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Int J Mol Epidemiol Genet. 2010; 1(3): 167–174.
Published online 2010 April 1.
PMCID: PMC2992359

Association of resistin promoter polymorphisms with plasma resistin levels and type 2 diabetes in women and men


This study's objective was to examine the associations between resistin (RETN) polymorphisms, plasma resistin levels, and type 2 diabetes risk. We conducted two nested case-control studies in postmenopausal women (359 incident cases and 359 controls) and middle-aged/elderly men (170 incident cases and 170 controls). Controls were matched (1:1) to cases by age, race, duration of follow-up, and time of blood draw. Circulating resistin levels were higher among carriers of the variant allele for rs34861192 (p<0.0001 for women, p=0.002 for men) but not rs1862513 (p=0.15 for women, p=0.14 for men). Neither polymorphism was significantly associated with risk of type 2 diabetes after adjusting for diabetes risk factors (exercise, smoking status, alcohol intake, family history of diabetes, and matching factors) among women (rs1862513: OR=1.19, 95% CI=0.80-1.77; rs34861192: OR=0.41, 95% CI=0.14-1.19) and men (rs1862513: OR=1.05, 95% CI=0.57-1.95; rs34861192: OR=0.64, 95% CI=0.14-2.89). In conclusion, RETN promoter polymorphism rs34861192 was associated with elevated circulating resistin levels, but rs1862513 was not. Neither polymorphism was associated with an increased risk for type 2 diabetes.

Keywords: Resistin, RETN, type 2 diabetes, CRP, TNFα-RII, BMI


The clinical and biological significance of the adipokine resistin in the development of type 2 diabetes remains uncertain. Current evidence from animal models indicates that a reduction in functional resistin protein (via gene knockout, anti-resistin immunoglobulin, and dominant inhibition) can lower blood glucose levels and improve insulin sensitivity [13]. In addition, elevated levels of serum resistin in diabetic db/db mice implicate resistin as a key mediator between adiposity and type 2 diabetes [3].

In humans, circulating resistin levels are higher among individuals with type 2 diabetes than among apparently healthy individuals [46]. Recently, two prospective cohort studies related baseline resistin levels to future development of type 2 diabetes, although this relation appeared to be mediated, in part, by BMI[4, 5] or inflammatory markers [4]. The suggestion of inflammatory pathway involvement is consistent with the fact that mononuclear cells are the primary source of resistin in humans [7]. In contrast, the causal relation between resistin levels and adiposity in humans remains unclear. Resistin levels are elevated in obese individuals [8], however, adiposity (estimated by BMI) does not consistently correlate with resistin levels across studies [9].

Studies of RETN polymorphisms are advantageous in clarifying the directionality of the relation between inflammation, adiposity, and resistin levels. The most widely studied RETN polymorphism is the promoter polymorphism rs1862513, whose G-allele has been associated with elevated resistin levels and type 2 diabetes risk in some [6, 10] but not all studies [1114]. Azuma, et al., observed that this polymorphism, in conjunction with another novel RETN promoter polymorphism (rs34861192), strongly influenced circulating resistin levels [15]. Based on these findings, we analyzed data from two well-characterized populations to further clarify the association between these RETN promoter polymorphisms, circulating resistin levels and type 2 diabetes risk.

Materials and methods

The Women's Health Study (WHS) is a randomized, controlled, 2×2 factorial trial which consisted of 39,876 female health professionals whose ages were 45 years or older and were free of cancer (except non-melanoma skin cancer) and cardiovascular disease at baseline [16, 17]. For the present analysis we conducted a nested case-control study using risk-set sampling. Cases and controls were selected from among original trial participants whose baseline blood samples were available (n=28,345) and who were postmenopausal and not using hormone replacement therapy at the time of blood collection (n=6,574). Cases included all women who developed type 2 diabetes by February 2005 (median follow-up of 10 years). Controls were matched to cases in a 1:1 ratio by age (within 1 year), follow-up duration (within 1 month), race, and fasting status at blood collection (72% provided fasting blood, defined as ≥10 hours since last meal). Based on these criteria, 359 cases and 359 controls were eligible for analysis in WHS.

The Physicians' Health Study II (PHS) is a randomized, controlled 2×2×2×2 factorial trial consisting of 14,641 male physicians aged 50.5 years or older who were free of cancer and cardiovascular disease at baseline [18]. In a similar manner as was done for WHS, cases and controls were selected from among original trial participants whose baseline blood samples were available (n=11,133). Cases included all men who developed type 2 diabetes during 8 years of follow-up. Controls were matched to cases in a 1:1 ratio by age (within 2 years), follow-up duration (within 1 month), race, and time of blood draw. Among the PHS cases and controls selected for the present analysis, 57% provided fasting blood (defined as ≥10 hours since their last meal). Based on these criteria, 170 cases and 170 controls were eligible for analysis in PHS.

