various players in the autoimmune process leading to β cell destruction and can be classified by the targets addressed: 1) general anti-inflammatory agents (e.g., cyclosporine, anti-IL-1 therapies); 2) immune modulators addressing T cell signalling and eliminating effector T cells (e.g., anti-CD3 and anti-CD20 monoclonal antibodies); 3) antigen-specific interventions and therapies aimed at Tregs, which potentially induce bystander suppression by spread of tolerance to other autoantigens (e.g. vaccination with GAD65, PPI or Peptide 277 of HSP60); and 4) combinations of the above. While such intervention studies mainly focus on recent-onset T1D patients, late intervention therapies aim at β cell replacement through islet cell transplantation or combined kidney-pancreas transplantation [135]. In addition, transplantation of autologous or umbilical cord stem cells is gaining traction. Stem cells may potentially modulate the autoimmune process with increments in peripheral Treg cells and less exogenous insulin requirement. There have been some encouraging early results [136, 137].
A number of authors have recently reviewed intervention trials in T1D [132-134, 138-141]. The outcomes of recent intervention trials were overall regarded as somewhat disappointing. However, the initial primary endpoints chosen in these studies often were clinical variables such as insulin dose or glycaemic control expressed as HbA1c, i.e. variables which are influenced by many factors. To improve comparability, studies should rather use primary endpoints that focus on the actual target of intervention: β cell function (as a surrogate for β cell mass), as reflected by stimulated C-peptide. As
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preserved stimulated C-peptide relates with a better long-term outcome in terms of disease complications, this is both a valid as well as a more realistic endpoint compared to full restoration of β cell function or mass as defined by insulin independence. C-peptide has been used as an outcome variable in various phase II trials that showed preservation of β cell function: treatment with anti-CD3 (oxelizumab and teplizumab), anti-CD20 (rituximab), and costimulation blockade (abatacept), for instance [142-144].
However, assessment of β cell function through stimulated C-peptide is inconvenient for the patient as it requires blood sampling after a standardized procedure rather than random and does not directly reflect immunological alterations initiated through treatment. Therefore, immune-based end-points or biomarkers remain much sought after, as will be discussed below [133].
Next to the above-mentioned interventions, there is the option of pancreatic islet cell transplantation, which can cure T1D, since it has the potential to achieve normoglycaemia with independence of exogenous insulin and near-physiological β cell function. However, long-term insulin independence after islet transplantation is not often reached due to graft dysfunction, inflammatory and immune reactions, and the use of immunosuppressive drugs that can be toxic for β cells or cause insulin resistance. Tools to improve understanding and prediction of the processes affecting graft survival would be of great value, for both patient selection as well as for decisions on immunosuppressive regimens.
We performed a pilot study to investigate whether biomarkers could be identified correlating with 1 year graft survival in T1D islet cell transplantation patients. Inflammatory mediators were hardly affected by islet transplantation and immunosuppression, at least after 1 year. Nevertheless, a set of serum mediators was identified that associated with clinical outcome.
These markers warrant validation, as biomarkers to guide patient selection for islet transplantation or choice of immunotherapy are much sought after.
The next era in intervention therapy in T1D
A number of intervention strategies appear to be at our doorstep. Firstly, combination therapies of various immune interventions may have a synergistic effect. Aims are to minimize toxicity while trying to maximize treatment synergy to enhance efficacy [145]. An example is anti-CD3 therapy followed by vaccination with an auto-antigen. Although initially the anti-CD3 effect was considered to be mediated by T cell receptor blocking,
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more recent studies point to a more intricate effect. Through selective depletion of pathogenic cells, the balance between pathogenic and regulatory T cells is restored [146]. Thus, combination therapy with anti-CD3 and vaccination aims at a reset of the immune system by eliminating autoreactive T cells, followed by re-education of the immune system through tipping the balance towards Tregs and thereby restoring tolerance. However, another example of combination therapy has led to renewed caution as a combination of IL-2 and rapamycin (sirolimus) not only failed to show an effect in autoantibody-positive otherwise healthy individuals, it actually transiently worsened clinical and metabolic parameters through a transient decrease in β cell function [147]. Thus, combination therapy does not automatically imply additive effects, let alone synergies [39].
A key factor for success of immune intervention might well be personalized intervention, both in timing and genetic make-up. This concept is presented in an elegant model, whereby the degree of disease state (prediabetic, new onset or established) determines therapy means and methods (single agent, vs double or three-way combination therapy) [148].
Secondly, immunotherapy with vitamin D-modulated dendritic cells (DCs). This therapy is based on the concept of the tolerizing capacity of combined dexamethasone and vitamin D treated DCs [149]. Vitamin D-treated DC therapy can be extended by loading these DCs with epitopes of autoreactive T cells. These approaches aim at either expansion of the in vivo T reg population or expanding regulatory T cells in vitro for subsequent therapy [149-151].
A third strategy is targeting of pro-inflammatory cytokines implicated in the pathogenesis of T1D. Selective blockade of cytokines such as IL-1, IL-6 and TNF aims to favour regulatory adaptive responses at the expense of pro-inflammatory responses. The effect of cytokine blockade might be improved by a combination with an antigen-specific therapy [138].
However, again, the proof of the pudding is in the eating. Two randomised placebo-controlled trials with IL-1 antagonists (anakinra (human IL-1 receptor antagonist) and canakinumab (a human monoclonal anti-IL-1 antibody)) in recent-onset T1D patients both did not positively affect β cell preservation after 9 / 12 months [152]. Again, timing might well be an issue; in addition, IL-1 blockade might be more suitable either in combination therapy or at an earlier stage in T1D development. Figure 7.1 illustrates the possible effect of pro- and anti-inflammatory interventions on β cell function.
