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Diabetes 56:3095-3100, 2007
DOI: 10.2337/db06-1647
© 2007 by the American Diabetes Association
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Brief Report |
Effects of Genetic Variation in the Human Retinol Binding Protein-4 Gene (RBP4) on Insulin Resistance and Fat Depot–Specific mRNA Expression
1 Interdisciplinary Centre for Clinical Research, University of Leipzig, Leipzig, Germany
2 Department of Medicine, University of Leipzig, Leipzig, Germany
3 Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, Massachusetts
4 Coordination Centre for Clinical Trials, University of Leipzig, Leipzig, Germany
5 Department of Surgery, University of Leipzig, Leipzig, Germany
Address correspondence and reprint requests to Michael Stumvoll, MD, Medical Department III, University of Leipzig, Philipp-Rosenthal-Str. 27, D-04103 Leipzig, Germany. E-mail: michael.stumvoll{at}medizin.uni-leipzig.de
Abbreviations:
FFA, free fatty acid; LD, linkage disequilibrium; RBP4, serum retinol binding protein 4; SNP, single nucleotide polymorphism; WHR, waist-to-hip ratio
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ABSTRACT |
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 TOP ABSTRACT RESEARCH DESIGN AND METHODSREFERENCES |
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OBJECTIVE— Serum retinol binding protein 4 (RBP4) is a new liver- and adipocyte-derived signal that may contribute to insulin resistance. Therefore, the RBP4 gene represents a plausible candidate gene involved in susceptibility to type 2 diabetes.
RESEARCH DESIGN AND METHODS— In this study, the RBP4 gene was sequenced in DNA samples from 48 nonrelated Caucasian subjects. Five novel and three known single nucleotide polymorphisms (SNPs) were identified. Furthermore, five recently reported SNPs were genotyped in 90 subjects. Six SNPs, representative of their linkage disequilibrium groups, were then genotyped in 934 diabetic and 716 nondiabetic subjects.
RESULTS— A haplotype of six common SNPs (A-G-G-T-G-C) was significantly increased in 934 case subjects with type 2 diabetes compared with 537 healthy control subjects with normal glucose tolerance (P = 0.02; odds ratio 1.37 [95% CI 1.05–1.79]). Furthermore, in the cohort of 716 nondiabetic Caucasian subjects, carriers of the A-G-G-T-G-C haplotype had significantly higher mean fasting plasma insulin and 2-h plasma glucose than subjects without the haplotype. Two single SNPs (rs10882283 and rs10882273) were also associated with BMI, waist-to-hip ratio, and fasting plasma insulin, and several SNPs were associated with circulating free fatty acids (all adjusted P < 0.05). In addition, subjects carrying a previously reported diabetes-associated haplotype had significantly higher mRNA levels in visceral adipose tissue (adjusted P < 0.05) in a subgroup of nondiabetic subjects (n = 170) with measurements of RBP4 mRNA expression in visceral and subcutaneous fat depots.
CONCLUSIONS— Our data indicate a role of RBP4 genetic variation in susceptibility to type 2 diabetes and insulin resistance, possibly through an effect on RBP4 expression.
Serum retinol binding protein 4 (RBP4) is a new adipocyte-derived signal linking adipose tissue dysfunction to systemic insulin resistance and thereby likely contributing to the pathogenesis of type 2 diabetes. Serum RBP4 is elevated in insulin-resistant mice, as well as in humans, with obesity and type 2 diabetes and can be normalized by insulin-sensitizing drugs (1). Moreover, RBP4 serum levels highly correlate with the degree of insulin resistance in subjects with obesity, impaired glucose tolerance, or type 2 diabetes, as well as in nonobese subjects with family history of type 2 diabetes (2). Recently, we found increased RBP4 mRNA expression in visceral compared with subcutaneous adipose tissue and serum RBP4 concentrations correlated with RBP4 mRNA expression, intra-abdominal fat mass, total body fat mass, A1C, and insulin resistance (3).
