OMICmAge quantifies biological age by integrating multi-omics with electronic medical records

Qingwen Chen; Varun B. Dwaraka; Natàlia Carreras-Gallo; Jenel F. Armstrong; Raghav Sehgal; M. Austin Argentieri; Anne Richmond; Andrea Aparicio; Kevin Mendez; Yulu Chen; Sofina Begum; Priyadarshini Kachroo; Nicole Prince; Tao Guo; Hannah Went; Tavis Mendez; Aaron Lin; Logan Turner; Mahdi Moqri; Su H. Chu; Rachel S. Kelly; Scott T. Weiss; Nicholas J. W. Rattray; Vadim N. Gladyshev; Elizabeth Karlson; Craig E. Wheelock; Ewy A. Mathé; Amber Dahlin; Michael J. McGeachie; Riccardo E. Marioni; Albert T. Higgins-Chen; Ryan Smith; Jessica Lasky-Su

doi:10.1038/s43587-026-01073-7

OMICmAge quantifies biological age by integrating multi-omics with electronic medical records

Qingwen Chen, Varun B. Dwaraka, Natàlia Carreras-Gallo, Jenel F. Armstrong, Raghav Sehgal, M. Austin Argentieri, Anne Richmond, Andrea Aparicio, Kevin Mendez, Yulu Chen, Sofina Begum, Priyadarshini Kachroo, Nicole Prince, Tao Guo, Hannah Went, Tavis Mendez, Aaron Lin, Logan Turner, Mahdi Moqri, Su H. ChuRachel S. Kelly, Scott T. Weiss, Nicholas J. W. Rattray, Vadim N. Gladyshev, Elizabeth Karlson, Craig E. Wheelock, Ewy A. Mathé, Amber Dahlin, Michael J. McGeachie, Riccardo E. Marioni, Albert T. Higgins-Chen, Ryan Smith, Jessica Lasky-Su^*

^*Corresponding author for this work

Institute of Genetics and Cancer

Research output: Contribution to journal › Article › peer-review

Abstract

Biological aging reflects complex cellular and biochemical processes
that can be measured across multiple omic layers. Using routine clinical
laboratory data from ~31,000 participants in the Mass General Brigham
Biobank, we developed EMRAge, a biomarker of mortality risk that can be
broadly recapitulated across electronic medical records. Here we show that
EMRAge can be modeled using elastic net regression with DNA methylation
and multi-omics to generate DNAmEMRAge and OMICmAge, respectively.
Both biomarkers are strongly associated with incident and prevalent chronic
diseases and mortality, performing comparably or better than current
biomarkers across discovery (Massachusetts General Brigham Aging
Biobank Cohort, n = 3,451) and validation cohorts (TruDiagnostic, n = 14,213;
Generation Scotland, n = 18,672). Importantly, OMICmAge leverages
epigenetic biomarker proxies to integrate proteomic, metabolomic and
clinical domains while remaining quantifiable from DNA methylation alone.
This framework establishes an accessible, scalable measure of biological
aging with potential to reveal molecular interconnections that shape
healthspan and disease risk.

Original language	English
Pages (from-to)	722-737
Journal	Nature Aging
Volume	6
Issue number	3
Early online date	25 Feb 2026
DOIs	https://doi.org/10.1038/s43587-026-01073-7
Publication status	Published - Mar 2026

Keywords / Materials (for Non-textual outputs)

Aged
Aged, 80 and over
Aging/genetics
Biomarkers
Cohort Studies
DNA Methylation
Electronic Health Records
Epigenesis, Genetic
Female
Genomics
Humans
Male
Metabolomics
Middle Aged
Multiomics
Proteomics/methods

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10.1038/s43587-026-01073-7Licence: CC BY-NC-ND

OMICmAge quantifies biological ageFinal published version, 5.18 MBLicence: Creative Commons: Attribution (CC-BY)

https://www.nature.com/articles/s43587-026-01073-7

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4 Finished

Exploiting genomic approaches to identify the environmental basis of depression
McIntosh, A. (Principal Investigator)
Wellcome Trust (Rcl)
1/06/21 → 31/05/27
Project: Research
Harnessing multiparametric biobank data to develop novel predictive models of frailty
Marshall, A. (Principal Investigator)
University of Strathclyde
4/09/23 → 26/09/25
Project: Research
Stratifying Resilience and Depression Longitudinally
McIntosh, A. (Principal Investigator), Deary, I. (Co-investigator), Evans, K. (Co-investigator), Haley, C. (Co-investigator) & Porteous, D. (Co-investigator)
UK-based charities
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Cite this