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Taking a different approach, therapies involving specific adipokines might potentially be beneficial in T1D. Leptin, secreted by AT and a key regulator of food intake as well as energy homeostasis and thus bodyweight, has been a natural candidate as in T1D leptin deficiency has been established [72, 153, 154]. Furthermore, leptin deficiency has been thought to contribute to insulin resistance in poorly controlled T1D [153]. Studies in rodent T1D models have showed improved glucose homeostasis after leptin administration [153]. Importantly, leptin also influenced lipogenic transcription factors and thereby reduced plasma and tissue lipids. Thus, leptin stands out as a promising treatment option for T1D by influencing both hyperglycaemia as well as chronic inflammation. Expectations on leptin, however, have been tempered as human studies in T2D failed to improve insulin sensitivity [155, 156].
The examples discussed here underline that β cell preservation currently is targeted from many different angles.
5 Biomarkers in paediatric type 1 diabetes and obesity
5.1 Definition of biomarkers
In paediatric patients, studying pathophysiological mechanisms in T1D and obesity is difficult. The possibilities for obtaining peripheral blood or other tissue for investigation is limited by strict regulations as well as the willingness of patients and their caretakers to participate in research projects. Therefore, variables that reflect disease state, “immune correlates” are much sought after. Such variables are referred to as biomarkers [157-159].
The NIH defines a biomarker as: “a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.” [160]. According to the National Institutes of Health (NIH) definition, biomarkers can for example be analytes measured in tissue or body fluids such as blood or urine, a cellular activity, a gene polymorphism or a biological characteristic (e.g. blood pressure) [157].
Disease-related biomarkers can provide insight into the aetiology of disease (aetiological marker); the disease state (diagnostic marker); or the development of disease or its complications, without or with treatment (prognostic marker). If a prognostic biomarker is to be of clinical relevance, a key requirement is that it correlates with a clinically relevant outcome, such as disease progression or remission, or long-term prognosis [161].
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5.2 Methodological considerations on biomarkers in paediatric type 1 diabetes and obesity
In paediatric research, sample volume of blood-based biomarkers is highly relevant given the burden that venepuncture imposes on patients. Therefore, the fact that many inflammatory mediators can now be measured in very small blood volumes with multiplex immuno assay (MIA) technology holds promise for their use as biomarkers. However, as information on large numbers of biomarkers can be obtained, the analysis of these datasets entails new challenges. Data could be used for clustering or pathway analyses thus bridging the gap between analyses of a single variable and systems biology [162, 163].
With the rapid growth of the use of biomarkers measured by MIA in both mechanistic studies and clinical trials, it is important to address how biological and technical variables affect detected levels of inflammatory mediators. Does time between venepuncture and sample handling affect inflammatory mediator levels? What external factors influence stability of the inflammatory mediators? Inflammatory mediator levels can be severely affected when conditions are not standardized. Some of these issues have been studied, but many remain to be clarified [164-166].
In this regard, prior experience on problems with T cell assays in T1D is instructive [40]. While evaluating outcome of autoreactive T cell assays in T1D, comparison of research data turned out to be obscured by technical differences in peripheral blood mononuclear cell (PBMC) isolation, (cryo)preservation, distribution and usage for detecting of the antigen-specific T cell responses [167]. These issues led to the initiative of the T cell Workshop Committee of the Immunology of Diabetes Society. This working group has published various consensus protocols on many aspects of T cell measurements, covering the whole process from “bed to bench” [33, 35, 159, 168]. In addition, researchers in the field are advised to report on all specified details when publishing their results [167].
Interestingly, the recently established international consortium UCAN-U (www.Ucan-U.org) has among its goals to improve standardization in bench to bedside research in JIA. This underlines the fact that the above-mentioned technical considerations hold true for research on inflammatory mediators not only in T1D but also in a variety of other autoimmune diseases. With an ever-expanding number of multicentre secondary prevention and intervention trials, agreement on many details in sample processing is imperative for a trustworthy evaluation of the effects of interventions on the underlying immunological mechanisms. From the moment a patient sample is drawn, including
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practicalities such as tube specification and fasting or fed state, through storage and transport conditions to thawing conditions and further technical aspects: all steps should be standardized and operators should be made aware of the effects protocol deviations might induce.
For many inflammatory mediators, reference data for “healthy adults” or “healthy obese” are not available, let alone for children. Thus, reference data sets need to be established. Research in children brings along a new set of challenges. How do inflammatory mediator levels fluctuate with age? How are they affected by sexual maturation and gender (effect of oestrogen and androgen levels)? Do diurnal patterns exist? What is the impact of body mass indices such as BMI? Has BMI been adjusted for age en gender by using a BMI-SD score?
In addition, ethical issues preclude obtaining large sets of reference data in otherwise healthy children and research on children more in general as well, as will be discussed below.
Although standardization of procedures is important for previously discussed reasons, this is not always feasible, for instance in the case of overnight fasting in very young children, or regarding invasive procedures requiring full patient cooperation. As an example, C-peptide in T1D is an important estimate of β cell function. For optimal assessment, C-peptide should be obtained in the fasting state and after a mixed meal tolerance test; both conditions can be problematic for a substantial percentage of children.
Of particular importance regarding biomarkers in T1D and obesity research is the question how glycaemia influences the mediators of interest. Publications on this matter support a cautious approach. Acute hyperglycaemia has been shown to induce an inflammatory response both in healthy volunteers as well as, more profoundly, in adult T1D patients and individuals with impaired glucose tolerance [169, 170]. A study in paediatric T1D patients similarly showed that hyperglycaemia influenced some but not all cytokines evaluated [171, 172]. Thus, in all studies involving individuals with possible alterations of glucose metabolism, rigorous standardization of sample scheduling with regard to fasting or fed state and concomitant evaluation of serum glucose levels might be inevitable; even then accounting for the effect of recent alterations in serum glucose levels remains unfeasible.
