Key result
AI model accurately predicts biological age using 27 routine clinical factors.
Why the study?
Current biological age prediction models are limited by reliance on conventional statistical methods and constrained clinical information.
Cohort (n=40,385)
Yes
An AI-driven biological age prediction model using routine health checkup data demonstrated high accuracy and clinical relevance for personalized health monitoring.
Answers are grounded in this paper and cite their sources.
High-accuracy AI biological age prediction from routine data is feasible; leaves open prospective outcome validation before clinical use.
Background: The global increase in life expectancy has not shown a similar rise in healthy life expectancy. Accurate assessment of biological aging is crucial for mitigating diseases and socioeconomic burdens associated with aging. Current biological age prediction models are limited by their reliance on conventional statistical methods and constrained clinical information. Objective: This study aimed to develop and validate an aging clock model using artificial intelligence, based on comprehensive health check-up data, to predict biological age and assess its clinical relevance. Methods: We used data from Koreans who underwent health checkups at the Seoul National University Hospital Gangnam Center as well as from the Korean Genome and Epidemiology Study. Our model incorporated 27 clinical factors and employed machine learning algorithms, including linear regression, least absolute shrinkage and selection operator, ridge regression, elastic net, random forest, support vector machine, gradient boosting, and K-nearest neighbors. Model performance was evaluated using adjusted R2 and the mean squared error (MSE) values. Shapley Additive exPlanation (SHAP) analysis was conducted to interpret the model's predictions. Results: The Gradient Boosting model achieved the best performance with a mean (SE) MSE of 4.219 (0.14) and a mean (SE) R2 of 0.967 (0.001). SHAP analysis identified significant predictors of biological age, including kidney function markers, gender, glycated hemoglobin level, liver function markers, and anthropometric measurements. After adjusting for the chronological age, the predicted biological age showed strong associations with multiple clinical factors, such as metabolic status, body compositions, fatty liver, smoking status, and pulmonary function. Conclusions: Our aging clock model demonstrates a high predictive accuracy and clinical relevance, offering a valuable tool for personalized health monitoring and intervention. The model's applicability in routine health checkups could enhance health management and promote regular health evaluations.
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Jeong et al. (2025) conducted a cohort in Healthy adults (n=40,385). Gradient Boosting biological age prediction model vs. Support Vector Machine (SVM) model was evaluated on Mean squared error (MSE) for biological age prediction. An artificial intelligence-driven Gradient Boosting model accurately predicted biological age using 27 routine clinical factors, achieving a mean squared error of 4.219 and an R2 of 0.967.
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