Endothelial nitric oxide synthase (eNOS) gene represents a promising candidate gene for coronary heart disease (CHD) because of its impact on eNOS activity. We systematically examined the associations of eight variants of the eNOS gene (two potentially functional variants [−786T>C and Glu298Asp] and six tagging single nucleotide polymorphisms) with CHD risk in a large cohort of diabetic patients. Among 861 diabetic men (>97% Caucasian) from the Health Professionals Follow-Up Study, 220 developed CHD, and 641 men without cardiovascular disease were used as control subjects. Genotype distributions of −786T>C and Glu298Asp polymorphisms were not significantly different between case and control subjects. CHD risk was significantly higher among men with the variant allele at the rs1541861 locus (intron 8 A/C) than men without it (adjusted odds ratio 1.5 [95% confidence interval 1.1–2.1]). Moreover, among control subjects, plasma soluble vascular cell adhesion molecule concentrations were significantly higher among carriers of this allele (P 0.019) and carriers of the variant allele of the −786T>C (P 0.010), or the Glu298Asp polymorphism (P 0.002), compared with noncarriers. In conclusion, our data suggested that −786T>C, Glu298Asp, and an intron 8 polymorphism of the eNOS gene are potentially involved in the atherogenic pathway among U.S. diabetic men.

Endothelial dysfunction and vascular inflammation contribute substantially to the accelerated atherogenesis associated with type 2 diabetes. Endothelial-derived nitric oxide (NO) is the primary compound responsible for vasodilation in arteries (1) and maintenance of normal vascular homeostasis. It can inhibit platelet aggregation and modulate leukocyte endothelium interactions by inhibiting cell adhesion molecule expression (i.e., intercellular cell adhesion molecule [ICAM] and vascular cell adhesion molecule [VCAM]), reducing monocyte adherence (1), and inhibiting the proliferation of smooth muscle cells (2). Endothelial-derived NO is synthesized from l-arginine by NO synthase encoded by endothelial NO synthase (eNOS or NOS3) gene, mapped on chromosome 7q36 (3). The eNOS gene knockout mouse was demonstrated to have an increased susceptibility to develop atherosclerosis independent of blood pressure changes (4). Both ex vivo and in vivo eNOS gene transfer to atherosclerotic arteries can improve acetycholine-induced endothelium-dependent vasodilation (5,6,7,8,9,10).

The association between several polymorphisms of the eNOS gene and coronary heart disease (CHD) risk has been studied recently. In particular, the −786T>C polymorphism in the 5′-flanking region and the Glu298Asp polymorphism in exon 7 have received the most attention; these two polymorphisms have been associated with alterations in eNOS activity in experimental studies (11,12,13,14) and with the existence, severity, and progression of CHD in some, although not all, epidemiological studies (12,1517). The association of these potentially functional polymorphisms with CHD risk has not been well studied among diabetic patients. Furthermore, we are unaware of studies that have systematically investigated the association of variants of the eNOS gene with CHD risk. In the present study, we examined associations of the two potentially functional single nucleotide polymorphisms (SNPs) and tagging SNPs of the eNOS gene with CHD risk among diabetic men. The associations of these eNOS SNPs with circulating levels of soluble VCAM-1 (sVCAM-1) and ICAM-1 were also examined.

The Health Professionals Follow-Up Study is a prospective cohort study of 51,529 American male health professionals aged 40–75 years at study initiation in 1986. This cohort has been and continues to be followed through biennial mailed questionnaires focusing on various lifestyle factors and health outcomes. In addition, between 1993 and 1999 (>95% of them between 1993 and 1995), 18,159 study participants provided blood samples by overnight courier. Among them, 999 (>97% are Caucasian) had confirmed type 2 diabetes (at baseline or during follow up: 1986–2000). From this group of diabetic men, we excluded those with reported CHD or stroke at baseline, those who developed stroke or other noncoronary cardiovascular disease during follow-up, and those who developed CHD before diabetes was diagnosed (total exclusion n = 138). Among the remaining 861 diabetic men, 220 who developed CHD (i.e., nonfatal myocardial infarction n = 66, fatal CHD n = 18, and coronary artery bypass grafting [CABG] n = 136) composed the case group, and 641 diabetic men who remained free of CHD and stroke composed the control group.

Diagnosis of type 2 diabetes.

