TY - UNPB
T1 - OMICmAge
T2 - an integrative multi-omics approach to quantify biological age with electronic medical records
AU - Chen, Qingwen
AU - Dwareka, Varun B.
AU - Carreras-Gallo, Natàlia
AU - Mendez, Kevin
AU - Chen, Yulu
AU - Begum, Sofina
AU - Kachroo, Priyadarshini
AU - Prince, Nicole
AU - Went, Hannah
AU - Mendez, Travis
AU - Lin, Aaron
AU - Turner, Logan
AU - Moqri, Mahdi
AU - Chu, Su H.
AU - Kelly, Rachel S.
AU - Weiss, Scott T.
AU - Rattray, Nicholas J.W.
AU - Gladyshev, Vadim N.
AU - Karlson, Elizabeth
AU - Wheelock, Craig
AU - Mathé, Ewy A.
AU - Dahlin, Amber
AU - McGeachie, Michael J.
AU - Smith, Ryan
AU - Lasky-Su, Jessica A.
PY - 2023/10/24
Y1 - 2023/10/24
N2 - 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.
AB - 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.
KW - epigenetics
KW - proteomics
KW - metabolomics
KW - biological aging
KW - multi-omics
KW - aging
KW - clock
KW - biobank
U2 - 10.1101/2023.10.16.562114
DO - 10.1101/2023.10.16.562114
M3 - Working Paper/Preprint
SP - 1
EP - 40
BT - OMICmAge
CY - Cold Spring Harbor, NY
ER -