PROJECT SUMMARY / ABSTRACT: Project 2 Environmental factors (i.e., ambient air pollution) are ubiquitous and potential causative factors in cardiovascular disease (CVD). Our preliminary studies using data from the NIH-funded GeoHealth Hub and the NHLBI-funded Center for cArdiometabolic Risk Reduction in South Asia Cohort (CARRS), indicate a strong temporal association for short- and long-term effects of ambient particulate matter (PM2.5)) exposure and elevated blood pressure and glycemic measures. Motivated by these findings, in Project 2 of Precision- CARRS, we will investigate the longitudinal association between PM2.5 and CVD subclinical and clinical CVD phenotypes (provided by the CVD Phenotyping Core). For atherosclerotic (ASCVD) and heart failure (HF) pathways. Precision-CARRS, will follow 21,864 socio-demographically diverse South Asians aged ≥20 years, representative of Delhi and Chennai, two cities with high levels of, and substantial spatio-temporal variability in, ambient air pollution. The cohort will be followed for up to 15 years, accruing 176,536 person-years and >1000 incident ASCVD (MI, stroke, CVD death) events and nearly 940 HF Stage C/D cases. PM2.5 will be assessed through a hybrid predictive model combining statistical and machine learning techniques using data from satellites, land use maps, meteorology, and emissions inventories. Our aims are: To assess the relationship between PM2.5 and: (Aim 1) Functional subclinical vascular and myocardial disease (arterial stiffness, and left ventricular dysfunction measured as either systolic and/or diastolic dysfunction using echocardiography); (Aim 2) Structural subclinical disease (artery calcium; carotid intimal media thickness, carotid plaque); (Aim 3) Clinical CVD events (i) ASCVD: incident nonfatal myocardial infarction, stroke, and CVD Death, (ii) HF: Stage C/D cases). In Aim 4, we will explore whether the pathophysiologic pathway-specific proteins measured in Project 1 (i.e., inflammation, immune dysregulation, myocardial ischemia and stress) modify the associations between PM2.5 and clinical ASCVD and HF. We will also contribute data to Project 3 to further examine the causal link between PM2.5 and CVD phenotypes using untargeted multi-omics, and Project 4 to separate social from environment influences in the production of CVD risk. We will contribute to the understanding of the connection between one of the leading adverse environmental exposures (i.e., air pollution) and CVD, to improving precision of risk prediction by accounting for environmental factors to advance precision CVD prevention, and generate data to facilitate global health policy to address air pollution.