Progress in Neuro-Psychopharmacology & Biological Psychiatry 48 (2014) 20–25

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The association between galactosylation of immunoglobulin Gand body mass index

Matea Nikolac Perkovic a, Maja Pucic Bakovic b, Jasminka Kristic b, Mislav Novokmet b,Jennifer E. Huffman c, Veronique Vitart c, Caroline Hayward c, Igor Rudan d,James F. Wilson d, Harry Campbell d, Ozren Polasek e, Gordan Lauc b, Nela Pivac a,⁎a Division of Molecular Medicine, Rudjer Boskovic Institute, Zagreb, Croatiab Genos Ltd., Glycobiology Laboratory, Zagreb, Croatiac MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdomd Centre for Population Health Sciences, Medical School, University of Edinburgh, Edinburgh, United Kingdome Department of Public Health, School of Medicine, University of Split, Split, Croatia

Abbreviations: BMI, body mass index; G0n, neutral gneutral glycans with two terminal galactoses; GWAS, gHILIC, hydrophilic interaction liquid chromatography; IgGmatoid arthritis.⁎ Corresponding author. Tel.: +385 1 4571 207; fax: +

E-mail address: npivac@irb.hr (N. Pivac).

0278-5846/$ – see front matter © 2013 Elsevier Inc. All rihttp://dx.doi.org/10.1016/j.pnpbp.2013.08.014

a b s t r a c t

a r t i c l e i n f o

Article history:

Received 27 July 2013Received in revised form 28 August 2013Accepted 28 August 2013Available online 5 September 2013

Keywords:Body mass indexGalactosylationGlycomeImmunoglobulin G

Objective: Obesity is becoming a fast-growing health problem worldwide. Glycosylation of proteins and theirvariations significantly affect protein structure and function, thus altering numerous physiological and patho-physiological cellular processes. Since plasma glycans were significantly associated with body mass index(BMI) in both Croatian and Chinese populations, the study evaluated the association between immunoglobulinG (IgG) glycome, which is closer to biological function, and BMI.Method: The study included individuals from two Croatian Adriatic islands, Vis and Korčula, and individuals fromNorthern Scottish Orkney Islands. A hydrophilic interaction chromatography onWaters BEH Glycan chromatog-raphy columnwas used to analyze N-glycans attached to IgG in plasma samples from a total of 3515 individuals.Results: A small but significant positive correlation between BMI and the level of neutral glycans without galac-toses was detected. After taking into account the influence of age and gender, correlation coefficients indicated

that BMI was responsible for up to 2.0% of variation in the level of neutral glycans without galactoses. Further-more, after adjusting the effects of age and gender, the level of neutral glycans with two terminal galactoseswas negatively associated with BMI in analyzed sample groups, suggesting that BMI could be responsible forup to 3.2% of variation in this glycan feature.Conclusion: Our study is the first large-scale study to indicate the association of BMI and changes in IgGgalactosylation. The observed loss of galactosewhich is associatedwith increased BMImight be related to chronicinflammation that accompanies the development of obesity.

© 2013 Elsevier Inc. All rights reserved.

1. Introduction

Obesity is becoming a fast-growing health problem and a recognizedrisk factor for many different diseases, including cardiovascular disease,type 2 diabetes and chronic inflammation, but there are also indicationsof a positive association with psychiatric disorders such as anxiety,depression, substance abuse, and personality disorders (McElroy et al.,2004; Simon et al., 2006; Stunkard et al., 2003). Recently, chronicinflammation, that often accompanies obesity, has been proposed as apossible link between obesity and depression (Kim et al., 2007;

lycans without galactoses; G2n,enome wide association study;, immunoglobulin G; RA, rheu-

385 1 456 1010.

ghts reserved.

Shelton and Miller, 2010). Therefore, the studies investigating easyobtainable biomarkers of obesity are essential to offer new strategieswith an aim to prevent the development of obesity and consequentlyto reduce the risk of development of other related disorders.

Nearly all membrane and secreted proteins (Apweiler et al.,1999), as well as numerous cytoplasmic proteins (Hart et al., 2007)are glycosylated. Glycosylation is an intricate carefully regulatedprocess, which results in the creation of specific branched oligosac-charide chains (glycans) that significantly affect protein structureand function (Cummings, 2009). In addition, virtually all interac-tions that take place at the cell surface are modulated by glycans(Gornik et al., 2012) and cell surface glycans are the primary attach-ment site for themajority of microorganisms (Sharon and Lis, 1989).

The structure of each protein is determined by nucleotide sequencein the corresponding gene, but for glycoproteins there are several addi-tional layers of complexity between genes and final structures. Genesdetermine the structure of proteins that participate in the process of

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glycan synthesis, but the structure of each glycan is not encoded directlyin the genome. Instead, glycans are determined by complex dynamicinteractions between numerous genetic and environmental factors.

We performed thefirst population study of the total plasma glycome(Knezevic et al., 2009) and, more recently also thefirst population studyof the immunoglobulin G (IgG) glycome (Pucic et al., 2011). Very highvariability in the composition of the total plasma glycomewasobserved.Interestingly, when glycome composition of the isolated IgG wasanalyzed, the observed variability was even greater than in the totalplasma glycome (Pucic et al., 2011), indicating that averaging of glycansacross the proteome is actually decreasing the observable variability ofglycosylation.