A diagnosis of diabetes was established when at least one of the following criteria was reported on a supplementary questionnaire sent to all men who reported a diagnosis of diabetes on any biennial follow-up questionnaire: 1) one or more classic symptoms (excessive thirst, polyuria, weight loss, hunger, or coma) plus a fasting plasma glucose concentration of ≥140 mg/dl or a random plasma glucose concentration of ≥200 mg/dl; 2) at least two elevated plasma glucose concentrations on different occasions (fasting ≥140 mg/dl and/or random ≥200 mg/dl and/or ≥200 mg/dl after 2 h or more on oral glucose tolerance testing) in the absence of symptoms; or 3) treatment with hypoglycemic medication (insulin or oral hypoglycemic agents). We used the National Diabetes Data Group criteria (18) to define diabetes because the majority of our case subjects were diagnosed before the release of the American Diabetes Association criteria (19). Men with type 1 diabetes were excluded. A validation study in a sub-sample of the Health Professionals Follow-Up Study demonstrated that our supplementary questionnaire is reliable in confirming diabetes diagnosis (20).

Diagnosis of cardiovascular end points.

The cardiovascular end points for this analysis comprised fatal CHD, nonfatal myocardial infarction, CABG, and percutaneous transluminal coronary angioplasty occurring between the return of the 1986 questionnaire and 31 January 2000. Nonfatal myocardial infarction was confirmed by a review of medical records based on World Health Organization criteria that included characteristic symptoms with either typical electrocardiographic changes or elevations of cardiac enzymes (21). Probable cases of myocardial infarction (no available records but confirmed by hospitalization and information from telephone interview/letter) were also included in the analysis. Confirmation of CABG or percutaneous transluminal coronary angioplasty was based on self-report only; hospital records obtained for a sample of 102 men in the Health Professionals Follow-Up Study confirmed the procedure for 96% (22). Deaths were reported by next of kin, through the postal system, and through records of the National Death Index. Using all sources combined, follow-up for deaths was >98% (23). Fatal CHD was confirmed by reviewing medical records or autopsy reports with the permission of the next of kin. Physicians who reviewed the records had no knowledge of genotype or the self-reported risk factor status of participants. Sudden deaths (i.e., deaths within 1 h of symptom onset in men without known disease that could explain death) were included in the fatal CHD category.

Ascertainment of characteristics of participants.

At baseline, participants were asked to report their height and current body weight; weight was then updated during biennial follow-up. Self-reports of body weight have been shown to be highly correlated with technician-measured weights (r = 0.97) in Health Professionals Follow-Up Study participants (24). Participants also provided information biennially on their cigarette smoking, aspirin use, and physical activity. History of high blood pressure and family history of CHD were reported in 1986. Alcohol intake was estimated with a dietary questionnaire in 1986.

Assessment of genotypes and biochemical variables.

Resequencing information from the Environmental Genome Project of the National Institute of Environmental Health Sciences at the University of Washington was used to generate haplotypes for the selection of tagging SNPs (25). There were 23 individuals reporting European descent with 119 genotyped SNPs in the database. After excluding SNPs with a minor allele frequency of <5%, haplotypes were reconstructed with PHASE (26). We selected six tagging SNPs based on both haplotype tagging SNPs algorithm with BEST (27) and linkage disequilibrium pairwise algorithm (28). They are at chromosome positions 150127045 (rs1800783), 150130092 (rs1800781), 150134981 (rs1541861), 150140689 (rs2566511), 150144031 (rs753482), and 150151284 (rs3800787) reported on the National Center for Biotechnology Information (NCBI) website (geneID 4846) (Appendix 1). Two other SNPs (−786T>C, rs2070744; and Glu298Asp, rs1799983), previously described as being associated with CHD risk, were also examined.

The collection and treatment of blood samples have been previously described (29). DNA was extracted from the buffy coat fraction of centrifuged blood using the QIAmp Blood kit (Qiagen, Chatsworth, CA). Case and control subjects were genotyped using the TaqMan system (Applied Biosystems, Foster City, CA). Primer and probe sequences are available from the authors on request. Replicate quality control samples (10%) were included and genotyped with ≥99% concordance. Genotype data were available for 828 (95% of case subjects and 97% of control subjects) of 861 study participants in the present study.

Biochemical markers were measured among diabetic men who developed CHD after their blood samples were collected in 1993–1994 (n = 120) and control subjects (n = 641). All markers were assayed in the laboratory of Dr. Nader Rifai (The Children’s Hospital, Boston, MA), which is certified by the NHLBI/CDC Lipid Standardization program. Circulating levels of soluble ICAMs (sICAMs) and sVCAMs were measured by enzyme-linked immunosorbent assay from R&D Systems (Minneapolis, MN). The day-to-day variability of the assays at concentrations of 64.2, 117, 290, and 453 ng/ml sICAM and at concentrations of 9.8, 24.9, and 49.6 ng/ml sVCAM was 10.1, 7.4, 6.0, and 6.1% and 10.2, 8.5, and 8.9%, respectively. C-reactive protein (CRP) was measured via an immunoturbidimetric assay using reagents and calibrators from Denka Seiken (Niigata, Japan). The day-to-day variability of the assay at concentrations of 0.91, 3.07, and 13.38 mg/l was 2.8, 1.6, and 1.1%, respectively. Fibrinogen was measured with an immunoturbidimetric assay using reagents and calibrators from Kamiya Biomedical (Seattle, WA). The day-to-day variability of the assay at concentrations of 4.92, 9.51, and 16.29 μg/l was 0.9, 1.1, and 1.5%, respectively.