Chen, Q., Dwaraka, V. B., Carreras-Gallo, N., Armstrong, J. F., Sehgal, R., Argentieri, M. A., Richmond, A., Aparicio, A., Mendez, K., Chen, Y., Begum, S., Kachroo, P., Prince, N., Guo, T., Went, H., Mendez, T., Lin, A., Turner, L., Moqri, M., ... Lasky-Su, J. (2026). OMICmAge quantifies biological age by integrating multi-omics with electronic medical records. Nature Aging, 6(3), 722-737. https://doi.org/10.1038/s43587-026-01073-7

@article{fd32b09654a54dd7a1baf632934e57fd,

title = "OMICmAge quantifies biological age by integrating multi-omics with electronic medical records",

abstract = "Biological aging reflects complex cellular and biochemical processes that can be measured across multiple omic layers. Using routine clinical laboratory data from \textasciitilde{}31,000 participants in the Mass General Brigham Biobank, we developed EMRAge, a biomarker of mortality risk that can be broadly recapitulated across electronic medical records. Here we show that EMRAge can be modeled using elastic net regression with DNA methylation and multi-omics to generate DNAmEMRAge and OMICmAge, respectively. Both biomarkers are strongly associated with incident and prevalent chronic diseases and mortality, performing comparably or better than current biomarkers across discovery (Massachusetts General Brigham Aging Biobank Cohort, n = 3,451) and validation cohorts (TruDiagnostic, n = 14,213; Generation Scotland, n = 18,672). Importantly, OMICmAge leverages epigenetic biomarker proxies to integrate proteomic, metabolomic and clinical domains while remaining quantifiable from DNA methylation alone. This framework establishes an accessible, scalable measure of biological aging with potential to reveal molecular interconnections that shape healthspan and disease risk.",

keywords = "Aged, Aged, 80 and over, Aging/genetics, Biomarkers, Cohort Studies, DNA Methylation, Electronic Health Records, Epigenesis, Genetic, Female, Genomics, Humans, Male, Metabolomics, Middle Aged, Multiomics, Proteomics/methods",

author = "Qingwen Chen and Dwaraka, \{Varun B.\} and Nat{\`a}lia Carreras-Gallo and Armstrong, \{Jenel F.\} and Raghav Sehgal and Argentieri, \{M. Austin\} and Anne Richmond and Andrea Aparicio and Kevin Mendez and Yulu Chen and Sofina Begum and Priyadarshini Kachroo and Nicole Prince and Tao Guo and Hannah Went and Tavis Mendez and Aaron Lin and Logan Turner and Mahdi Moqri and Chu, \{Su H.\} and Kelly, \{Rachel S.\} and Weiss, \{Scott T.\} and Rattray, \{Nicholas J. W.\} and Gladyshev, \{Vadim N.\} and Elizabeth Karlson and Wheelock, \{Craig E.\} and Math{\'e}, \{Ewy A.\} and Amber Dahlin and McGeachie, \{Michael J.\} and Marioni, \{Riccardo E.\} and Higgins-Chen, \{Albert T.\} and Ryan Smith and Jessica Lasky-Su",

year = "2026",

month = mar,

doi = "10.1038/s43587-026-01073-7",

language = "English",

volume = "6",

pages = "722--737",

journal = "Nature Aging",

issn = "2662-8465",

publisher = "Springer Nature",

number = "3",

}

Chen, Q, Dwaraka, VB, Carreras-Gallo, N, Armstrong, JF, Sehgal, R, Argentieri, MA, Richmond, A, Aparicio, A, Mendez, K, Chen, Y, Begum, S, Kachroo, P, Prince, N, Guo, T, Went, H, Mendez, T, Lin, A, Turner, L, Moqri, M, Chu, SH, Kelly, RS, Weiss, ST, Rattray, NJW, Gladyshev, VN, Karlson, E, Wheelock, CE, Mathé, EA, Dahlin, A, McGeachie, MJ, Marioni, RE, Higgins-Chen, AT, Smith, R & Lasky-Su, J 2026, 'OMICmAge quantifies biological age by integrating multi-omics with electronic medical records', Nature Aging, vol. 6, no. 3, pp. 722-737. https://doi.org/10.1038/s43587-026-01073-7

TY - JOUR

T1 - OMICmAge quantifies biological age by integrating multi-omics with electronic medical records

AU - Chen, Qingwen

AU - Dwaraka, Varun B.

