• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
JAMA. Author manuscript; available in PMC Oct 15, 2009.
Published in final edited form as:
PMCID: PMC2762126
NIHMSID: NIHMS147544

Interaction Between Poor Glycemic Control and 9p21 Locus on Risk of Coronary Artery Disease in Type 2 Diabetes

Alessandro Doria, MD, PhD, MPH,1,2,6 Joanna Wojcik, MD,1,2 Rui Xu, MD, PhD,1,2 Ernest V. Gervino, ScD,2,3 Thomas H. Hauser, MD, MMSc, MPH,2,3 Michael T. Johnstone, MD,4 David Nolan, BS,1 Frank B. Hu, MD, PhD,2,5,6 and James H. Warram, MD, ScD1

Abstract

Context

A common allele on chromosome 9p21 has been repeatedly associated with increased risk of coronary artery disease (CAD) in the general population. However, the magnitude of this effect in the population with diabetes has not been well characterized.

Objective

To examine the association of the 9p21 variant with CAD in individuals with type 2 diabetes and evaluate its interaction with poor glycemic control.

Design, Setting, and Participants

1. Case-control study of 734 type 2 diabetic patients (322 with angiographically-diagnosed CAD and 412 with no evidence of CAD) who were recruited in 2001–2006 at the Joslin Clinic/Beth Israel Deaconess Medical Center, 2. Independent cohort study of 475 type 2 diabetic patients from the Joslin Clinic whose survival status was monitored from their recruitment in 1993–1996 until December 31, 2004. Study subjects were genotyped for a representative SNP at 9p21 (rs2383206) and characterized for their long-term glycemic control by averaging multiple hemoglobin A1c (HbA1c) measurements taken in the years before study entry.

Main Outcome Measures

Case-control study: association between SNP rs2383206 and CAD defined as angiographically documented stenosis greater than 50% in a major coronary artery or a main branch thereof. Cohort study: cumulative 10-year mortality.

Results

Individuals homozygous for the risk allele were significantly more frequent in case than control subjects (42.3 vs. 28.9%, p=0.0002). This association was unaffected by adjustment for cardiovascular risk factors, but the effect of the risk genotype was significantly magnified (adjusted p for interaction = 0.048) in the presence of poor glycemic control (worst tertile of the distribution of HbA1c at examination). Relative to the CAD risk for patients with neither a 9p21 risk allele nor poor glycemic control, the CAD risk for subjects having two risk alleles but not poor glycemic control was increased two-fold (OR=1.99, 1.17–3.41), whereas the risk for study subjects with the same genotype and with poor glycemic control was increased four-fold (OR=4.27, 2.26–8.01). The interaction was stronger (adjusted p=0.005) when a measure of long-term glycemic control (7-year average rather than most recent HbA1c) was used, with ORs of 7.83 (3.49–17.6) for subjects having two risk alleles and a history of poor glycemia and 1.54 (0.72–3.30) for subjects with the same genotype but without this exposure. A similar interaction between 9p21 variant and poor glycemic control was observed with respect to cumulative 10-year mortality in the cohort study (43.6% in patients with two risk alleles and poor glycemic control, 23.1% in those with only the two risk alleles, 30.0% in those with only poor glycemic control, and 31.6% in those with neither factor, p for interaction=0.036).

Conclusions

In this study population, the CAD risk associated with the 9p21 variant was increased in the presence of poor glycemic control in type 2 diabetes.

INTRODUCTION

Among the known risk factors for cardiovascular disease, diabetes mellitus ranks as one of the most potent. It increases the lifetime risk of a major cardiac event by a factor of 2 to 4 relative to people without diabetes (1) and compounds the impact of such events with increased risks of re-infarction, congestive heart failure, and death (2). It is also a major determinant of peripheral artery disease and stroke (3,4). These profound effects result from an acceleration of atherosclerosis induced by hyperglycemia and other aspects of the diabetic milieu, such as hyperlipidemia (particularly small LDL cholesterol particles) and hypertension (5).

Based on family studies, a substantial proportion of cardiovascular risk is under the control of genetic factors (6,7). While this evidence has been gathered mainly in the general population, studies of indicators of preclinical atherosclerosis suggest that genetic factors also play a role in the development of atherosclerosis in the presence of diabetes (8,9). One may suppose that the genes that play a role in the absence of diabetes are likely to play a role also in its presence. If so, it invites the question whether the increased cardiovascular disease risk seen in diabetes is mediated by interaction of those same genes with the diabetic milieu.

Four recent genome wide association studies found association of a common allele on chromosome 9p21 with coronary artery disease in the general population (1013). In this study, we examined the association of this allele with coronary artery disease in individuals with type 2 diabetes and whether the association is modified by the severity of hyperglycemia, the defining characteristic of diabetes.

