GENETIC EPIDEMIOLOGY OF AGING AND BODY COMPOSITION

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Conducting a modern genetic epidemiologic study of aging poses formidable logistic and analytic challenges. The key logistic challenge is to have sufficient familial data from individuals aged from middle age on up; the key analytic challenge is that from the process of aging biologically meaningful phenotypes suitable for analysis must be obtained. Significant body composition changes occur in both men and women throughout their adult lives. These changes affect the amount of bone and muscle, and the amount and distribution of fat. These changes in body composition contribute to the risks for complex diseases such as osteoporosis, sarcopenia and heart disease. The logistic goal of the proposed study is to create from existing resources a genetic epidemiologic study of an essential aspect of aging-changes in body composition. The analytic goal is to elucidate the genetic architecture of age-related changes in body composition, broadly defined to include (and acknowledge the inter-relatedness of) measures of bone, muscle and fat, cardiopulmonary functioning, lipids and hormones. Familial data for the proposed study will consist of data collected as part of four currently conducted studies, and data to be specifically collected for the proposed study from these study participants and their relatives. These studies are the Fels Longitudinal Study, the Genetics of Hypertension Follow-Up Study, the Health Assessment 2000 Study, and the Osteoporosis Risk Study. The proposed study has three specific aims: 1) Pedigree organization and expansion - will focus on individuals 40 years of age and older; recruited individuals will be selected to maximize the number of relationships between them and their kin; 2) Data organization and expansion - will focus on quantitative measures of body composition and related traits that pertain to specific disease risks, including osteoporosis risk (e.g., bone mineral density), overweight and obesity (e.g., percent body fat), sarcopenia (e.g., fat-free mass), cardiopulmonary functioning (e.g, heart rate, blood pressure, and oxygen uptake), and cardiovascular disease (e.g., lipid and endocrine assays); and 3) Statistical genetic analyses - will proceed from univariate quantitative genetic analyses of cross-sectional data, to multivariate quantitative genetic analyses of cross-sectional and longitudinal data, to linkage analyses of both cross- sectional and longitudinal data in a subset of the data.