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 theautonomic 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 asautonomic age. The model was used to estimate the deviation from expected healthy aging, theautonomic 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 increasedautonomic age gapof 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.