| Title: | Artificial intelligence-derived photoplethysmography age as a digital biomarker for cardiovascular health |
| Journal: | Communications Medicine |
| Published: | 19 Nov 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41258400/ |
| DOI: | https://doi.org/10.1038/s43856-025-01188-9 |
| URL: | https://www.nature.com/articles/s43856-025-01188-9.pdf |
Publication 18885
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Abstract
BackgroundPhotoplethysmography (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.MethodsWe 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).ResultsAfter 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).ConclusionsAI-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.</p>
5 Authors
- Guangkun Nie
- Qinghao Zhao
- Gongzheng Tang
- Yaxin Li
- Shenda Hong
1 Application
| Application ID | Title |
|---|---|
| 90018 | Environment, genotype, AI-derived phenotype, and the risk of cardiovascular diseases, metabolic diseases, and chronic kidney disease |
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