Lack of Association Between a Common Polymorphism Near the INSIG2 Gene and BMI, Myocardial Infarction, and Cardiovascular Risk Factors

Authors


  • The first two authors contributed equally to this work.

(christian.hengstenberg@klinik.uni-regensburg.de)

Abstract

Epidemiological studies revealed an increasing prevalence of and a steep increase in obesity, a risk factor for cardiovascular disease. Because significant influence of a polymorphism, rs7566605, near the INSIG2 gene on BMI has been shown in the general population and in obesity cohorts, we hypothesized that this polymorphism might also act through an elevated BMI on the development of coronary artery disease (CAD) or myocardial infarction (MI). We pursued two strategies: First, the polymorphism rs7566605 was investigated for association with BMI, CAD/MI, and cardiovascular risk factors in a large German cohort at high risk for CAD and MI (n = 1,460 MI patients) as compared to unrelated healthy controls (n = 1,215); second, we extended our analyses on the families of MI patients and performed family-based association testing (n = 5,390 individuals). The polymorphism rs7566605 was analyzed using TaqMan technology. No deviation from Hardy–Weinberg equilibrium could be observed, and the call rate was 98.2%. No significant associations of rs7566605 with CAD/MI, BMI, and classical cardiovascular risk factors could be detected in the full sample size or in the subgroups. A total of 6,878 individuals were investigated in a population of German MI patients and their family members. Although the number of individuals was large enough, no influence of the rs7566605 INSIG2 polymorphism was detected on BMI and CAD/MI. We therefore conclude that in our sample the SNP rs7566605 near the INSIG2 gene does not influence BMI and is not associated directly with CAD/MI or indirectly through cardiovascular risk factors.

Introduction

Epidemiological studies revealed an increasing cardiovascular disease prevalence in the presence of “classical” risk factors often summarized as the “Metabolic Syndrome”. An elevated BMI and obesity as intermediate phenotypes are related to several components of the metabolic syndrome and account as risk factors for cardiovascular disease (1). The heritability of BMI has been assessed in family studies, and it was shown that genetic factors contribute significantly to BMI, though the identification of these genetic determinants has proved difficult (2,3). Furthermore, the worldwide increasing prevalence of obesity indicates a strong influence of lifestyle on the risk of obesity, which makes it even harder to ferret out its genetic part (4). In a genome-wide association study involving 86,604 SNPs, Herbert and colleagues identified one SNP (rs7566605) situated 10 kb upstream of the INSIG2 gene (insulin-induced gene 2) showing strong evidence for association with BMI in multiple cohorts (odds ratio (OR) for obesity = 1.22; 95% confidence interval 1.05–1.42; P = 0.008 in meta-analysis of six cohorts) (5). Homozygotes for the minor C allele (around 10% of the population) had a BMI about 1 kg/m² higher than did homozygotes for the major G allele or GC heterozygotes (5). This significant influence of rs7566605 on BMI was shown in both, the general population and in obesity cohorts (5). However, this association is not consistent in all cohorts (5,6) as some studies failed to replicate these findings (6,7,8,9,10,11,12,13,14). In the present study, we investigated a sample of the German Myocardial Infarction Family Study, which was initiated to collect families with a strong genetic background of coronary artery disease (CAD). In the present investigation, patients with myocardial infarction (MI) and unrelated healthy controls were genotyped for rs7566605 to reveal the effect of this SNP on BMI and CAD as well as MI.

Methods and Procedures

Populations

The participants of this study (n = 6,878) were recruited from the German MI Family Study. Recruitment process, selection criteria, and study details have been reported previously (15,16). In brief, we identified families from all parts of Germany with accumulation of premature MI or severe CAD. Control individuals were unaffected spouses of MI family members and had no genetic relationship to cases.

The Ethics committee of the University of Regensburg approved the study protocol, and all participants gave their written informed consent at the time of inclusion and again at the time of follow-up investigations. The study was in accordance with the principles of the current version of the Declaration of Helsinki.

Phenotyping

The baseline home visit was performed by a physician and included a standardized questionnaire, physical examination, and biochemical analyses. A standardized follow-up interview regarding new medical events was carried out by specially trained telephone interviewers after 2 and 5 years. Cardiovascular events at study entry and follow-up were validated by reviewing medical records.

