Quantifying cardiovascular autonomic aging with machine learning

作者：Andy Schumann, Yubraj Gupta, Maria Geisler, Feliberto de la Cruz, Denis Gerstorf, Ilja Demuth, Maja Olecka, Christian Gaser, Karl-Jürgen Bär · 发表于：American Journal of Physiology-Heart and Circulatory Physiology · 年份：2025 · DOI：10.1152/ajpheart.00693.2025 · 被引用次数：1 · 研究领域：Heart Rate Variability and Autonomic Control、Blood Pressure and Hypertension Studies、ECG Monitoring and Analysis

The cardiovascular autonomic age (CAA) gap is a new machine learning-based marker that reveals when the body ages faster than the clock. Using resting-state cardiovascular recordings from 1,000+ participants, we show that individuals with higher cardiovascular risk exhibit accelerated autonomic aging. The CAA gap could become a sensitive, interpretable tool for early detection and long-term monitoring.