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Using non-invasive behavioral and physiological data to measure biological age in wild baboons.

Chelsea J WeibelMauna R DasariDavid A JansenLaurence R GesquiereRaphael S MututuaJ Kinyua WarutereLong'ida I SiodiSusan C AlbertsJenny TungElizabeth A Archie
Published in: GeroScience (2024)
Biological aging is near-ubiquitous in the animal kingdom, but its timing and pace vary between individuals and over lifespans. Prospective, individual-based studies of wild animals-especially non-human primates-help identify the social and environmental drivers of this variation by indicating the conditions and exposure windows that affect aging processes. However, measuring individual biological age in wild primates is challenging because several of the most promising methods require invasive sampling. Here, we leverage observational data on behavior and physiology, collected non-invasively from 319 wild female baboons across 2402 female-years of study, to develop a composite predictor of age: the non-invasive physiology and behavior (NPB) clock. We found that age predictions from the NPB clock explained 51% of the variation in females' known ages. Further, deviations from the clock's age predictions predicted female survival: females predicted to be older than their known ages had higher adult mortality. Finally, females who experienced harsh early-life conditions were predicted to be about 6 months older than those who grew up in more benign conditions. While the relationship between early adversity and NPB age is noisy, this estimate translates to a predicted 2-3 year reduction in mean adult lifespan in our model. A constraint of our clock is that it is tailored to data collection approaches implemented in our study population. However, many of the clock's components have analogs in other populations, suggesting that non-invasive data can provide broadly applicable insight into heterogeneity in biological age in natural populations.
Keyphrases
  • early life
  • electronic health record
  • endothelial cells
  • big data
  • physical activity
  • genetic diversity
  • mental health
  • machine learning
  • cardiovascular events
  • young adults
  • cross sectional
  • community dwelling