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

Qingwen Chen; Varun Bhamidipati Dwaraka; Natàlia Carreras-Gallo; Kevin M. Mendez; Yulu Chen; Sofina Begum; Priyadarshini Kachroo; Nicole Prince; Hannah Went; Tavis L. Mendez; Aaron Lin; Logan Turner; Mahdi Moqri; Su H. Chu; Rachel S. Kelly; Scott T. Weiss; Nicholas J. W. Rattray; Vadim N. Gladyshev; Elizabeth W. Karlson; Craig Edward Wheelock; Ewy A. Mathé; Amber Dahlin; Michael J. McGeachie; Ryan C. Smith; Jessica Ann Lasky-Su

doi:10.1101/2023.10.16.562114

BioinformaticsbioRxivOct 16, 2023

Preprint

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

Qingwen Chen iD, Varun Bhamidipati Dwaraka iD, Natàlia Carreras-Gallo iD, Kevin M. Mendez iD, Yulu Chen iD, Sofina Begum iD, Priyadarshini Kachroo iD, Nicole Prince iD, Hannah Went, Tavis L. Mendez, Aaron Lin, Logan Turner, Mahdi Moqri iD, Su H. Chu iD, Rachel S. Kelly iD, Scott T. Weiss iD, Nicholas J. W. Rattray iD, Vadim N. Gladyshev iD, Elizabeth W. Karlson iD, Craig Edward Wheelock iD, Ewy A. Mathé iD, Amber Dahlin iD, Michael J. McGeachie iD, Ryan C. Smith iD, Jessica Ann Lasky-Su iD

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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