Population studies enabled the first genome wide association study(GWAS) of both plasma (Huffman et al., 2011; Lauc et al., 2010) andIgG glycomes (Lauc et al., 2013). In comparable sizes of studied cohortsGWASof the IgG glycome revealedmanymore andmuch stronger asso-ciations with genetic loci than GWAS of the plasma glycome, indicatingthat the analysis of a single protein glycosylation is much closer tobiological function, than the analysis of the total plasma glycome.

Since obesity is a worldwide public health problem that increasesthe risk of somatic and psychosocial complications, our study aimedto determine to what extent the IgG N-glycome correlates with theoccurrence of excessive weight and obesity. Recently we reported asso-ciations between plasma glycans and body mass index (BMI) in bothCroatian (Knezevicet al., 2010) and Chinese populations (Lu et al.,2011). In this study we expanded this analysis to the IgG glycome,thus eliminating effects of varying concentrations of plasma proteinswhich might complicate the interpretation of the changes observed inthe total plasma glycome.

2. Materials and methods

2.1. Study populations

Blood samples were collected from individuals who were recruitedwithin a large genetic epidemiology program (Rudan et al., 1999,2006, 2009) on two Croatian Adriatic islands Vis and Korčula and theNorthern Scottish Orkney Islands (McQuillan et al., 2008). Samplingof the subjects from Croatia was based on the information from thevoting register of Croatia, and adult inhabitants, over 18 years of age,were included in the program. Volunteers from the Northern ScottishOrkney Islands were recruited within the Orkney Complex DiseaseStudy (ORCADES).

The sample from Vis Island consisted of 795 subjects (medianage 57, age range from 18 to 91 years of age, 41.3% of male subjects)and Korčula Island sample consisted of 834 subjects (median age 57,age range from 18 to 98 years of age, 35.4% of male subjects). Thethird group of subjects from Orkney Islands included 1886 individuals(median age 54, age range from 18 to 91 years of age, 40.0% of malesubjects).

All members of all three sample groups were interviewed by thetrained surveyors regarding their demographic characteristics.Body weight and height were measured during the physical exami-nation by trained nurses and physicians. Blood samples were collect-ed in test tubes containing EDTA anticoagulant following overnightfasting. The samples were processed immediately and blood plasmawas separated from whole blood to be stored at−70 °C until furtheranalysis.

Before starting the study, permissions were obtained fromthe local Ethics Committees in both Croatia and Scotland. Writteninformed consent was obtained from all participants. All studieshave been executed with the full cooperation of examinees,adequate understanding, and have therefore been performed inaccordance with the ethical standards laid down in the 1964Declaration of Helsinki.

2.2. IgG isolation

The IgG was isolated using protein G monolithic plates as describedpreviously (Pucic et al., 2011). Briefly, 50 μl of plasma was diluted 10×with PBS, applied to the protein G plate and instantly washed. IgGswere eluted with 1 ml of 0.1 M formic acid and neutralized with 1 Mammonium bicarbonate.

2.3. Glycan analysis

Glycan release and labeling of Vis and Korčula samples were per-formed as reported by Royle et al. (2008). Briefly, IgG was immobilizedin a block of sodium dodecyl sulfate-polyacrylamide gel in a 96-wellmicrotiter plate and N-glycans were released by digestion with recom-binant N-glycosidase F (ProZyme, San Leandro, CA, USA). Released gly-cans were labeled with 2-aminobenzamide and purified using solid-phase extraction (SPE) with Whatman 3MM chromatography paper.Glycan release and labeling of IgG isolated from Orkney samples weredone as described by Ruhaak et al. (2010b). In short, IgG was denaturedby incubation with 20 μl of 2% SDS at 60 °C for 10 min. Subsequently,10 μl 4% NP-40 and 1.25 mU of N-glycosidase F (ProZvme) in 10 μl 5×PBS were added to the samples and incubated overnight at 37 °C. Gly-cans were labeled with 2-aminobenzamide using 2-picoline-boraneas reductant. Labeled glycanswere purifiedwith hydrophilic interactionliquid chromatography (HILIC) solid phase extraction (SPE) usingmicrocrystalline cellulose (Ruhaak et al., 2010a).

IgG N-glycans were separated by HILIC on a Waters Acquity UPLCinstrument (Walters Corporation, Milford, MA, USA) into 24 chromato-graphic peaks and quantified as relative contributions of individualpeaks to the total IgG N-glycome (Pucic et al., 2011). From these 24directly measured traits an additional 54 derived traits describing thepercentage of galactosylation, sialylation, fucosylation, etc., were calcu-lated. For a list of major glycan structures present in each chromato-graphic peak and for a list of all derived traits see Supplementarycontent (Tables S1 and S2).

