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Related Experiment Video

Updated: May 14, 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 Bennett, Jaime L Speiser, John D Olson

    Biorxiv : the Preprint Server for Biology
    |May 13, 2026
    PubMed
    Summary
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    Developing new "Aging Resilience" (AR) metrics for non-human primates (NHPs) using clinical data. These metrics, derived from non-linear models, accurately predict mortality, offering insights into biological aging and healthspan.

    Area of Science:

    • Gerontology and comparative biology
    • Veterinary clinical science
    • Computational biology and bioinformatics

    Background:

    • Quantifying biological aging is essential for understanding functional decline and healthspan.
    • Existing aging and frailty measures are limited for non-human primates (NHPs) due to unique data challenges.
    • Non-human primate clinical data presents variability and lacks normative values, hindering aging assessment.

    Purpose of the Study:

    • To develop and validate novel "Aging Resilience" (AR) metrics for non-human primates (NHPs) using routine clinical data.
    • To address challenges in NHP data, including lack of normative values and data variability.
    • To establish a scalable framework for monitoring biological aging in translational models.

    Main Methods:

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  • Utilized longitudinal clinical data from 4,328 baboons and 281 rhesus macaques.
  • Trained five computational models (Linear Mixed-Effects, Random Forest, RNN) to predict chronological age.
  • Derived AR metrics (Rate of Aging, Normalized Cumulative Aging) from non-linear models (RNN, Random Forest).

    • Main Results:

    • Non-linear models (RNN, Random Forest) demonstrated strong predictive validity for mortality (Pearson's r > 0.8).
    • AR metrics derived from non-linear models better captured biological resilience than models predicting chronological age.
    • Linear models accurately predicted chronological age (R² up to 0.99) but correlated poorly with lifespan.

    Conclusions:

    • Models accurately predicting chronological age do not necessarily reflect biological resilience or healthspan.
    • Aging Resilience (AR) metrics derived from non-linear models offer robust prediction of mortality in NHPs.
    • This study provides a scalable framework for biological aging assessment in translational research using veterinary records.