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Human Molecular Genetics Advance Access originally published online on April 27, 2006
Human Molecular Genetics 2006 15(11):1914-1920; doi:10.1093/hmg/ddl113
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Genetic variation in IL6 gene and type 2 diabetes: tagging-SNP haplotype analysis in large-scale case–control study and meta-analysis

Lu Qi1,3,*, Rob M. van Dam1, James B. Meigs5, JoAnn E. Manson2,3,4, David Hunter1,2,3 and Frank B. Hu1,2,3

1Department of Nutrition, Harvard School of Public Health, Boston, MA, USA, 2Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA, 3Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA, 4Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA and 5General Medicine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

* To whom correspondence should be addressed at: Department of Nutrition, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA. Tel: +1 6174324116; Fax: +1 617 432 2435; Email: nhlqi{at}channing.harvard.edu or frank.hu{at}channing.harvard.edu

Received March 10, 2006; Accepted April 21, 2006


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Interleukin-6 (IL-6, gene symbol IL6) is a proinflammatory cytokine. High circulating IL-6 levels have been associated with insulin resistance and greater risk of type 2 diabetes. Using a linkage disequilibrium (LD)-based approach, we sought to investigate the associations of the common polymorphisms comprehensively defining the genetic variability at the IL6 locus with diabetes risk. We conducted a case–control study of 2691 cases of type 2 diabetes (1692 women and 999 men) and 3237 control subjects (2238 women and 999 men) from the Nurses' Health Study and the Health Professional Follow-up Study. Pairwise LD analysis indicated that all the IL6 polymorphisms (rs2069827, rs1800797, rs1800795, rs1554606, rs2069849, rs2069861 and rs1818879) were in strong LD. We did not find significant associations between IL6 polymorphisms and the risk of type 2 diabetes in women or men, individually or in haplotypes. In addition, none of the IL6 polymorphisms was significantly associated with the plasma levels of IL-6 in the control subjects. Our meta-analysis of 5383 diabetes case and 12 069 controls indicated a null association between the best-studied 5' promoter polymorphism –174G>C (rs1800795) and diabetes risk. Diversity in adiposity, age and sex could not account for the heterogeneity across different studies. In summary, the data in this study do not support substantial associations between the common polymorphisms in IL6 gene and circulating IL-6 levels and the risk of type 2 diabetes.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Persuasive evidence indicates that low-grade inflammation plays a pivotal role in the pathogenesis of type 2 diabetes (1Go,2Go). Interleukin-6 (IL-6, gene symbol IL6) is a proinflammatory cytokine secreted by immune cells, adipose tissue and muscles, and is able to accelerate or inhibit the inflammatory processes (3Go,4Go). There is a significant correlation between adipose IL6 mRNA expression and insulin resistance (5Go). Recently, several prospective studies have associated increased plasma IL-6 levels with a higher risk of type 2 diabetes (1Go,6Go). These data suggest that IL6 is an appealing candidate gene for type 2 diabetes.

To date, about a dozen studies have examined the associations between the variability in IL6 gene and the risk of type 2 diabetes (7Go–16Go). Most studies focused on a single 5' promoter polymorphism –174G>C (rs1800795), which has been associated with an increased expression of IL6 and also with energy expenditure, inflammatory response and insulin sensitivity (12Go,17Go–19Go). However, the reported associations between polymorphism –174G>C and diabetes risk are conflicting. No study has investigated the relation between the IL6 gene and type 2 diabetes using comprehensive genetic markers that capture the overall variability at this locus.

In this study, we selected five tagging polymorphisms of IL6 gene, using a linkage disequilibrium (LD)-based approach (20Go) and also included polymorphisms that were previously associated with diabetes risk (9Go). We examined the associations between IL6 polymorphisms and type 2 diabetes in two large-scale case–control studies of women and men from the Nurses' Health Study (NHS) and the Health Professional Follow-up Study (HPFS) cohorts. In addition, we assessed the associations between IL6 polymorphisms and plasma levels of IL-6 in a subgroup of women in our cohorts. We also systematically reviewed the association of polymorphism –174G>C with diabetes risk in a meta-analysis combining our data with data from previous studies.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Case–control study in US women and men
In US women and men, the allele frequency of IL6 polymorphisms ranged from 0.03 (rs2069849) to 0.44 (rs1554606) (Table 1). All genotypes fit Hardy–Weinberg equilibrium (P>0.05) and were similarly distributed in women and men. All the pairwise LD measure D' are above 0.95, whereas r2 (which accounts for differences in allele frequencies) ranges from 0.003 to 0.92 (Fig. 1B).


