9-1-1 Call Data Modeling to Assess the Influence of a Mass Gathering on Host Community Metrics for Syndromic Surveillance.

Biological aging is the progressive accumulation of cellular damage leading to degeneration and organismal death (). DNA methylation patterns at CpG sites across the genome correlate strongly with the aging process, an effect that has been quantified using statistical models called ‘methylation clocks’ (). The first generation of methylation clocks were trained to predict chronological age from methylation levels at selected CpGs from across the genome (; ; ). A second generation of clocks were trained to use methylation levels to predict mortality risk as proxied by a combination of biomarkers of frailty and physiological decline (; ). Finally, a third generation of clocks have been trained to predict the rate of aging based on cohorts with longitudinal data on biomarkers of frailty (). Greater predicted DNA methylation age compared to an individual’s chronological age, known as methylation age acceleration, has been associated with an increased risk of many age-related diseases, including coronary heart disease, white matter hyperintensities, Type 2 diabetes mellitus, Parkinson’s disease, and Alzheimer’s disease (AD; ; ; ; ; ; ; ). As such, methylation clocks show potential as predictive biomarkers of the aging process and age-related health outcomes, and may capture relevant biological signals associated with aging. The clocks are also increasingly being used in social epidemiology research to quantify associations of methylation aging with exposure to adverse social and environmental factors that often differ across groups (; ; ; ). While methylation is shaped by the environment of an individual, it is also strongly influenced by genetic variation (). Millions of methylation quantitative trait loci (meQTLs)—genetic variants that associate with the methylation level of a CpG site across individuals—have been identified (). MeQTL influence methylation levels via many mechanisms, including disruption of CpGs and effects on transcription factor binding, gene expression, and other gene regulatory processes (; ). Methylation patterns also vary between human groups, and approximately 75% of variance in methylation between human groups associates with genetic ancestry (). This suggests that methylation levels and meQTLs often vary in frequency in different genetic ancestries.