RBP4 is encoded by the RBP4 gene, which maps to chromosome 10q23-q24, a region that has been linked to increased risk for type 2 diabetes in different populations (4,5). Despite known physiology as well as chromosomal location, to date, very few studies on the effects of genetic variation in the RBP4 gene on increased metabolic risk in humans have been reported (6,7). We therefore investigated whether genetic variants within the RBP4 gene might be responsible for observed changes in RBP4 mRNA expression and whether it may affect obesity/type 2 diabetes and pathophysiologically relevant traits in humans. We screened the gene for prevalent and functionally relevant variants and genotyped six informative single nucleotide polymorphisms (SNPs) in 934 patients with type 2 diabetes and 716 healthy German Caucasian subjects. To identify genetic variants, all six exons (National Center for Biotechnology Information reference NM_006744), including intron/exon splicing sites, the 5' region (
1,500 bp upstream of the first translation initiation site, which also included the 5' untranslated region [exon 1 and the part of exon 2] and intron 1), and the 3' untranslated region, were sequenced in DNA samples from 48 nonrelated Caucasian subjects (24 nondiabetic with normal glucose tolerance and 24 with type 2 diabetes). Eight SNPs (five novel and three known database SNPs) were identified (Fig. 1). Three SNPs were in the 5' region, and five SNPs were in introns. Based on a recent publication (7), we selected five additional SNPs (rs36014035, rs12265684, rs34571439, rs12766992, and rs10882273) with minor allele frequency >5% in Caucasian samples (7), which we genotyped in 90 subjects, including all 48 sequenced DNA samples.
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We estimated linkage disequilibrium (LD) among the variants (EMLD software; available from https://epi.mdanderson.org/
qhuang/software/pub.htm) (8) (supplemental Fig. 1 [available in an online appendix at http://dx.doi.org/10.2337/db07-1647]). Among these variants, c.248+13C>T, c.248+28C>G, c.248+44T>C, and c.355+41G>C were in complete LD; rs3758539 was in complete LD with c.111+25G>A; rs36014035 was in LD with rs10882273; and rs12265684, rs34571439, and rs12766992 were in LD among each other (Fig. 1). For association studies, only c.248+44T>C, rs3758539, rs12265684, and rs10882273 were selected as representative variants for all four LD groups and genotyped in all subjects for association analyses. In addition, rs3758538 and rs10882283, which were unique among SNPs, were also selected for further association analyses. The genotype distributions for all SNPs were consistent with Hardy-Weinberg equilibrium.
Using the PHASE v.2.1 software (9,10), we identified five common haplotypes among the six different SNPs genotyped in all subjects (Table 1). These five haplotypes, A-G-T-T-G-T, A-G-G-T-G-C, A-A-G-T-C-C, C-G-T-T-G-T, and C-G-T-C-G-C accounted for 90% of the all observed haplotypes, where haplotypes are defined by the composition of alleles at each SNP in following order: rs3758538, rs3758539, rs10882283, c.248+44T>C, rs12265684, rs10882273. No single variant was associated with type 2 diabetes in 934 case subjects and 537 healthy control subjects with normal glucose tolerance (supplemental Table 1 of the online appendix), but the A-G-G-T-G-C haplotype frequency was significantly increased in type 2 diabetic compared with control subjects (P = 0.02; odds ratio [OR] 1.37 [95% CI 1.05–1.79]) (Table 1). Furthermore, in the cohort of 716 nondiabetic Caucasian subjects, the rs10882283 G-allele was associated with higher BMI and waist-to-hip ratio (WHR) (adjusted P < 0.05 in a recessive mode of inheritance), and the rs10882273 C-allele was significantly associated with increased BMI, plasma insulin, and circulating free fatty acid (FFA) concentrations (adjusted P < 0.05) (Table 2). Because these two alleles are part of the diabetes risk haplotype (A-G-G-T-G-C), it is not surprising that subjects carrying the A-G-G-T-G-C haplotype had significantly higher mean fasting plasma insulin and 2-h plasma glucose levels than subjects without the haplotype (Table 3), which further supports the observed association with type 2 diabetes. We are aware that we have not corrected our statistical analyses for the number of comparisons made (given the number of tested traits and six SNPs); the results therefore must be interpreted with caution. However, our data strongly support the findings reported by Craig et al. (7), who investigated the RBP4 gene and its role in type 2 diabetes in two Caucasian cohorts (Utah and Arkansas) and in individuals of African-American ancestry. Similarly to our present study, they showed significant effects of RBP4 genetic variation on insulin resistance in Caucasians. In addition, one haplotype was significantly increased in subjects with type 2 diabetes, and the authors therefore suggest that noncoding variants may increase diabetes susceptibility and may contribute to insulin resistance (7). Although the haplotype composition in our present study was extended by two additional variants (rs3758538 and rs10882283), the A-G-G-T-G-C haplotype corresponds to the diabetes risk haplotype described by Craig et al. (7). Thus, our findings provide further independent evidence for the involvement of RBP4 gene variants in susceptibility to type 2 diabetes.
