OMICmAge quantifies biological age by integrating multi-omics with electronic medical records

作者信息Qingwen Chen, Varun B Dwaraka, Natàlia Carreras-Gallo, Jenel F Armstrong, Raghav Sehgal, M Austin Argentieri, Anne Richmond, Andrea Aparicio, Kevin Mendez, Yulu Chen, Sofina Begum, Priyadarshini Kachroo, Nicole Prince, Tao Guo, Hannah Went, Tavis Mendez, Aaron Lin, Logan Turner, Mahdi Moqri, Su H Chu, Rachel S Kelly, Scott T Weiss, Nicholas J W Rattray, Vadim N Gladyshev, Elizabeth Karlson, Craig E Wheelock, Ewy A Mathé, Amber Dahlin, Michael J McGeachie, Riccardo E Marioni, Albert T Higgins-Chen, Ryan Smith, Jessica Lasky-Su
PMID41741793
期刊Nat Aging
发布时间2026-03
DOI10.1038/s43587-026-01073-7
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摘要

Biological aging reflects complex cellular and biochemical processes that can be measured across multiple omic layers. Using routine clinical laboratory data from ~31,000 participants in the Mass General Brigham Biobank, we developed EMRAge, a biomarker of mortality risk that can be broadly recapitulated across electronic medical records. Here we show that EMRAge can be modeled using elastic net regression with DNA methylation and multi-omics to generate DNAmEMRAge and OMICmAge, respectively. Both biomarkers are strongly associated with incident and prevalent chronic diseases and mortality, performing comparably or better than current biomarkers across discovery (Massachusetts General Brigham Aging Biobank Cohort, n = 3,451) and validation cohorts (TruDiagnostic, n = 14,213; Generation Scotland, n = 18,672). Importantly, OMICmAge leverages epigenetic biomarker proxies to integrate proteomic, metabolomic and clinical domains while remaining quantifiable from DNA methylation alone. This framework establishes an accessible, scalable measure of biological aging with potential to reveal molecular interconnections that shape healthspan and disease risk.

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