METABOLIC PHENOTYPES OF DIABETIC KIDNEY DISEASE

Ville-Petteri Mäkinen University of California, Los Angeles, USA

Imperial College, London, UK South Australian Health and Medical Research Institute, Adelaide, AU

Figuring out biology from complex data:

NATIONAL HEALTH AND NUTRITION EXAMINATION SURVEY 2005-2010, AGE ≥ 20

United States Renal Data System 2012 Annual Data Report

MORTALITY, AGING AND KIDNEY DISEASE

Age

Moody WE, Edwards NC, Chue CD et al. Heart 2013:365–372Foley RN et al. Am J Kidney Dis 1998:S112–19

TYPE 1 DIABETESAutoimmune disease

No insulin secretion

High blood glucose

Complications

BLOOD GLUCOSE IN T1D

Meals, physical activity, sleep patterns etc. make it difficult to inject optimal amounts of insulin during the day.

• Insulin resistance (life style) ⇒ increased insulin demand

• Insufficient insulin response (genetics)

• Vicious cycle of glucose and lipid toxicity

• Chronic inflammation and end-organ damage

TYPE 1 DIABETESAutoimmune disease

No insulin

High blood glucose

Complications

TYPE 2 DIABETES*

*Any diabetes where an exact cause cannot be identified.

Thorn et al. (2005) Diabetes Care 28:2019–2024

TYPE 1 DIABETESAutoimmune disease

No insulin

High blood glucose

Life style and genetics

Insulin deficiency

Diabetes (type 2)

Complications

Vascular diseases

SYSTEMIC METABOLIC DISEASE

Obesity

Metabolic stress

COMPLEX DISEASE CHALLENGE• Combination of environment and genes

- network of inter-dependent causes. • Chronic diseases, gradual development

- ambiguous lines between health and disease. • Quantitative biology, qualitative end-points

- diagnostic definitions used as risk factors.

COMPLEX DISEASE CHALLENGE• Noisy diagnoses and stochastic end-points

- poor predictive performance. • Datasets and experiments are unique

- poor performance outside original study. • Individual baseline state is unique

- single time point is of limited value.

OBJECTIVES• Understand the statistical patterns within a

cohort of patients with type 1 diabetes. • Typical lipoprotein characteristics associated

with complications and mortality. • Conceptually simple framework that can

handle large number of variables, discrete and continuous traits, and missing data.

• Four traits: - hairiness - head size - eye size - mood.

Basic statistics - Easy interpretation - Pre-defined groups - Impractical for complex data

Full data - Easy interpretation - High resolution - Impractical for large datasets

Regression and classification - Interpreted through parameters - High descriptive power - Danger of over-fitting

ORGANIZE SAMPLES IN 2D BASED ON SIMILARITY

SUMMARIZE LOCAL SAMPLES

N samples

K model phenotypes, K ≪ N

SUMMARY OF PHENOTYPIC DIVERSITY

Bar height indicates statistically normalized deviation

VISUALIZATION OF TRAIT VALUES

Eye size Hairiness Mood

TRAIT ASSOCIATIONS

Eye size

Hai

rine

ss

Tukiainen et al. Biochem Biophys Res Commun, 375:356-361, 2008Cognitive decline and Alzheimer's

Kumpula et al. J Lipid Res 51:431-439, 2010Lipoprotein structure and composition

Intima media thicknessWurtz et al 2010

Mäkinen et al. J Proteome Res 11:1782-1790, 2012Kidney disease progression

Diabetic complicationsMäkinen et al. Mol Syst Biol 4:167, 2008

Lipoprotein subclasses in type 1 diabetesMäkinen et al. J Intern Med 273:383–395, 2013

STUDY DESIGN

The Finnish Diabetic Nephropathy Study Folkhälsan Research Center, Helsinki, Finland

Healthy C-peptide > 0.2 nmol/L

DIABETES TYPE

Target HbA1c < 7%

DIABETES MANAGEMENT

Healthy AER < 30 mg/24h

AER = albumin excretion rate

KIDNEY DISEASE

Glycated Hb (%)

Log

AER

RAW DATA POINTSr = 0.25 −log P > 17

AER

(m

g/24

h)

HbA1c (%)

300

30

10

8.0 9.0

AER = albumin excretion rate

AER

(m

g/24

h)

HbA1c (%)

300

30

10

8.0 9.0

AER = albumin excretion rate

AER

(m

g/24

h)

HbA1c (%)

300

30

10

8.0 9.0

AER = albumin excretion rate

MODEL PHENOTYPES

AER

(m

g/24

h)

HbA1c (%)

300

30

10

8.0 9.0

AER = albumin excretion rate

Biomarkers used in diagnoses were included in training set.

Sex differences were adjusted before analysis.