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Interestingly, circulating serum FFA concentrations were significantly associated with three SNPs (Table 2). These effects were reflected in the haplotype analysis where the A-A-G-T-C-C haplotype was associated with elevated FFA levels (Table 3). Our findings are consistent with recently reported data from RBP4 knockout mice (1). It has been shown that genetic deletion of RBP4 in Rbp4–/– knockout mice results in lower levels of serum FFAs, which was suggested to be linked to their improved insulin sensitivity. However, FFAs were not changed in insulin-resistant transgenic mice with overexpressed RBP4, in RBP4-injected mice, or in adipose-Glut4–/– mice, suggesting that regulation of circulating FFA levels does not seem to be the principal mechanism by which RBP4 regulates insulin sensitivity (1). Nevertheless, we believe that the association of FFAs with RBP4 SNPs, together with the data from RBP4–/– knockout mice manifesting lower levels of serum FFAs provokes further studies aimed to pinpoint the mechanisms by which RBP4 might alter circulating levels of FFA.
Increased RBP4 gene expression in visceral adipose tissue is a likely source for elevated RBP4 serum concentrations in patients with increased visceral fat mass and type 2 diabetes and could therefore contribute to mechanisms linking visceral fat accumulation to the development of insulin resistance (3). Therefore, we examined whether genetic variants could affect RBP4 mRNA expression in visceral and subcutaneous fat as well as serum RBP4 concentrations. Although we found no significant impact of the single variants on either visceral and subcutaneous mRNA expression or on serum RBP4 (online appendix supplemental Table 2), the A-G-G-T-G-C diabetes risk haplotype carriers had a higher mean visceral and subcutaneous expression as well as serum RBP4 concentrations compared with noncarriers. Most likely due to the small sample size, this did not reach statistical significance (adjusted P > 0.05; online appendix supplemental Table 3). However, when we restricted the analysis to the haplotypes comprising only the variants covering the RBP4 haplotypes previously reported by Craig et al. (7) (rs3758539, c.248+44T>C, rs12265684, and rs10882273), the type 2 diabetes–associated haplotype from the Utah study was significantly associated with increased RBP4 mRNA expression in visceral adipose tissue (geometric mean 3,458 AU [95% CI 1,907–6,273] vs. 1,566 [953–2,574]; P < 0.05 after adjusting for age, sex, BMI, and percentage body fat). Although Craig et al. reported haplotypes of eight common SNPs, these eight SNPs fall into four LD groups in our study and could therefore be presented by four common SNPs (rs3758539, c.248+44T>C, rs12265684, and rs10882273). Considering the association of this haplotype with type 2 diabetes and related traits, it is noteworthy that visceral RBP4 mRNA level was the strongest factor significantly affecting glucose infusion rate in a multivariate generalized linear model analysis also including age, sex, BMI, WHR, and percentage body fat (data not shown). This suggests a role of RBP4 genetic variation in susceptibility to insulin resistance, possibly through an effect on RBP4 expression. Regarding the lack of statistically significant genetic association with serum RBP4 concentrations, we need to point out that this subgroup of subjects had a high mean BMI (30.0 ± 6.9 kg/m2), which may have masked the effect of genetic variants on serum RBP4 levels.
