A Wearable-Based Aging Clock Associates with Disease and Behavior
Abstract: Biomarkers of aging play a vital role in understanding human longevity and have the potential to inform clinical decisions and assess interventions. Existing aging clocks are typically based on blood, saliva, vital signs, or imaging collected in a clinical setting. Wearables, however, can make convenient, frequent, and inexpensive measurements throughout daily life, scaled to an entire population. We propose PpgAge, a biomarker that is non-invasively, passively, and longitudinally measurable by photoplethysmography (PPG) at the wrist from a consumer wearable. With data from the Apple Heart & Movement Study (n=213,593 participants and over 149 million participant-days), we develop a model of healthy aging and study its association with disease and behavior. PpgAge predicts chronological age with mean absolute error (MAE) of 2.43 years (95% CI 2.33–2.53) in a healthy cohort and 3.18 years (95% CI 3.16–3.19) in a general cohort. Among participants with a PpgAge gap (i.e., the deviation between predicted age and chronological age) larger than 6 years, diagnosis rates of heart disease, heart failure, and diabetes are 1.5–5 times the age- and sex-adjusted average. PpgAge gap also predicts incident disease — a PpgAge gap of 6 years is associated with a significant increased risk of adverse cardiac events (hazard ratio 1.40 [95% CI 1.26–1.54]) when controlling for other risk factors. PpgAge also associates with behavior, including smoking, exercise, and sleep. In longitudinal analyses, PpgAge exhibits a sharp increase during pregnancy and around the time of certain types of cardiac events. With additional evidence, PpgAge may be a useful surrogate for healthy aging in the study of human longevity and the treatment of age-related conditions. (ClinicalTrials.gov Identifier NCT04198194.)
Bio: Guillermo Sapiro was born in Montevideo, Uruguay, on April 3, 1966. He received his B.Sc. (summa cum laude), M.Sc., and Ph.D. from the Department of Electrical Engineering at the Technion, Israel Institute of Technology, in 1989, 1991, and 1993 respectively. After post-doctoral research at MIT, Dr. Sapiro became a Member of Technical Staff at the research facilities of HP Labs in Palo Alto, California. He was with the Department of Electrical and Computer Engineering at the University of Minnesota, where he held the position of Distinguished McKnight University Professor and Vincentine Hermes-Luh Chair in Electrical and Computer Engineering. From 2012 to 2025 he was a James B. Duke School Professor with Duke University.
He is also a Distinguished Engineer with Apple, Inc., where he leads a team on Health AI.
G. Sapiro works on theory and applications in computer vision, computer graphics, medical imaging, image analysis, and machine learning. He has authored and co-authored over 500 papers in these areas and has written a book published by Cambridge University Press, January 2001.
G. Sapiro was awarded the Gutwirth Scholarship for Special Excellence in Graduate Studies in 1991, the Ollendorff Fellowship for Excellence in Vision and Image Understanding Work in 1992, the Rothschild Fellowship for Post-Doctoral Studies in 1993, the Office of Naval Research Young Investigator Award in 1998, the Presidential Early Career Awards for Scientist and Engineers (PECASE) in 1998, the National Science Foundation Career Award in 1999, and the National Security Science and Engineering Faculty Fellowship in 2010. He received the Test-of-Time award at ICCV 2011 and at ICML 2019, only researcher to receive Test-of-Time awards in both computer vision and machine learning major venues. He was elected to the American academy of Arts and Sciences in 2018 and to the National Academy of Engineering in 2022. G. Sapiro is also a Fellow of IEEE, SIAM. G. Sapiro was the founding Editor-in-Chief of the SIAM Journal on Imaging Sciences.