METHODS

Study Subjects

Case-control study

We studied a group of 734 individuals with type 2 diabetes living in the greater Boston area and attending the Joslin Clinic and/or the Beth Israel Deaconess Medical Center (BIDMC). All subjects were Non-Hispanic Whites. The study protocol and informed consent procedures were approved by the Joslin Committee on Human Studies and the BIDMC Committee on Clinical Investigations. All subjects gave written informed consent. Type 2 diabetes was defined as diabetes that was diagnosed after age 30 according to ADA criteria (14) and did not require insulin treatment for at least two years after its diagnosis. Study subjects included 322 cases with diagnosed coronary artery disease (CAD) and 412 controls who did not have clinical evidence of CAD. CAD-positive case patients were a random sample of patients with type 2 diabetes who had a stenosis greater than 50% in a major coronary artery or a main branch thereof that was documented by cardiac catheterization at the Beth Israel Deaconess Medical Center (BIDMC) between 2001 and 2006. All cases were enrolled in the study at the time of catheterization and examined within one month from the procedure. Sixty-two percent of the case patients attended the Joslin Clinic for the management of their diabetes (the Joslin Clinic serves as the BIDMC Diabetes Clinic). CAD-negative controls were randomly selected from among 903 Joslin patients who were identified between 2001 and 2006 as fulfilling the following criteria: 1. current age between 55 and 74 years; 2. type 2 diabetes for five years or more; 3. negative cardiovascular history (i.e., normal resting EKG, absence of cardiac symptoms, and no hospitalization for cardiovascular events); and 4. normal response to an exercise treadmill test (ETT, [15]) performed for screening purposes. All controls were recruited within 6 months from the ETT. History of myocardial infarction, smoking, hypertension, and hypercholesterolemia and treatment with glucose-lowering drugs were determined by a questionnaire administered at the time of examination. Data on medications were confirmed by review of medical records.

Prospective study

The interaction between 9p21 variant and poor glycemic control with respect to all-cause and cardiovascular mortality was evaluated in a group of 516 Joslin patients who had been recruited between 1993 and 1996 for genetic studies of type 2 diabetes and its complications. The present study was limited to the 475 members of the cohort for whom DNA samples were still available in 2007. Eleven of these subjects overlapped with those in the case-control study. The study protocol and informed consent procedures were approved by the Joslin Committee on Human Studies. All subjects gave written informed consent. Study participants were a random sample of type 2 diabetic patients from the Joslin Clinic enriched with individuals with proteinuria (16). All subjects had diabetes diagnosed after age 25 according to WHO criteria and were treated with diet or oral agents for at least two years after the diagnosis. Their survival status was updated as of December 31, 2004 by matching with the National Death Index, and causes of death were extracted for deceased cohort members. A death was ascribed to cardiovascular causes if the primary cause of death was ICD-9 code 401 to 448.9 or ICD-10 code I10 to I74.9, or if diabetes or renal failure were listed as the primary cause of death and cardiovascular disease was the secondary cause.

Severity of Hyperglycemia

For the case-control study, HbA1c was measured at examination on a random blood sample by HPLC (Tosoh Bioscience, South San Francisco, CA) by the Joslin Clinical Laboratory. The intra- and inter-assay coefficients of variation for this measurement were 0.25% and 2.1%, respectively. Additional HbA1c values measured between two months and seven years before study entry were abstracted from the Joslin electronic medical records. For the prospective study, all HbA1c values measured in these subjects since 1990 were abstracted from the Joslin electronic medical records. For measurements taken before 1994, HbA1c values were derived from HbA1 values as previously described (17).

SNP Genotyping

All study subjects were typed for SNP rs2383206 and rs10757278 by the Joslin DERC Genetics Core by means of TaqMan assays implemented on an ABI PRISM 7700 HT Sequence Detection System (Applied Biosystems, Foster City, CA). Genotyping quality was tested by including six blinded duplicate samples in each 96-well assay. The average agreement rate of duplicate samples was >99%.

Statistical analysis

Case-control study

All statistical analyses were conducted in SAS V 9.1 (SAS Institute, Cary, NC). Genotype distributions were tested at both polymorphic loci for departure from Hardy-Weinberg equilibrium and compared between study groups by Fisher exact tests. Allele frequencies were derived from genotype counts and compared between groups also by Fisher exact tests. Odds ratios of CAD for SNP rs2383206 and other relevant predictors were estimated by logistic regression analysis using first a univariable model for each predictor and then a multivariable model including all variables showing a significant effect (p<0.05). For examining the interaction of genotype with hyperglycemia, rs2383206 was represented as an additive model (number of risk alleles), HbA1c as an indicator variable for the highest tertile, and the interaction as the product of the HBA1c variable by an indicator variable for patients homozygous for the risk allele. Power was estimated by means of the software QUANTO (http://hydra.usc.edu/gxe), assuming a risk allele frequency of 0.55. For both SNPs, there was 80% power (α=0.05) to detect associations with CAD with ORs as low as 1.35 per risk allele and to detect a 2.5-fold difference in the ORs for risk allele homozygotes between top and lower two tertiles of HbA1c. The latter calculation assumed a CAD prevalence of 0.25 and marginal ORs of 1.80 for G/G vs. A/G+A/A and 1.45 for the top tertile vs. the lower two tertiles of HbA1c.

