A Machine Learning Approach to Predict Biological Age and its Longitudinal Drivers

TL;DR

This study presents a machine learning pipeline that predicts biological age by incorporating longitudinal biomarker changes, outperforming traditional models and emphasizing the importance of health trajectories for personalized aging assessments.

Contribution

The paper introduces a novel feature engineering approach capturing biomarker change rates, significantly enhancing biological age prediction accuracy over static models.

Findings

Engineered slope features improve age prediction accuracy.

Longitudinal health trajectories are key determinants of biological age.

Model outperforms traditional linear and ensemble models.

Abstract

Predicting an individual's aging trajectory is a central challenge in preventative medicine and bioinformatics. While machine learning models can predict chronological age from biomarkers, they often fail to capture the dynamic, longitudinal nature of the aging process. In this work, we developed and validated a machine learning pipeline to predict age using a longitudinal cohort with data from two distinct time periods (2019-2020 and 2021-2022). We demonstrate that a model using only static, cross-sectional biomarkers has limited predictive power when generalizing to future time points. However, by engineering novel features that explicitly capture the rate of change (slope) of key biomarkers over time, we significantly improved model performance. Our final LightGBM model, trained on the initial wave of data, successfully predicted age in the subsequent wave with high accuracy ($R^2 = 0.515$ for males, $R^2 = 0.498$ for females), significantly outperforming both traditional linear models and other tree-based ensembles. SHAP analysis of our successful model revealed that the engineered slope features were among the most important predictors, highlighting that an individual's health trajectory, not just their static health snapshot, is a key determinant of biological age. Our framework paves the way for clinical tools that dynamically track patient health trajectories, enabling early intervention and personalized prevention strategies for age-related diseases.