OMICmAge, a multi-omics biological age biomarker integrating epigenetic, proteomic, metabolomic, and clinical domains while remaining quantifiable from DNA methylation alone, is strongly associated with incident and prevalent chronic diseases and mortality, performing comparably or better than current biomarkers across discovery and validation cohorts.
Key Findings
Methods
EMRAge, a biomarker of mortality risk derived from routine clinical laboratory data, was developed using data from approximately 31,000 participants in the Mass General Brigham Biobank.
EMRAge was constructed from routine clinical laboratory data available in electronic medical records.
The development cohort comprised ~31,000 participants from the Mass General Brigham Biobank.
EMRAge was designed to be broadly recapitulated across electronic medical records systems.
Methods
DNAmEMRAge was developed by modeling EMRAge using elastic net regression with DNA methylation data.
Elastic net regression was used to train DNAmEMRAge from DNA methylation features.
DNAmEMRAge represents the epigenetic recapitulation of the EMRAge mortality risk biomarker.
DNAmEMRAge was evaluated in the discovery cohort (Massachusetts General Brigham Aging Biobank Cohort, n = 3,451) and validation cohorts.
Methods
OMICmAge was developed by modeling EMRAge using elastic net regression with multi-omics data, integrating proteomic, metabolomic, and clinical domains.
OMICmAge incorporates epigenetic biomarker proxies to integrate proteomic, metabolomic, and clinical domains.
Despite integrating multiple omic layers, OMICmAge remains quantifiable from DNA methylation alone.
The multi-omics integration framework allows OMICmAge to capture molecular interconnections across biological layers.
Results
Both DNAmEMRAge and OMICmAge were strongly associated with incident and prevalent chronic diseases and mortality.
Associations were demonstrated in a discovery cohort (Massachusetts General Brigham Aging Biobank Cohort, n = 3,451).
Results were validated in two independent validation cohorts: TruDiagnostic (n = 14,213) and Generation Scotland (n = 18,672).
Both biomarkers showed associations with both incident (new diagnoses) and prevalent (existing) chronic diseases.
Results
OMICmAge and DNAmEMRAge performed comparably or better than current biological age biomarkers across discovery and validation cohorts.
Performance comparisons were made against existing biological aging biomarkers in the discovery cohort (n = 3,451).
Validation was conducted in TruDiagnostic (n = 14,213) and Generation Scotland (n = 18,672) cohorts.
The biomarkers demonstrated consistent performance across multiple independent cohorts.
Conclusions
OMICmAge leverages epigenetic biomarker proxies to integrate multiple omic layers while remaining quantifiable from DNA methylation alone, establishing an accessible and scalable measure of biological aging.
The framework uses DNA methylation-based proxies for proteomic, metabolomic, and clinical features.
This design allows OMICmAge to be computed from a single omic assay (DNA methylation) while capturing multi-omics information.
The approach is described as establishing 'an accessible, scalable measure of biological aging with potential to reveal molecular interconnections that shape healthspan and disease risk.'
Chen Q, Dwaraka V, Carreras-Gallo N, Armstrong J, Sehgal R, Argentieri M, et al.. (2026). OMICmAge quantifies biological age by integrating multi-omics with electronic medical records.. Nature aging. https://doi.org/10.1038/s43587-026-01073-7