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

• Published in: bioRxiv : the preprint server for biology
• Main Authors: Chen, Qingwen, Dwaraka, Varun B, Carreras-Gallo, Natàlia, Mendez, Kevin, Chen, Yulu, Begum, Sofina, Kachroo, Priyadarshini, Prince, Nicole, Went, Hannah, Mendez, Tavis, Lin, Aaron, Turner, Logan, Moqri, Mahdi, Chu, Su H, Kelly, Rachel S, Weiss, Scott T, Rattray, Nicholas J W, Gladyshev, Vadim N, Karlson, Elizabeth, Wheelock, Craig, Mathé, Ewy A, Dahlin, Amber, McGeachie, Michae J, Smith, Ryan, Lasky-Su, Jessica A
• Format: Journal Article
• Language: English
• Published: United States 24.10.2023
• Subjects: biological aging; proteomics; metabolomics; multi-omics; aging; clock; biobank; epigenetics
• DOI: 10.1101/2023.10.16.562114

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, , that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model with DNA-methylation (DNAm) and multiple omics, generating and 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, 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.