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  		Table 1. Common IL6 polymorphisms genotyped in the study subjects
 

Figure 1131
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  		Figure 1. (A) The location of IL6 polymorphisms (not drawn to scale). The exons are indicated by black boxes and the promoter and 3'-UTR are denoted by smaller gray boxes. The direction of transcription is labeled with arrows. The position and identity of polymorphisms are indicated with lines. (B) Pairwise LD matrix. D' is presented above the diagonal and r2 is presented below the diagonal.

 
The associations between the IL6 polymorphisms and type 2 diabetes were examined in two case–control studies of women (1692 cases and 2238 controls) and men (999 cases and 999 controls). For each IL6 polymorphism, there was no significant difference in allele distribution between diabetic and non-diabetic subjects in either gender (Table 2). Analysis of the genotype distribution generated similar results, and adjustment for the conventional risk factors for diabetes did not appreciably change the associations (data not shown). Also, the common haplotypes (frequency >0.05, covering 92–96% of allelic variance) encompassing IL6 polymorphisms were not significantly associated with diabetes risk in women or men (Table 3). The associations remained not significant when only incident cases were used (1468 women and 625 men, data not shown).


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  		Table 2. Allele frequencies of IL6 polymorphisms in type 2 diabetes cases and controls
 

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  		Table 3. Distribution of IL6 haplotypes (frequency ≥0.05) in cases of type 2 diabetes and control subjects
 
The associations between IL6 polymorphisms and plasma IL-6 levels were examined in a subgroup of control women. No polymorphism showed significant association with IL-6 levels (Table 4). Adjustment for age, body mass index (BMI) and other covariates did not appreciably change the results. Haplotypes inferred from IL6 polymorphisms were not associated with IL-6 levels.


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  		Table 4. Blood concentration of interleukin 6 (ng/ml) by IL6 genotypes in a subset of control women, means (SE)
 
Meta-analysis of the association between IL6 polymorphism –174G >C (rs1800795) and type 2 diabetes
We conducted a meta-analysis of the association between the best-studied IL6 polymorphism –174G>C and diabetes risk. Because –174G allele was initially associated with an increased risk of diabetes, we defined the inheritance model by treating allele G as the ‘risk’ allele. Ten previous studies (7Go–16Go) and the present study were included in the meta-analysis. The association observed for Native Americans in Vozarova et al.'s study (7Go) was not included because the onset of diabetes was young and the observed association was likely due to the confounding of population admixture.

In the analysis including all available studies, the GG homozygotes were not significantly associated with the risk of type 2 diabetes compared with the CC or GG+GC genotypes (Table 5). However, the GG genotype was associated with a small but significant increase in risk of diabetes compared with GC genotype. We noted that the significantly increased odds of diabetes were observed only in Stephens et al.'s study (16Go). The result from this study, however, may be biased by the unbalanced study design (cases, ~58% men; controls, 100% men; cases and controls were from different source populations). By excluding Stephens et al.'s study, the summary odds ratio (OR) became non-significant [OR=1.09, 95% confidence interval (95% CI) 0.99–1.18] and comparable with our estimates (in women, OR=1.12, 95% CI 0.95–1.32; in men, OR=1.07, 95% CI 0.85–1.35) (Fig. 2). No evidence of publication bias was found in the analyses.


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  		Table 5. Pooled estimates of ORs (95% CI) for the association between IL6 polymorphism –174G>C (rs1800795) and type 2 diabetes
 

Figure 1132
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  		Figure 2. Meta-analysis of the association between IL6 polymorphism –174G>C and type 2 diabetes. Eleven studies, including this study, were included (7Go–16Go). The ORs from women and men in the present study were reported separately. Black square indicates the OR in each study, with the size of the square inversely proportional to its variance, and horizontal lines represent the 95% CI. The pooled OR and its 95% CI are indicated by the unshaded diamond.

 
IL-6 is partly secreted by adipose tissue and it has been suggested that the degree of obesity may affect the association between IL6 genotype and diabetes risk (9Go). We, therefore, further conducted a meta-regression to evaluate the potential influence of adiposity on the heterogeneity in associations across studies. The mean BMI was calculated for each study (cases and controls). For the studies without BMI information, the average BMI from other studies was applied. BMI did not appreciably explain study heterogeneity for any inheritance models (test of regression slope!=0, P=0.27 for GG versus CC and P=0.59 for GG+GC versus CC). Age and sex (percentage of men) also did not explain the heterogeneity (data not shown).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
There is compelling evidence that augmented levels of IL-6 are associated with type 2 diabetes (1Go,6Go). To date, about a dozen studies have examined the associations between IL6 genetic variations and the risk of type 2 diabetes (7Go–16Go). Most of the studies focused on a single polymorphism –174G>C and generated mixed results. The initial study reported that the G allele of this polymorphism was associated with an increased risk of type 2 diabetes (7Go), whereas a recent study found that genotype GG might be protective for the disease (15Go). The majority of the studies, however, failed to identify a significant association. Moreover, none of the previous studies has investigated the association between the IL6 gene and the risk of type 2 diabetes using genetic markers representing the overall variability at this locus. Therefore, the relation between the variance of IL6 gene, taken as a whole, and diabetes risk remains unclear.

