Aging dimensions and markers as relative predictors of mortality in a longitudinal epidemiological sample

• Published in: PloS one Vol. 20; no. 6; p. e0324156
• Main Authors: Markon, Kristian E., Mann, Frank D., Freilich, Colin D., Cole, Steve, Krueger, Robert F.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.06.2025; Public Library of Science (PLoS)
• Subjects: Adipose tissue; Adult; Age; Aged; Aged, 80 and over; Aging; Aging - genetics; Aging - physiology; Analysis; Biology and Life Sciences; Biomarkers - metabolism; Blood pressure; Cognition; Cognition - physiology; Data collection; Discriminant analysis; DNA methylation; Epidemiology; Epigenetics; Executive function; Female; Forecasts and trends; Genetic testing; Glucose; Humans; Inflammation; Lipids; Longitudinal Studies; Male; Medical imaging; Medicine and Health Sciences; Memory; Middle Aged; Mortality; Neuroimaging; Physical Sciences; Prediction models; Research and Analysis Methods; Survival; Twins; United States - epidemiology; Variables; United States; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0324156

Measurement of aging is critical to understanding its causes and developing interventions, but little consensus exists on what components such measurements should include or how they perform in predicting mortality. The aim of this study was to identify factors of aging among a comprehensive set of indicators, and to evaluate their relative performance in predicting mortality. Measurements on 34 clinical, survey, and neuroimaging variables, along with epigenetic age markers, were obtained from two waves (2004–2021) of the Midlife in the United States (MIDUS) study. Mortality data was also available on 11875 participants, including 1908 twins. Factor analyses were used to identify aging factors, and these were used to predict mortality as of 2022. Twin data were used to model predictors of mortality within families. Factor analyses identified 9 major dimensions of aging: frailty, cognition, adiposity, glucose, blood pressure, inflammation, lipids, adaptive functioning, and neurological functioning. The strongest predictors of survival among the aging dimensions were cognition, adaptive functioning, and inflammation, and among the epigenetic markers, the decline-predictive markers (GrimAge and DunedinPACE). When entered in joint prediction models, cognition remained a significant predictor of mortality, but the epigenetic markers did not. Cognition, adaptive functioning, and inflammation remained significant predictors of mortality within twin pairs as well. Aging is a multidimensional construct, with cognition, adaptive functioning, and inflammation being the strongest predictors of survival among the aging dimensions examined. Their association with mortality is observed within families, suggesting that early developmental factors cannot entirely account for their association with survival. Interventions and assessments should prioritize cognition in measures of aging quality, along with adaptive functioning and inflammation.