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Ethical issues
Approval of non-therapeutic studies with children when involving more than ‘minimal’ risks and burdens is forbidden by ethical codes and regulations including the Dutch WMO. This puts important limits on the study of pathophysiology and treatment of diseases in children. The question how to facilitate more studies to improve medical care for children as a group while still adequately protecting individual research subjects has recently been addressed in a doctoral thesis [173] [http://hdl.handle.net/1887/1775]. Among the study recommendations were suggestions to 1) distinguish between therapeutic and non-therapeutic procedures instead of between therapeutic and non-therapeutic studies to more accurately identify those research risks and burdens that have to be minimal. 2) To define minimal risk as: ‘empirical data and/or expert opinions suggest that in the persons concerned, the likelihoods that the procedure(s) will cause small, moderate, and/or serious harm are < 1/100, < 1/10,000, and < 1/1,000,000, respectively’, and to define minimal burden as: ‘empirical data, expert opinions and/or the procedural characteristics suggest that at most a quarter of the persons concerned will experience considerable discomfort’ [173]. Studies addressing or at least including paediatric patients are of great importance, as intervention outcomes that hold true in adults may not do so in children. For example, in T1D, decline in β cell function is much faster in children and some therapeutics aiming at preservation of β cell function have hinted at or actually shown more promising effects in paediatric (sub)populations while “failing” in adults (e.g. anti-CD20 monoclonal antibody rituximab; anti-CD3 monoclonal antibody teplizumab) [143, 174]. Thus, excluding children from studies might actually hamper the development of adequate treatment possibilities. It is to be hoped, therefore, that the current proposal to cautiously broaden the options for research involving children in The Netherlands will be accepted by the authorities.
After study approval, obtaining parent approval is of major importance. Parental interviews showed that willingness to participate was affected by whether a child is healthy or affected by more or less severe pathology [175]. Factors positively affecting approval were 1) if the disease studied ran in the family, 2) doctor encouragement and 3) small risk of harm; while 1) too high a chance of harm and 2) the sentiment “not to let my child be a guinea pig” were negatively affecting approval [http://www.mottnpch.org/reports-surveys/children-research-many-parents-willing-if-risk-harm-small]. Finally, patient cooperation presents a challenge, especially in young children, as many ethically approved protocols for obtaining blood samples direct cancellation of blood withdrawal
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if the child shows clear signs of physical resistance, which often is the case in babies, toddlers and pre-school children.
5.3 Established biomarkers in type 1 diabetes
Valid and useful biomarkers can be developed despite the difficulties discussed above. An example is glycated haemoglobin (HbA1c), a marker for the average plasma glucose concentration over 6 weeks to 3 months. Assays have been standardized and reference values have recently been unified on a global base [176, 177]. HbA1c is used in daily practice as a measure of glycaemic control; it correlates reasonably well with long-term disease complications [178-181]. Therefore, HbA1c has often been used as an outcome measure in intervention studies. Nevertheless, HbA1c shows wide inter-individual variations in correlation with glycaemia [182]; in addition it is a not a direct measure of how (immune) interventions affect β cell function, nor a perfect measure of risk of complications, and such markers are needed [39].
Autoantibodies to glutamic acid decarboxylase (anti-GAD) are another example of a useful biomarker [157]. Although anti-GAD antibodies are not pathogenic in T1D and although they are not 100% sensitive, they have been shown to be useful as a diagnostic marker of T1D, especially when the clinical presentation is not typical, as there is no gold standard definition to discern T1D from T2D [http://www.ispad.org/content/ispad-clinical-practice-consensus-guidelines-2009]. Furthermore, anti-GAD functions as a marker of risk of development of T1D in non-diabetic individuals, and as an aid in the selection of individuals for secondary prevention trials [9, 183-186].
Connecting (C-) peptide, a splice product of insulin synthesis, is a third example of a useful biomarker as it directly reflects endogenous insulin production. C-peptide is a direct biomarker for β cell function. Actually, it is a better marker for β cell function than insulin itself [187, 188], due to a longer half life, higher systemic levels and independence of exogenous insulin admission. Unfortunately, C-peptide is not an optimal biomarker from a methodological point of view. C-peptide assays have not been standardized and various measurement units are reported, thus caution is required when comparing C-peptide levels [183]. From a clinical perspective, C-peptide as biomarker of β cell function is of importance to guide patient classification in cases where distinction between T1D, T2D or monogenetic forms of diabetes is less clear [183, 189]. In addition, C-peptide has been used as a biomarker for graft function in islet transplantation [190, 191]. In research,
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preservation of C-peptide response has become the gold standard, as HbA1c as outcome measure of intervention trials appeared to be less suitable. For primary evaluation of β cell function, a non-fasting, random C-peptide can be measured [192]. More standardized assessment of β cell function, regarded as the gold standard, is measurement of stimulated C-peptide in a mixed meal tolerance or glucagon test [193]. Of renewed importance in the light of recent findings of preserved β cell mass in longstanding T1D patients without residual β cell function is the fact that C-peptide estimates β cell function but does not reflect β cell mass [39, 194].