The diagnosis of MI was established according to the MONICA (Monitoring Trends and Determinants in Cardiovascular Disease) diagnostic criteria (http:www.ktl.fipublicationsmonicamanualindex.htm). Severe CAD was defined as treatment with percutaneous coronary intervention or coronary artery bypass grafting. Resting blood pressure was taken according to MONICA guidelines after participants had been resting in a sitting position (17). Hypertension was defined as blood pressure ≥ 140/90 mm Hg or ongoing antihypertensive therapy. Body weight was determined with subjects wearing light clothing. BMI was tested as weight in kilograms divided by the square of height in meters. Obesity was defined as BMI ≥ 30 kg/m². Sampling of serum was carried out from nonfasting individuals. Serum levels of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol were measured by standard enzymatic methods. Hypercholesterolemia was defined as low-density lipoprotein cholesterol ≥160 mg/dl or intake of lipid lowering medication. History of diabetes mellitus or intake of antidiabetic medication was used to define type 2 diabetes. Smoking was defined as current or former smoking habit.

CAD/MI case–control sample

A large case–control sample was established from the German MI Family Study, including 1,473 CAD/MI cases (856 men, 617 women) and 1,241 unrelated CAD/MI-free control individuals (336 men, 905 women). Index patients were defined to have suffered from MI or severe CAD before the age of 60 years. First-degree relatives were classified as affected when suffering from MI or severe CAD before the age of 70 years. Cardiovascular risk factors and phenotypic details are summarized in Table 1.

Table 1.  Characteristics of successfully genotyped CAD/MI case and control study sample
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Sample for family-based analyses

For the family-based association analysis, a subset of 5,257 family members from 1,362 families were selected from our German MI Family Study, as for these individuals a full data set including rs7566605 genotype information and BMI information was available. Unaffected family members were investigated up to 10 years after recruitment and were free from CAD. The family structure was used to calculate the effect of rs7566605 on BMI.

SNP selection and genetic analyses

The SNP rs7566605 located near the INSIG2 gene (Figure 1) was selected due to its association with obesity in several studies (5,6).

Figure 1.

Schematic representation of INSIG2 gene structure and linkage disequilibrium (LD) pattern. The 50 kb genomic region on chromosome 2q14.1 (118,550,001-118,600,000 on NCBI build 36) contains the INSIG2 gene and intergenic region. INSIG2 is oriented from left to right with horizontal lines corresponding to introns and vertical bars depicting the six exons of INSIG2. Position of marker rs7566605 is given as well as the position of the five additional markers on GeneChip Human Mapping 500K Array Set from Affymetrix present in this region. Representation of LD structure in the INSIG2 region. Pairwise r2 values between markers from CEU samples of HapMap project phase II release 22 (http:www.hapmap.orgindex.html) are shown (red denotes perfect LD with r2 = 1; white denotes no LD with r2 = 0; light denotes r2 between 0 and 1 with intermediate LD).

Genomic DNA was isolated from whole blood samples using the PureGene DNA Blood Kit (Gentra, Minneapolis, MN). DNA samples were genotyped using 5′ exonuclease TaqMan technology (Applied Biosystems, Foster City, CA). For each genotyping experiment 10 ng DNA was used in a total volume of 5 µl containing 1x TaqMan Genotyping Master Mix (Applied Biosystems). PCR and post-PCR endpoint plate read was carried out according to the manufacturer's instructions using the Applied Biosystems 7900HT Real-Time PCR System. Sequence Detection System software version 2.3 (Applied Biosystems) was used to assign genotypes applying the allelic discrimination test. For CAD/MI analysis, case and control DNA was genotyped together on the same plates with duplicates of samples (15%) to assess intraplate and interplate genotype quality. No genotyping discrepancies were detected. Assignment of genotypes was performed by a person without knowledge of the proband's affection status. Total call rate was 98.2% (n = 6,878 individuals). A total of 1,460 MI patients and 1,215 control individuals were genotyped successfully and used for further analysis.

Analysis of genome-wide data on MI and CAD

Genome-wide data from two recently published screens for polymorphisms associated with CAD or MI were employed to survey the complete INSIG2 gene region (18). Data of the HapMap (http:www.hapmap.org) phase II release 22 data were used to assess linkage disequilibrium patterns of the INSIG2 gene region (19) and define the boundaries for analysis of genome-wide data.