Prospective study

Life-table methods were used to estimate the cumulative 10-year mortality and its standard error within each stratum defined by degree of glycemic control and rs2383206 genotype. The significance of the interaction between these two variables was determined by comparing the effect of glycemic control on cumulative mortality in G/G homozygotes with that in carriers of other genotypes. This linear contrast, divided by its standard error, was compared to the standard normal distribution. The presence of interaction was also tested by adding a cross-product term to a Cox proportional hazard model including glycemic control and rs2383206 genotype together with age at baseline and gender as main effects. The assumption of proportionality of the hazards was tested by adding time interaction terms to the model.

RESULTS

Clinical characteristics of cases and controls

Clinical characteristics of the study subjects with type 2 diabetes are summarized in Table 1 according to their coronary artery disease (CAD) status. Case subjects had significant CAD (angiographically-confirmed) and control subjects had a negative cardiovascular history and normal exercise treadmill test. Age at examination, age at diagnosis of diabetes, and body weight were similar in the two groups. HbA1c (a measure of poor glycemic control) averaged slightly higher in those with rather than without CAD. This difference was entirely due to an excess of CAD cases in the worst tertile of HbA1c, consistent with a non-linear relationship between poor glycemic control and CAD risk. Treatment with insulin was more frequent in cases with CAD than control subjects (50.9% as compared with 40.0%), as was a history of hypertension (80.4% as compared to 70.4%). A history of smoking was almost twice as common in case subjects as in control subjects (65.5% as compared with 37.6%). Almost half of the cases with CAD (45.7%) had a previous myocardial infarction.

Table 1
Clinical characteristics of study subjects with type 2 diabetes according to coronary artery disease status.

Association between 9p21 variant and CAD

Two SNPs on chromosome 9 (rs2383206 and rs10757278) that were reported to be associated with CAD in the general population (10,11) were genotyped in both study groups. For both polymorphisms, the genotypes were in Hardy-Weinberg equilibrium in cases as well as controls. The genotype distributions for both SNPs were significantly different between cases and controls (p=0.0002 for rs2383206 and p=0.0049 for rs10757278) (Table 2). For both, homozygotes for the G allele were more frequent in study subjects with CAD than those without, the same pattern reported in the general population (10,11). No significant differences in genotype distributions were observed between case subjects who attended the Joslin Clinic and those who did not, or between those who had a previous myocardial infarction and those who had not. Haplotype analysis indicated that the effect of rs10757278 was secondary to its strong linkage disequilibrium with rs2383206 (D′=1, r2=0.78). The G allele of rs2383206 was associated with CAD regardless of the rs10757278 allele that was present on the same haplotype, whereas the A allele of rs10757278 was protective only when it occurred together with the protective allele of rs2383206. The primary role of rs2383206 in our study population was confirmed by the fact that the association with CAD disappeared for rs10757278 (p=0.51) whereas remained significant with rs2383206 (p=0.013) when the two SNPs were analyzed together in a multivariable model. Thus, only rs2383206 was considered in further analyses.

Table 2
Genotype and allele distributions in individuals with type 2 diabetes according to coronary artery disease status.

The significant associations with CAD in Table 1 and those concerning the genotypes for rs2383206 are re-expressed in Table 3 as odds ratios, both from univariable analysis and after adjustment in a multivariable model that included all the variables in the table. The univariable ORs were 1.45 (0.94–2.22) for rs2383206 heterozygotes and 2.37 (1.52–3.70) for allele G homozygotes, consistent with an additive mode of inheritance. Among the other variables, association with smoking was strongest (OR=3.24, 2.39–4.40), followed by gender (OR=1.75, 1.28–2.38), antihypertensive therapy (OR=1.73, 1.22–2.45), insulin therapy (OR=1.53, 1.13–2.08), poor glycemic control (OR=1.44, 0.98–2.1, for the highest tertile of HbA1c at examination versus the lowest), and history of hypercholesterolemia (OR=1.29, 0.87–1.92). Except for insulin therapy and history of hypercholesterolemia, all variables remained significant in a multivariable model including all other predictors. The ORs for rs2383206, both for G/G homozygotes and for heterozygotes, were similar to those obtained in univariable analysis, indicating that the effect of this SNP was not mediated by an effect on the other cardiovascular risk factors.

Table 3
Odds ratios for characteristics associated with coronary artery disease in patients with type 2 diabetes.