5.4 Biomarker development: Inflammatory mediators
The three examples of established biomarkers discussed above demonstrate that biomarkers can be useful, even when they are not perfect. Inflammatory mediators can add to the established biomarkers in T1D and obesity when they represent, more reliably than is currently achievable, one of the following: 1) risk of development of diabetes; 2) risk of disease progression; 3) β cell function or, preferably, β cell mass; 4) chronic inflammation and risk of cardiovascular disease; and 5) effect of (immune) intervention trials. Especially for this group, biomarkers for therapeutic safety, immunological efficacy and therapeutic efficacy are much sought after [157, 173]. Next, inflammatory biomarkers might add to our knowledge of the interplay between local (tissue-specific, including AT) and systemic inflammation. In this regard, adipokines enter the stage as biomarkers for chronic inflammation. Adipokines are signalling molecules secreted by white AT. They function as circulating hormones communicating with other organs such as liver, brain, the immune system as well as AT itself. In addition, adipokines have the capability to modulate inflammation and insulin resistance. Most adipokines are pro-inflammatory; exceptions are the anti-inflammatory adipokines adiponectin, vaspin and omentin-1. An imbalance in adipokine levels leads to the development of a chronic inflammatory state and contributes to obesity-induced insulin resistance [195, 196]. Furthermore, dysregulation of adipokines has been implicated in the development of cardiovascular disease [197]. Thus, adipokines might well serve as biomarkers for the ongoing inflammation. Below, two potential adipokine biomarkers will be discussed in detail.
Chemerin
Chemerin is induced by inflammatory molecules such as IL-1β and is highly expressed in AT [198]. Chemerin, an agonist of the orphan G-protein coupled receptor chemokine-
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like receptor 1 (CMKLR1, ChemR23) expressed by cells of the innate immune system, is activated in coagulatory, fibrinolytic and inflammatory cascades. Once activated, chemerin induces recruitment of APCs. Furthermore, it has been implicated in adipocyte differentiation and lipolysis [199, 200]. Thus, chemerin links obesity and inflammation [201]. Chemerin levels have been associated with metabolic syndrome-related variables including BMI, fasting serum insulin, triglycerides and HDL cholesterol [202, 203]. However, the emerging picture is that of systemic chemerin as a marker of inflammation rather than obesity. Firstly, normal weight T2D patients showed higher chemerin levels than matched healthy controls [201]. In our study on inflammatory mediators in obesity, higher chemerin levels were associated with enhanced systemic inflammation in vitamin D deficiency rather than with mere obesity [107]. Secondly, insulin resistance and inflammation are BMI-independent predictors of elevated chemerin serum concentrations, while decreased chemerin serum concentrations significantly correlated with improved insulin sensitivity and reduced C-reactive protein levels independently of changes in BMI [204]. Thirdly, while chemerin levels correlated well with disease activity of RA, a negative correlation between chemerin levels and BMI was found in RA patients [205]. Thus, chemerin seems primarily a marker for inflammation. The association of chemerin with obesity, dyslipidaemia and insulin resistance may partly depend on its inflammatory role.
In view of the above, chemerin is an interesting candidate biomarker for chronic inflammation in T1D and obesity. In our study describing adipokine levels in T1D, we showed that chemerin levels were increased compared to healthy controls [68]. Thus far, there are no further publications on chemerin in T1D. Furthermore, data suggests that chemerin may serve as an independent marker in diagnosing chronic inflammatory conditions even before they become clinically symptomatic [203]. In RA, chemerin was shown to have potential as a biomarker for disease activity [205].
Unfortunately, the methodological considerations previously discussed also hold true for chemerin, as assays have not been unified. In addition, investigation into the predictive value of chemerin may be hampered by the fact some drugs (e.g. angiotensin-converting enzyme (ACE) inhibition, insulin sensitizers) have been reported to influence systemic levels [206-208].
Future research should address whether there is a relation between chemerin as a biomarker of chronic ongoing inflammation in T1D and development of diabetes-related complications. A second question is whether chemerin holds potential to identify T1D
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individuals developing a component of insulin resistance (double diabetes) next to insulin deficiency. Thirdly, the evolution of chemerin levels over time might be used in decision making regarding whether to start additional insulin-sensitizing drugs. Finally, chemerin could be studied as a biomarker to reflect the effect of targeting inflammation through blocking of inflammatory mediators such as IL-6 or IL-1β. Some of these questions may be best addressed in large cohort studies. For others, the elegance of using chemerin as a biomarker may lie in immediate evaluation of cause and effect in small studies.
Cathepsin S
Cathepsin is the second candidate biomarker. Kathépsein, meaning to digest or to boil down, was first identified in gastric juice during the 1920s. Cathepsins are lysosomal proteases; many different cathepsins have been identified. They are classified in families with the cysteine cathepsin family, including cathepsin S, being the largest and predominantly expressed in APCs [209, 210]. Cathepsin S is involved in MHC class II antigen presentation through moderating the invariant chain degradation; hereby antigen presentation to CD4+ T cells can be influenced [211]. Next to their role in antigen processing and presentation, cathepsins directly process proteins involved in extracellular matrix remodelling, atherosclerosis, obesity /adipogenesis and kidney disease among others [212]. Cathepsin S is produced by many cells, including adipocytes and smooth muscle cells.
In a T1D mouse model, deficiency of cathepsin S was protective against T1D development and led to a decreased incidence in diabetes [212]. Importantly, cathepsin S has with other family members been implicated in the loss of peri-islet basement membrane (BM) and subjacent interstitial matrix in the development of T1D in mice and human T1D. Sites of loss of peri-islet BM and subjacent interstitial matrix matched leukocyte infiltration into islets. Cathepsin S activity was co-localized with a macrophage subpopulation at sites of leukocyte penetration of peri-islet BM. Finally, subsiding inflammation was accompanied by reconstitution of peri-islet BM. These observations are of key importance, as they underscore the potential of cathepsin S as a biomarker reflecting the insulitic process, next to chronic inflammation. Furthermore, the observation that subsiding inflammation is accompanied by tissue recovery holds promise for therapeutic options involving maintaining or replacing β cells [213].