Statistical analysis.

Frequency distributions of characteristics of study participants were examined according to case-control status. Student’s t tests were used for comparisons of means. χ2 Tests were used to assess whether genotype distributions were deviated from Hardy-Weinberg equilibrium and to determine differences in genotype frequencies and proportions of categorical variables between case and control subjects.

Unconditional logistic regression was used to calculate odds ratios (ORs) and 95% CIs for the association between SNP and CHD risk adjusted for risk factors for CHD. Adjustment for baseline variables (i.e., age, smoking status [never, past, or current smoker], and alcohol consumption [nondrinker or <5.0, <10.0, or >10.0 g/day]) and duration of diabetes changed the OR slightly, but we kept these variables in the final model because of their recognition as risk factors for CHD and interpretable variables that may account for the heterogeneity of study participants. Adjustment for other covariates such as family history of myocardial infarction, aspirin use, history of hypertension, and hypercholesterolemia at baseline did not show a significant effect on the ORs, and because they might be intermediates for the association, they were not included in the final model. Generalized linear models were used to compare geometric mean concentrations of log-transformed biomarkers across the eNOS genotype groups among diabetic men without CHD adjusting for multiple covariates. In addition, to account for multiple statistical testing, we constructed permutation null-distribution datasets (10,000 resamples) to guide the interpretation of our results (30).

Two measures of linkage disequilibrium, squared correlation coefficient (r2) and Lewontin’s standardized disequilibrium coefficient (D′), were computed between pairs of SNPs. Haplotype frequencies were estimated with the expectation-maximization algorithm, as implemented in SAS PROC Haplotype (SAS Institute, Cary, NC). Global/Omnibus tests of haplotype association and haplotype-specific ORs were calculated by haplotype replacement regression (31), assuming an additive model using the probability of carrying each haplotype provided by PROC Haplotype. Men who were homozygous for the most common haplotype were used as the reference, and rare haplotypes (combined frequency <5%) were collapsed together. We assumed an additive model, in which haplotype-specific parameters represent the per-haplotype increase in log odds of disease. All reported P values are two-tailed, and statistical significance was defined at the α = 0.05 level. All analyses were performed with SAS version 8.12 software (SAS Institute, Cary, NC).

Compared with those without CHD, diabetic men who developed CHD tended to be older and were more likely to have a family history of myocardial infarction and to have hypertension and hypercholesterolemia at baseline (Table 1). They were also more likely to use aspirin at baseline, probably because of their elevated cardiovascular risk factors.

We first examined the association of the two potentially functional SNPs, eNOS −786T>C and the Glu298Asp, with CHD risk. Genotype frequency distributions of them did not deviate from Hardy-Weinberg equilibrium among study participants (combined case and control subjects, P = 0.47 for −786T>C and 0.07 for Glu298Asp) (Table 2 and Appendix 2). No statistically significant association between each of these two polymorphisms and CHD risk was observed (minor allele frequency [95% CI] for case versus control subjects: 38.4 [33.8–43.1] vs. 40.8 [38.1–43.3] for −786T>C; 30.5 [26.7–35.1] vs. 30.6 [27.5–32.8] for Glu298Asp). Because of the relatively small number of homozygous carriers of the variant allele in each polymorphism among case subjects, we combined data of heterozygotes with homozygotes in the following analysis. Adjustment for smoking status, alcohol consumption, duration of diabetes, and BMI did not change the results materially.

Among cardiovascular disease–negative control subjects, plasma concentrations of both sVCAM-1 and sICAM-1 were significantly higher for participants with the 298T variant allele (genotype GT/TT) than those without this allele (genotype GG) (multivariate adjusted mean 1,385 vs. 1,299 ng/ml, P = 0.002 for sVCAM-1; 354 vs. 339 ng/ml, P = 0.020 for sICAM-1) (Fig. 1). Similarly, plasma concentrations of sVCAM-1 and ICAM-1 were higher among carriers of the −786 C variant allele (genotype CT/CC), although the difference in ICAM levels was not statistically significant. No statistically significant difference in CRP and fibrinogen levels according to genotypes of these two SNPs was observed.

We next examined the hypothesis that there might be common noncoding variants that influence the risk of CHD. We conducted haplotype analyses according to the linkage disequilibrium pattern. The linkage disequilibrium analyses (Appendix 3) revealed two regions characterized by relatively strong linkage disequilibrium: block 1, including SNP1 and SNP2 in promoter as indicate by D′ and r2, and block 2, including SNPs 4–7, which meets criteria for haplotype “block” as defined by Gabriel et al. (32) (haploview 4.0, 2004). SNPs 3 and 8 appeared not to belong to these two blocks. We examined their associations with CHD risk separately; no significant association was observed (Table 3). Within block 2, haplotype 4 was significantly associated with CHD risk, but the global test was not significant. Because of the current controversies in defining haplotype blocks, we also examined the association of haplotypes with CHD risk using a sliding window approach. The haplotypes containing the variant allele of SNPs 4 and/or 5 were associated with a significantly increased risk for CHD.