Interaction between 9p21 variant and poor glycemic control at examination

The magnitude of the association between 9p21 locus and CAD appeared to be larger than that described in the general population. In a meta-analysis of the studies in ref. 1011, including a total of 9,583 CAD cases and 32,292 controls from the general population, the ORs were 1.24 (1.17–1.32) for heterozygotes and 1.46 (1.37–1.57) for risk allele homozygotes, as compared to 1.45 (0.94–2.22) and 2.37 (1.52–3.70) in our study. The contrast between the ORs in our study and those from the meta-analysis was significant in the case of risk allele homozygotes (p=0.037) but not for heterozygotes (p=0.49). The contrast with studies in ref. 1213 that used SNPs in incomplete linkage disequilibrium with rs2383206 gave similar results but did not reach significance for either genotype. While the higher OR for risk allele homozygotes in our study could plausibly be attributed to selection of controls from the extreme low end of a liability distribution, it was also plausibly due to an enhanced effect of the G/G genotype in the intensely atherogenic milieu present in diabetes. In particular, we considered the possibility of an interaction with hyperglycemia, since it is the distinguishing characteristic of diabetes and excess glucose has potent proatherogenic effects in vitro (18). To explore this hypothesis, we divided study subjects according to the three rs2383206 genotypes and whether they were in the worst tertile of HbA1c values at examination (HbA1c>7.6), the measure of glycemic control most associated with CAD in Table 1. Relative to the CAD risk for patients with neither a rs238206 risk allele nor the worst glycemic control, the risks for those with only poor glycemic control, or only one risk allele, or only one risk allele and poor glycemic control were similarly increased, although not significantly (Figure 1A) (OR=1.25, 0.56–2.82; OR=1.47, 0.87–2.46, and OR=1.70, 0.86–3.01, respectively). By contrast, the CAD risk for subjects having two risk alleles but not poor glycemic control was increased two-fold (OR=1.99, 1.17–3.41), whereas the risk for study subjects with the same genotype but poor glycemic control was increased fourfold (OR=4.27, 2.26–8.01). This effect magnification had a p value for interaction (i.e., deviation from additivity in the log scale) between G/G genotype and glycemic control of 0.071 in a univariable analysis and 0.048 in a multivariable model including other cardiovascular risk factors.

Figure 1
Synergism between poor glycemic control and SNP rs2383206 on the odds of CAD in type 2 diabetes

Interaction between 9p21 variant and history of poor glycemic control

The single HbA1c value measured at entry to this study does not necessarily reflect the glycemic exposure of the case and control subjects during the years during which CAD did or did not develop. Fortunately, the majority of them were patients of the Joslin Clinic for a number of years before study entry. Excluding the 60 days prior to examination, 68% of the study group (347 controls and 151 cases) had at least two HbA1c measurements in their medical records during the preceding seven years (median=11 measurements, IQR 6–16). As compared to the rest of study group, these subjects had a younger age at diabetes diagnosis (mean=51±8 as compared to 55±9 years) and included less women (41% vs. 56.9% in controls and 24% vs. 35% in cases). In this subset, the magnitude of the interaction between rs2383206 G/G genotype and poor glycemic control, when defined by the HbA1c at examination, was similar to that in the full case-control study (unadjusted regression coefficient for interaction = 0.49±0.40 and 0.58±0.32, respectively). HbA1c measurements spanned one year before study entry in 6.4% of subjects, two years in 10.3%, three years in 10.9%, four years in 13.6%, and five years or more in 58.8%. Although the time-weighted average HbA1c value over these years was significantly correlated with the HbA1c value at examination (Spearman r=0.64, p<0.0001), use of the top tertile of the average values as the criterion for poor glycemic control resulted in a stronger interaction with the G/G genotype at rs2383206 (unadjusted regression coefficient for interaction=1.11 ± 0.40) (Figure 1B). The odds ratio was 7.83 (3.49–17.6) for subjects having both the G/G genotype and long-term poor glycemic control as compared to 1.54 (0.72–3.30) for those with the G/G genotype but not long-term poor glycemia (Figure 1B). This interaction was significant (unadjusted p=0.0052, adjusted p=0.0049) despite the reduced sample size and was not affected by further adjustment for mean arterial pressure and LDL and HDL cholesterol levels, suggesting that it was not due to a confounding effect of worse blood pressure or lipid control among individuals with high HbA1c values.