The role of cathepsin S in AT is mediated through extracellular matrix remodeling which leads to adipogenesis and/or adipose cell hypertrophy [214]. Through AT expansion triggering hypoxia the path leads to low-grade inflammation and insulin resistance.
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Indeed, cathepsin S activity appeared to be involved in the early dysregulation of glucose and insulin metabolism [214]. Our results, with higher cathepsin S levels both at onset of T1D and in longstanding disease, underline a role for cathepsin S as a marker of inflammation, although we cannot differentiate between association with insulitis or chronic low grade inflammation [68]. Next, we found higher cathepsin S levels to be related with vitamin D deficiency in obesity [107]. Interestingly, cathepsin S inhibitors have been implicated as immunomodulatory targets for autoimmune and inflammatory diseases as well cancer and obesity [210, 215].
Thus, cathepsin S may well hold potential as a biomarker for 1) insulitis and disease progression in T1D, 2) chronic inflammation and risk of cardiovascular disease and 3) responses to therapeutic interventions. A number of studies are ongoing in genetically susceptible individuals with antibodies being the best marker thus far to predict loss of β cell function and T1D development [125, 216, 217]. Do systemic cathepsin S levels somehow represent insulitis? Can cathepsin S levels be used for monitoring efficacy of primary prevention studies? These questions could be answered in focused cohort studies. Furthermore, after islet transplantation cathepsin S may function as a marker of recurrent insulitis. Next, cathepsin S as a biomarker for chronic inflammation could be studied comparing various populations, e.g. normal and overweight T1D patients; those with good versus poor metabolic control or those with and without diabetes-related complications. In obesity, the effect of interventions such as weight loss or initiation of insulin-sensitizing drugs in patients with impaired glucose tolerance on chronic inflammation could be assessed.
In conclusion, adipokines such as chemerin and cathepsin S have potential as biomarkers for chronic inflammation in T1D and obesity; in addition cathepsin S may well be a biomarker of insulitis in T1D.
Of note, pattern analysis of inflammatory mediators may help to explore inflammatory networks, and improve insight in pathophysiological processes. The fact that MIA technology allows for simultaneous measurement of multiple inflammatory mediators, among which chemerin and cathepsin S, adds to the explanatory potential of these mediators.
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6 Conclusion and further perspectives
Can inflammatory mediators provide useful insights in T1D and obesity? The examples provided in this thesis illustrate that indeed they can. At the same time, one single inflammatory mediator that will tell all clearly does not exist. Again, the examples provided in this thesis illustrate this point. Single inflammatory mediators were not sufficient to show who has vitamin D deficiency in obesity, nor who benefits from islet cell transplantation in T1D. Nevertheless, while one piece of a puzzle reveals little of the picture that will emerge, combination with additional pieces is likely to be much more informative. Therefore, the way forward may well be in combining inflammatory mediators, as used in this thesis. From a clinical research point of view, there is a great need for biomarkers to explore issues such as the role of adipose tissue in T1D and obesity, the concept of double diabetes, and intervention trials in T1D. In doing so, it is likely that a more refined picture of patient categories will emerge than currently exists, which in turn is a first step towards a more personalized approach towards care and cure of children with chronic diseases.
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Summary
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Summary
The incidence of type 1 diabetes (T1D) is increasing worldwide, while at the same time the age of presentation is decreasing. Thus, for families as a whole the burden of T1D is increasing as it has been estimated that globally 78,000 children develop T1D every year. Obesity also has a major impact on paediatric health and incidence is increasing as well, with a fair chance of persisting into adulthood with important long-term complications. T1D, caused by selective destruction of β cells by autoreactive T cells, is an inflammatory disorder as is obesity. This thesis addresses inflammatory features in both conditions (Chapter 1), with focus on inflammatory mediators and the role of adipose tissue (AT).
In Part II, Specific aspects of immune tolerance in T1D, immune regulation and loss of immune control in T1D are discussed. In addition, potential immune modulators to restore the immunological balance are studied. Chapter 2 discusses heat shock protein (HSP)60 as a modulator of the immune response with the capacity to mediate an anti-inflammatory response. HSP60 peptide p277, specifically, was shown to induce a tolerogenic response in adult patients with T1D. In this thesis, we investigated whether other HSP60 peptides can be identified with similar immunomodulatory potential. Recognition of pan HLA-DR-binding HSP60 epitopes by T cells in paediatric T1D patients and healthy controls was investigated. Firstly, HSP60 epitopes were found to induce low peptide-specific proliferative responses. Secondly, some intracellular peptide-specific cytokine production in T1D patients was detected. In addition to these primary research questions on overall recognition of pan HLA-DR binding peptides, it was found that peptide-specific immune responses could not predict clinical remission. In Chapter 3, autoreactive T cells, thought to be important in β cell destruction in T1D, are discussed. Intriguingly, these autoreactive T cells escape thymic deletion in T1D. In order to explore the role of HLA binding in thymic deletion, autoreactive CD8 T cells recognizing preproinsulin (PPI) peptides were studied with regard to HLA-binding affinity of these peptides. Strikingly, whereas T cells recognizing peptides with intermediate and high HLA-A2 binding affinity appeared to be deleted, T cells responsive to PPI peptides with low HLA-A2 binding affinity escape thymic selection. Recognition of the immunogenic potential of low affinity HLA-binding peptides may add to our understanding of loss of tolerance, an early phenomenon in disease pathogenesis.