2.4. Statistical analysis

All glycan traits were adjusted for polygenic effects to control forfamilial relatedness using the polygenic() function of theGenABEL pack-age (Aulchenko et al., 2007) for R. This was done once for the rawglycan traits and once for the trait adjusted for age and sex since BMIand IgG N-glycan levels are often influenced by age and gender(Parekh et al., 1988; Pucic et al., 2011; Ruhaak et al., 2010c; Shikataet al., 1998). The resulting “pgresiduals” were used for correlationtests. Since most of the glycan traits deviated from the normal distribu-tion (tested with Kolmogorov–Smirnov test) we used Spearman's rankcorrelation for analyzing the association between glycan structures andBMI. All statistical analyseswere performed using SPSS version 13 (SPSSInc., Chicago, IL, USA). P-values ≤ 0.001 were regarded statisticallysignificant to account for multiple testing.

3. Results

Descriptive data for N-glycans attached to IgG, age and BMI in allthree populations are shown in Table 1 (for the rest of the descriptivedata see Supplementary content, Table S3).

When BMI was correlated with all themain features of IgG glycome,after adjusting the relatedness in the sample, an association betweenBMI and galactosylation of IgG glycans in all three sample groups wasdetected. The correlation between BMI and IgG glycome is shown indetails in the Supplementary content (Table S4), while Table 2 showsthe results for the main derived features of IgG glycome.

The calculated correlation coefficients showed a significant positivecorrelation between BMI and neutral glycans without galactoses (G0n)in Vis (r = 0.26; P = 2.81E−13), Korčula (r = 0.31; P = 7.29E−20)

Table 1Descriptive parameters of IgG glycans in investigated populations from islands of Vis, Korčula and Orkney.

Descriptive statistics Population

Vis Korčula Orkney

Median Percentiles Median Percentiles Median Percentiles

25th 75th 25th 75th 25th 75th

Age (years) 57 45 69 57 47 66 55 43 66BMI (kg/m2) 27.24 24.17 30.19 27.71 25.15 30.55 26.96 24.33 30.26

IgG glycan traitsTotal IgG glycans (neutral + charged)

SialylationFGS / (FG + FGS) 28.16 25.92 30.42 29.51 27.13 31.64 26.45 24.27 28.83FBGS / (FBG + FBGS) 43.12 38.57 47.47 43.01 38.84 46.65 36.32 31.64 40.52FGS / (F + FG + FGS) 19.71 17.12 23.09 20.84 18.07 23.89 19.87 17.27 22.99FBGS / (FB + FBG + FBGS) 29.95 26.20 34.19 29.37 25.72 33.37 26.05 22.31 29.96FG1S1 / (FG1 + FG1S1) 11.49 10.35 12.99 11.79 10.67 13.01 10.23 9.24 11.48FG2S1 / (FG2 + FG2S1 + FG2S2) 40.02 38.15 41.47 40.72 39.13 42.38 39.57 37.68 41.31FG2S2/(FG2 + FG2S1 + FG2S2) 8.99 7.55 10.81 10.05 8.52 11.91 6.68 5.37 8.15FBG2S1 / (FBG2 + FBG2S1 + FBG2S2) 37.19 34.78 39.48 36.26 33.95 38.61 36.33 33.86 39.30FBG2S2 / (FBG2 + FBG2S1 + FBG2S2) 41.10 38.00 43.89 40.70 37.90 43.64 35.83 31.98 39.20FtotalS1 / FtotalS2 3.04 2.70 3.53 2.95 2.63 3.32 4.18 3.55 5.00FS1 / FS2 6.15 5.21 7.16 5.49 4.80 6.32 7.79 6.53 9.56FBS1 / FBS2 0.90 0.82 1.00 0.89 0.80 0.98 1.01 0.90 1.17

Bisecting N-GlcNAcFBStotal / FStotal 0.38 0.31 0.45 0.36 0.30 0.42 0.26 0.21 0.32FBS1 / FS1 0.21 0.17 0.26 0.20 0.16 0.24 0.15 0.12 0.19FBS1 / (FS1 + FBS1) 0.17 0.15 0.20 0.17 0.14 0.19 0.13 0.11 0.16FBS2 / FS2 1.38 1.17 1.62 1.21 1.03 1.43 1.13 0.96 1.37FBS2 /( FS2 + FBS2) 0.58 0.54 0.62 0.55 0.51 0.59 0.53 0.49 0.58

Neutral IgG glycansGalactosylationG0n 37.04 30.90 43.49 37.18 31.36 43.06 30.81 24.97 37.01G1n 42.63 40.26 44.74 42.75 40.30 44.70 46.09 44.12 47.60G2n 19.18 15.40 24.17 18.97 15.38 23.65 21.88 17.71 27.30