Written informed consent was obtained from all participants in both WHS and PHS. Both studies were conducted according to the ethical guidelines of Brigham and Women's Hospital, Harvard Medical School, and the UCLA institutional review boards.

Methods used to ascertain incident type 2 diabetes in the present analyses have been described in detail previously [19]. Briefly, participants with type 2 diabetes at baseline were excluded. The remaining participants were asked annually whether and when they had been diagnosed with type 2 diabetes during follow-up. Self-reported cases of type 2 diabetes were confirmed by supplementary questionnaires. A small validation study in WHS compared self-reported type 2 diabetes to physician-led telephone interviews, supplementary questionnaires, and medical record reviews; the positive predictive value of self-reported type 2 diabetes was at least 91% in each comparison [20].

Centrifuged blood samples were stored in liquid nitrogen freezers. Case-control pairs were handled identically. Laboratory personnel were blinded to case-control status. Each matched pair was assayed in random order and in the same analytical run. Plasma levels of resistin were measured by an enzymatically amplified “two-step” sandwich-type ELISA immunoassay (R&D Systems, Minneapolis, MN). TNFα-receptor II (TNF-RII) was also measured by ELISA (R&D Systems, Minneapolis, MN). C-reactive protein (CRP) was measured at a core laboratory facility using a validated, high-sensitivity assay (Denka Seiken) [21]. The CRP assay was conducted only in WHS.

Two RETN SNPs–rs 1862513 (RETN -420C>G) and rs34861192 (RETN -638G>A)–were successfully genotyped in >98% of the samples in both WHS and PHS (providing 96% of the total case-control pairs for genetic analysis). Polymorphism selection was based on a review of the past literature and HapMap. Priority was given to polymorphisms that were associated with differences in plasma resistin levels, associated with type 2 diabetes, had a relatively high minor allele frequency, and found in the RETN gene promoter region. All DNA samples were genotyped using the ABI Taqman system (Applied Biosystems, Foster City, CA). Endpoint fluorescence of PCR-amplified DNA was read by the ABI PRISM 7900HT Sequence Detection System (SDS). Genotypes were assigned using SDS 2.2.2 Allelic Discrimination Software (Applied Biosystems, Foster City, CA) by two independent technicians blinded to the identification number and case-control status of each sample. Predesigned Taqman SNP genotyping probes were used for rs1862513, and custom probes were designed for rs34861192 (Applied Biosystems, Foster City, CA).

Biomarker values with skewed distributions were log-transformed to enhance compliance with normality assumptions. McNemar's χ2 test (for categorical variables) and Wilcoxon signed rank test (for continuous variables) were used to compare baseline characteristics between cases and controls. We calculated geometric means for biomarkers and arithmetic means for BMI by genotype. Arithmetic means for BMI were multivariably adjusted for physical activity levels, smoking status, alcohol intake, and ever having a first-degree relative with diabetes. Geometric means for biomarkers were further adjusted for BMI. Differences in genotype-specific means were assessed by generalized linear models. Conditional logistic regression provided odds ratios (OR) and 95% CI on the association between RETN SNPs and type 2 diabetes. ORs in the present analyses yield unbiased estimates of relative risks (RR), specifically rate ratios, since risk-set sampling was used. Estimates from the WHS and PHS were pooled using a random-effects meta-analysis model [22] (Stata 10.0, StataCorp, Texas Station, TX). All other analyses were conducted in SAS 9.1.3 (SAS Institute Inc., Cary, NC).


Demographic characteristics, circulating bio-marker levels, and genotypic frequencies for rs1862513 and rs34861192 are presented in Table 1. The minor allele frequencies among the controls were 30% and 34% for rs1862513, and 3% and 5% in rs34861192 among women and men, respectively. Among control participants, Hardy-Weinberg equilibrium was observed for rs1862513 (p<0.0001 among women, p=0.0001 among men) but not for rs34861192 (p=0.89 among women, p=0.73 among men). Still, allele frequencies for both SNPs were consistent between the men and women in our analysis and those of HapMap CEU Europeans and the Environmental Genome Project (EGP) CEPH Europeans.

Table 1
Baseline characteristics of case and control participants from the Women's Health Study (n=718) and Physicians' Health Study II (n=340)

The adjusted means of resistin levels, BMI, TNF-RII, and CRP did not differ substantially between rs1862513 genotypes in either men or women (Table 2). Compared to non-carriers, rs34861192 variant A-allele carriers were similar in most measures, but had higher resistin levels (p<0.0001 among women, p=0.002 among men) and lower BMI (p=0.003 among men). Additive and dominant genetic models led to similar results, while the recessive model possessed weaker discriminatory ability (data not shown).