In Part III, Adipose tissue inflammation and the role of adipokines in T1D and obesity, the role of AT in immune dysregulation is addressed. Both T1D and obesity can be viewed as pro-inflammatory conditions. AT has many functions besides fat storage. Apart from its
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role as endocrine mediator, AT has immunological potential. In this thesis, we investigated the role of adipokines, immune mediators secreted by AT, in T1D and obesity. Chapter 4 focuses on AT; in chronic, stable, well-regulated T1D, AT mass usually increases. Both hyperglycaemia and metabolic dysregulation seem involved in the increase in adiposity. To investigate how T1D and AT interact, circulating adipokines and the effect of diabetic plasma on in vitro cultured adipocytes were studied. Levels of various adipokines were increased in children with new-onset and longstanding T1D compared with healthy controls, while some adipokines further increased with longer duration of T1D. Specific plasma factors (but not glucose and free fatty acids) were shown to influence adipocyte differentiation as well as secretion of adipokines in vitro. In Chapter 5, the impact of vitamin D deficiency on inflammation and glucose homeostasis in obese children was investigated. Low grade inflammation, present in childhood obesity, drives insulin resistance. Vitamin D can act as an immune modulator, and thereby ameliorate glucose homeostasis. High levels of several inflammatory mediators were found in obese vitamin D deficient children, compared to their non-deficient counterparts. Together with increased inflammation, vitamin D deficient obese children exhibited decreased insulin sensitivity.
In Part IV, Inflammatory mediators in pancreatic ! cell transplantation in T1D, focus is on one possible immune intervention for T1D; islet transplantation. The potential of islet transplantation depends on graft survival. However, predictors for graft survival are vital. In Chapter 6 we investigated whether inflammatory mediators, collectively viewed as the serum secretome, can predict graft survival and guide patient selection. Inflammatory mediators pre- and one-year post-islet cell transplantation were studied. Graft function was assessed by glucose variability and insulin requirement. The serum secretome remained relatively unaffected by islet transplantation and immunosuppression, at least after one year. In addition, a set inflammatory markers was identified that may predict or associate with outcome of the islet cell transplantation.
These findings are discussed in a broader perspective in Chapter 7. Anti-inflammatory therapies, including vitamin D, treatment with HSP60 peptides and cytokine blockade, have the potential to positively affect β cell function while AT inflammation might further compromise β cell function. Next, the role of inflammatory mediators as biomarkers in paediatric T1D and obesity is discussed. Although, in our studies, no single inflammatory mediator was identified predicting outcome of various interventions in T1D, combining inflammatory mediators to evaluate interventions may hold promise, specifically in a personalised approach.
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Addendum
PART VI
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Chapter 1
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Samenvatting voor niet-ingewijden
A
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Addendum
A
Samenvatting voor niet-ingewijden
Type 1-diabetes (T1D) (suikerziekte) is een chronische ziekte die een grote belasting betekent voor zowel het kind met de aandoening als zijn of haar omgeving, waaronder het gezin maar bijvoorbeeld ook de school. Het aantal mensen, zowel kinderen als (jong)volwassenen, dat type 1 diabetes krijgt neemt al jarenlang toe. Bovendien presenteert de ziekte zich ook op steeds jongere leeftijd.
Er zijn verschillende vormen van diabetes waarvan type 1 en type 2 de meest bekende zijn. Bij T1D kan een specifiek onderdeel van de alvleesklier (de β-cellen in de eilandjes van Langerhans) niet meer voldoende insuline maken om de bloedsuikerspiegels bin-nen strakke grenzen te houden. Vervolgens ontstaan door te hoge bloedsuikerspiegels de klassieke symptomen van diabetes: veel plassen en veel drinken. In de cellen is er dan een tekort aan brandstof. Door het tekort aan insuline kan de energie in de vorm van suiker niet de cel in om als brandstof te dienen. Bij type 2-diabetes wordt nog wel insu-line gemaakt, maar het lichaam is niet meer goed gevoelig voor de insuline. Dit leidt ook tot te hoge bloedsuikerwaarden. Ook bij veel mensen met ernstig overgewicht, obesitas genoemd, kan een situatie ontstaan waarin de bloedsuikerregulatie verstoord raakt.
T1D is een voorbeeld van een auto-immuunziekte. Auto-immuunziekten zijn aandoe-ningen waarbij het lichaam ziek wordt doordat lichaamseigen cellen weefsel van het eigen lichaam kapot maken. Ander voorbeelden van auto-immuunziekten zijn reuma en schildklierziekten. Bij al deze ziekten is er sprake van een acuut ontstekingsproces, waarbij ook een meer chronisch ontstekingsproces kan ontstaan. Chronische ontsteking speelt een rol bij zowel T1D als overgewicht. Bij chronische ontsteking spelen verschil-lende typen signaalstoffen een rol: cytokines, belangrijk voor het contact tussen cellen; chemokines, stoffen die een rol spelen bij het aantrekken van cellen naar de plaats van de ontsteking; en adipokines, signaalstoffen gemaakt door vetweefsel (deel I).
In dit proefschrift worden verschillende aspecten van het (chronische) ontstekingspro-ces in T1D en bij overgewicht beschreven, met speciale aandacht voor verschillende signaalstoffen.
