Quantifying Cardiovascular-Kidney-Metabolic(CKM) Health Age Using Wearables and lab Biomarkers

[2603.27787] Cardiovascular-Kidney-Metabolic Health Age Using Wearables and Blood Biomarkers Skip to main content Search Submit Donate Log in Quantitative Biology > Quantitative Methods arXiv:2603.27787 (q-bio) [Submitted on 29 Mar 2026 ( v1 ), last revised 11 Sep 2026 (this version, v4)] Title: Cardiovascular-Kidney-Metabolic Health Age Using Wearables and Blood Biomarkers Authors: Zeinab Esmaeilpour , A. Ali Heydari , Daniel McDuff , Anthony Z Faranesh , Conor Heneghan , Shwetak Patel , Mark Malhotra , Cathy Speed , Javier L. Prieto , Ahmed A. Metwally View PDF HTML (experimental) Abstract: Cardiovascular-Kidney-Metabolic (CKM) syndrome represents a growing public health crisis, yet its subclinical heterogeneity remains underexplored. Early detection of physiological deviation is critical for preventing irreversible organ damage. Here, we characterize the prevalence and interplay of CKM impairment in a US cohort (N=819) by integrating continuous wearable data with clinical biomarkers. We assessed cardiovascular, kidney via clinical biomarkers, namely Chol/HDL, eGFR, as well as metabolic health risk through Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). We show that while metabolic and cardiovascular disruptions are significantly associated (r=0.26, $p<0.001$), early-stage kidney impairment manifests independently. Utilizing a normalized deviance score, we identified significant health impairments in 29.0% of the cohort. Cardiovascular deviation was the most prevalent singular phenotype (13.3%), followed by metabolic (9.1%) and renal (6.25%) deviations, with dual metabolic-cardiovascular impairment occurring in only 2.2% of participants. These findings suggest that high system-specific deviance may serve as an indicator for accelerated physiological aging within the respective organ system. Furthermore, Step count, Active Zone Minutes, and resting heart rate were identified as the key wearable predictors of cardiovascular and metabolic decline. Leveraging these and CKM metrics, we developed a Health Age model that tracks physiological health, aligning with established clinical standards (Metabolic syndrome severity and modified AHA Life's Essential 8) and user-reported overall health. These findings underscore the necessity of a multi-system subtyping approach, demonstrating that wearable-derived phenotypes can facilitate the early, targeted interventions required to manage the complex landscape of CKM syndrome. Comments: Replacing to update the title slightly to better reflect journal review feedback; otherwise identical to v1 Subjects: Quantitative Methods (q-bio.QM) Cite as: arXiv:2603.27787 [q-bio.QM] (or arXiv:2603.27787v4 [q-bio.QM] for this version) https://doi.org/10.48550/arXiv.2603.27787 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zeinab Esmaeilpour [ view email ] [v1] Sun, 29 Mar 2026 17:44:21 UTC (5,193 KB) [v2] Wed, 26 Aug 2026 17:09:51 UTC (9,141 KB) [v3] Thu, 27 Aug 2026 21:20:05 UTC (9,141 KB) [v4] Fri, 11 Sep 2026 00:51:44 UTC (9,141 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.QM < prev | next > new | recent | 2026-03 Change to browse by: q-bio References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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