The Physiologic Response to Weather Changes and Extremes in an Elderly Cohort

描述（由申请人提供）：全球变暖将产生更多的极端天气事件以及更高的温度，这些都与发病率和死亡率增加有关。易感性的机制和来源尚不清楚，了解它们可能会导致更有针对性的干预措施，并改善风险评估。拟议的项目将研究一些与天气相关的暴露（温度，湿度，气压）和多种临床结果之间的关联，每种结果都与四个主要生物途径之一有关-心脏自主功能，炎症，血液动力学和肺功能。该项目将利用现有的1，597名老年男性队列，这些男性已被纵向跟踪数十年。该队列中的受试者每3至5年接受一次临床检查，收集人口统计学、心理社会学和其他流行病学数据。此外，在每次访视时收集生物标志物测量结果，例如血压、炎症血液标志物、肺功能和自主神经功能测量结果。在这项研究中，暴露的特点是通过使用现有的固定天气监测器和预测温度在每个主题的住所，可从时空土地利用回归模型，以前已经开发了这个队列。这一队列中的每个受试者也都被很好地描述了他们每天暴露于空气污染物的情况。为了解释对每个受试者的重复测量，该项目将采用多变量混合回归模型来检查天气参数与每个观察到的生物结果之间的关系，同时调整相关的潜在混杂因素，如空气污染暴露。将使用相互作用术语来测试效应修正，并确定以下任何因素是否会导致对天气健康影响的易感性-空气污染、年龄、认知功能、心理社会压力和共病状况。最后，独特的统计方法，如结构方程模型和时间聚类分析，将能够避免多重比较，了解天气和发病率之间关系的潜在中间体（如DNA甲基化），以及更全面地检查多个天气和空气污染参数之间的相互作用。该项目有可能解决以前研究中发现的一些差距和局限性，包括更准确的暴露评估，能够查看相关的生物中间结果以了解影响发病率和死亡率的机制，以及评估某些特征和条件是否使某些人群特别容易受到天气对健康的影响。

DESCRIPTION (provided by applicant): Global warming will produce increased extreme weather events as well as higher temperatures and these have been associated with increased morbidity and mortality. The mechanisms and the sources of susceptibility are not clear, and understanding them may lead to more focused interventions, and improved risk assessment. The proposed project will examine the association between a number of weather-related exposures (temperature, humidity, barometric pressure) and multiple clinical outcomes, each related to one of four overarching biological pathways - cardiac autonomic function, inflammation, hemodynamics and lung function. This project will make use of access to an existing cohort of 1,597 elderly men who have been longitudinally followed for decades. Subjects in this cohort receive a clinical examination every 3 to 5 years, at which demographic, psychosocial, and other epidemiologic data are collected. Additionally, biomarker measurements, such as blood pressure, inflammatory blood markers, pulmonary function, and autonomic function measurements are collected at each visit. In this study, exposure will be characterized through the use of an existing stationary weather monitor and predicted temperature at each subject's residence, available from a spatio-temporal land use regression model that has previously been developed for this cohort. Each subject in this cohort has also been well characterized for their daily exposure to air pollutants. To account for the repeated measurements on each subject, the project will employ multivariate mixed regression models to examine the relationship between weather parameters and each of the observed biological outcomes, while adjusting for relevant potential confounders, such as air pollution exposure. Interaction terms will be used to test for effect modification and determine if any of the following confer susceptibility to the health effects of weather - air pollution, age, cognitive function, psychosocial stress, and co-morbid conditions. Finally, unique statistical methods, such as structural equation models and temporal clustering analysis, will enable avoidance of multiple comparisons, understanding of potential intermediates (e.g. DNA methylation) in the relationship between weather and morbidity, and more comprehensive examination of interactions between multiple weather and air pollution parameters. This project has the potential to address a number of gaps and limitations seen in previous studies, including more accurate exposure assessment, the ability to look at relevant biological intermediate outcomes to understand mechanisms affecting morbidity and mortality, and the evaluation of whether certain characteristics and conditions make some populations uniquely susceptible to the health effects of weather.