Environmental Exposures and the Development of Subclinical and Clinical Vascular and Myocardial Disease

项目总结/摘要：项目2 环境因素（即，环境空气污染）是普遍存在的潜在致病因素， 心血管疾病（CVD）。我们的初步研究使用了来自NIH资助的GeoHealth Hub和 NHLBI资助的南亚队列心脏代谢风险降低中心（CARRS）表明， 环境颗粒物（PM2.5）暴露的短期和长期影响的时间关联， 血压升高和血糖测量。受这些发现的启发，在精密度的项目2中- CARRS，我们将研究PM2.5与亚临床和临床CVD之间的纵向关联 表型（由CVD表型核心提供）。动脉粥样硬化（ASCVD）和心力衰竭（HF） 途径。Precision-CARRS将随访21，864名年龄≥20岁的社会人口统计学多样化的南亚人， 德里和钦奈的代表，这两个城市的高水平，和大量的时空变化， 环境空气污染。该队列将被随访长达15年，累计176，536人-年， ASCVD（MI、卒中、CVD死亡）事件和近940例HF C/D期病例。PM2.5将被评估 通过结合统计和机器学习技术的混合预测模型， 卫星、土地使用图、气象学和排放清单。我们的目标是：评估 PM2.5和：（目的1）功能性亚临床血管和心肌疾病（动脉僵硬，左 使用超声心动图测量的心室功能障碍为收缩和/或舒张功能障碍）;（目的 2)结构性亚临床疾病（动脉钙;颈动脉内膜中层厚度，颈动脉斑块）;（目标3） 临床CVD事件（i）ASCVD：非致死性心肌梗死、卒中和CVD死亡事件，（ii）HF：分期 C/D病例）。在目标4中，我们将探索在肿瘤组织中测量的病理生理学病理特异性蛋白质是否与肿瘤组织中的病理生理学病理特异性蛋白质相关。 项目1（即，炎症、免疫失调、心肌缺血和应激）改变了 PM2.5与临床ASCVD和HF之间的关系。我们亦会为项目3提供数据，以进一步研究 使用非靶向多组学和项目4将社会因素与PM2.5和CVD表型之间的因果关系分开， 在CVD风险的产生中受到环境影响。我们将为理解 主要不利环境暴露之一（即，空气污染）和CVD， 通过考虑环境因素提高风险预测的精度，以推进精确CVD 预防，并产生数据，以促进全球卫生政策，以解决空气污染。

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.