AU - Carreras-Gallo, Natàlia

AU - Armstrong, Jenel F.

AU - Sehgal, Raghav

AU - Argentieri, M. Austin

AU - Richmond, Anne

AU - Aparicio, Andrea

AU - Mendez, Kevin

AU - Chen, Yulu

AU - Begum, Sofina

AU - Kachroo, Priyadarshini

AU - Prince, Nicole

AU - Guo, Tao

AU - Went, Hannah

AU - Mendez, Tavis

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 E.

AU - Mathé, Ewy A.

AU - Dahlin, Amber

AU - McGeachie, Michael J.

AU - Marioni, Riccardo E.

AU - Higgins-Chen, Albert T.

AU - Smith, Ryan

AU - Lasky-Su, Jessica

PY - 2026/3

Y1 - 2026/3

N2 - Biological aging reflects complex cellular and biochemical processes that can be measured across multiple omic layers. Using routine clinical laboratory data from ~31,000 participants in the Mass General Brigham Biobank, we developed EMRAge, a biomarker of mortality risk that can be broadly recapitulated across electronic medical records. Here we show that EMRAge can be modeled using elastic net regression with DNA methylation and multi-omics to generate DNAmEMRAge and OMICmAge, respectively. Both biomarkers are strongly associated with incident and prevalent chronic diseases and mortality, performing comparably or better than current biomarkers across discovery (Massachusetts General Brigham Aging Biobank Cohort, n = 3,451) and validation cohorts (TruDiagnostic, n = 14,213; Generation Scotland, n = 18,672). Importantly, OMICmAge leverages epigenetic biomarker proxies to integrate proteomic, metabolomic and clinical domains while remaining quantifiable from DNA methylation alone. This framework establishes an accessible, scalable measure of biological aging with potential to reveal molecular interconnections that shape healthspan and disease risk.

AB - Biological aging reflects complex cellular and biochemical processes that can be measured across multiple omic layers. Using routine clinical laboratory data from ~31,000 participants in the Mass General Brigham Biobank, we developed EMRAge, a biomarker of mortality risk that can be broadly recapitulated across electronic medical records. Here we show that EMRAge can be modeled using elastic net regression with DNA methylation and multi-omics to generate DNAmEMRAge and OMICmAge, respectively. Both biomarkers are strongly associated with incident and prevalent chronic diseases and mortality, performing comparably or better than current biomarkers across discovery (Massachusetts General Brigham Aging Biobank Cohort, n = 3,451) and validation cohorts (TruDiagnostic, n = 14,213; Generation Scotland, n = 18,672). Importantly, OMICmAge leverages epigenetic biomarker proxies to integrate proteomic, metabolomic and clinical domains while remaining quantifiable from DNA methylation alone. This framework establishes an accessible, scalable measure of biological aging with potential to reveal molecular interconnections that shape healthspan and disease risk.

KW - Aged

KW - Aged, 80 and over

KW - Aging/genetics

KW - Biomarkers

KW - Cohort Studies

KW - DNA Methylation

KW - Electronic Health Records

KW - Epigenesis, Genetic

KW - Female

KW - Genomics

KW - Humans

KW - Male

KW - Metabolomics

KW - Middle Aged

KW - Multiomics

KW - Proteomics/methods

U2 - 10.1038/s43587-026-01073-7

DO - 10.1038/s43587-026-01073-7

M3 - Article

C2 - 41741793

SN - 2662-8465

VL - 6

SP - 722

EP - 737

JO - Nature Aging

JF - Nature Aging

IS - 3

ER -

OMICmAge quantifies biological age by integrating multi-omics with electronic medical records

Abstract

Keywords / Materials (for Non-textual outputs)

Access to Document

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Projects

Exploiting genomic approaches to identify the environmental basis of depression

Harnessing multiparametric biobank data to develop novel predictive models of frailty

Stratifying Resilience and Depression Longitudinally

Cite this