Statistical analyses

To determine whether the genotypes of the INSIG2 SNP (rs7566605) deviated from Hardy–Weinberg equilibrium, actual and predicted genotype counts were compared by χ2-test. Differences in allele frequencies between dichotomous traits were tested employing the same method. Genotypes were coded for both dominant and recessive effects (genotype 22 + 12 vs. 11 and genotype 22 vs. 11 + 12, respectively, with the minor allele coded as 2). The additive genetic model was tested using Armitage's trend test. Multiple logistic regression analysis was used to examine the association of rs7566605 with either phenotype allowing adjustment for relevant covariates (Table 1). Differences in continuous variables between groups were tested using a two-tailed unpaired t-test. Prevalence ORs with their 95% confidence intervals were reported. A two-sided P value ≤ 0.05 was considered statistically significant.

Association analyses and permutation testing were performed using JMP IN 7.0.1 (SAS Institute, Cary, NC) and PLINK v1.01 (http:pngu.mgh.harvard.edupurcellplink) (20). Power analysis was carried out using G*Power 3.0.8 (http:www.psycho.uni-duesseldorf.deabteilungenaapgpower3) (21), comparing allele frequencies between 1,460 MI cases and 1,215 controls in a post-hoc exact test assuming a minor allele frequency of 0.33.

Family-based association testing was carried out using the QFAM option implemented in PLINK v1.01 (20).

Results

Population characteristics

In our large case–control sample for CAD/MI, the incidence of established cardiovascular risk factors, such as male gender, type 2 diabetes, hypercholesterolemia, hypertension, and smoking, as well as increased BMI, was higher in CAD/MI cases (n = 1,460) as compared to controls (n = 1,215) (Table 1).

Genetic analyses

Association analysis of INSIG2 SNP rs7566605 with CAD/MI. Genotype distributions and allele frequencies of rs7566605 in the CAD/MI case–control cohort are shown in Table 2. There was no significant deviation from Hardy–Weinberg equilibrium in both CAD-free controls (P = 0.845) and CAD/MI cases (P = 0.236).

Table 2.  rs7566605 association analysis results in CAD/MI case–control sample
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No association of rs7566605 with CAD/MI was observed (P = 0.824; OR = 1.01; 95% confidence interval 0.90–1.14) (Table 2). Multivariate logistic regression analysis adjusting for cardiovascular risk factors (age at first manifestation, male gender, type 2 diabetes, hypercholesterolemia, hypertension, and smoking) did not significantly influence the result (P = 0.144). In addition to allele frequency and genotype distribution, we analyzed additive, dominant, and recessive genetic models with no substantial increase in significance (Table 2).

Separate analyses in men and women revealed similar ORs in allele frequency (Table 2). There was no significant deviation from Hardy–Weinberg equilibrium, both in male and female CAD-free controls (P = 0.702 and P = 1.000, respectively) as well as in male and female CAD/MI cases (P = 0.596 and P = 0.115, respectively).

Our power calculation for comparison of allele frequencies indicated that at a significance level of 0.05 and with a two-sided alternative hypothesis, our CAD/MI sample with n = 1,460 cases and n = 1,215 CAD-free controls (Table 1) had >97% power to detect a significant association with an assumed OR of 1.4 between the tested polymorphism (allele frequency 33%) and the phenotype MI. For smaller effect sizes (OR = 1.2), the power was still 60%. The number of individuals in the subgroups is limited under the hypothesis of a recessive model, proposed by Herbert et al. for the trait obesity (5). This working group estimated in a meta-analysis an OR of 1.22 [95% confidence interval from 1.05 to 1.42] for this SNP and obesity under a recessive model. Accordingly, we have estimated the power of our study on this SNP and obesity using G-Power in a Fisher's exact test (i) comparing allele frequencies and (ii) under a recessive model. To detect an effect with an OR of 1.22, we had a respective power of 47.1% and 26.6%, for an OR of 1.42 a respective power of 92.1% and 67.4%, and for an OR of 1.05 a respective power of 7.2% and 5.9%.

