Physiologic Response to Weather Changes and Extremes in Elderly Cohort
NIEHS Grant: R21AG040027
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.