Artificial intelligence-derived photoplethysmography age as a digital biomarker for cardiovascular health

Photoplethysmography (PPG), increasingly available through wearable devices, provides a non-invasive means of monitoring human hemodynamics. In this study, we introduce artificial intelligence-derived photoplethysmography (AI-PPG) age, a deep learning-based estimate of biological age from raw PPG signals, and evaluate its potential as a digital biomarker for cardiovascular health. We developed a deep learning model with a distribution-aware loss function to reduce bias from imbalanced data. The model was trained and evaluated on the UK Biobank cohort (N = 212,231). We analyzed the association between the AI-PPG age gap (AI-PPG age minus calendar age) and multiple cardiovascular and metabolic outcomes, assessed its longitudinal value using serial PPG measurements, and externally validated its generalizability in an independent MIMIC-III-derived cohort (N = 2343). After adjusting for key confounders, participants with an AI-PPG age gap greater than 9 years have a significantly higher risk of major adverse cardiovascular and cerebrovascular events (hazard ratio of 2.37, p = 8.46 × 10−80), as well as seven secondary outcomes including coronary heart disease and myocardial infarction (all p < 0.005). Conversely, those with a gap below −9 years show a lower risk profile. Longitudinal analysis demonstrates that changes in AI-PPG age add predictive value over time. In the external validation cohort, each one-year increase in AI-PPG age gap is associated with higher in-hospital mortality (odds ratio of 1.02, p = 0.01). AI-PPG age is a scalable, non-invasive biomarker for cardiovascular health assessment. Integrated with wearable devices, it may enable population-level screening, personalized monitoring, and early intervention. Wearable devices can measure tiny changes in blood flow using light. We developed a computer method that turns this information into a measure called “PPG age”, which shows how old the blood vessels appear. In our study of over 200,000 people, those with a PPG age much higher than their actual age were more likely to develop heart problems, such as coronary heart disease. People with a younger PPG age had lower risks. Tracking changes in this measure over time also provided useful clues about future health. Because it works with simple wearable sensors, this approach could support large-scale heart health screening and personalized prevention in everyday life. Nie, Zhao et al. develop a deep learning approach to estimate biological age from wearable photoplethysmography signals. They show that the gap between estimated and calendar age predicts major cardiovascular events and mortality, highlighting its value as a scalable digital biomarker for cardiovascular health.