Because SNP4 (Glu298Asp) alone was not significantly associated with CHD risk in our analyses, we examined whether SNP5 was associated with CHD risk. We observed that the frequency of heterozygous carriers of the variant allele was significantly higher among case subjects than control subjects (52 vs. 39%, P = 0.005) (Table 4). After adjustment for age, BMI, duration of diabetes, smoking, and alcohol consumptions, heterozygous carriers had 1.8-fold increased risk of CHD compared with participants without the variant allele (95% CI 1.3–2.6, P = 0.004). Frequency distributions of homozygous carriers of the variant allele were more similar among control subjects than case subjects. Because of the small number of the homozygous carriers among case subjects, we combined heterozygous and homozygous data in the following analysis. Carriers of the variant allele (including heterozygous and homozygous) had higher risk of CHD than noncarriers (multivariate adjusted OR 1.5 [95% CI 1.1–2.1]; P = 0.03). Moreover, plasma concentrations of sVCAM-1 and sICAM-1 were higher among carriers than those without this allele in control subjects (multivariate adjusted mean 1,366 vs. 1,300 ng/ml, P = 0.019 for sVCAM-1; 350 vs. 340 ng/ml, P = 0.12 for sICAM-1) (Fig. 1). No statistically significant difference in CRP and fibrinogen levels according to genotypes of this SNP was observed. After accounting for multiple testing, we observed that empirical P values exceeded 0.05 for the association between the intron 8 SNP and CHD risk (corrected P value = 0.17; Appendix 2) and sVCAM concentrations (corrected P value = 0.07), whereas empirical P values remained <0.05 for the associations of the Glu298Asp and −786T>C SNPs with sVCAM-1 concentrations.

In the present study of diabetic men, we did not observe significant associations between the two potentially functional eNOS polymorphisms (i.e., −786T>C and Glu298Asp) and CHD risk. However, we did find a significant elevation in the endothelial dysfunction markers sVCAM-1 and sICAM-1 associated with these two polymorphisms. Another main finding was that carriers of the variant allele of a SNP located at intron 8 were associated with increased risk of CHD among diabetic men. The haplotype analyses confirmed this association. Moreover, plasma concentrations of sVCAM-1 were significantly higher among carriers of this allele.

The Glu298Asp polymorphism is the only common variant identified so far in the coding region of eNOS gene. Mechanistic studies indicated that this substitution alters function. For instance, associations have been described between this polymorphism and eNOS activity (11,33) or endothelial function (34,35). A mechanism by which eNOS Glu298Asp might reduce NO bioavailability has also been reported. eNOS Asp298 is subject to selective proteolytic cleavage in endothelial cells and vascular tissues, which could lead to reduced vascular NO generation (11,33) because the cleaved fragments would be expected to lack NO synthase activity. A functional effect for the −786 T>C variant was observed as assessed by in vitro reporter gene assays; the T−786C variant suppressed eNOS gene transmission and resulted in a significant reduction in the eNOS gene promoter activity (12). Lower eNOS mRNA and serum nitrite/nitrate levels have been found in individuals with the −786C variant in some studies as well (36,37,38).

Despite the substantial evidence that endothelial dysfunction and vascular inflammation contribute to the accelerated atherogenesis associated with type 2 diabetes, very few studies have been conducted among diabetic patients. Findings from epidemiological studies on the association between these two functional polymorphisms and CHD risk are conflicting, and studies of the association of eNOS polymorphism with CHD risk among Caucasian diabetic patients are sparse. In a recent meta-analysis of 26 studies, Casas et al. (15) found that the 298 polymorphism was associated with higher risk of ischemic heart disease (OR 1.34 [95% CI 1.03–1.75]), whereas no significant association of the −786 polymorphism with CHD risk was observed. Insufficient sample size may have limited our power to detect a significant association between the Glu298Asp polymorphism and CHD risk. We estimated a priori detectable ORs on the basis of available data. We set type 1 error at α = 0.05, sample size as 220 case subjects and 641 control subjects, power at β = 0.80, and minor allele frequency as reported previously in NCBI database (minimum 0.33). The estimated detectable OR of the Glu298Asp variant (1.54) fell into the range of reported ORs (1.0–4.2) for the association between this SNP and CHD risk among Caucasians (15). However, if true effect of this SNP on CHD risk was smaller, we would have had lower power and may have failed to detect an association.