P = 0.66 −log P > 13 −log P > 20

−log P > 15 −log P > 12 −log P > 8

Height (cm)

Trig

lyce

ride

s (m

mol

/L)

RAW DATA POINTS

r = −0.10 −log P > 8

MODEL PHENOTYPES

Height (cm)

Trig

lyce

ride

s (m

mol

/L)

Wadén et al. (2005) Diabetes 28:2019–2024

Laser-treated for eye disease n = 1181 Large vessel diseases

n = 343n = 268

Laser-treated for eye disease n = 1181 Large vessel diseases

n = 343n = 268

−log P > 30 −log P > 22

Prevalence of retinopathy

Prevalence of vascular disease

MODEL PHENOTYPES

FULL STUDY DESIGN

The Finnish Diabetic Nephropathy Study Folkhälsan Research Center, Helsinki, Finland

−log P > 30 −log P > 22

−log P > 6 −log P > 17 −log P > 28

Urinary AER (mg/24h)

All-

caus

e m

orta

lity

MODEL PHENOTYPES

STANDARDIZED MORTALITY RATE

AER < 30 30 < AER < 300 AER > 300 End-stage renal disease

0.8

2.7

9.2

18.3

Groop et al. (2009) Diabetes 58:1651-1658

−log P > 6 −log P > 17 −log P > 28

P = 0.086 P = 0.036 P < 0.001

TAKE-HOME MESSAGE?• Carefully investigate observed lipoprotein

characteristics. • Determine overall measures that are

- applicable to all lipoprotein subclasses - easy to relate to existing medical literature.

• Test if these phenotypes are related to clinical data.

LIPOPROTEIN LIPIDSTriglycerides

Cholesterol

SIMPLIFIED LIPOPROTEIN LIPIDS

Phenotype I Lowest cholesterol

Phenotype II Lowest TG:C ratio

Phenotype III Highest TG:C ratio

Normal AER at baseline 93% 77% 8%Microalbuminuria at baseline 5% 12% 7%Macroalbuminuria at baseline 2% 5% 57%End-stage renal disease at baseline <1% 4% 27%

Cholesterol (mmol/L) 3.9 5.0 5.3Triglycerides (mmol/L) 0.76 0.81 2.11HDL cholesterol (mmol/L) 1.22 1.82 1.06

Recommended cholesterol < 5.0 mmol/L

Recommended triglycerides < 1.7 mmol/L

Recommended HDL cholesterol > 1.1 mmol/L (men) Recommended HDL cholesterol > 1.3 mmol/L (women)

Phenotype I Lowest cholesterol

Phenotype II Lowest TG:C ratio

Phenotype III Highest TG:C ratio

Type 1 diabetes duration (years) 12 (11 - 13) 19 (21 - 23) 30 (28 - 31)

Body-mass index (kg/m2) 23.4 (23.1 - 23.8) 23.8 (23.6 - 24.2) 25.6 (25.0 - 26.4)Insulin dose (IU/kg) 0.67 (0.63 - 0.71) 0.66 (0.64 - 0.68) 0.69 (0.66 - 0.72)

Systolic blood pressure (mmHg) 124 (122 - 126) 131 (129 - 133) 145 (141 - 149)Diastolic blood pressure (mmHg) 76 (75 - 77) 80 (79 - 81) 84 (82 - 85)

Hemoglobin A1c (%) 7.5 8.1 9.1

Urinary albumin excretion (mg/24h) 8.6 13 596Estimated GFR (mL/min per 1.73m2) 105 94 48

C-reactive protein (mg/L) 1.15 1.44 3.86

Serum adiponectin (mg/L) 9.3 16 18

Phenotype I Lowest cholesterol

Phenotype II Lowest TG:C ratio

Phenotype III Highest TG:C ratio

Progression from normal AER 4% (2% - 6%) 2% (1% - 4%) 10% (4% - 20%)

Progression from microalbuminuria <1% 15% (6% - 29%) 38% (18% - 57%)

Progression from macroalbuminuria 1% (0 - 4%) 13% (3% - 26%) 42% (31% - 51%)

Deceased at follow-up <3% 6% (4% - 9%) 40% (32% - 47%)

Excess enrichment of triglycerides across every lipoprotein subclass is a part of a metabolic phenotype with high vascular risk in type 1 diabetes

Life style and genetics

Insulin deficiency

Diabetes Vascular diseases

SYSTEMIC METABOLIC DISEASE

Obesity

Metabolic stress

SCORING SYSTEM FOR METABOLIC SYNDROME

• Obesity • High blood glucose

(or diabetes) • Abnormal blood lipids

(TG and HDLC) • High blood pressure

Unhealthy life

Systemic metabolic stress

Insulin resistance

Type 2 diabetes

Heart attack Stroke

Obesity

Unhealthy life

Systemic metabolic stress

Insulin resistance

Kidney disease

Heart attack Stroke

Obesity

Type 1 diabetes

Aging

Basic statistics - Easy interpretation - Pre-defined groups - Impractical for complex data

Full data - Easy interpretation - High resolution - Impractical for large datasets

Regression and classification - Interpreted through parameters - High descriptive power - Danger for over-fitting

ACKNOWLEDGMENTSFolkhälsan Research Center The FinnDiane Group Prof Per-Henrik Groop Carol Forsblom Markku Lehto Lena M Thorn Valma Harjutsalo University of Oulu &

University of Eastern Finland Prof Mika Ala-Korpela Pasi Soininen Tuulia Tynkkynen Antti Kangas

Aalto University School of Science and Tech. Prof Kimmo Kaski Tomi Peltola