1. Methods

1.1. Study subjects

The cardiovascular health study is a prospective population-based cohort study of 5888 older adult men and women recruited from four U.S. field centers: Forsyth County, North Carolina; Sacramento County, California; Washington County, Maryland; and Pittsburgh, Pennsylva- nia [6]. Those eligible to participate included all persons 65 years of age or older living in the household of each individual sampled. The original cohort (n = 5201) was recruited from 1989to1990.Asecondcohort(n = 687)ofAfrican-American participants was recruited between 1992 and 1993. All pro- cedures were conducted under institutionally approved pro- tocols for use of human subjects, and all subjects provided written informed consent.

Because available genotype data were from a case–control sample nested within the cohort, we weighted the data for each participant according to the reciprocal of his or her known sampling probability. Adjustment for sampling prob- ability recreates the frequency distribution of each case and control group in the entire cohort, thereby giving unbiased es- timates of genotype–longevity risk [13]. To obtain unbiased confidenceintervals(CI)andtestsofsignificance,weusedthe Huber–White (sandwich) method, as implemented in Stata 8.0 (College Station, TX) [14]. Hardy–Weinberg equilibrium was assessed by performing Pearson’s Chi-squared test us- ing the genotype distributions estimated for the entire cohort, but re-weighted a second time according to the case–control sample size, in order to more closely approximate the true sampling variance.

Clinical events, including fatal events due to all causes, were adjudicated by physician review panel according to medical records, death certificates, supplemented by Medi- care utilization data. We used the National Death Index to

All primary analyses were performed stratified by sex and by race. To assess associations of genotype with the continu- ous outcome variables (years of life, years of healthy life), we

A.P. Reiner et al. / Atherosclerosis 181 (2005) 175–183

medication use, cardiovascular risk factors, and prevalence of other attributes were similar in the 2224 included participants and the remaining 3664 CHS subjects.

used multiple linear regression, adjusted for age at baseline and clinic site. In two additional regression models, we also adjustedforbaselinehealthstatus(self-ratedhealth,smoking, cardiovascular disease, cancer, hypertension, and diabetes), or indicators of socioeconomic status (years of education, in- come, and marital status). The associations of genotype with mortality (overall and cause-specific) were assessed with Cox proportional hazards models [15].

After 10 years of follow-up, there were 918 deaths, in- cluding 451 cardiovascular deaths (303 from coronary heart disease) and 467 non-cardiovascular deaths (156 from can- ± cer). The mean ( S.D.) number of years lived per subject ± during those 10 years was 8.32 2.69; the mean number of ± healthy years was 6.07 3.43. Age, male sex, black race, baseline fair or poor self-reported health status, smoking, di- abetes, obesity, hypertension, and history of cardiovascular disease were strongly associated with fewer years of life and fewer years of healthy life (all P < 0.001).

Subjects having the most common genotype served as the reference group, unless otherwise noted. Covariate-adjusted P-values for genetic main effects were calculated from the composite Wald test for indicator variables corresponding to the two minor genotypes. Because our primary hypotheses required performing multiple, independent tests of four un- ≤ linked genes, P-values 0.01 were considered statistically significant.

Except for TAFI 438 G/A, the variant promoter alle- les were less common among blacks than whites (Table 2). Genotype distributions did not differ significantly according to other baseline health-related variables such as obesity, hy- pertension, diabetes, cardiovascular disease, cancer, and fair or poor health status (all P > 0.10).

In hypothesis-generating analyses, we explored the in- teractions of inflammatory response genotypes with three risk factors that influence inflammation: cigarette smoking, obesity (body mass index ≥ 30 kg/m2), and baseline use of any medication having anti-inflammatory properties (aspirin, NSAIDs, ACE inhibitors, or statins). Models were adjusted for sex, sex-by-genotype interaction, baseline cardiovascu- lar disease, current smoking, diabetes, and hypertension. P- values for interaction were calculated from Wald tests for the cross-product interaction terms. Because of the exploratory nature of these analyses, we set statistical significance at P ≤ 0.05.

Among white participants, the alleles of the TAFI − 438 G/A polymorphism deviated significantly from Hardy– Weinberg proportions (Table 2). The Hardy–Weinberg dis- equilibrium was strongly present among men (P = 0.0007), but not among women (P = 0.26). When adjusted for mul- tiple comparisons, the disequilibrium remained signifi- cant (P-values 0.03 overall, 0.01 in white men). Possible, but less extreme, deviations from Hardy–Weinberg equi- librium were noted for the PAI-1 4G/5G polymorphism

2. Results

The mean age at study entry was 73 years, 84% of the par- ticipants were white, and 54% were women (Table 1). Demo- graphic characteristics, overall health and functional status,