Table 2
Adjusted means and geometric means* (95% CI) by RETN genotype (dominant genetic model) among control women (n=359) and men (n=170)

The risk of type 2 diabetes did not differ significantly between carriers and non-carriers of the rs1862513 variant allele in any of our statistical models (Final model: pooled RR=1.15, 95% CI=0.82-1.60) (Table 3). The crude and BMI-adjusted models for rs34861192 suggested a lower risk of type 2 diabetes among minor allele carriers; however, this association was no longer statistically significant after controlling for additional type 2 diabetes risk factors and BMI in the final model (pooled RR=0.48, 95% CI=0.20-1.13). Further adjustment for TNF-RII (or CRP, when available) in the final models did not alter estimates substantially (data not shown).

Table 3
Association between resistin genotype (dominant genetic model) and risk of type 2 diabetes*


The present analysis examined two SNPs with putative function in the promoter sequence of the RETN gene in relation to plasma resistin levels and risk of type 2 diabetes. In the widely studied rs1862513 no significant differences in circulating resistin levels nor risk of type 2 diabetes were observed across genotypes. However, elevated resistin levels were observed among carriers of the rs34861192 variant allele. Interestingly, the risk of type 2 diabetes among rs34861192 variant allele carriers did not differ significantly from non-carriers. In addition, circulating inflammatory markers levels were similar across RETN genotypes.

RETN -420C>G (rs1862513) has been studied extensively in regards to circulating resistin levels and type 2 diabetes risk. While the majority of studies found elevated resistin levels [13, 23, 24] among carriers of the variant G-allele, our analysis corroborates the findings of a recent longitudinal study [25] showing no substantial differences in resistin levels nor in type 2 diabetes risk across rs1862513 genotypes. Racial/ethnic differences may explain these differences. Studies that showed such associations were exclusively in Asian populations [9]; whereas, studies of predominantly Caucasian populations showed little to no differences between rs1862513 genotypes [25].

RETN -638G>A (rs34861192) is a novel RETN polymorphism that has only been examined recently for its association with type 2 diabetes. Previously, Azuma, et al., reported that, in conjunction with rs1862513, the rs34861192 polymorphism strongly influenced resistin levels, potentially by eliminating recognition sites for heat shock transcription factors [15]. In our analysis the variant A-allele was associated with increased levels of circulating resistin, yet conferred slightly lower risk of type 2 diabetes, although this result should be interpreted with caution due to low minor allele frequency in our population. Asano, etal., observed rs34861192 to be strongly associated with plasma resistin levels but not with diabetes-associated traits in a Japanese population [23].

The lack of association between RETN promoter polymorphisms and type 2 diabetes risk in our study may be due, in part, to the fact that we lacked the statistical power to detect modest genetic associations. While the promoter polymorphisms that we examined were not significantly associated with type 2 diabetes risk, we could exclude the possibility that other rare SNPs across the RETN gene may influence risk of insulin resistance and related phenotypes [26]. Fine mapping of the RETN gene revealed that circulating resistin levels may be more influenced by polymorphisms in the 3′ region than by promoter SNPs [25]. Nevertheless, these prospective data are consistent with previous experimental findings [27], which suggest the resistin protein as a marker of adipose tissue inflammation and not a significant mediator in the development of type 2 diabetes. This may explain the observed correlations between BMI, inflammatory markers, and resistin levels in our previous analysis [4] and the lack of association between BMI, inflammatory markers, and RETN SNPs in the current analysis.

Limitations from this study included the lack of data on RETN 3′UTR polymorphisms, low minor allele frequency for rs34861192, and inadequate statistical power that may have precluded the detection of modest genetic effects and/or gene-environment interactions. While population stratification could not be ruled out completely, matching of cases and controls by race in these two prospective studies of well-characterized, relatively homogeneous populations minimize such a bias. The strength of this study included the use of RETN promoter polymorphisms, as surrogate measures of circulating resistin levels, reducing the possibility of reverse causation in examining the influence of resistin levels on adiposity and inflammatory markers.

In conclusion, we did not find any significant relation between the two RETN polymorphism with risk of type 2 diabetes in two prospective cohorts of men and women. However, the rs34861192 promoter polymorphism was associated with elevated circulating resistin levels whose functional role warrants further investigation.


We are indebted to the dedicated and committed participants of the WHS and PHS II. We also acknowledge the contributions of the entire staff of the WHS and PHSII.

This study was supported by grant DK-066401 from the National Institutes of Health, Be-thesda, MD. The Women's Health Study (WHS) is supported by grants HL-043851, HL-080467 and CA-047988 from the National Institutes of Health, Bethesda, MD. The Physicians' Health Study II (PHS II) is supported by grants CA-34944, CA-40360, CA-097193, HL-26490, and HL-34595 from the National Institutes of Health, Bethesda, MD. Mr. Chen is supported by a grant (T32-HG002536) from the National Human Genome Research Institute, the National Institutes of Health, Bethesda, MD. Dr. Song is supported by a grant (K01-DK078846) from the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institutes of Health, Bethesda, MD. Dr. Ding was supported by a postdoctoral fellowship grant from the American Diabetes Association and a fellowship from the Paul and Daisy Soros Foundation, New York. The funding sources had no role in the study conduct and analysis.


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