Identifying significant associations of genetic variants with complex qualitative trait as the CHD may demand a larger sample size than that for quantitative traits. When examining the association of these two polymorphisms with quantitative trait, we observed that these two variants were associated with higher concentrations of cellular adhesion molecules (i.e., soluble ICAM and/or VCAM). Cellular adhesion molecules mediate the adhesion and migration of leukocytes, steps proposed to play a critical role in early atherogenesis (39,40). Consistent with findings from the present study, circulating levels of VCAM-1 have been positively associated with clinical atherosclerosis (41,42,43). Although we are unaware of studies that have directly examined the association of eNOS polymorphisms with CAM in diabetic patients, our results are consistent with findings from a recent study, which demonstrated that gene transfer of eNOS suppressed arterial VCAM-1 expression in an animal model (44).

There are a few plausible explanations for the observed association between the intron 8 variant and CHD risk. It is plausible that the intron 8 locus could act as a marker for another functional, yet unidentified polymorphism, in the eNOS gene or functional SNPs in other genes. In addition to the eNOS gene, chromosome 7q36 contains a few other genes that are plausibly implicated in the atherogenic pathway because of their functional significance, for example, insulin-induced gene 1 (154.48–154.49 Mb; MIM 602055) (45), protein kinase, AMP-activated, 2 noncatalytic subunit (MIM 602743), and fatty acid binding protein 5-like 3 (46). It is possible that the association we found for the intron 8 locus is the result of linkage disequilibrium with certain SNPs in genes within this area. Another possibility is that the intron 8 allele has intrinsic functional significance. Although this variant lies within an intron, it could affect mRNA stability and enzyme levels by affecting splicing. In the present study, the associations of CHD risk with the intron 8 SNP was only due to the heterozygous genotype. This could indicate only a weak dominant effect or could be due to the lack of power to detect a significant effect for the homozygotes of the rare allele because of the small number of study subjects falling into this category.

Lastly, our observed positive association may be due to chance, in particular considering that multiple variants at a locus may increase type I error (47,48). Stringent statistical criteria to correct for multiple testing and replication were recommended to determine whether the observed associations are false-positive (49). We constructed 10,000 permutation null datasets and found that empirical P values exceeded 0.05 for the association between the intron 8 SNP and CHD risk, whereas empirical probability values remained <0.05 for the associations of the Glu298Asp and −786T>C SNPs with sVCAM-1 concentrations. Thus, despite observing nominal significant associations of the intron 8 SNP with phenotypes (i.e., CHD risk and sVCAM concentrations) among diabetic patients, declaration of a genuine association between this polymorphism and phenotypes would be premature. Instead, we regarded our observed associations of the intron 8 SNP with CHD risk as a hypothesis generating observations and meriting testing in other cohorts of diabetic patients. However, it should also be noted that, by controlling the false-positive rate through empirical methods, we may have increased our false-negative rate (48). Rigid adherence to an empirical P < 0.05 significance threshold could be overly conservative and obscure some true positive associations (50). Finally, the Health Professionals Follow-Up Study does not represent a random sample of U.S. diabetic men. The relative socioeconomic homogeneity of this population, on the other hand, tends to reduce the impact of unknown confounders.

In conclusion, our data suggested that two potentially functional polymorphisms (i.e., 786T>C and Glu298Asp) and an intron 8 polymorphism of the eNOS gene are potentially involved in the atherogenic pathway among diabetic men. It will be important to confirm these findings with additional investigations among larger samples of diabetic patients. The biological importance of the intron SNP is currently unclear, and further functional testing of this SNP is required if this finding is confirmed by further studies.

Appendix 1

Descriptions of selected SNPS in the eNOS gene

SNPsChromosome positionDbSNPLocationMinor allele frequency
NCBI*Current sampleDetectable ORY
150127045 rs1800783     5′ (A/T) 40.0–56.5 40.8 1.51 
150127727 rs2070744     5′ (C/T) 36.0–41.0 41.3 1.52 
150130092 rs1800781 Intron 2 (A/G) 15.0–19.6 17.3 1.65 
150133759 rs1799983 Exon 7 (G/T) 33.3–50.0 30.6 1.54 
150134981 rs1541861 Intron 8 (A/C) 45.8–47.8 37.2 1.50 
150140689 rs2566511 Intron 13 (C/T) 22.7–28.3 28.4 1.56 
150144031 rs753482 Intron 18 (G/T) 30.4 22.0 1.54 
150151284 rs3800787     3′-UTR (C/G) 40.9 40.2 1.51 
SNPsChromosome positionDbSNPLocationMinor allele frequency
NCBI*Current sampleDetectable ORY
150127045 rs1800783     5′ (A/T) 40.0–56.5 40.8 1.51 
150127727 rs2070744     5′ (C/T) 36.0–41.0 41.3 1.52 
150130092 rs1800781 Intron 2 (A/G) 15.0–19.6 17.3 1.65 
150133759 rs1799983 Exon 7 (G/T) 33.3–50.0 30.6 1.54 
150134981 rs1541861 Intron 8 (A/C) 45.8–47.8 37.2 1.50 
150140689 rs2566511 Intron 13 (C/T) 22.7–28.3 28.4 1.56 
150144031 rs753482 Intron 18 (G/T) 30.4 22.0 1.54 
150151284 rs3800787     3′-UTR (C/G) 40.9 40.2 1.51 

Data are percent and OR.

