PROJECT SUMMARY: Cardiovascular disease (CVD) is a leading cause of disability and death in the United States; and the risks for disease are greatest among older adults. For more than half a century, CVD risk calculators have been used to estimate a person’s risk of developing CVD and to guide treatment strategies to prevent disease. Unfortunately, the tool recommended by current guidelines performs poorly in older adults and among dis/advantaged population groups. The proposed reasons for these shortcomings are twofold. First, the existing model does not account for changes in risk factors—e.g., cholesterol, blood pressure (BP), etc.—that occur with age-related declines in vascular function and/or the use of preventive therapies. Second, the existing model does not account for social determinants of health (SDOH) and other non-clinical factors that have been shown to improve risk prediction across diverse population groups. Without addressing these issues, a vital tool for CVD prevention will remain suboptimal and opportunities to reduce the development of CVD in adults who are at greatest risk of disease will remain urgent and unmet. Our central hypothesis is that CVD risk prediction and its translation to disease prevention can be greatly improved in older adults by synchronizing age-related changes among multiple established risk factors while accounting for SDOH and other factors that contribute to CVD risks. Using nationally-representative data (Health and Retirement Study [HRS]) and pooled community-based cohorts (Atherosclerosis Risk in Communities [ARIC], Cardiovascular Health Study [CHS], Multiethnic Study of Atherosclerosis [MESA], Framingham [FHS] Original, and FHS Offspring), our aims are threefold: First, we will use group-based trajectory models to classify age-related changes within and among the major risk factors for CVD in a nationally-representative sample of older adults (65+). Results will then be validated with external (pooled cohort) data to identify the most efficient and robust classifications of age-related changes among the multiple established risk factors. Second, we will predict the onset of CVD based on the multi-trajectory profiles of risk factors. By accounting for changes in risk factors and trajectory group-membership with advancing age, this approach will mirror a real-world clinical setting where follow-up measures become available during routine care and a patient’s changes in risk factors translate into greater (or lesser) risks for a CVD event. Third, we will examine a wide array of socioeconomic, psychosocial, and behavioral factors to identify SDOH and other key factors related to individual risk profiles and CVD outcomes. The major aims of this proposal are highly aligned with the National Institutes of Health’s (NIH) mission to better understand the interdisciplinary dynamics of aging to prevent disease in older adults. Our study responds to this call by using multiple datasets and innovative methods ...