In deel II wordt een aantal factoren besproken die betrokken zijn bij het ontstaan van T1D. Het tekort aan insuline in T1D ontstaat doordat de β-cellen kapot worden gemaakt. De boosdoeners hierbij zijn speciale cellen van het afweermechanisme, waaronder de zogenaamde T-cellen. Deze afweercellen reageren op stukjes eiwit die van lichaams-vreemde maar ook van lichaamseigen weefsels afkomstig kunnen zijn. Er zijn heel veel
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verschillende T-cellen; een bepaalde T-cel komt pas in actie op een bepaalde plaats in het lichaam als hij wordt ingeschakeld door een eiwit dat goed genoeg past bij die bepaalde T-cel. Er zijn twee belangrijke groepen T-cellen, de zogenaamde T-helper cellen en de ‘killer’ T-cellen. Beide typen T-cellen spelen een grote rol in het afweermechanisme. Ze zorgen er aan de ene kant voor dat het lichaam invloeden van buitenaf reguleert. Ze helpen in het onderscheid tussen ‘goed en kwaad’; zo kunnen ze het afweersysteem helpen kalmeren en bijvoorbeeld een afstotingsreactie voorkomen (een mooi voorbeeld is zwangerschap). Aan de andere kant zijn de T-cellen betrokken bij het bestrijden van infecties en kunnen ze de afweer juist aanzwengelen. Soms raakt de balans tussen het afzwakken en aanzwengelen van een ontstekingsreactie verstoord en slaat de weegschaal door naar een overmaat aan ontsteking; dit is het geval bij auto-immuunziekten.
Een bepaald type eiwitten, de heat shock eiwitten (HSP’s), kan de functie van de T-cel beïnvloeden en helpen de T-cel te (her)programmeren tot een beschermende T-cel. Zulke eiwitten kunnen van grote waarde zijn voor behandeling van auto-immuunziekten. Inge-wikkeld hierbij is dat HSP voor dit herprogrammeren gebruik maken van een hulpstuk dat tussen mensen verschilt van bouw. Recent zijn stukjes HSP-eiwit gevonden die op de meeste van deze hulpstukken passen. Bij sommige ziektes zijn deze stukjes HSP-eiwit inderdaad in staat gebleken de functie van de T-cellen te beïnvloeden. In T1D vinden we bescheiden effecten van deze stukjes HSP-eiwit op de functie van de T-cellen (hoofdstuk 2). Daarmee lijkt er op basis van onze studie niet een grote rol weggelegd voor deze spe-ciale stukjes HSP-eiwit bij het herprogrammeren van T-cellen in T1D.
Bij gezonde personen wordt vroeg in het aanmaakproces van T-cellen bekeken welke T-cellen goed zijn voor het lichaam en welke T-cellen een risico zijn omdat ze misschien onbedoeld het lichaam kunnen ‘aanvallen’ en dus moeten worden opgeruimd. In de zwezerik, een klier in de hals, vindt de centrale selectie plaats tussen goede en gevaar-lijke T-cellen. In hoofdstuk 3 wordt onderzocht hoe het selectieproces in de zwezerik verloopt en in het bijzonder hoe het lichaam besluit welke ‘killer’ T-cellen wel en niet op te ruimen dan wel als nuttig te bestempelen. Waarom worden de T-cellen die zorgen voor het ontstaan van T1D niet uit voorzorg verwijderd? Dat blijft de kernvraag. Maar in ieder geval blijkt dat T-cellen ingeschakeld door minder goed passende vormen van een bepaald eiwit blijven bestaan, terwijl T-cellen die worden ingeschakeld door beter passende vormen van dit eiwit worden opgeruimd. Juist T-cellen die maar een matige eiwitovereenkomst hebben lijken dus te overleven en wellicht zijn die betrokken bij het ontstaan van T1D.
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In deel III wordt de rol van vetweefsel in T1D en overgewicht verder onderzocht. Bij het ontstaan van afwijkende bloedsuikerspiegels bij mensen met overgewicht speelt vetweef-sel een belangrijke rol. Vetweefsel, vooral het zogenaamde buikvet, heeft namelijk niet alleen een opslagfunctie, maar maakt zelf ook allerlei stoffen aan, waaronder stoffen met signaalfuncties. Deze door vetweefsel gemaakte signaalstoffen noemen we adipokines. Adipokines kunnen de insuline-aanmaak en de gevoeligheid van het lichaam voor insuline beïnvloeden (waardoor de insulineproductie toeneemt, zeker in het begin). Tegelijkertijd kunnen hoge insulinespiegels bijdragen aan overgewicht.
De interactie tussen T1D en vetweefsel is het onderwerp van hoofdstuk 4. We vonden hogere spiegels van een aantal adipokines in het bloed van kinderen met T1D vergeleken met gezonde vrijwilligers. In sommige gevallen waren de spiegels nóg hoger wanneer de diabetes al langer aanwezig was. Om uit te zoeken of er factoren in het bloed zijn bij T1D-patiënten die het vetweefsel beïnvloeden werd bloedplasma van kinderen met diabetes of van gezonde personen toegevoegd aan gekweekte vetcellen. Hierbij bleek er inderdaad een verschil te zijn met meer groei van de vetcellen en een hogere aanmaak van adipokines als aan de cellen bloedplasma van kinderen met T1D was toegevoegd. Dit effect bleek niet te worden veroorzaakt door alleen de bloedsuikerspiegel of het vetgehalte van het bloedplasma. Een wisselwerking tussen T1D en het vetweefsel lijkt er dus zeker te zijn, en het is aannemelijk dat dit de chronische ontsteking in T1D beïnvloedt.