Interaction between 9p21 variant and poor glycemic control on mortality

A similar interaction between 9p21 high risk genotype and poor glycemic control was observed with respect to mortality in a study of 475 Joslin patients who had been recruited between 1993 and 1996 for genetic studies of type 2 diabetes and its complications (Table 4). As in the case-control study, multiple HbA1c measurements spanning several years before study entry were available for these subjects in the Joslin medical records (median number of measures = 10, IQR=6–15). On average, HbA1c levels were about one point higher than in the case-control study (8.3 vs. 7.4%) due to a secular trend towards lower HbA1c values in the Joslin population during the last decade or so. Table 5 shows the cumulative 10-year mortality in this cohort according to whether or not individuals carried the G/G genotype and whether or not they had a history of poor glycemic control (defined as the top tertile of the average HbA1c before study entry [HbA1c >8.9]). Among individuals who did not carry the G/G genotype, no significant differences were observed in either all-cause or cardiovascular mortality between glycemic control groups. By contrast, among G/G carriers, mortality was about twice as high in individuals with poor glycemic control as compared to those in relatively good control (p=0.021 and p=0.020 for all-cause and cardiovascular mortality, respectively) (Table 5). The p value for interaction between poor glycemic control and G/G genotype was 0.036 for all-cause and 0.049 for cardiovascular mortality. Similar evidence of interaction was obtained by means of a proportional hazard regression analysis adjusted for age and gender (p=0.028 and p=0.060 for all-cause and CVD mortality, respectively). Exclusion of 11 individuals who were also part of the case-control study did not change these results (data not shown).

Table 4
Baseline characteristics of the Joslin cohort investigated in the prospective study.
Table 5
Cumulative 10-year mortality in 475 Joslin patients with type 2 diabetes according to degree of glycemic control before study entry and 9p21 genotype.

COMMENT

One or more genetic variants located on chromosome 9p21 and tagged by SNP rs2383206 are major risk factors for coronary artery disease among individuals with type 2 diabetes. In our population of diabetic subjects, this effect is stronger than that reported in the general population due to a positive interaction between the genetic variant(s) and hyperglycemia. As was found in the general population, this association is not mediated by an effect of these genetic variants on other cardiovascular risk factors, since it is not attenuated by adjustment for these variables (10,11). This synergism between 9p21 locus and hyperglycemia on the risk of coronary artery disease translates into a similar interaction with respect to cardiovascular mortality among individuals with type 2 diabetes.

Other genes, including ADIPOQ, ADIPOR1, ENPP1, and TNFAIP3, have been reported to host polymorphisms influencing cardiovascular risk in type 2 diabetes (1924). However, the present finding stands out from the previous ones in two respects. First, it concerns a genetic effect that was identified through a genome-wide approach and has been extensively replicated in the general population (1013). Second, it is the first to demonstrate synergism with poor glycemic control.

The interaction between 9p21 allele and glycemic control may help explain the discrepancy between the potent proatherogenic effects of glucose observed in vitro and the evidence from large clinical trials of limited benefit of good glycemic control on cardiovascular outcomes in diabetic subjects (2,18,2528). Poor glycemic control has an especially strong impact on cardiovascular risk in individuals who are homozygous for allele G at rs2383206, about 30% of individuals with type 2 diabetes. The other 70% are not as sensitive to the atherogenic effects of hyperglycemia. This heterogeneity could explain the past difficulties in demonstrating an association between glycemic control and cardiovascular outcomes.

Our findings are at variance with those by Broadbent et al., who found that the strength of the association between 9p21 variant and CAD was similar among diabetic and non-diabetic subjects (29). That study, however, included individuals with both type 1 and type 2 diabetes. Furthermore, the estimate of the association between 9p21 variants and CAD in the diabetic stratum was based on a small number of controls (n=156) in whom asymptomatic CAD had not been excluded as had been in ours. Most importantly, that study did not present data on history of glycemic control. Thus, a fair comparison with our study is not possible.

SNP rs2383206 is placed in a 190 Kb region of high linkage disequilibrium containing two known genes (CDKN2A and CDKN2B), which code for three proteins (p16INK4a, ARF, and p15INK4b) that are expressed at high levels in a wide range of cell types, including endothelial and inflammatory cells. All three proteins are inhibitors of cyclin-dependent kinases controlling cell proliferation, cell aging, and apoptosis – functions that are all potentially relevant to the atherosclerotic process (3032). Excess glucose is believed to foster atherosclerosis through multiple pathways involving the build-up of advance glycation end-products or AGE, activation of protein kinase C, increased production of polyols and hexosamine, and increased oxidative stress (18,25). At what level the cellular pathways controlled by the 9p21 polymorphism(s) and those induced by high glucose intersect remain to be determined. However, one should also consider the possibility that metabolic alterations that are associated with poor glycemic control, rather than hyperglycemia per se, are the actual factors responsible for the synergism with the 9p21 locus. The finding that the interaction was unaffected by adjustment for some of these metabolic traits such as blood pressure and cholesterol levels at examination is against his hypothesis, but further analyses using more precise indicators based on repeated measures are certainly warranted. Another aspect that remains to be fully clarified is whether the association with type 2 diabetes reported in a region immediately centromeric to that associated with CAD (3335), plays any role in the observed interaction, On the other hand, a recent multicentric study has shown that the SNP associated with type 2 diabetes (rs10811661) is not associated with increased risk of CAD or other arterial disorders (36). Similarly, in an exploratory study of our cases and controls, this SNP was not associated with CAD nor was there any evidence that this SNP mediated the interaction between 9p21 variant and HbA1c levels by determining worse glycemic control (Doria et al., unpublished data).