To assess the possibility that other markers located within or close to the INSIG2 gene could exert influence on susceptibility to CAD or MI, we analyzed the data from two recently conducted genome-wide association studies (18). Both from British (WTCCC; http:www.wtccc.org.ukinfosummary_stats.shtml) and German (Cardiogenics; http:www.cardiogenics.imbs-luebeck.de) case–control samples, P values were obtained for association with CAD/MI (WTCCC) and MI (Cardiogenics). A total of 38 SNP markers located in the expanded INSIG2 gene region (215.470 kb) were analyzed. None of the SNPs showed association at a P < 0.05 with CAD/MI both in the WTCCC and in Cardiogenics cohorts (data not shown).

Association analysis of INSIG2 SNP rs7566605 with cardiovascular risk factors. To assess the impact of rs7566605 on classical cardiovascular risk factors, we tested for association with male gender, type 2 diabetes, hypercholesterolemia, hypertension, and smoking. No significant association could be detected (Table 3).

Table 3.  rs7566605 genotype distribution according to affection status on dichotomized cardiovascular risk factors in CAD/MI case–control background
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Association analysis of INSIG2 SNP rs7566605 with BMI. Within the study population of MI patients, no association between the INSIG2 polymorphism rs7566605 and BMI could be observed in a sex-stratified analysis. The genotype frequencies in 305 female MI cases were 46.2% for GG carriers (mean BMI 27.0 ± 4.0 kg/m2), 40.7% for GC heterozygous individuals (mean BMI 27.7 ± 4.2 kg/m2), and 12.8% for CC homozygous carriers (mean BMI of 26.9 ± 4.2 kg/m2). Thus, no significant effect of the rs7566605 genotype on BMI could be observed (P = 0.244), and adjustment for age did not alter the result (P adjusted for age = 0.393) (Table 4).

Table 4.  rs7566605 genotype distribution and BMI in MI cases
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In the male MI patients, similar results were observed. The genotype frequencies in the 1,155 male patients were 44.6% for GG (mean BMI of 27.0 ± 3.1 kg/m2), 43.6% for GC (mean BMI of 27.3 ± 3.3 kg/m2), and 11.8% for CC (mean BMI of 27.4 ± 3.2 kg/m2) genotype, respectively. As in female MI patients, no significant association between rs7566605 genotype and BMI could be observed (P = 0.204) and did not change after adjustment for age (P = 0.300) (Table 4).

We also tested for association between the polymorphism and BMI as a continuous trait independent of MI in the whole case–control sample (n = 2,675). No significant results where observed, and adjustment for sex and age did not significantly alter the results. The mean BMI was nearly the same for all genotypes. Carriers of the GG genotype (n = 1,197) had a mean BMI of 27.0 ± 3.8 kg/m2, carriers of GC (n = 1,169) had a mean BMI of 26.9 ± 3.8, and those carrying CC (n = 308) had a mean BMI of 26.7 ± 3.8 kg/m2.

Family-based association analysis of INSIG2 SNP rs7566605 with BMI. To use the full power of all investigated families, a family-based association analysis was performed using the QFAM option implemented in PLINK to examine the effect of rs7566605 on BMI. Due to missing BMI data from family members, 98 families were excluded prior to analysis. Accordingly, testing was performed in 5,257 family members from 1,362 families. Family-based association analysis revealed no significant association of rs7566605 genotype with BMI (P = 0.5). In PLINK, the QFAM procedure allows family-based tests of association with quantitative phenotypes. PLINK performs a simple linear regression of phenotype on genotype but then uses a special permutation procedure to correct for family structure. The QFAM procedure was performed with 100,000 permutations (20).

Discussion

Recently, an association of a polymorphism near the INSIG2 gene with BMI has been identified in a genome-wide analysis (5). This finding was supported by replication in several but not all cohorts (5,6). We hypothesized that this polymorphism that exerts a strong effect on BMI could also play an important role for the development of CAD in a high-risk population. For this purpose, we examined the association between rs7566605 with CAD/MI and with BMI in a large German MI sample. No significant association could be detected.