Core fucosylation and bisecting GlcNAcFn total 95.76 94.42 96.67 95.74 94.36 96.71 97.24 96.43 97.82FG0n total / G0n 97.11 96.03 97.96 97.24 95.98 98.06 98.18 97.46 98.72FG1n total / G1n 97.78 96.78 98.43 97.75 96.83 98.40 98.70 98.21 99.05FG2n total / G2n 90.98 87.90 93.02 90.86 88.37 92.73 94.24 92.86 95.23Fn 78.28 76.08 80.64 77.94 75.59 80.30 81.78 79.51 83.83FG0n / G0n 77.02 74.39 79.52 76.15 73.21 79.14 79.35 76.74 81.77FG1n / G1n 79.48 77.20 81.74 79.66 77.23 81.68 82.33 80.02 84.38FG2n / G2n 80.38 77.18 82.99 79.47 76.45 82.06 84.66 82.74 86.50FBn 16.94 15.23 18.69 17.27 15.67 19.24 15.24 13.44 17.25FBG0n / G0n 19.86 17.61 22.17 20.66 18.45 23.12 18.64 16.56 20.86FBG1n / G1n 17.78 15.98 19.88 17.74 15.95 19.95 16.22 14.28 18.29FBG2n / G2n 10.28 9.20 11.60 11.10 9.63 12.70 9.22 8.19 10.49FBn / Fn 0.22 0.19 0.24 0.22 0.19 0.25 0.19 0.16 0.22FBn / Fn total 17.76 15.94 19.67 18.21 16.32 20.24 15.75 13.84 17.80

IgG glycan traits are not corrected for the effect of familial relatedness.For description of glycan features, see Supporting information, Table S2 and for an extended list of descriptive statistics for all glycan features, see Supporting information, Table S3.

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and Orkney (r = 0.25; P = 2.32E−27) population, indicating that BMIcould be responsible for up to 9.6% of variation observed in G0n. Afteradditional corrections for the effect of age and gender, a positive correla-tion between the level of G0n and BMI (Table 2) in Korčula Island sample(r = 0.14; P = 3.41E−5)was confirmed. This correlationwasmarginal,although not significant (due to correction for multiple testing), in thecase of the individuals from the Orkney Islands (r = 0.07; P = 0.003).In Vis Island sample, the significant correlation between G0n and BMIwas lost after correcting the effect of age and gender (r = 0.07; P =0.052). The correlation between the level of G0n and BMI indicatedthat BMI could be responsible for up to 2.0% of variation in this glycanfeature, which is not estimated by the other independent variables inthe equation.

Calculated correlation coefficients also showed a significant neg-ative correlation between BMI and the level of neutral glycans withtwo terminal galactoses (G2n), after adjusting just for familial related-ness, in Vis (r = −0.28; P = 2.56E−15), Korčula (r = −0.33; P =1.17E−22) and Orkney (r = −0.25; P = 1.12E−28) population,suggesting that the influence of BMI could be associated with up to

10.9% of variation observed in the level of G2n. The level of G2n

remained negatively associated with BMI (Table 2) after adjusting theeffects of age and gender in Korčula (r = −0.18; P = 2.15E−7) andOrkney (r = −0.09; P = 5.54E−5) samples, while this correlationwasmarginal (andnot significant due to correction formultiple testing)in individuals from Vis (r = −0.09; P = 0.010). Calculated correlationcoefficients suggest that up to 3.2% of variance in this glycan featurecould be explained by the influence of BMI, after the effects of age andgender have been removed.

To confirm our results, our subjects were grouped according to theirBMI into three categories: normal (18.5 ≤ BMI b 25.0), overweight(25.0 ≤ BMI b 30.0) and obese (BMI ≥ 30.0). The sample from VisIsland consisted of 29.1% of subjects with normal BMI, 44.3% were over-weight and 26.7% were obese individuals. The similar distributionof BMI categories was found in the Korčula Island sample (23.6% ofsubjects had normal BMI, 47.2% were overweight and 29.1% wereobese), and in the group from Orkney Islands (30.1% had normal BMI,42.8% were overweight and 27.0% were obese subjects). Again, thesimilar pattern of correlation between IgG galactosylation and BMI

Table 2Correlation between IgG N-glycan features and BMI in all three populations, after adjusting the effects of familial relatedness, age and gender.

IgG glycans Vis Korčula Orkney

r P r P r P

Total IgG glycans (neutral + charged)SialylationFGS / (FG + FGS) 0.02 0.601 −0.09 0.013 −0.03 0.136FBGS / (FBG + FBGS) −0.04 0.276 −0.02 0.523 −0.02 0.362FGS / (F + FG + FGS) −0.02 0.539 −0.12 0.001 −0.06 0.009FBGS / (FB + FBG + FBGS) −0.06 0.093 −0.05 0.118 −0.04 0.134FG1S1 / (FG1 + FG1S1) 0.06 0.104 −0.07 0.059 0.02 0.368FG2S1 / (FG2 + FG2S1 + FG2S2) 0.03 0.336 −0.06 0.095 −0.01 0.832FG2S2 / (FG2 + FG2S1 + FG2S2) 0.08 0.034 0.09 0.012 0.02 0.506FBG2S1 / (FBG2 + FBG2S1 + FBG2S2) −0.01 0.723 0.05 0.126 0.06 0.013FBG2S2 / (FBG2 + FBG2S1 + FBG2S2) 0.05 0.205 0.06 0.070 −0.02 0.506FtotalS1 / FtotalS2 −0.03 0.472 −0.11 0.002 0.00 0.875FS1 / FS2 −0.05 0.159 −0.10 0.006 0.01 0.841FBS1 / FBS2 −0.03 0.346 −0.02 0.538 0.04 0.054