Two identified SNPs (rs3758538 and rs3758539) are located 5' upstream of the translation start site in a putative promoter region. We therefore used the Transcription Element Search System (TESS; available from http://www.cbil.upenn.edu/tess) to examine transcriptional regulatory sequences surrounding these genetic variants, which might modify RBP4 expression. The highly conserved region surrounding rs3758539 matches human transcriptional binding sites for MAZ (11) and R1/R2/Sp1 for the major allele G (12,13) and c-Ets-2 for the minor allele A (14). Furthermore, this SNP seems to influence the transcription efficiency in a hepatocarcinoma cell line as well as the binding efficiency of hepatocyte nuclear factor 1
to the motif (6). Regarding the association of the RBP4 haplotype with mRNA levels, this also indicates a potential functional relevance of the noncoding RBP4 variants in the putative promoter region. However, only additional functional experiments on these SNPs might assign causality for the associated phenotypes. Alternatively, the RBP4 haplotypes might harbor a variant, which controls RBP4 mRNA levels but was not tested in our present study because only exons and potentially regulatory regions were sequenced.
In conclusion, consistent with previously reported findings in mice and in human studies (1,6,7), several RBP4 SNPs and their haplotypes are likely to affect measures of insulin resistance (fasting plasma insulin and 2-h plasma glucose) and related traits (BMI, WHR, and circulating FFAs), as well as RBP4 mRNA levels in visceral adipose tissue in humans. These effects may ultimately result in type 2 diabetes, which is in line with the observed association of the A-G-G-T-G-C haplotype with increased risk of type 2 diabetes in the present study. Thus, our data indicate a role of RBP4 genetic variation in susceptibility to type 2 diabetes and insulin resistance, possibly through an effect on RBP4 expression.
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RESEARCH DESIGN AND METHODS |
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TOPABSTRACTRESEARCH DESIGN AND METHODSREFERENCES |
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A total of 934 patients with type 2 diabetes and 716 healthy subjects were recruited at the University Hospital in Leipzig, Germany. The healthy subjects included 269 men and 447 women (mean age ± SD 47.2 ± 14.6 years, mean BMI 27.4 ± 5.3 kg/m2, mean WHR 0.96 ± 0.19), and patients with type 2 diabetes included 477 men and 457 women (mean age 64.5 ± 10.8 years, mean BMI 29.6 ± 5.0 kg/m2, mean WHR 1.13 ± 0.13). In addition, oral glucose tolerance test and fasting plasma insulin measurements were performed in all nondiabetic subjects as described elsewhere (15). Of 716 subjects, 179 had impaired glucose tolerance. Because impaired glucose tolerance is a type 2 diabetes predicting factor, only the remaining 537 subjects with normal glucose tolerance were included as healthy control subjects in the type 2 diabetes case-control study.
In a subgroup of 403 nondiabetic subjects, body fat content was measured by dual-energy X-ray absorptiometry. Insulin sensitivity was assessed with the euglycemic-hyperinsulinemic clamp method, as previously described (16,17).
In addition, paired samples of visceral and subcutaneous adipose tissue were obtained from a subgroup of 218 Caucasian men (n = 108) and women (n = 110) who underwent open abdominal surgery for gastric banding, cholecystectomy, weight reduction surgery, abdominal injuries, or explorative laparotomy (described in detail elsewhere) (18). The age ranged from 23 to 99 years and BMI from 20.8 to 54.1 kg/m2. Serum RBP4 concentrations were also measured in these subjects. Only nondiabetic subjects (n = 170) were included in association analyses.
All studies were approved by the ethics committee of the University of Leipzig, and all subjects gave written informed consent before taking part in the study.
Measurement of serum RBP4.
RBP4 was measured in serum by enzyme-linked immunosorbent assay (ALPCO) or by quantitative Western blotting with purified human RBP4 standards, as described in detail elsewhere (2).
