OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records
Dwaraka, V; Chen, Q; Carreras-Gallo, N; Mendez, K; Chen, Y; Begum, S; Kachroo, P
Published in: Physiology
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 with balanced sex representation, 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. Furthermore, both DNAmEMRAge and OMICmAge exhibit greater accuracy (average ROC = 0.88) in predicting 5-year and 10-year mortality than current biological age predictors. 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. This work was funded in part by TruDiagnostic for data generation and manuscript writing. Efforts for RK, KM, YC, SB, and JALS are supported by R01HL123915 from the NIH/NHLBI; PK is supported by K99HL159234 from the NIH/NHLBI; RSK is supported by K01HL146980 from the NIH/NHLBI; SHC is supported by K01HL153941 from the NIH/NHLBI; YC and JAILS are supported by R01HL141826 from the NIH/NHLBI; AD is supported by K01HL130629 from the NIH/NHLBI; AD and JALS are supported by 1R01HL152244 from the NIH/NHLBI; MM and JALS are supported by R01HL155742 from the NIH/NHLBI; MM is supported by R01HL139634 from the NIH/NHLBI; JALS, STW, EWK are supported by the NIH U01HG008685; VNG is supported by NIH AG065403 and AG064223; CEW is supported by the JSPS KAKENHI Grant (JP19K21239), the Japanese Environment Research and Technology Development Fund (No. 5- 1752), JPJ SBP-1201854), HLF 20180290, HLF 20200693), and the Swedish Research Council (2016-02798); NJWR is supported by MR/Y010736/1 and IF\R1\231034; EM is supported by the NIH (ZICTR000410-03). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