Bisecting N-GlcNAcFBStotal / FStotal −0.04 0.220 0.08 0.016 0.02 0.307FBS1 / FS1 −0.04 0.292 0.08 0.027 0.03 0.192FBS1 / (FS1 + FBS1) −0.03 0.407 0.08 0.024 0.04 0.106FBS2 / FS2 −0.06 0.097 0.01 0.886 0.01 0.618FBS2 / (FS2 + FBS2) −0.05 0.148 0.01 0.752 0.03 0.273

Neutral IgG glycansGalactosylationG0n 0.07 0.052 0.14 3.41E-5 0.07 0.003G1n 0.03 0.476 −0.00 0.975 0.02 0.409G2n −0.09 0.010 −0.18 2.15E−7 −0.09 5.54E−5

Core fucosylation and bisecting GlcNAcFn total 0.04 0.314 0.06 0.091 0.06 0.015FG0n total / G0n 0.01 0.751 0.01 0.896 0.03 0.240FG1n total / G1n 0.04 0.320 0.03 0.342 0.08 0.001FG2n total / G2n 0.03 0.425 0.05 0.159 0.05 0.048Fn −0.02 0.528 0.03 0.462 0.02 0.475FG0n / G0n 0.01 0.740 0.04 0.287 0.05 0.035FG1n / G1n −0.02 0.630 0.01 0.881 0.01 0.718FG2n / G2n 0.01 0.684 0.04 0.220 0.02 0.492FBn 0.05 0.175 0.02 0.659 0.01 0.579FBG0n / G0n −0.01 0.724 −0.06 0.096 −0.05 0.020FBG1n / G1n 0.04 0.304 0.00 0.898 0.01 0.656FBG2n / G2n 0.02 0.539 0.00 0.945 0.05 0.043FBn / Fn 0.04 0.239 0.00 0.912 0.00 0.996FBn / Fn total 0.05 0.193 0.01 0.857 0.01 0.843

Significant correlations of IgG glycome features with BMI are highlighted in bold.For description of glycan features, see Supporting information, Table S2. Results of Spearman's rank correlation, analyzing association of all the other glycan featureswith BMI, are availableas Supporting information, Table S4.

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(Table S5) was detected. Calculated correlation coefficients showed asignificant positive correlation between BMI categories and G0n

in Korčula population (r = 0.14; P = 9.54E−5). A similar trend wasobserved in the sample from Orkney Islands (r = 0.07; P = 0.005)but not in the sample fromVis Island (r = 0.07; P = 0.064), suggestingthat BMI could be responsible for up to 2.0% of variation observed in G0n

after taking into account the effect of age, gender and relatedness(Table S5). After dividing the subjects into BMI categories and adjustingfor age, gender and relatedness, BMI was also responsible for up to 2.9%of variation in the level of G2n (Table S5).

4. Discussion

The prevalence of the increased BMI, overweight and obesity isrising worldwide, representing a major health hazard (Shelton andMiller, 2010). In US, according to the data from the National Healthand Nutrition Examination Survey (NHANES), 30–40% subjects wereoverweight and 35%were obese. Present study confirmed that, accordingto the BMI categories, 24–30% of included subjects had normal weight,while 43–47% were overweight and 27–28% were obese. When twoCroatian populations were collapsed, this prevalence was similar(χ2 = 3.607; df = 2; P = 0.165) to the reported BMI categories fromthe population representative sample of healthy control Croatian subjectsin our previous study (Kozaric-Kovacic et al., 2009), and was in line

(35.4% of subjects had normal weight, while 41.3% were overweightand 22.3% were obese) with the most recent BMI data for Croatia,according to World Health Organization (WHO) Global database onbody mass index (WHO, 2013). Our data revealed that distribution ofBMI categories in subjects from Orkney Islands was similar to the mostrecent data for United Kingdom (Great Britain and Northern Ireland),where 34% had normal weight while 38% were overweight and 23%were obese. Therefore, increased BMI continues to be a major healthproblemworldwide, and identification of biomarkers thatmight translatenew knowledge into prevention and treatment of obesity is needed.

We used three remote and isolated populations in our study todetermine a possible associationbetweenBMI and changes in IgG glyco-sylation. Isolated populations are often characterized by reduction indiversity and are often genetically different compared with each otherand compared to main population (Igl et al., 2010; Vitart et al., 2006),whichmakes them a useful tool for genetic studies of complex diseasesand traits (Kristiansson et al., 2008). In addition, isolated populationsare exposed to environmental factors which are critical risk factorsfor complex diseases (Kristiansson et al., 2008), such as obesity. Ourstudy is a first large-scale study that demonstrates a possible associationbetween BMI and changes in IgG galactosylation. The results from thepresent study suggest that the observed loss of galactose (G0n) inour samples is associated with increased BMI (and age), whilehigher galactosylation levels (G2n) were observed in patients with

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lower (i.e., “normal”) BMI. Our results showed a positive correlationbetween the level of G0n and BMI in all three populations and a neg-ative correlation between BMI and the level of G2n. The observedcorrelations remained significant or marginally significant for thesamples from Korčula and Orkney Islands even after correcting theeffects of age and gender, while these correlations were no longerstatistically significant for the Vis population due to correction formultiple testing (but a similar trend was found).

