PROJECT SUMMARY/ABSTRACT Existing heart disease and stroke prediction models (e.g., Framingham) tend to overestimate risk for Hispanics/Latinxs (H/L)s. This inaccuracy has significant economic and public health impacts associated with inaccurate surveillance, intervention targeting, and medical management. Model inaccuracies likely stem from pervasive underrepresentation of H/Ls in model development and validation efforts. Consequently, traditional risk factors for cardiovascular disease (CVD) may be specific to the populations upon whom they were derived, and not generalizable to H/Ls. In addition, there may be unique disease determinants for H/Ls that remain untested or unincorporated leading to error in prediction. Importantly, resilience factors such as culturally- moderated social capital may be critical to understanding risk in this population. Addressing these gaps will lead to better understanding of CVD risk with corresponding implications for targeted intervention strategies. This K99/R00 MOSAIC proposal will use secondary data to inform current 10-year CVD risk models using theory and data-driven methods to increase CVD prediction model accuracy in H/Ls. The proposed training plan establishes a solid foundation for a career investigating H/L CVD risk and resilience factors. The training plan leverages substantial resources at The University of Arizona and a mentoring team of senior content experts. The candidate will gain the following, 1) expertise in H/L CVD disparities, 2) advanced knowledge in CVD epidemiology, risk, and etiology and pathophysiology of atherosclerotic disease, 3) applied machine learning, cross-validation, and selection of risk prediction models, and 4) cultural factors and social capital influencing H/L CVD. The research proposal has three aims focused on evaluating and informing existing 10- year CVD prediction in H/Ls. Using secondary data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), the candidate will (Aim 1 – K99) evaluate the prediction accuracy of current 10-year CVD risk models using a large H/L sample with significant representation of diverse H/Ls (HCHS/SOL). (Aim 2 – R00) the candidate will use available data to identify a group of target risk factors that improve risk prediction in H/Ls. (Aim 3 – R00) the candidate will test whether adding a social resilience component to CVD risk models will improve their prediction accuracy for this group. Machine learning will be used to identify valid predictors of 10-year CVD in Latinos. The social resilience component will capture the multi-dimensionality of social environments (e.g. spouse, family, neighborhood) using data reduction methods. The proposed research proposal adopts a holistic view of cardiovascular health to elucidate both risk and resilience factors in this growing ethnic group.