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Related Concept Videos

Aging01:26

Aging

26
Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
26

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Related Experiment Video

Updated: May 13, 2025

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
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Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development

Chang-Uk Jeong1, Jacob S Leiby1, Dokyoon Kim1,2

  • 1Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

JMIR Aging
|April 15, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an AI-powered aging clock using health data to accurately predict biological age. The model shows high accuracy and clinical relevance for personalized health monitoring and disease prevention.

Keywords:
AIagingaging clockartificial intelligencebiological ageclinical relevanceelderlygeriatricgerontologyhealth checkuphistorylife expectancymachine learningmortalityolderpredictionpredictiverecord

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Area of Science:

  • Biomedical Science
  • Gerontology
  • Artificial Intelligence in Healthcare

Background:

  • Global life expectancy is rising, but healthy life expectancy lags, necessitating accurate biological age assessment.
  • Aging-related diseases and socioeconomic burdens require advanced methods for evaluating biological aging.
  • Existing biological age prediction models are limited by conventional statistics and restricted clinical data.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-based aging clock model.
  • To predict biological age using comprehensive health check-up data.
  • To assess the clinical relevance of the AI-driven biological age prediction.

Main Methods:

  • Utilized health check-up data from Korean individuals.
  • Incorporated 27 clinical factors and employed various machine learning algorithms (e.g., Gradient Boosting, Random Forest).
  • Evaluated model performance using adjusted R2 and Mean Squared Error (MSE); employed Shapley Additive exPlanations (SHAP) for interpretation.

Main Results:

  • The Gradient Boosting model demonstrated superior performance (MSE: 4.219, R2: 0.967).
  • SHAP analysis identified key predictors: kidney function, gender, HbA1c, liver function, and anthropometrics.
  • Predicted biological age correlated strongly with metabolic status, body composition, fatty liver, smoking, and pulmonary function.

Conclusions:

  • The developed aging clock model offers high predictive accuracy and clinical utility.
  • This AI tool can enhance personalized health monitoring and interventions.
  • Integration into routine health checkups can improve health management and encourage regular screenings.