The results from our previous study (Knezevic et al., 2010) sug-gested that N-glycosylation of plasma proteins is age and sex specific,and that changes in the levels of glycan features can be associatedwith body fat parameters, as well as lipid status. These data suggestedthat BMI is associated with position of fucose, degree of branching,levels of sialylation of biantennary glycans and levels of galactosylation.However, after including all body fat parameters and age inmultivariateanalysis, BMI remained related only to sialylation of biantennary struc-tures (Knezevic et al., 2010). Lu et al. (2011) associated BMI with anincrease in triantennary plasma glycans and the decrease in corefucosylated glycans.

Our results could be associated with chronic inflammation as oneof the characteristics of metabolic disorders, like obesity and metabolicsyndrome (Hotamisligil, 2006). Inflammation is a process associatedwith different disorders such as cardiovascular disease (Wang andNakayama, 2010), rheumatoid arthritis (RA) (Dickens et al., 2002;Zautra et al., 2004), multiple sclerosis (Siegert and Abernethy, 2005)and, recently, neuropsychiatric diseases (Cavanagh and Mathias,2008; McElroy et al., 2004; Simon et al., 2006; Stunkard et al.,2003). Adipocytes behave as immune cells and all of thesedisorders are characterized by increased production of proinflammatorycytokines and adipokines. IgG molecules are also one of the key playersin inflammatory processes. Post-translational modifications of IgG mole-cules by glycosylation, defined by the attachment of oligosaccharides,are important for structural integrity of IgGmolecules and they play a cru-cial role in activation of complement (Malhotra et al., 1995), in interac-tions with Fc receptors (Adler et al., 1995) and in antibody-dependentcell mediated cytotoxicity (Shinkawa et al., 2003).

IgG galactosylation has been intensively investigated in connectionwith pathogenesis of RA. It has been reported that patients with RAhave a reduced degree of IgG oligosaccharide chains terminating ingalactose (Parekh et al., 1985). Hypogalactosylation was found toreverse during pregnancy in patients with RA (Alavi et al., 2000; vandeGeijn et al., 2009), probably due to changes in hormonal and cytokinelevels (van de Geijn et al., 2009). There have also been indications thatsuggest an association of IgG galactosylation and sialylation (van deGeijn et al., 2009), which is important for the function of IgG as pro-inflammatory or anti-inflammatory agent (Kaneko et al., 2006). Incom-plete galactosylation exposes terminal N-acetylglucosamine (GlcNAc)residues of IgG glycoforms to the environment. This can trigger an inap-propriate pro-inflammatory response, probably through IgG binding tomannose-binding lectin which subsequently activates the complementsystem (Malhotra et al., 1995). Our results, showing the loss of galactoseassociated with increased BMI, might be interpreted to be in line withthe proposed strong interplay between obesity, inactivity, and inflam-mation, which could contribute to depression in some people (Sheltonand Miller, 2010).

5. Conclusion

The results of this study suggest a possible association between BMIand the observed loss of galactose which might be related to chronicinflammation that accompanies the development of obesity. Theobserved increased levels of galactosylation in patients with lower(i.e., “normal”) BMI could, therefore, indicate possible anti-inflammatoryproperties of IgG galactosylation. Future studies should focus on mecha-nisms behind the changes in IgG galactosylation and their exact roleand function in increasedBMI andobesity, in order tofindnovel strategies

aimed to prevent the development of obesity and to reduce the risk ofdevelopment of various somatic and psychiatric disorders associatedwith obesity.

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.pnpbp.2013.08.014.

Acknowledgments

The CROATIA-Vis and CROATIA-Korcula studies in the Croatianislands of Vis and Korcula were supported by grants from the MedicalResearch Council (UK); the Ministry of Science, Education, and Sportof the Republic of Croatia (grant number 108-1080315-0302); and theEuropean Union Framework Program 6 European Special PopulationsResearch Network Project (contract LSHG-CT-2006-018947). ORCADESwas supported by the Chief Scientist Office of the Scottish Government,the Royal Society, and the European Union Framework Programme 6EUROSPAN Project (contract LSHG-CT-2006-018947). Glycome analysiswas supported by the Croatian Ministry of Science, Education, andSport (grant numbers 309-0061194-2023 and 216-1080315-0302);the Croatian Science Foundation (grant number 04-47); the EuropeanCommission EuroGlycoArrays (contract #215536), GlycoBioM (contract#259869), and HighGlycan (contract #278535) grants.

The authors of the CROATIA-Vis andCROATIA-Korcula studieswouldlike to acknowledge the invaluable contributions of the recruitmentteam (including those from the Institute of Anthropological Researchin Zagreb) in Vis and Korcula, the administrative teams in Croatia andEdinburgh, and the people of Vis and Korcula. ORCADES would liketo acknowledge the invaluable contributions of Lorraine Anderson, theresearch nurses in Orkney, and the administrative team in Edinburgh.