*

Data are from UW-FHRCRC-PGA European ancestry and Hapmap European ancestry in the NCBI SNP website.

Case and control subjects combined.

Detectable OR estimated under the condition α = 0.05, power = 0.80, and minor allele frequency reported from NCBI. UTR, untranslated region.

Appendix 2

Comparison of minor allele frequency distributions between CHD case and control subjects among diabetic men

SNPsMinor allele frequency (95% CI)
P value*Corrected P value*
CHD case subjects (n = 220)Control subjects (n = 641)
37.2 (32.8–41.7) 40.8 (38.3–43.3) 0.19 0.57 
39.4 (33.8–43.1) 41.7 (38.1–43.3) 0.46 0.80 
17.9 (14.2–21.4) 17.3 (15.1–19.5) 0.61 0.80 
30.6 (26.7–35.1) 30.3 (27.5–32.8) 0.34 0.75 
38.3 (33.7–42.8) 37.2 (34.0–40.1) 0.03 0.17 
28.9 (24.8–33.3) 27.6 (25.1–30.2) 0.31 0.75 
19.8 (16.3–23.8) 22.5 (20.0–24.8) 0.52 0.80 
42.8 (38.3–47.7) 39.2 (36.4–42.2) 0.09 0.31 
SNPsMinor allele frequency (95% CI)
P value*Corrected P value*
CHD case subjects (n = 220)Control subjects (n = 641)
37.2 (32.8–41.7) 40.8 (38.3–43.3) 0.19 0.57 
39.4 (33.8–43.1) 41.7 (38.1–43.3) 0.46 0.80 
17.9 (14.2–21.4) 17.3 (15.1–19.5) 0.61 0.80 
30.6 (26.7–35.1) 30.3 (27.5–32.8) 0.34 0.75 
38.3 (33.7–42.8) 37.2 (34.0–40.1) 0.03 0.17 
28.9 (24.8–33.3) 27.6 (25.1–30.2) 0.31 0.75 
19.8 (16.3–23.8) 22.5 (20.0–24.8) 0.52 0.80 
42.8 (38.3–47.7) 39.2 (36.4–42.2) 0.09 0.31 
*

P value from Fisher’s exact tests of difference based on dominant model.

Corrected P value based on permutation test of 10,000 resampling under null distribution.

Appendix 3

Pairwise measure of linkage disequilibrium (D′) between eNOS SNPs

SNPSNP
rs1800783rs2070744rs1800781rs1799983rs1541861rs2566511rs753482rs3800787
rs1800783 — 0.923 0.192 0.177 0.078 0.028 0.293 0.0002 
rs2070744 0.969 — 0.193 0.200 0.093 0.022 0.289 0.0005 
rs1800781 0.797 0.791 — 00.086 0.115 0.470 0.051 0.234 
rs1799983 0.538 0.559 −0.976 — 0.682 0.162 0.477 0.104 
rs1541861 0.302 0.326 −0.963 0.972 — 0.228 0.372 0.114 
rs2566511 0.221 0.199 0.934 −0.986 −0.990 — 0.094 0.430 
rs753482 0.834 0.823 −0.918 0.838 0.871 −0.916 — 0.117 
rs3800787 0.014 0.022 −0.858 −0.607 −0.540 0.856 −0.786 — 
SNPSNP
rs1800783rs2070744rs1800781rs1799983rs1541861rs2566511rs753482rs3800787
rs1800783 — 0.923 0.192 0.177 0.078 0.028 0.293 0.0002 
rs2070744 0.969 — 0.193 0.200 0.093 0.022 0.289 0.0005 
rs1800781 0.797 0.791 — 00.086 0.115 0.470 0.051 0.234 
rs1799983 0.538 0.559 −0.976 — 0.682 0.162 0.477 0.104 
rs1541861 0.302 0.326 −0.963 0.972 — 0.228 0.372 0.114 
rs2566511 0.221 0.199 0.934 −0.986 −0.990 — 0.094 0.430 
rs753482 0.834 0.823 −0.918 0.838 0.871 −0.916 — 0.117 
rs3800787 0.014 0.022 −0.858 −0.607 −0.540 0.856 −0.786 — 

r2 values are given above the diagonal, and D′ values are given below the diagonal.

FIG. 1.

