OMICmAge : an integrative multi-omics approach to quantify biological age with electronic medical records

Chen, Qingwen and Dwareka, Varun B. and Carreras-Gallo, Natàlia and Mendez, Kevin and Chen, Yulu and Begum, Sofina and Kachroo, Priyadarshini and Prince, Nicole and Went, Hannah and Mendez, Travis and Lin, Aaron and Turner, Logan and Moqri, Mahdi and Chu, Su H. and Kelly, Rachel S. and Weiss, Scott T. and Rattray, Nicholas J.W. and Gladyshev, Vadim N. and Karlson, Elizabeth and Wheelock, Craig and Mathé, Ewy A. and Dahlin, Amber and McGeachie, Michael J. and Smith, Ryan and Lasky-Su, Jessica A. (2023) OMICmAge : an integrative multi-omics approach to quantify biological age with electronic medical records. Other. bioRxiv, Cold Spring Harbor, NY. (https://doi.org/10.1101/2023.10.16.562114)

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Abstract

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

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