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Aging under Climate Stress: How Extreme Temperatures Shape Multi-System Biological Aging

Investigators:

Eun Young Choi, Jennifer A. Ailshire, and Eileen M. Crimmins

Funding:

NIA R61AG086854 (CACHE)

Data sources:

  • Health and Retirement Study (HRS). This project utilizes sensitive and restricted data from the HRS. Sensitive data include biomarker measures used to calculate our primary outcome, biological age, drawn from the 2016 Venous Blood Study and the 2014 and 2016 Biomarker data files. Restricted data include respondents’ geographic identifiers and contextual data from the HRS Contextual Data Resource products. Researchers must apply for access to these datasets through the HRS website [https://hrs.isr.umich.edu].
  • GridMet. Daily meteorological variables (e.g., temperature, humidity, wind speed) for the contiguous US [https://climatologylab.org/gridmet.html].
  • Environmental Protection Agency. Daily concentrations of PM₂.₅ and ozone (O₃) across the contiguous US [https://epa.gov/hesc/rsig-related-downloadable-data-files].
  • Centers for Disease Control and Prevention. Social Vulnerability Index that ranks US census tracts based on 15 social factors that may adversely affect communities that encounter disasters [https://atsdr.cdc.gov/place-health/php/svi/index.html].
  • National Neighborhood Data Archive US census tract-level cooling/warming amenities (e.g., public buildings and private low-cost businesses) and national land cover database [https://nanda.isr.umich.edu].

Measures:

  • Health Measures: Biological age is estimated using the “Expanded Biological Age” measure, based on 22 clinically relevant blood-based biomarkers. This measure captures functioning across physiological systems (e.g., cardiovascular, metabolic, renal, immune). Biological age is further regressed on chronological age, and the residual is used as an indicator of biological age acceleration (value > 0) or deceleration (< 0), expressed in years.
  • Aging Measures: The analysis includes US adults aged 56 years and older. We include chronological age a covariate in the model.
  • Climate Measures: Number of extreme heat and cold days; more details below.
  • Source of Susceptibility Measures: We draw on multiple datasets to capture potential socioeconomic factors contributing to susceptibility. At the community level, variables include the Social Vulnerability Index, neighborhood social capital, availability of cooling and warming amenities, and land cover characteristics. At the familial or individual level, measures include household income and wealth, educational attainment, personal social networks, health-related behaviors, and housing type and physical conditions.

Project Summary:

Extreme heat and cold are increasingly associated with morbidity and mortality in aging populations. However, little is known about how these exposures affect biological aging, an important process that precedes the onset of chronic diseases and functional decline. The physiological burden of temperature extremes may not manifest immediately as clinical conditions but instead silently accelerate biological deterioration. Thus, examining biological aging and system-specific damage at an intervenable stage holds substantial public health significance for mitigating long-term health risks from climate stress. Animal studies provide strong evidence that heat and cold stress induce biological decline linked to aging. However, it remains unclear whether these well-characterized biological impacts of heat and cold in model systems translate to human populations. Existing human studies are often restricted to small, regionally selective samples, lacking sociodemographic and geographic representation.

This project is among the first to examine how outdoor extreme temperatures are associated with biological age acceleration measured with 22 biomarkers. Leveraging data from the nationally representative sample of US community-dwelling older adults, we examine whether older adults living in areas with more extreme heat or cold days have greater biological age acceleration and identify which physiological systems are most affected. We assess short-term exposures over the 7 days prior to blood collection for acute biological responses and long-term exposures over a 10-year period for the effects of chronic climatic conditions on biological age acceleration. To disentangle system-specific effects, we also test associations across each biomarker reflecting components (e.g., cardiovascular, immune) of biological age. Further, we identify high-risk subgroups through a novel neural network–based model (regression-guided neural networks; ReGNN [https://doi.org/10.48550/arXiv.2409.13205]) developed by our team. ReGNN complements traditional regression by capturing complex interactions across multiple sources of heterogeneity and generates individual-level susceptibility scores.

On the creation of the weather variables:

Outdoor temperature exposure is measured using the Heat Index and Wind Chill Index that incorporate air temperature with humidity or wind speed, respectively, to reflect temperature as experienced by the human body. Because no single epidemiologic threshold is universally accepted, we adopt a multi-pronged approach to define an “extreme heat and cold.” First, we apply absolute thresholds from the National Weather Service: for heat, ≥80°F (Caution), ≥90°F (Extreme Caution), ≥103°F (Danger); for cold, ≤0°F (Mild), ≤-10°F (Moderate), ≤-25°F (Severe). Second, we apply relative thresholds to account for physiological adaptations and regional acclimatization. Extreme days will be defined as those exceeding the 90th, 95th, 99th percentile (heat) or below the 10th, 5th, 1st percentile (cold) of tract-specific historical index values (1979-1999), constrained to biologically relevant thresholds (Heat Index ≥ 79°F; Wind Chill ≤20°F). For each participant, we calculate the annual mean number of extreme days for two periods: (1) short-term exposure, from the day of blood collection (BC-day) to prior 7 days; (2) long-term exposure, from BC-day to prior 10 years. Residential mobility will be accounted for using cross-wave respondents’ census tract identifiers verified from biennial HRS surveys and self-reported moving month/year data.

Outputs:

Conference presentations, peer-reviewed publications, grant proposals, documented codes to integrate HRS datasets relevant to this work.