The associations of −786T>C, Glu298Asp, and the intron 8 (rs1541861) polymorphisms with plasma concentrations of sVCAM-1 and sICAM-1 among diabetic men without CHD at blood drawing. The data are presented as means. P values for test of difference, using log-transformed sVCAM-1 and sICAM-1, were adjusted for age, duration of diabetes, BMI, smoking status, and alcohol consumption. □, homozygous for common allele; ▪, carriers of rare allele.

FIG. 1.

The associations of −786T>C, Glu298Asp, and the intron 8 (rs1541861) polymorphisms with plasma concentrations of sVCAM-1 and sICAM-1 among diabetic men without CHD at blood drawing. The data are presented as means. P values for test of difference, using log-transformed sVCAM-1 and sICAM-1, were adjusted for age, duration of diabetes, BMI, smoking status, and alcohol consumption. □, homozygous for common allele; ▪, carriers of rare allele.

TABLE 1

Comparison of cardiovascular risk factors between CHD case and control subjects at baseline (1986) among U.S. diabetic men

CharacteristicsCHD case subjectsControl subjectsP value
n 220 641  
Age (years) 59.4 ± 7.3 55.0 ± 8.6 <0.001 
BMI (kg/m227.9 ± 4.3 27.6 ± 4.1 0.52 
Physical activity (MET/week) 14.4 ± 20.7 14.0 ± 17.7 0.81 
Alcohol consumption (g/day) 8.9 ± 16.4 11.0 ± 16.5 0.10 
Family history of myocardial infarction (%) 20.0 11.9 0.003 
History of hypertension (%) 47.7 31.4 <0.001 
History of hypercholesterolemia (%) 25.9 12.2 <0.001 
Never smokers (%) 37.3 39.4 0.59 
Aspirin users (%) 44.6 30.0 <0.001 
Race/ethnicity (% white) 99.0 97.4 0.79 
ICAM-1 (ng/ml)* 379.1 ± 80.0 356.7 ± 88.6 0.10 
VCAM-1 (ng/ml)* 1,425.4 ± 384.5 1,387.8 ± 367.4 <0.001 
CRP (mg/l) 3.4 ± 4.5 2.9 ± 4.8 0.04 
Fibrinogen (mg/dl) 491.4 ± 145.4 464.8 ± 125.2 0.14 
CharacteristicsCHD case subjectsControl subjectsP value
n 220 641  
Age (years) 59.4 ± 7.3 55.0 ± 8.6 <0.001 
BMI (kg/m227.9 ± 4.3 27.6 ± 4.1 0.52 
Physical activity (MET/week) 14.4 ± 20.7 14.0 ± 17.7 0.81 
Alcohol consumption (g/day) 8.9 ± 16.4 11.0 ± 16.5 0.10 
Family history of myocardial infarction (%) 20.0 11.9 0.003 
History of hypertension (%) 47.7 31.4 <0.001 
History of hypercholesterolemia (%) 25.9 12.2 <0.001 
Never smokers (%) 37.3 39.4 0.59 
Aspirin users (%) 44.6 30.0 <0.001 
Race/ethnicity (% white) 99.0 97.4 0.79 
ICAM-1 (ng/ml)* 379.1 ± 80.0 356.7 ± 88.6 0.10 
VCAM-1 (ng/ml)* 1,425.4 ± 384.5 1,387.8 ± 367.4 <0.001 
CRP (mg/l) 3.4 ± 4.5 2.9 ± 4.8 0.04 
Fibrinogen (mg/dl) 491.4 ± 145.4 464.8 ± 125.2 0.14 

Data are means ± SD unless otherwise indicated.

*

Only for subjects whose blood sample was available before CHD was identified (120 CHD case subjects and 641 control subjects).

TABLE 2

Associations of functional eNOS genotypes with the risk for CHD among U.S. diabetic men

GenotypesCHD case subjectsControl subjectsOR (95% CI)*OR (95% CI)
−786T>C     
    TT 101 (47) 319 (51) 1.00 1.00 
    Carrier CT/CC 113 (52) 311 (49) 0.9 (0.6–1.3) 0.9 (0.7–1.3) 
    CT 95 (44) 240 (38) 0.9 (0.6–1.3) 1.0 (0.7–1.4) 
    CC 18 (8) 71 (11) 0.9 (0.6–1.5) 0.9 (0.5–1.7) 
Glu298Asp (G894T)     
    GG 81 (38) 218 (34) 1.00 1.00 
    Carrier GT/TT 134 (62) 413 (66) 1.2 (0.8–1.6) 1.2 (0.9–1.8) 
    GT 103 (48) 313 (50) 1.2 (0.9–1.7) 1.3 (0.9–1.9) 
    TT 31 (14) 100 (16) 0.9 (0.5–1.5) 0.9 (0.5–1.7) 
GenotypesCHD case subjectsControl subjectsOR (95% CI)*OR (95% CI)
−786T>C     
    TT 101 (47) 319 (51) 1.00 1.00 
    Carrier CT/CC 113 (52) 311 (49) 0.9 (0.6–1.3) 0.9 (0.7–1.3) 
    CT 95 (44) 240 (38) 0.9 (0.6–1.3) 1.0 (0.7–1.4) 
    CC 18 (8) 71 (11) 0.9 (0.6–1.5) 0.9 (0.5–1.7) 
Glu298Asp (G894T)     
    GG 81 (38) 218 (34) 1.00 1.00 
    Carrier GT/TT 134 (62) 413 (66) 1.2 (0.8–1.6) 1.2 (0.9–1.8) 
    GT 103 (48) 313 (50) 1.2 (0.9–1.7) 1.3 (0.9–1.9) 
    TT 31 (14) 100 (16) 0.9 (0.5–1.5) 0.9 (0.5–1.7) 