In this study, we evaluated the associations between common polymorphisms that comprehensively capture the variability of IL6 gene and the risk of type 2 diabetes in US women and men from two independent prospective cohorts. None of the polymorphisms showed significant association with the disease. In addition, we did not find appreciable associations between the IL6 polymorphisms and plasma IL-6 levels. In our meta-analysis including 5383 diabetes case and 12 069 controls, polymorphism –174G>C was not significantly associated with the risk of type 2 diabetes.

It has been documented that polymorphism –174G>C affects the transcriptional activity of IL6 gene (21Go). However, data on the effects of this polymorphism on IL-6 levels in vivo have led to conflicting results (17Go,22Go). Likewise, previous studies of the relation between IL6 polymorphisms and insulin sensitivity also generate mixed results (17Go,18Go). Our results do not support an effect of polymorphism –174G>C on plasma IL-6 levels. Nevertheless, we cannot exclude the possibility that this polymorphism may affect the adipose abundance of IL-6, as suggested by recent data (23Go).

The reasons underlying the discrepancy among studies are unclear. It is documented that ~30% of circulating IL-6 is derived from adipose tissue (24Go). Earlier evidence suggests that body adiposity may modulate the association between IL6 genetic variability and diabetes risk. In Illig et al.'s study (9Go), the genetic effect appears to be more evident in the leaner subjects (BMI<28.7 kg/m2). However, the effect modification by adiposity was not observed in our study. In addition, adiposity (BMI) did not appear to explain the variance across studies in our meta-analysis of polymorphism –174G>C. It has been suggested that the effect of IL6 polymorphism is stronger in men than in women (9Go). However, our data do not support a sex-specific effect.

In population genetic studies, replication is regarded as a key criterion for convincing genetic association (25Go). As a major strength, this study was conducted in diabetic cases and controls from two large-scale, independent cohorts, which ensure our conclusions were based on replication observations. In addition, for the first time, we used LD-tagging approach to examine the association between genetic variations in IL6 gene and diabetic risk. Recent advances in genomics allow the comprehensive evaluation of the associations between a specific locus and phenotypic variation by taking advantage of LD structure. Neighboring polymorphisms are often correlated and co-inherited. This offers possibility to adequately capture the genetic variance across a specific region using a subset of tagging-single-nucleotide polymorphisms (SNPs), which can infer the allelic state of all the common polymorphisms and cover maximum genetic variability. The utilization of tagging SNPs in genetic association studies has been posited as an efficient method to narrow association signal and localize susceptibility variants (26Go).

One potential limitation of our study is that some of our controls may have undiagnosed diabetes that may bias the results toward the null. However, the prevalence of undiagnosed diabetes in our study of health professionals, with ready access to health care, is much lower than that in the general population, according to our previous validation study (27Go). Population stratification may influence the observed associations. However, our populations are racially homogeneous, with the majority of the participants being white (~96%). Further adjustment for ethnicity or removing that for minority ethnicity from the analyses did not change the associations. In addition, blood IL-6 concentration was measured only in a subgroup of women and this limits the study power to identify moderate difference between genotypes and to generalize the inference to men.

In conclusion, our results and meta-analysis do not support a substantial main effect of IL6 polymorphisms on the risk of type 2 diabetes. Also, we did not find significant associations between IL6 polymorphisms and plasma IL-6 levels.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Study population
The Nurses' Health Study was established in 1976 when 121 700 female registered nurses aged 30–55 years and residing in 11 large US states completed a mailed questionnaire on their medical history and lifestyle (28Go). The lifestyle factors, including smoking, menopausal status and postmenopausal hormone therapy and body weight, have been updated by validated questionnaires every 2 years. Of those, 32 826 women provided blood samples between 1989 and 1990. The Health Professional Follow-up Study is a prospective cohort study of 51 529 US male health professionals aged 40–75 years at study initiation in 1986 (29Go). Information about health and disease is assessed biennially by a self-administered questionnaire. Between 1993 and 1999, 18 159 men provided blood samples.