Bij kinderen met overgewicht is er ook sprake van een sluimerend chronisch ontste-kingsproces. Dit ontstekingsproces heeft effect op de gevoeligheid van het lichaam voor insuline bij het regelen van de bloedsuikerhuishouding. In hoofdstuk 5 wordt gekeken naar het verband tussen vitamine D en chronische ontsteking bij kinderen met ernstig overgewicht. Vitamine D is niet alleen een belangrijk hormoon voor de aanmaak van sterke botten, vitamine D heeft ook een belangrijke rol in het immuunsysteem; het kan ontsteking helpen onderdrukken. Bij kinderen met overgewicht komt helaas juist vaak een tekort aan vitamine D voor. Ook in de studie beschreven in dit proefschrift, waarbij we kinderen met een normaal gewicht vergelijken met kinderen met ernstig overgewicht, vinden we bij meer dan 50% van de kinderen met ernstig overgewicht een te lage vitamine D-spiegel. Bij kinderen met de combinatie van ernstig overgewicht en een tekort aan vitamine D werden hogere spiegels van bepaalde adipokines gemeten. Ook lijken deze kinderen minder gevoelig voor insuline te zijn dan kinderen met overgewicht zonder deze combinatie. Chronische ontsteking, insulinegevoeligheid en vitamine D lijken op basis van deze bevindingen inderdaad met elkaar in verband te staan.
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Eén mogelijke behandeling van T1D is het vervangen van de kapotte alvleeskliercellen door transplantatie. Er zijn verschillende vormen van transplantatie, zoals transplantatie van de hele alvleesklier of alleen transplantatie van de eilandjes van Langerhans. Deze procedures worden bij mensen nu alleen uitgevoerd in onderzoeksverband. In deel IV wordt verder gekeken naar eilandjestransplantatie in T1D en in het bijzonder naar de rol van signaalstoffen daarbij en naar het verloop van deze signaalstoffen in de loop van de tijd. De transplantaties “slaan niet altijd aan“, om verschillende redenen. Eén van de manieren om het succes van een transplantatie te beoordelen is te kijken of de patiënt kan stoppen met het spuiten van insuline. In hoofdstuk 6 is onderzocht of bloedplasmaspiegels van de signaalstoffen kunnen helpen bij het selecteren van patiënten voor transplantatie. Patiënten werden verdeeld in twee groepen: patiënten mét en zónder een goede functie van het transplantaat in het eerste jaar na transplantatie, dat wil zeggen ten minste een periode waarin geen insuline meer hoefde te worden gebruikt. Opvallend was dat de spiegels van signaalstoffen in bloedplasma voor en één jaar na transplantatie per patiënt vaak vrij constant waren ondanks de transplantatie en afweeronderdrukkende medicijnen. Een aantal individuele signaalstoffen uit een panel van totaal 94 stoffen, net voor en één jaar na de transplantatie gemeten, verschilt tussen de patiënten met een goede en slechte uitkomst; hetzij vooraf, hetzij na één jaar. Mogelijk dat deze signaalstoffen kunnen helpen bij het verfijnen van de selectie van patiënten voor eilandjestransplantatie.
Chronische ontsteking speelt dus een rol in T1D en obesitas. Dit ontstekingsproces is mogelijk ook een nieuw aangrijpingspunt voor ontstekingsremmende behandeling. Hierbij kan worden gedacht aan behandeling met vitamine D, maar ook aan het gebruik maken van HSP-eiwitten of het blokkeren van signaalstoffen. Al deze behandelingen hebben als doel de insulineproductie zo goed mogelijk in stand te houden. In de hier beschreven onderzoeken is ook gekeken naar de rol van signaalstoffen. Kan één of kun-nen enkele signaalstoffen worden gebruikt om het effect van behandeling snel te meten of om te voorspellen wie ergens baat bij zal gaan hebben? Het is onwaarschijnlijk dat een dergelijke rol voor één individuele signaalstof is weggelegd. Maar het combineren van verschillende metingen kan misschien wel helpen in het per persoon opstellen van een plan voor behandeling bij kinderen met type 1-diabetes en (ernstig) overgewicht.
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About the author
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About the author
Annemarie Agatha Verrijn Stuart was born 20 February 1970 in Naarden, The Netherlands. She attended St. Vitus college in Bussum and graduated in 1988. Subsequently, at the University of Groningen, she started her study at the Faculty of Economics. After a master’s degree thesis on ‘reserves en voorzieningen in de jaarrekening van academische ziekenhuizen’, under supervision of Prof. D.W. Feenstra and Prof. G.J. van Helden she obtained her masters degree in Corporate Finance in March 1996. In addition, she started her medical study in 1991 at the same university and after internships in ‘Sophia Ziekenhuis’ and ‘Ziekenhuis de Weezenlanden’ in Zwolle, she obtained her medical degree in 1997 (cum laude).
In 1998 she worked as a medical officer in paediatrics in ‘Sophia Ziekenhuis’ in Zwolle (supervisor Dr W. Baerts) and VU University Medical Center Amsterdam (supervisor Prof. J.J. Roord). From January 1999 to July 2003 she was a resident in paediatrics in Wilhelmina’s Children’s Hospital / University Medical Center, Utrecht (WKZ/UMCU; under supervision of Prof. J.L.L. Kimpen). As part of this residency, she worked 1 year in the Catharina Hospital, Eindhoven (2000; supervisor Dr J.J. Waelkens) and 6 months in Tygerberg Hospital, Stellenbosch University, South Africa (2001; supervisor Prof. B. van der Merwe). After registration as a paediatrician in July 2003, she did a one year fellowship in Clinical Genetics (UMCU; supervisor Prof. D. Lindhout). From July 2004 she trained as a fellow in paediatric endocrinology (WKZ/UMCU; supervisor: Dr M. Jansen). Since January 2008 she has worked as a paediatric endocrinologist in the same hospital. In 2007, she started her PhD studies at the Department of Pediatric Translational Research & Center for Cellular and Molecular Intervention (CMCI; WKZ/UMCU) and the Department of Immunohematology and Blood Transfusion, Leiden University Medical Center (LUMC), under supervision of Prof. A.B.J. Prakken and Prof. B.O. Roep; results are presented in this thesis.
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