The autonomic age gap: a machine learning approach to assess biological-calendar age deviations

Machine learning has emerged as a valuable tool in precision medicine and aging research. Here, we introduce the autonomic age gap , a novel metric quantifying the individual deviation between machine-learning–estimated biological age and chronological age, based on autonomic nervous system function. We collected high-resolution electrocardiograms and continuous blood pressure recordings at rest from 1,012 healthy individuals. From these signals, 29 autonomic indices were extracted, encompassing time-, frequency-, and symbol-domain heart rate variability, cardiovascular coupling, pulse wave dynamics, and QT interval features. Based on those parameters, a Gaussian process regression model was trained on 879 participants to estimate chronological age referred to as autonomic age . The model was used to estimate the deviation from expected healthy aging, the autonomic age gap , in an independent validation set and two test sets stratified by cardiovascular risk (CVR) using the Framingham. The was evaluated via the autonomic age gap.

High CVR individuals had a significantly increased autonomic age gap of 9.7 years compared to the low CVR group and the validation set. In contrast, the low CVR group had a negative age gap of -2.2 years on average. Predictions in the validation sample closely matched calendar age with a deviation below 0.5 years. Additionally, in the high-risk group, the slope of predicted versus actual age suggested accelerated physiological aging.

These findings highlight the autonomic age gap as a sensitive and interpretable marker of cardiovascular risk and aging, offering potential clinical utility for early risk detection and longitudinal health monitoring.