Analysis of human RBP4 expression.
Human RBP4 mRNA expression was measured by quantitative real-time PCR in a fluorescent temperature cycler using the TaqMan assay, and fluorescence was detected on an ABI Prism 7000 sequence detector (Applied Biosystems, Darmstadt, Germany), as described in detail elsewhere (3,19).
Sequencing of RBP4.
Sequencing of the RBP4 gene was performed using the Big Dye Terminator (Applied Biosystems) on an automated DNA capillary sequencer (ABI Prism 3100 Avant; Applied Biosystems). Sequence information for all oligonucleotide primers used for variant screening is available upon request.
Genotyping of RBP4 SNPs.
Genotyping of selected SNPs in all study subjects was done using the TaqMan assay (Applied Biosystems) for the variants rs3758538, rs3758539, c.248+44T>C, rs12265684, and rs10882273 and by restriction fragment–length polymorphism technique for the SNP rs10882283. Oligonucleotide sequences are available upon request. The TaqMan genotyping reaction was performed according to the manufacturer's protocol on an ABI Prism 7000 or ABI Prism 7700 sequence detector (Applied Biosystems). The restriction fragment–length polymorphism genotypes were determined by PCR amplification of the respective fragments from exon 1 of the RBP4 gene on the GeneAmp PCR system 9700 (95°C for 5 min, 95°C for 30 s, 60°C for 1 min, and 72°C for 30 s for 35 cycles and 72°C for 10 min), subsequent digestion with the SchI (Fermentas Life Sciences) restriction enzyme, and size fractionation and visualization by electrophoresis. To assess genotyping reproducibility, a random 10% selection of the sample was regenotyped in all four SNPs; all genotypes matched initial designated genotypes.
Statistical analyses.
Before statistical analysis, non-normally distributed parameters were logarithmically transformed to approximate a normal distribution. Differences in genotype frequencies between the diabetic and healthy control subjects were compared using logistic regression and differences in haplotype frequencies compared using the 
2 test. Multivariate linear relationships were assessed by generalized linear regression models. P values <0.05 were considered statistically significant and are presented without correction for multiple hypothesis testing. Based on minor allele frequencies in the present study, we had >85% power ( = 0.05) to detect a difference in allele frequency of 6–9%, corresponding to an OR of 1.5–1.9; hence, smaller effects were likely to be missed. The analysis of associations with RBP4 serum concentrations and mRNA in adipose tissue was restricted to nondiabetic subjects to avoid diabetes status masking potential effects of the variants on these phenotypic traits. Although we performed separate analyses also in subjects with type 2 diabetes only, no associations with the above-mentioned parameters were found. This is most likely due to lacking statistical power in this very small sample size and given the very low haplotype frequencies; these data are therefore not shown. Statistical analyses were performed using the SPSS software package (SPSS, Chicago, IL) and the statistical analysis system of the SAS Institute (Cary, NC).
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ACKNOWLEDGMENTS |
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This work was supported by grants from the Interdisciplinary Centre for Clinical Research at the University of Leipzig (B27 to M.S., P.K., and A.T. and N06 to P.K., B.E., J.B., and D.S.), from the German Diabetes Association (to Y.B., A.T., and P.K.), from the University of Leipzig (Formel.1-94 to Y.B.), from the Deutsche Forschungsgemeinschaft (BL 580/3-1 to M.B., KFO-152 to M.S., and project BL 833/1-1 to M.B.), and the National Institutes of Health (R01 DK43051 to B.B.K. and K08 DK69624 to T.E.G.).
We thank all those who participated in the studies. We appreciate the help of the nurses and physicians who performed the clinical examinations and data collection.
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FOOTNOTES |
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Published ahead of print at http://diabetes.diabetesjournals.org on 29 August 2007. DOI: 10.2337/db07-1647.
P.K. and M.G. contributed equally to this work.
Additional information for this article can be found in an online appendix at http://dx.doi.org/10.2337/db07-1647.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received for publication November 24, 2006 and accepted in revised form August 23, 2007
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