We thank Carolien Koeleman for expert technical assistance, andDr. Eoin Cosgrave and Dr. Jonathan Bones for their critical reading ofthe manuscript.

References

Adler Y, Lamour A, Jamin C, Menez JF, Le Corre R, Shoenfeld Y, et al. Impaired bindingcapacity of asialyl and agalactosyl IgG to Fc gamma receptors. Clin Exp Rheumatol1995;13:315–9.

Alavi A, Arden N, Spector TD, Axford JS. Immunoglobulin G glycosylation and clinicaloutcome in rheumatoid arthritis during pregnancy. J Rheumatol 2000;27:1379–85.

Apweiler R, Hermjakob H, Sharon N. On the frequency of protein glycosylation, as deducedfrom analysis of the SWISS-PROT database. Biochim Biophys Acta 1999;1473:4–8.

Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: an R library for genome-wideassociation analysis. Bioinformatics 2007;23:1294–6.

Cavanagh J, Mathias C. Inflammation and its relevance to psychiatry. Adv Psychiatr Treat2008;14:248–55.

Cummings RD. The repertoire of glycan determinants in the human glycome. Mol Biosyst2009;5:1087–104.

Dickens C, McGowan L, Clark-Carter D, Creed F. Depression in rheumatoid arthritis: asystematic review of the literature with meta-analysis. Psychosom Med 2002;64:52–60.

Gornik O, Pavic T, Lauc G. Alternative glycosylation modulates function of IgG and otherproteins — implications on evolution and disease. Biochim Biophys Acta 2012;1820:1318–26.

Hart GW, Housley MP, Slawson C. Cycling of O-linked beta-N-acetylglucosamine onnucleocytoplasmic proteins. Nature 2007;446:1017–22.

Hotamisligil GS. Inflammation and metabolic disorders. Nature 2006;444:860–7.Huffman JE, Knezevic A, Vitart V, Kattla J, Adamczyk B, NovokmetM, et al. Polymorphisms

in B3GAT1, SLC9A9 and MGAT5 are associated with variation within the humanplasma N-glycome of 3533 European adults. Hum Mol Genet 2011;20:5000–11.

Igl W, Johansson A, Gyllensten U. The Northern Swedish Population Healthy Study(NSPHS) — a paradigmatic study in a rural population combining community healthand basic research. Rural Remote Health 2010;10:1363.

Kaneko Y, Nimmerjahn F, Ravetch JV. Anti-inflammatory activity of immunoglobulin Gresulting from Fc sialylation. Science 2006;313:670–3.

Kim Y-K, Na K-S, Shin K-H, Jung H-Y, Choi S-H, Kim J-B. Cytokine imbalance in thepathophysiology of major depressive disorder. Prog Neuropsychopharmacol BiolPsychiatry 2007;31:1044–53.

Knezevic A, Polasek O, Gornik O, Rudan I, Campbell H, Hayward C, et al. Variability,heritability and environmental determinants of human plasmaN-glycome. J ProteomeRes 2009;8:694–701.

Knezevic A, Gornik O, Polasek O, Pucic M, Redzic I, Novokmet M, et al. Effects of aging,body mass index, plasma lipid profiles, and smoking on human plasma N-glycans.Glycobiology 2010;20:959–69.

25M. Nikolac Perkovic et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 48 (2014) 20–25

Kozaric-Kovacic D, Ilic MG, Romic Z, Vidovic A, Jendricko T, Pivac N. Body mass indexin male Caucasian veterans with or without posttraumatic stress disorder. ProgNeuropsychopharmacol Biol Psychiatry 2009;13:1447–50.

Kristiansson K, Naukkarinen J, Peltonen L. Isolated populations and complex disease geneidentification. Genome Biol 2008;9:109.

Lauc G, Essafi A, Huffman JE, Hayward C, Knezevic A, Kattla JJ, et al. Genomics meetsglycomics — the first GWAS study of human N-Glycome identifies HNF1alpha as amaster regulator of plasma protein fucosylation. PLoS Genet 2010;6:e1001256.

Lauc G, Huffman JE, Pucic M, Zgaga L, Adamczyk B, Muzinic A, et al. Loci associated withN-glycosylation of human immunoglobulin g show pleiotropy with autoimmunediseases and haematological cancers. PLoS Genet 2013;9:e1003225.

Lu JP, Knezevic A, Wang YX, Rudan I, Campbell H, Zou ZK, et al. Screening novelbiomarkers for metabolic syndrome by profiling human plasma N-glycans in ChineseHan and Croatian populations. J Proteome Res 2011;10:4959–69.

Malhotra R, Wormald MR, Rudd PM, Fischer PB, Dwek RA, Sim RB. Glycosylation changesof IgG associated with rheumatoid arthritis can activate complement via themannose-binding protein. Nat Med 1995;1:237–43.

McElroy SL, Kotwal R, Malhotra S, Nelson EB, Keck PE, Nemeroff CB. Are mood disordersand obesity related? A review for the mental health professional. J Clin Psychiatry2004;65:634–51.

