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Predicting superagers: a machine learning approach utilizing gut microbiome features.

Ha Eun Kim1, Bori R Kim2, Sang Hi Hong3

  • 1Department of Artificial Intelligence Convergence, Ewha Womans University, College of Artificial Intelligence, Seoul, Republic of Korea.

Frontiers in Aging Neuroscience
|September 24, 2024
PubMed
Summary

Superagers maintain youthful cognitive function, and their gut microbiome differs from typical aging adults. Machine learning models using gut bacteria can predict superager status, offering insights into successful aging.

Keywords:
AlistipesLightGBM algorithmgut microbiomemachine learningsuperagers

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Area of Science:

  • Neuroscience
  • Microbiology
  • Gerontology

Background:

  • Cognitive decline is often seen as a normal part of aging.
  • Superagers, older adults with preserved cognition, offer insights into successful aging.
  • The microbiome-gut-brain axis highlights the gut microbiome's influence on brain health.

Purpose of the Study:

  • To investigate unique gut microbiome patterns in superagers.
  • To develop machine learning models for differentiating superagers from typical agers.
  • To identify specific gut microbial features associated with superior cognitive function.

Main Methods:

  • Recruited 161 participants, analyzing 102 (57 superagers, 45 typical agers) based on memory performance.
  • Collected stool samples for gut microbiome sequencing.
  • Utilized the LightGBM algorithm and SHAP analysis for predictive modeling and feature importance.

Main Results:

  • Machine learning models achieved high performance (AUC up to 0.861) in distinguishing superagers.
  • Key differentiating microbiome features included genera like Alistipes, Leuconostoc, and specific unclassified genera (PAC001137_g, PAC001138_g, PAC001115_g).
  • Higher abundance of PAC001138_g and PAC001115_g positively correlated with superager classification.

Conclusions:

  • Machine learning models effectively differentiate superagers from typical agers using gut microbiome data.
  • Gut microbiome composition is a potential biomarker for cognitive resilience in aging.
  • Further research can explore microbiome-targeted interventions for cognitive health.