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Clinical Manifestations.

Emma P Fischer1, Zahra Khodakarami1, Christopher A Brown1

  • 1University of Pennsylvania, Philadelphia, PA, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning model predicts cognitive age, identifying individuals resilient or vulnerable to aging. Removing race from the model revealed socioeconomic disparities in cognitive vulnerability.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Gerontology

Background:

  • Traditional cognitive assessments for neurodegenerative diseases can be variable and biased.
  • A cognitive age model offers a more robust measure of cognitive status by predicting age from cognitive scores and demographics.
  • This approach aids in identifying resilience or vulnerability to normal aging across diverse populations.

Purpose of the Study:

  • To develop and assess a machine learning model for predicting cognitive age.
  • To investigate the influence of demographic variables, including race and socioeconomic status (Area of Deprivation Index - ADI), on cognitive aging.
  • To identify factors contributing to cognitive resilience and vulnerability.

Main Methods:

  • A random forest model was built using psychometric and demographic data from 11,752 cognitively unimpaired individuals (NACC UDS 3).
  • The model predicted 'cognitive' age, and percentile ranks were calculated to denote vulnerability or resilience.
  • The model was evaluated with and without self-identified race as a predictor, and percentile ranks were correlated with the Area of Deprivation Index (ADI).

Main Results:

  • The model excluding race showed a stronger association between cognitive age percentile rank and ADI, indicating race-covarying may mask socioeconomic effects.
  • Excluding race increased percentile ranks for Black participants, suggesting higher vulnerability.
  • Preliminary MRI analysis indicated greater anterior cingulate and medial frontal cortex thickness in resilient individuals compared to vulnerable ones.

Conclusions:

  • The machine learning cognitive age model effectively analyzes cognition and identifies abnormal aging patterns in large datasets.
  • It successfully identifies individuals exhibiting cognitive resilience or vulnerability.
  • Understanding demographic influences on model predictions enhances insights into factors driving resilient and vulnerable aging.