Whether knowledge of modest genetic effects can improve disease prediction and treatment of common disorders remains uncertain (37). However, the magnitude of the joint effect of poor glycemic control and 9p21 locus favors some clinical benefit. If the probability of clinically significant CAD is about 30% for unselected type 2 diabetic subjects, one can estimate from our data that this probability goes up to 60% for diabetic individuals who have Hba1c values in the upper tertile of the distribution and carry the high risk genotype. On the other hand, the availability of a test improving prediction does not necessarily imply that such test should be adopted in clinical practice. This decision should be based on an investigation of the cost-effectiveness of prevention strategies targeted at high-risk individuals rather than to the entire population of diabetic subjects. This analysis must weigh the costs of the genetic test and available prevention strategies and the effectiveness of these strategies specifically in these high-risk patients.

Our study has two unique strengths, namely the contrast achieved by comparing angiographically-confirmed CAD cases with controls for whom CAD was ruled out by an exercise stress test, and the accurate assessment of long-term glycemic control through multiple HbA1c measurements spanning many years before study entry, in contrast to the cross-sectional measurements available to most studies of CAD in type 2 diabetes. However, some limitations should be acknowledged. One is that the analysis of the interaction between genotype and glycemic control was based on small effective sample sizes. This translated into relatively high p values for interaction (0.004–0.05), raising the concern of a false-positive result. The fact that we found a similar interaction between glycemic control and the 9p21 locus for a related outcome (cardiovascular mortality) in an independent study based on a different design makes this possibility less likely. However, additional replication studies are necessary to establish the interaction with statistical confidence. Another limitation concerns generalizability. Cases and controls were recruited at the Joslin Diabetes Center and Beth Israel Deaconess Medical Center. It is possible that the strength of the association in our study may have been overestimated due to selective referral of especially severe CAD cases to these specialized centers. However, this does not seem to be the case since only half of the cases had three stenotic vessels and less than half had a previous myocardial infarction. Generalizability is also affected by the fact that the additional risk to patients with the risk genotype was assessed against a select group of controls rather than the general population of patients with type 2 diabetes. Finally, a potential limitation concerns the effectiveness of the exercise treadmill test to exclude CAD in controls. Large meta-analyses evaluating the accuracy of the exercise treadmill test for the detection of CAD in the setting of normal ECG have determined the sensitivity of this test to be 72% with a specificity of 77% (15,38,39). Although the prevalence of asymptomatic CAD in diabetic patients has not been thoroughly investigated, a single, large prospective study found myocardial perfusion defects in 16% of asymptomatic diabetic patients (40). Assuming this prevalence of CAD in the population eligible for entry into the control group, the negative predictive value of our combined selection criteria is 93.5%. Therefore, only a small proportion of the individuals in the control group might have had asymptomatic obstructive CAD. Furthermore, such misclassification, if present, would have biased the results towards the null hypothesis, making our findings of association even more notable.

In conclusion, 9p21 locus and poor glycemic control interact in determining the risk of CAD in type 2 diabetes. This finding may have implications for our understanding of atherogenesis in diabetes and for the design of more effective prevention strategies. More broadly, it illustrates the complex etiology of multifactorial disorders and highlights the importance of accounting for gene-environment and gene-gene interactions in the quest for genetic factors contributing to these conditions.

Acknowledgments

Funding/Support: This study was supported by National Institutes of Health Grants HL73168, HL71981, and DK36836 (Genetics Core of the Diabetes & Endocrinology Research Center at the Joslin Diabetes Center), and a grant from the Donald W. Reynolds Foundation.

Role of the Sponsors: None of the above funding agencies had any role in the design and conduct of the study, in the collection, management, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.

Footnotes

Conflict of interest: The authors do not report conflicts of interest with this research.

Author Contributions: Dr. Doria had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Doria, Gervino, Hauser, Johnstone, Warram

Acquisition of data: Doria, Wojcik, Xu, Gervino, Hauser, Johnstone, Nolan, Warram

Analysis and interpretation of data: Doria, Wojcik, Xu, Gervino, Hauser, Hu, Warram

Drafting of the manuscript: Doria, Wojcik, Xu, Hauser, Hu, Warram

Critical revision of the manuscript for important intellectual content: Doria, Wojcik, Hauser, Johnstone, Hu, Gervino, Nolan, Warram

Statistical analysis: Doria, Hu, Warram

Obtained funding: Doria, Hu

Administrative, technical, or material support: Doria, Wojcik, Xu, Nolan, Johnstone, Warram

Study supervision: Doria, Gervino, Hauser, Warram

.