So far, those studies reporting positive associations between rs7566605 and obesity have been conducted in samples selected for obesity (5,22), while most studies investigating samples not primarily selected for obesity phenotypes failed to show significant association (5,8,9). A study by Hall and colleagues aimed to reveal the association of rs7566605 with BMI in a sample selected for hypertension, a disease pattern also strongly related to obesity, and could not show a significant association result (9). A recently published study by Andreasen et al. also failed to replicate the association results for rs7566605 and obesity, but it reports an interaction between this SNP and self-reported physical activity for the association with BMI (14). Furthermore, obesity is known to be a major risk factor for esophageal cancer, and the influence of rs7566605 on risk of esophageal cancer in a BMI-stratified sample was investigated without significant results (8). These results, together with our own findings, indicate that rs7566605 has little if any effect on BMI within the normal to moderately overweight range.

The overall cardiovascular risk is caused by factors like smoking, hypercholesterolemia, hypertension, type 2 diabetes, abdominal obesity, psychosocial factors, physical activity, and nutrition (23). Additionally, genetic variations modulate not only some of these classical cardiovascular risk factors but act also in a complex manner (24). Alleles directly influencing susceptibility to CAD or MI in a still unknown fashion were consistently found in three recent genome-wide association studies (18,25,26). For rs7566605, no significant association with CAD/ MI could be detected. According to power analysis, our study had >97% and 60% power to detect effects of rs7566605 on MI with assumed ORs of 1.4 and 1.2, respectively. Lack of power could be an issue, as also the frequently observed phenomenon that replication studies do obtain less extreme ORs than the initial study first reporting the association (27).

Furthermore, no association of rs7566605 with the cardiovascular risk factors hypercholesterolemia, hypertension, type 2 diabetes, obesity, and smoking could be found. Other studies investigating the effects of rs7566605 on cardiovascular risk factors were also not able to show significant association (7,9,11,12).

Several limitations of our study have to be considered. The CAD/MI phenotype was assessed retrospectively from patient records and medical history. We only included control individuals without history of CAD/MI. However, in the CAD/MI sample, an imbalance on gender existed due to the selection approach of the control individuals in our German MI Family Study (spouses of cases for a disease having a much higher prevalence in men). Therefore, we adjusted for male gender and other cardiovascular risk factors with no increase in significance. Moreover, it was discussed by Lyon et al. that association effects could be decreased in samples containing cases and controls matched for late-onset diseases as BMI increases with age (6). Therefore, in our study, effect size could be decreased as cases where ascertained after their first MI/CAD event with a mean age of 58.2 years and controls were of comparable age (56.4 years). It was speculated that the effect of rs7566605 on BMI is predominant in already obese subjects (9,14). In our study, only 494 individuals where obese, and a potential effect of this SNP on BMI would not have been detected in this subgroup.

Though the association results for rs7566605 with obesity-related phenotypes are inconsistent, a recent study by Krapivner (28) identified a new polymorphism in the INSIG2 gene promoter region showing association with BMI in two cohorts of old and middle-aged men. For this new SNP, INSIG2-102G/A, an effect on the binding capacity of nuclear factors to the promoter of INSIG2 was shown presumably influencing the rate of transcription (28). In the future, a systematic investigation of SNPs in the upstream region of the INSIG2 gene together with further functional studies might help to uncover the function of the single variations and feasible interactions possibly explaining the so far inconsistent association results for INSIG2 gene polymorphisms and obesity-related phenotypes.

In conclusion, our study demonstrates strong evidence that the polymorphism rs7566605 near the INSIG2 gene is not associated with an elevated BMI in a cohort with a high susceptibility to CAD. Furthermore, this SNP is unlikely to play a causal role in the pathogenesis of CAD or MI and is not linked to cardiovascular risk factors.

Acknowledgments

We appreciate the invaluable contribution of participants of the German MI Family Study. We gratefully acknowledge the excellent technical assistance of Dagmar Glatz, Josef Simon, and Michaela Vöstner. This study was supported in part by the National Genome Network (01GS0418 to Drs Hengstenberg, Erdmann and Schunkert; 01GR0466) and by the National Genome Network Plus to Dr Hengstenberg, both networks sponsored by the German Federal Ministry of Education and Research (BMBF). Further funding was received by the Deutsche Forschungsgemeinschaft (He1921/9-1), by the Wilhelm-Vaillant-Stiftung, and by the European Union sponsored project Cardiogenics (LSHM-CT 2006-037593). SW received a scholarship from the Deutsche Stiftung für Herzforschung.

Disclosure

The authors declared no conflict of interest.

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