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Related Experiment Video

Updated: May 8, 2026

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage
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Developing and internally validating AI-based aging resilience biomarkers in non-human primates.

Robert F Bennett1, Jaime L Speiser2, John D Olson3

  • 1Section of Molecular Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States.

The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences
|May 7, 2026
PubMed
Summary

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Developing novel "Aging Resilience" (AR) metrics for non-human primates (NHPs) offers a new way to measure biological aging. These metrics, derived from clinical data, predict mortality better than chronological age predictors.

Area of Science:

  • Gerontology and Aging Research
  • Translational Medicine
  • Comparative Biology

Background:

  • Quantifying biological aging is essential for understanding functional decline and morbidity.
  • Existing aging and frailty measures are limited for non-human primates (NHPs) due to unique data challenges.
  • Lack of normative values and data variability in NHP clinical records hinder aging assessment.

Purpose of the Study:

  • To develop and validate novel "Aging Resilience" (AR) metrics using longitudinal clinical data from NHPs.
  • To create a scalable framework for monitoring biological aging in translational models.

Main Methods:

  • Utilized longitudinal, routine clinical data from 4,328 baboons and 281 rhesus macaques.
  • Trained five computational models (Linear Mixed-Effects, Random Forest, RNN) to predict chronological age.
Keywords:
GeroscienceHealthspanLongitudinal analysisMachine learningMortality prediction

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  • Derived AR metrics: Rate of Aging (velocity) and Normalized Cumulative Aging (cumulative burden).

    • Main Results:

    • Linear models accurately predicted chronological age (R2 up to 0.99) but poorly correlated with lifespan.
    • Non-linear models (RNN, Random Forest) generated AR metrics with strong mortality prediction (Pearson's r > 0.8).
    • Models predicting chronological age best did not necessarily capture biological resilience or healthspan.

    Conclusions:

    • Aging Resilience (AR) metrics derived from non-linear models effectively predict mortality in NHPs.
    • This study establishes a scalable framework for biological aging assessment in translational models using veterinary records.
    • Biological resilience, not just chronological age prediction, is key for understanding healthspan.