Additional Contributions: We are grateful to Vincenzo Trischitta, MD (University of Rome, Italy) and Andrzej S. Krolewski, MD PhD (Joslin Diabetes Center, Boston) for their valuable comments, and to Drs. Richard W. Nesto, MD (Lahey Clinic, Burlington, MA) for his initial help with the recruitment of study subjects. VD, ASK, and RWN did not received compensation for their contributions. We are also grateful to the following employees of the Joslin Diabetes Center for their technical help: Christine Powers, Ryan Thompson, Maya Becker, Aviva Bashan, Jill Duffy, Helen Kim, Rachel Sagor, and Celeste Amundsen. We acknowledge the invaluable contribution by the individuals who participated in this study.

References

1. Stamler J, Vaccaro O, Neaton JD, Wentworth D. Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the Multiple Risk Factor Intervention Trial. Diabetes Care. 1993;16:434–444. [PubMed]
2. Beckman JA, Creager MA, Libby P. Diabetes and atherosclerosis: epidemiology, pathophysiology, and management. JAMA. 2002;287:2570–81. [PubMed]
3. Abbott RD, Brand FN, Kannel WB. Epidemiology of some peripheral arterial findings in diabetic men and women: experiences from the Framingham Study. Am J Med. 1990;88:376–81. [PubMed]
4. Folsom AR, Rasmussen ML, Chambless LE, et al. Prospective associations of fasting insulin, body fat distribution, and diabetes with risk of ischemic stroke. Diabetes Care. 1999;22:1077–83. [PubMed]
5. Warram JH, Kopczynski J, Janka HU, Krolewski AS. Epidemiology of non-insulin-dependent diabetes mellitus and its macrovascular complications. A basis for the development of cost-effective programs. Endocrinol Metab Clin North Am. 1997;26:165–188. [PubMed]
6. Shea S, Ottman R, Gabrieli C, Stein Z, Nichols A. Family history as an independent risk factor for coronary artery disease. J Am Coll Cardiol. 1984;4:793–801. [PubMed]
7. Marenberg M, Risch N, Berkman LF, Floderus B, de Faire U. Genetic susceptibility to death from coronary artery disease in a study of twins. N Engl J Med. 1994;330:1041–1046. [PubMed]
8. Wagenknecht LE, Bowden DW, Carr JJ, Langefeld CD, Freedman BI, Rich SS. Familial aggregation of coronary artery calcium in families with type 2 diabetes. Diabetes. 2001;50:861–6. [PubMed]
9. Lange LA, Bowden DW, Langefeld CD, et al. Heritability of carotid artery intima-medial thickness in type 2 diabetes. Stroke. 2002;33:1876–81. [PubMed]
10. Helgadottir A, Thorleifsson G, Manolescu A, et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science. 2007;316:1491–3. [PubMed]
11. McPherson R, Pertsemlidis A, Kavaslar N, et al. A common allele on chromosome 8 associated with coronary artery disease. Science. 2007;316:1488–91. [PMC free article] [PubMed]
12. Welcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447:661–78. [PMC free article] [PubMed]
13. Samani NJ, Erdmann J, Hall AS, et al. Genomewide association analysis of coronary artery disease. N Engl J Med. 2007;357:443–53. [PMC free article] [PubMed]
14. The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care. 1997;20:1183–97. [PubMed]
15. Gibbons RJ, Balady GJ, Bricker JT, et al. ACC/AHA 2002 guideline update for exercise testing: summary article. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Update the 1997 Exercise Testing Guidelines) J Am Coll Cardiol. 2002;40:1531–40. [PubMed]
16. Freire MB, Ji L, Onuma T, Orban T, Warram JH, Krolewski AS. Gender-specific association of M235T polymorphism in angiotensinogen gene and diabetic nephropathy in NIDDM. Hypertension. 1998;31:896–9. [PubMed]
17. Krolewski AS, Laffel LM, Krolewski M, Quinn M, Warram JH. Glycosylated hemoglobin and the risk of microalbuminuria in patients with insulin-dependent diabetes mellitus. N Engl J Med. 1995;332:1251–5. [PubMed]
18. Rask-Madsen C, King GL. Proatherosclerotic mechanisms involving protein kinase C in diabetes and insulin resistance. Arterioscler Thromb Vasc Biol. 2005;25:487–96. [PubMed]
19. Bacci S, Menzaghi C, Ercolino T, et al. The +276 G/T single nucleotide polymorphism of the adiponectin gene is associated with coronary artery disease in type 2 diabetic patients. Diabetes Care. 2004;27:2015–20. [PubMed]
20. Lacquemant C, Froguel P, Lobbens S, Izzo P, Dina C, Ruiz J. The adiponectin gene SNP+45 is associated with coronary artery disease in Type 2 (non-insulin-dependent) diabetes mellitus. Diabet Med. 2004;21:776–781. [PubMed]