Subjects for the present study were selected from those who provided blood samples. Diabetes cases were defined as self-reported diabetes confirmed by a validated supplementary questionnaire. For cases before 1998, diagnosis was made using criteria consistent with those proposed by the National Diabetes Data Group (NDDG) (30Go). We used the American Diabetes Association's diagnostic criteria for the diagnosis of diabetes cases during the 1998 and 2000 cycles (31Go). This study included 1692 female cases (228 diagnosed during/before 1976 and 1468 diagnosed during follow-up through 2000) and 999 male cases (374 diagnosed during/before 1986 and 625 diagnosed during follow-up through 2000). The cases were matched to 2238 (women) and 999 (men) non-diabetic control subjects, respectively, on age, month and year of blood draw and fasting status. For the female cases diagnosed in 1996 or earlier, two control subjects were matched to each case subject. One of the two control subjects was also matched according to BMI (±1 kg/m2). For the female cases diagnosed after 1996, one control subject was matched to each case subject.

Assessment of plasma IL-6 and covariates
Blood sample collection and processing were previously described (2Go,32Go). Plasma concentration of IL-6 was measured in a subset of women (687 diabetes cases and 750 control subjects), using a quantitative sandwich enzyme immunoassay technique (Quantikine HS Immunoassay kit). The coefficient of variation (CV) was 5.9%. BMI was calculated as weight in kilograms divided by the square of height in meters. Physical activity was expressed as metabolic equivalent task hours on the basis of self-reported types and durations of activities over the previous year.

SNP selection and genotype determination
We selected tagging SNPs from the SeattleSNPs database that uses a clustering approach (ldSelect program) to bin SNPs with similar r2 for one threshold (0.64) (20Go) (http://droog.gs.washington.edu/ldSelect.html). We did not include the polymorphisms with a minor allele frequency <5%. One tag SNP was chosen for each cluster bin giving a priority to those located in the coding region, 5' promoter and 3'-UTR. Five polymorphisms (rs2069827, rs1554606, rs2069849, rs2069861 and rs1818879) were selected (Table 1, Fig. 1). In addition, we included two polymorphisms (rs1800797, rs1800795) that were associated with type 2 diabetes in previous studies (9Go). DNA was extracted from the buffy coat fraction of centrifuged blood using the QIAmp Blood Kit (Qiagen, Chatsworth, CA). The polymorphisms were genotyped using Taqman SNP allelic discrimination by means of an ABI 7900HT (Applied Biosystems, Foster City, CA). Replicate quality control samples were included and genotyped with 100% concordance.

Statistical analyses
{chi}2 test and unconditional logistic regression were used to compare the genotype and allele frequencies between case and control subjects and to estimate ORs. The geometric mean levels of plasma IL-6 were compared among the genotype groups using ANOVA. In the multivariate analysis, we adjusted for age, physical activity (<1.5, 1.5–5.9, 6.0–11.9, 12–20.9 and ≥21.0 metabolic equivalent hours/week), smoking (never, past and current), alcohol intake [non-drinker and drinker (0.1–4.9, 5–10 or >10 g/day)], BMI (<23, 23–24.9, 25–29.9, 30–34.9 or ≥35 kg/m2) and menopausal status [pre- or postmenopausal (never, past or current hormone use); women only]. The SAS (SAS Institute, Cary, NC) statistical package was used for the analyses (SAS, Version 8.2 for UNIX). Haplotype analysis was conducted on the basis of the Stochastic-EM (SEM) algorithm using THESIAS program (33Go).

A meta-analysis was conducted using STATA (STATA, College Station, TX, Version 7.0). Relevant studies were identified by searching the MEDLINE, PubMed and Online Mendelian Inheritance in Man (OMIM) databases for all published genetic association studies up to January 2006. In general, we included studies that provided estimates of relative risks or frequency data that permitted estimation of these parameters. For the studies that reported a multivariate-adjusted OR, we used the adjusted estimates rather than the crude; when the adjusted ORs were not provided by the original studies, we calculated the estimates (non-adjusted) according to the genotypic distribution. Publication bias was assessed by means of a funnel plot (34Go). Formal tests of heterogeneity were assessed by a {chi}2 statistic. Fixed-effect model was used when data were not heterogeneous and the summary ORs were obtained by averaging the natural logarithms of the ORs from individual studies, weighted by the inverse of their variances. When heterogeneity was detected, ORs were estimated using the DerSimonian and Laird random-effect model to incorporate both within study and between study variabilities (35Go). We also conducted a meta-regression analysis of study-specific ORs by means of weighted linear regression to evaluate the potential influence of age, sex and BMI on the heterogeneity of effects across studies. All P-values are two-sided.


   ACKNOWLEDGEMENTS
 
This study was supported by NIH grant DK58845 and CA87969. J.B.M. is supported by an American Diabetes Association Career Development Award. The authors gratefully acknowledge the contribution of the SeattleSNPs database (http://pga.mbt.washington.edu/) in the selection of genetic markers.

Conflict of Interest statement. There are no conflicts of interest associated with this work.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

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