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Journal of Diabetes 6 (2014) 514–518

O R I G I N A L A RT I C L E

Genetic variants determining body fat distribution and sex hormone-binding globulin among Chinese female young adults Juan SHI,1* Lijuan LI,1* Jie HONG,1 Lu QI,2 Bin CUI,3 Weiqiong GU,1 Yifei ZHANG,1 Lin MIAO,1 Rui WANG,1 Weiqing WANG1 and Guang NING1,3 1

Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrinology and Metabolism, Endocrine and Metabolic E-Institutes of Shanghai Universities (EISU) and Key Laboratory for Endocrinology and Metabolism of Chinese Health Ministry, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, 3Laboratory of Endocrinology and Metabolism, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences/Shanghai Jiao-Tong University School of Medicine, Shanghai, China, and 2The Department of Nutrition, Harvard School of Public Health, and Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA

Correspondence Jie Hong, Department of Endocrinology and Metabolism, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai 200025, China. Tel: +86 21 6437 0045, extn 665340 Fax: +86 21 6437 3514 Email: [email protected] *These author contributed equally to this work. Received 15 September 2013; revised 1 March 2014; accepted 3 March 2014. doi: 10.1111/1753-0407.12146

Abstract Background: Measures of body fat distribution (i.e. waist : hip ratio [WHR]) are major risk factors for diabetes, independent of overall adiposity. The genetic variants related to body fat distribution show sexual dimorphism and particularly affect females. Substantial literature supports a role for sex hormone-binding globulin (SHBG) in the maintenance of glucose homeostasis. The aim of the present study was to examine the association of the genetic risk score of body fat distribution with SHBG levels and insulin resistance in young (14–30 years) Chinese females. Methods: In all, 675 young Chinese females were evaluated in the present study. A genetic risk score (GRS) was calculated on the basis of 12 established variants associated with body fat distribution. The main outcome variable was serum SHBG levels and homeostasis model assessment of insulin resistance (HOMA-IR). Results: The GRS of body fat distribution was significantly associated with decreasing serum SHBG levels (P = 0.018), independent of body mass index and WHR. In addition, the GRS and SHBG showed additive effects on HOMA-IR (P = 0.004). Conclusions: The GRS of body fat distribution reflects serum SHBG levels, and the GRS and SHBG jointly influence the risk of insulin resistance.

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Keywords: body fat distribution, genetic risk score (GRS), genetic susceptibility, insulin resistance.

Significant findings of the study: The genetic risk score of body fat distribution is significantly associated with decreasing serum sex hormone-binding globulin (SHBG) levels. The genetic factors and SHBG showed additive effects on the risk of insulin resistance. What this study adds: The genetic variants of body fat distribution may affect SHBG levels through pathways other than the waist : hip ratio. The present study also provides insights into the joint effect of body fat distribution and SHBG on insulin resistance.

Introduction Body fat distribution, measured by waist : hip ratio (WHR), has been shown to be an independent predictor 514

of diabetes.1,2 Measures of body fat distribution are highly heritable3 and independent of overall adiposity measured by body mass index (BMI).4 There is substantial gender-specific difference in fat distribution, and

© 2014 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd

J. SHI et al.

such disparity is likely to be determined by genetic factors.5 A recent genome-wide association study (GWAS) meta-analysis among 200 000 individuals of European descent by the Genetic Investigation of Anthropometric Trials (GIANT) consortium identified 14 loci associated with body fat distribution.6 Interestingly, it was noted that nearly half the identified loci showed significant sexual dimorphism in their associations, particularly affecting women. However, little is known the potential mechanism underlying these observations.6 Body fat distribution patterns have been closely related to sex hormones in previous studies;7 however, the causality remains unclear. It was recently found that low circulating levels of sex hormone-binding globulin (SHBG) predicted type 2 diabetes (T2D) in women and men.8 Moreover, clinical studies have associated low circulating levels of SHBG with impaired glucose control,9–12 implicating the globulin in the maintenance of glucose homeostasis. However, studies exploring the joint effect of genetic variants determining body fat distribution and serum SHBG levels on insulin resistance are scarce. In the present study, we examined the association of body fat distribution-related genetic variants with serum SHBG levels and insulin resistance in young Chinese females.

Methods Study population

Assessment of anthropometrics and biochemical markers All participants were subjected to a 75-g oral glucose tolerance test (OGTT). On the day of the OGTT, height and weight (light clothes and without shoes), waist (midway between the lateral lower ribs and the iliac crests) and hip (the widest part over the greater trochanters) circumference, and seated blood pressure were determined by the same experienced physician. The average of