21. Qi L, Li T, Rimm E, et al. The +276 polymorphism of the APM1 gene, plasma adiponectin concentration, and cardiovascular risk in diabetic men. Diabetes. 2005;54:1607–10. [PubMed]
22. Soccio T, Zhang YY, Bacci S, et al. Common haplotypes at the adiponectin receptor 1 (ADIPOR1) locus are associated with increased risk of coronary artery disease in type 2 diabetes. Diabetes. 2006;55:2763–2770. [PubMed]
23. Bacci S, Ludovico O, Prudente S, et al. The K121Q polymorphism of the ENPP1/PC-1 gene is associated with insulin resistance/atherogenic phenotypes including earlier onset of type 2 diabetes and of myocardial infarction. Diabetes. 2005;54:3021–3025. [PubMed]
24. Boonyasrisawat W, Eberle D, Bacci S, et al. Tag polymorphisms at the A20 (TNFAIP3) locus are associated with lower gene expression and increased risk of coronary artery disease in type 2 diabetes. Diabetes. 2007;56:499–505. [PubMed]
25. Libby P, Plutzky J. Diabetic macrovascular disease: the glucose paradox? Circulation. 2002;106:2760–3. [PubMed]
26. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) UK Prospective Diabetes Study (UKPDS) Group. Lancet. 1998;352:837–53. [PubMed]
27. Haffner SM. Epidemiological studies on the effects of hyperglycemia and improvement of glycemic control on macrovascular events in type 2 diabetes. Diabetes Care. 1999;22 (Suppl 3):C54–6. [PubMed]
28. Wild SH, Dunn CJ, McKeigue PM, Comte S. Glycemic control and cardiovascular disease in type 2 diabetes: a review. Diabetes Metab Res Rev. 1999;15:197–204. [PubMed]
29. Broadbent HM, Peden JF, Lorkowski S, et al. Susceptibility to coronary artery disease and diabetes is encoded by distinct, tightly linked SNPs in the ANRIL locus on chromosome 9p. Hum Mol Genet. 2008;17:806–14. [PubMed]
30. Kamb A, Gruis NA, Weaver-Feldhaus J, et al. A cell cycle regulator potentially involved in genesis of many tumor types. Science. 1994;264:436–40. [PubMed]
31. Pomerantz J, Schreiber-Agus N, Liegeois NJ, et al. The Ink4a tumor suppressor gene product, p19Arf, interacts with MDM2 and neutralizes MDM2’s inhibition of p53. Cell. 1998;92:713–723. [PubMed]
32. Hannon GJ, Beach D. p15INK4B is a potential effector of TGF-beta-induced cell cycle arrest. Nature. 1994;371:257–61. [PubMed]
33. Saxena R, Voight BF, Lyssenko V, et al. Genome-Wide Association Analysis Identifies Loci for Type 2 Diabetes and Triglyceride Levels. Science. 2007;316:1331–6. [PubMed]
34. Scott LJ, Mohlke KL, Bonnycastle LL, et al. A Genome-Wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants. Science. 2007;316:1341–5. [PMC free article] [PubMed]
35. Zeggini E, Weedon MN, Lindgren CM, et al. Replication of Genome-Wide Association Signals in U.K. Samples Reveals Risk Loci for Type 2 Diabetes. Science. 2007;316:1336–41. [PMC free article] [PubMed]
36. Helgadottir A, Thorleifsson G, Magnusson KP, et al. The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm. Nat Genet. 2008;40:217–24. [PubMed]
37. Arnett DK, Baird AE, Barkley RA, et al. Relevance of genetics and genomics for prevention and treatment of cardiovascular disease: a scientific statement from the American Heart Association Council on Epidemiology and Prevention, the Stroke Council, and the Functional Genomics and Translational Biology Interdisciplinary Working Group. Circulation. 2007;115:2878–901. [PubMed]
38. Gianrossi R, Detrano R, Mulvihill D, et al. Exercise-induced ST depression in the diagnosis of coronary artery disease: a metaanalysis. Circulation. 1989;80:87–98. [PubMed]
39. Detrano R, Gianrossi R, Froelicher V. The diagnostic accuracy of the exercise electrocardiogram: a meta-analysis of 22 years of research. Prog Cardiovasc Dis. 1989;32:173–206. [PubMed]
40. Wackers FJT, Young LH, Inzucchi SE, et al. Detection of silent myocardial ischemia in asymptomatic diabetic subjects the DIAD study. Diabetes Care. 2004;27:1954–1961. [PubMed]
PubReader format: click here to try

Formats:

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...

Links

  • MedGen
    MedGen
    Related information in MedGen
  • PubMed
    PubMed
    PubMed citations for these articles
  • SNP
    SNP
    PMC to SNP links
  • Substance
    Substance
    PubChem Substance links

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...