Impact of Physical Activity, Sleep, and Genetic Background on Cardiovascular Risk in the All of Us Program

项目总结 心血管疾病及其危险因素是造成健康负担和过早死亡的主要因素。物理 活动和睡眠模式是重要的行为，与心血管发病率和 死亡率。此外，个体的遗传易感性会增加或降低罹患癌症的风险 这些条件。可改变的活动和睡眠行为与遗传背景相结合的程度 影响心血管风险的因素尚不清楚。这是一个重要的知识鸿沟，因为当代 体力活动建议没有考虑到遗传变异性。我们所有人的研究计划 提供来自可穿戴设备的长期活动和睡眠数据的独特组合，全基因组 测序，以及寻求护理的患者的临床结果。这些数据源提供了一个机会 了解行为如何与遗传因素相互作用，从而增加突发疾病风险。我们假设 增加体力活动和改善睡眠对于减少过量的遗传风险是必要的。物理 活动、睡眠持续时间和质量可以通过可穿戴设备进行量化和跟踪，这些可穿戴设备现在被广泛使用 公众。这些设备能够高质量、纵向地收集这些措施，以集成到信息 对疾病的影响。遗传风险是心血管疾病的一个重要因素，也是 在量化可修改行为的作用时要考虑这一点。遗传背景代表着 哪些行为和环境相互作用来决定疾病的发病和严重程度。目前尚不清楚 体力活动和睡眠等行为可能需要调整到什么程度以适应特定的基因 个人的背景。在使用我们所有数据的初步工作中，我们进行了全组现象 步数与慢性病发病关系的相关性研究。超过590万 监测人日、心血管危险因素(肥胖、糖尿病、高血压和严重抑郁症) 在1700个表型中，与步数较低的关联最强。我们现在建议 扩展我们的工作，以衡量潜在的遗传风险以及活动和睡眠模式对 心血管风险。目标1将量化遗传风险和体力活动对修改事件的交互作用 使用肥胖、高血压、血脂异常、糖尿病和高血压的多基因风险评分的心血管危险因素 抑郁症。二次分析将考察遗传风险对心血管结果的影响。目标2将 评估睡眠时间对突发心血管危险因素的影响 遗传风险。我们的调查团队是唯一有资格最大限度地利用 我们所有人都要量化睡眠、活动和遗传因素对心血管风险的综合影响。我们有 心血管疾病、基因组分析、电子健康记录队列、睡眠方面的集体专业知识 Fitbit数据的研究和使用。这项工作的结果将为实现个性化提供初步的步骤 结合遗传背景的活动和睡眠指导。

PROJECT SUMMARY Cardiovascular disease and its risk factors are major contributors to health burden and early death. Physical activity and sleep patterns are important behaviors that are causally tied to cardiovascular morbidity and mortality. Additionally, an individual’s genetic predisposition contributes to either increased or decreased risk of these conditions. The extent to which modifiable activity and sleep behaviors combine with genetic background to influence cardiovascular risk is not known. This is an important knowledge gap because contemporary physical activity recommendations do not account for genetic variability. The All of Us Research Program offers a unique combination of long-term activity and sleep data from wearable devices, whole-genome sequencing, and clinical outcomes from patients seeking care. These data sources provide an opportunity to understand how behaviors interact with genetic factors to contribute to incident disease risk. We hypothesize that increased physical activity and improved sleep will be necessary to mitigate excess genetic risk. Physical activity and sleep duration and quality can be quantified and tracked by wearables that are now widely used by the public. These devices enable high quality, longitudinal collection of these measures to integrate to inform impact on disease. Genetic risk is a significant contributor to cardiovascular disease and an important factor to consider when quantifying the role of modifiable behaviors. Genetic background represents a risk floor upon which behavior and environment interact to determine disease onset and severity. It is currently unclear to what degree behaviors such as physical activity and sleep might need to be adjusted to the specific genetic background of the individual. In preliminary work using All of Us data, we performed a phenome-wide association study of the association between step counts and incident chronic disease. Over 5.9 million person-days of monitoring, cardiovascular risk factors (obesity, diabetes, hypertension, and major depression) emerged among 1,700 phenotypes as most strongly associated with lower step counts. We now propose to extend our work to measure the impact of underlying genetic risk and activity and sleep patterns on cardiovascular risk. Aim 1 will quantify the interaction of genetic risk and physical activity on modifying incident cardiovascular risk factors using polygenic risk scores for obesity, hypertension, dyslipidemia, diabetes, and depression. Secondary analyses will examine the impact of genetic risk on cardiovascular outcomes. Aim 2 will assess the impact of sleep duration on incident cardiovascular risk factors with and without integration of genetic risk. Our investigative team is uniquely qualified to maximally leverage the available sources of data in All of Us to quantify the combined impact of sleep, activity, and genetics on cardiovascular risk. We have collective expertise in cardiovascular disease, genomic analysis, electronic health record cohorts, sleep research, and use of Fitbit data. The results of this work will provide an initial step toward personalization of activity and sleep guidance that incorporates genetic background.