To further characterize the nature of the association between socioeconomic status (SES), health behaviors and early cardiovascular disease in the Vietnam Era Twin (VET) Registry, a sample of over 4,000 twin pairs.

BACKGROUND:

Cardiovascular disease (CVD) affects approximately 60,800,000 Americans each year, claiming the lives of nearly one million of these people (American Heart Association, 2001). CVD is likely to be complex in etiology, reflecting the combined effect of both genes and environment, as well as gene x gene and gene x environment interaction. Examination of environmental factors thought to affect CVD risk in the context of a genetically informative design can help elucidate the relative contribution of genetic and environmental factors and potentially aid in identifying environmental factors that may interact with genetic vulnerability in predicting CVD. Cardiovascular morbidity and mortality are over-represented among individuals in lower socioeconomic strata, as are behaviors that increase the likelihood of cardiovascular events, including smoking, heavy alcohol consumption and physical inactivity (Adler et al., 1994; Anderson & Armstead, 1995). Thus, it is commonly assumed that socioeconomic status (SES) serves as an important environmental influence on health and health behaviors. Twin studies partition genetic and environmental variance and detect gene x environment interaction and provide a unique opportunity to study the association between SES and health.

DESIGN NARRATIVE:

The study will characterize the nature of the association between socioeconomic status (SES), health behaviors and cardiovascular disease (CVD) in the existing Vietnam Era Twin (VET) registry, a sample of over 4000 male twin pairs with a mean age of 51 in 1999. Specifically, the study will: 1) determine how strongly environmental factors contribute to individual differences in SES, health behaviors and CVD, relative to genetic factors; 2) examine whether measures of SES are associated with health behaviors and CVD mortality when controlling for concomitant genetic influences; and 3) investigate whether SES interacts with genetic vulnerabilities to predict health behaviors and CVD (gene x environment interaction). Using VETdatasets, the primary method of analysis will be twin structural equation modeling.

No eligibility criteria