McQuillan R, Leutenegger AL, Abdel-Rahman R, Franklin CS, Pericic M, Barac-Lauc L, et al.Runs of homozygosity in European populations. Am J Hum Genet 2008;83:359–72.

Parekh RB, Dwek RA, Sutton BJ, Fernandes DL, Leung A, Stanworth D, et al. Association ofrheumatoid arthritis and primary osteoarthritis with changes in the glycosylationpattern of total serum IgG. Nature 1985;316:452–7.

Parekh R, Roitt I, Isenberg D, Dwek R, Rademacher T. Age-related galactosylation of theN-linked oligosaccharides of human serum IgG. J Exp Med 1988;167:1731–6.

PucicM, Knezevic A, Vidic J, AdamczykB, NovokmetM, PolasekO, et al. High throughput iso-lation and glycosylation analysis of IgG-variability and heritability of the IgG glycome inthree isolated human populations. Mol Cell Proteomics 2011;10(M111):010090.

Royle L, Campbell MP, Radcliffe CM, White DM, Harvey DJ, Abrahams JL, et al. HPLC-basedanalysis of serum N-glycans on a 96-well plate platform with dedicated databasesoftware. Anal Biochem 2008;376:1–12.

Rudan I, Campbell H, Rudan P. Genetic epidemiological studies of eastern Adriatic Islandisolates, Croatia: objective and strategies. Coll Antropol 1999;23:531–46.

Rudan I, Biloglav Z, Vorko-Jovic A, Kujundzic-Tiljak M, Stevanovic R, Ropac D, et al. Effectsof inbreeding, endogamy, genetic admixture, and outbreeding on human health: a(1001 Dalmatians) study. Croat Med J 2006;47:601–10.

Rudan I, Marusic A, Jankovic S, Rotim K, Boban M, Lauc G, et al. “10001 Dalmatians:”Croatia launches its national biobank. Croat Med J 2009;50:4–6.

Ruhaak LR, Hennig R, Huhn C, Borowiak M, Dolhain RJ, Deelder AM, et al. Optimizedworkflow for preparation of APTS-labeled N-glycans allowing high-throughputanalysis of human plasma glycomes using 48-channelmultiplexed CGE-LIF. J ProteomeRes 2010a;9:6655–64.

Ruhaak LR, Steenvoorden E, Koeleman CA, Deelder AM, Wuhrer M. 2-Picoline-borane:a non-toxic reducing agent for oligosaccharide labeling by reductive amination.Proteomics 2010b;10:2330–6.

Ruhaak LR, Uh HW, Beekman M, Koeleman CA, Hokke CH, Westendorp RG, et al.Decreased levels of bisecting GlcNAc glycoforms of IgG are associated with humanlongevity. PLoS One 2010c;5:e12566.

Sharon N, Lis H. Lectins as cell recognition molecules. Science 1989;246:227–34.Shelton RC, Miller AH. Eating ourselves to death (and despair): the contribution of

adiposity and inflammation to depression. Prog Neurobiol 2010;91:275–99.Shikata K, Yasuda T, Takeuchi F, Konishi T, Nakata M, Mizuochi T. Structural changes

in the oligosaccharide moiety of human IgG with aging. Glycoconj J 1998;15:683–9.

Shinkawa T, Nakamura K, Yamane N, Shoji-Hosaka E, Kanda Y, Sakurada M, et al. Theabsence of fucose but not the presence of galactose or bisecting N-acetylglucosamineof human IgG1 complex-type oligosaccharides shows the critical role of enhancingantibody-dependent cellular cytotoxicity. J Biol Chem 2003;278:3466–73.

Siegert RJ, Abernethy DA. Depression in multiple sclerosis: a review. J Neurol NeurosurgPsychiatry 2005;76:469–75.

SimonGE, Von Korff M, Saunders K,Miglioretti DL, Crane PK, van Belle G, et al. Associationbetween obesity and psychiatric disorders in the US adult population. Arch GenPsychiatry 2006;63:824–30.

Stunkard AJ, Faith MS, Allison KC. Depression and obesity. Biol Psychiatry 2003;54:330–7.

van de Geijn FE, Wuhrer M, Selman MH, Willemsen SP, de Man YA, Deelder AM, et al.Immunoglobulin G galactosylation and sialylation are associated with pregnancy-induced improvement of rheumatoid arthritis and the postpartum flare: resultsfrom a large prospective cohort study. Arthritis Res Ther 2009;11:R193.

Vitart V, Biloglav Z, Hayward C, Janicijevic B, Smolej-Narancic N, Barac L, et al. 3000 years ofsolitude: extreme differentiation in the island isolates of Dalmatia, Croatia. Eur J HumGenet 2006;14:478–87.

Wang Z, Nakayama T. Inflammation, a link between obesity and cardiovascular disease.Mediators Inflamm 2010;2010:1–17.

World Health Organization (WHO). Global database on body mass index. Available at:http://apps.who.int/bmi/index.jsp, 2013. [Accessed July 26, 2013].

Zautra AJ, Yocum DC, Villanueva I, Smith B, Davis MC, Attrep J, et al. Immune activationand depression in women with rheumatoid arthritis. J Rheumatol 2004;31:457–63.