two waist and hip circumference measurements was used for analysis. The mean value of three blood pressure measurements at 5-min intervals using a standard brachial cuff technique was reported. Blood was collected after a 12-h fast for biochemical analysis. Glucose, serum insulin, total cholesterol (TC), triglycerides (TG), high-density lipoprotein–cholesterol (HDL-C) and low-density lipoprotein-cholesterol (LDL-C) were measured as described previously.14 Serum SHBG concentrations were determined using an immunoradiometric assay kit (DSL-7400 ACTIVE; Diagnostic Systems Laboratories, Webster, TX, USA), with an interassay coefficient of variation of 11.5%. Homeostasis model assessment of insulin resistance (HOMA-IR) was used to assess insulin resistance as fasting insulin (μIU/ mL) × fasting glucose (mmol/L)/22.5. Genotyping and computation of genetic risk score Fourteen single-nucleotide polymorphisms (SNPs) at 14 independent loci that tagged genome-wide significant SNPs identified through recent meta-analysis of GWAS in Europeans6 were selected. Genomic DNA was extracted from peripheral blood leukocytes using an QIAmp blood kit (Qiagen, Chatsworth, CA, USA). The 14 SNPs were genotyped by using the LightCycler480 Taqman PCR system (Roche Diagnostics, Rotkreuz, Switzerland).13 The call rates ranged from 92% (rs9491696) to 98% (rs6861681). All SNPs were in Hardy–Weinberg equilibrium. Two methods were used to create a genetic risk score (GRS): (i) a simple count method (count GRS); and (ii) a weighted method (weighted GRS). Given the known sexual dimorphism of body fat distribution and the evidence from the recent GIANT study that 12 of the 14 SNPs reached genome-wide significance in women,6 we summed the number of risk alleles of the 12 WHRassociated SNPs for each individual to produce the GRS.15 The count method assumes that each SNP in the panel contributes equally to the risk of obesity. For the weighted GRS, each SNP was weighted by β-coefficients obtained from the initial meta-analysis by Heid et al.6 as follows: 0.050 (RSPO3, rs9491696), 0.052 (VEGFA, rs6905288), 0.034 (TBX15-WARS2, rs984222), 0.044 (NFE2L3, rs1055144), 0.054 (GRB14, rs10195252), 0.059 (LYPLAL1, rs4846567), 0.029 (DNM3-PIGC, rs1011731), 0.042 (ITPR2-SSPN, rs718314), 0.031 (LY86, rs1294421), 0.040 (HOXC13, rs1443512), 0.038 (ADAMTS9, rs6795735), and 0.030 (ZNRF3KREMEN1, rs4823006). We assigned missing genotypes the average genotype at that locus. We observed similar results for both analyses, therefore we only report the results for the count GRS.

© 2014 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd

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This cross-sectional study consisted of 675 young Chinese females (aged 14–30 years) living in an eastern area of China. All 675 female subjects were from our previous study,13 and details of the study sample have been described elsewhere.13 This study was approved by the Institutional Review Board of the Ruijin Hospital, Shanghai JiaoTong University School of Medicine and was performed in accordance with the principle of the Helsinki Declaration II. Written informed consent was obtained from each participant, the next of kin, carers or guardians on the behalf of minors involved in the study.

Body fat distribution genetics

Body fat distribution genetics

J. SHI et al.

Statistical analyses

Results

All statistical analyses were performed using SPSS 13.0 (SPSS Inc., Chicago, IL, USA) and PLINK version 1.07 (http://pngu.mgh.harvard.edu/purcell/plink/). Data were tested for normal distribution and logarithmically transformed for statistical analysis when required. Deviation from Hardy–Weinberg equilibrium for genotypes at individual loci was assessed using the Chisquared test. An anova was used to compare age, BMI, WHR, HOMA-IR and the GRS across tertiles of serum SHBG levels. Correlations were calculated by Spearman correlation coefficients. Logarithmically transformed SHBG was regressed on the GRS (tertiles) with adjustment for covariates (three models). The joint effect of the GRS (tertiles) and SHBG (tertiles) on HOMA-IR was assessed by introduction of a new variable that was recoded to 1–9 according to the distribution of the tertiles of the GRS and SHBG in the linear regression. Mean (± SE) values of HOMA-IR in each group were derived by a general linear model, adjusting for age, systolic blood pressure (SBP), diastolic blood pressure (DBP), TC, TG, HDL-C, and LDL-C.

The characteristics of the 675 study subject are given in Table 1. Three of the 14 SNPs showed statistical association with WHR in 675 females after adjusting for age and BMI (VEGFA rs6905288, β = 0.007566, P = 0.040; LYPLAL1 rs4846567, β = 0.006889, P = 0.025; and HOXC13 rs1443512, β = 0.009508, P = 0.023; see Table S1 available as Supplementary Material to this paper). We calculated a simple count GRS on the basis of 12 WHR-associated SNPs, following a previously reported method.15 The association between GRS and WHR was not significant after adjusting for age and BMI (P = 0.191). We further examined the associations among the GRS, SHBG, and HOMA-IR. Serum SHBG levels were significantly correlated with HOMA-IR (r = –0.697, P < 0.001). Increasing tertiles of the GRS were significantly associated with decreased serum SHBG levels after adjustment for age, SBP, DBP, lipid profiles, and BMI (P = 0.016), and the significance remained after further controlling for WHR (P = 0.018; Table 2). However, the GRS

Table 1

General demographic and laboratory characteristics of the 675 study participants SHBG (nmol/L)

No. participants SHBG (nmol/L) Age (years) BMI (kg/m2) WHR HOMA-IR (μIU·mol/L2) GRS

Total

Tertile 1 (≤21.2 nmol/L)

Tertile 2 (21.3–58.8 nmol/L)

Tertile 3 (>58.8 nmol/L)

Unadjusted Ptrend

675 35.6 (17.5–68.0) 19.4 ± 4.6 27.7 ± 8.6 0.85 ± 0.09 2.62 (1.36–5.18) 11.0 (10.0–13.0)

225 14.3 (11.1–17.7) 20.5 ± 4.5 35.4 ± 5.5 0.92 ± 0.07 5.35 (3.76–7.66) 12.0 (10.0–13.0)

225 36.3 (27.2–47.4) 19.4 ± 4.9 25.8 ± 7.4 0.84 ± 0.08 2.03 (1.34–4.20) 11.5 (10.0–13.0)

225 81.6 (68.6–97.9) 17.9 ± 4.0 20.7 ± 4.8 0.78 ± 0.07 1.26 (0.89–1.66) 11.0 (10.0–13.0)

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Genetic variants determining body fat distribution and sex hormone-binding globulin among Chinese female young adults.

Measures of body fat distribution (i.e. waist : hip ratio [WHR]) are major risk factors for diabetes, independent of overall adiposity. The genetic va...
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