Data are n (%) and OR (95% CI).

*

Adjusted for age and duration of diabetes.

Adjusted for age, duration of diabetes, BMI, smoking status, race/ethnicity, and alcohol consumption.

TABLE 3

Association of haplotypes of the eNOS gene with the risk of CHD among U.S. diabetic men

SNPsCase subjects (%)Control subjects (%)OR*
12345678
rs1800783 rs2070744 rs1800781 rs1799983 rs1541861 rs2566511 rs753482 rs380078    
 −786T>C  GLU298ASP        
Block 1           
    0       61.4 58.3 1.0 
    1       37.0 40.0 1.1 (0.7–1.5) 
    Others (Frq <5%)        1.6 1.7  
    P for global test* 0.33          
SNP3       82.2 82.7 1.0 
       17.8 17.3 1.0 (0.8–1.3) 
Block 2    31.0 33.1 1.0 
    26.4 28.0 1.3 (0.9–1.7) 
    23.0 19.9 1.1 (0.5–9.2) 
    14.7 12.8 1.6 (1.1–2.4) 
    Others (Frq <5%)        4.9 6.2  
    P for global test* 0.16          
SNP8       57.2 60.8 1.0 
       42.8 39.2 1.4 (0.9–1.9) 
SNPsCase subjects (%)Control subjects (%)OR*
12345678
rs1800783 rs2070744 rs1800781 rs1799983 rs1541861 rs2566511 rs753482 rs380078    
 −786T>C  GLU298ASP        
Block 1           
    0       61.4 58.3 1.0 
    1       37.0 40.0 1.1 (0.7–1.5) 
    Others (Frq <5%)        1.6 1.7  
    P for global test* 0.33          
SNP3       82.2 82.7 1.0 
       17.8 17.3 1.0 (0.8–1.3) 
Block 2    31.0 33.1 1.0 
    26.4 28.0 1.3 (0.9–1.7) 
    23.0 19.9 1.1 (0.5–9.2) 
    14.7 12.8 1.6 (1.1–2.4) 
    Others (Frq <5%)        4.9 6.2  
    P for global test* 0.16          
SNP8       57.2 60.8 1.0 
       42.8 39.2 1.4 (0.9–1.9) 

1, major allele; 0, minor allele.

*

Adjusted for age, BMI, duration of diabetes, smoking status, race/ethnicity, and alcohol consumption.

TABLE 4

Associations of genotypes of the intron 8 locus of the eNOS Gene (rs1541861, SNP5) with the risk for CHD among U.S. diabetic men

GenotypesCHD case subjectsControl subjectsOR (95% CI)*OR (95% CI)
AA 69 (33) 267 (44) 1.0 1.0 
AC/CC (carriers of the minor allele) 141 (67) 353 (56) 1.4 (1.1–1.9) 1.5 (1.1–2.1) 
AC 109 (52) 244 (39) 1.5 (1.2–2.1) 1.8 (1.3–2.6) 
CC 32 (15) 109 (17) 1.1 (0.7–1.7) 1.1 (0.7–1.9) 
GenotypesCHD case subjectsControl subjectsOR (95% CI)*OR (95% CI)
AA 69 (33) 267 (44) 1.0 1.0 
AC/CC (carriers of the minor allele) 141 (67) 353 (56) 1.4 (1.1–1.9) 1.5 (1.1–2.1) 
AC 109 (52) 244 (39) 1.5 (1.2–2.1) 1.8 (1.3–2.6) 
CC 32 (15) 109 (17) 1.1 (0.7–1.7) 1.1 (0.7–1.9) 

Data are n (%) and OR (95% CI).

*

Adjusted for age and duration of diabetes.

Adjusted for age, duration of diabetes, BMI, smoking status, race, and alcohol consumption.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact

This research has received National Institutes of Health Grants HL-65582 and HL-35464. F.B.H. is partly supported by an American Heart Association Established Investigator Award.

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