Extended Data Table 1.

Blood-based^* biomarkers of aging validated in cross-population studies.

	Biomarker	Type	Assay	Variable selection method	Biomarker components
Panel	“Phenotypic aging”11	Blood biomarkers	Blood biochemistry and hematology	Penalized Cox regression model, 42 variables	9 blood measures
	“Agglomerative algorithm”12	Blood biomarkers	Blood biochemistry and hematology	Hierarchical clustering, 40 variables	19 blood measures
	BloodAge 13	Blood biomarkers	Blood biochemistry and hematology	Deep Neural Networks	45 blood measures
Omic	DNAmRS 14	Epigenetics	DNAm microarray	EWAS on mortality, ∼450K CpGs	Weighted DNAm levels at 10 CpGs
	PhenoAge11	Epigenetics	DNAm microarray	EN regression model with 20169 CpGs	Weighted DNAm levels at 513 CpGs
	GrimAge 15	Epigenetics	DNAm microarray	EN Cox regression model with ∼450K CpGs	Weighted DNAm levels at 1030 CpGs
	GrimAge2 16	Epigenetics	DNAm microarray	EN Cox regression model of time-to-death, 1030 CpGs	Weighted DNAm levels at 1030 CpGs
	DunedinPACE 17	Epigenetics	DNAm microarray	EN regression model on 81239 CpGs with high test-retest reliability	Weighted DNAm levels at 173 CpGs
	bAge 18	Epigenetics	DNAm microarray	EN Cox regression model of time-to-death, ~ 370K CpGs	35 EpiScores based on weighted DNAm levels
	MetaboHealth score 19	Metabolomic	Nuclear magnetic resonance	Stepwise Cox proportional hazards model, 226 variables	Concentration of 14 metabolites
	MetaboAge score 20	Metabolomic	Nuclear magnetic resonance	Linear model trained on chronological age, 56 variables	Concentration of 56 metabolites
	M-metabo-score 21	Metabolomic	Liquid chromatography-mass spectrometry	Cox proportional hazards model, 470 variables	Concentration of 17 metabolites
	“Proteomic signature” 22	Proteomic	SOMAscan 1.3K	Cox proportional hazard model, 1301 variables	Concentration of 76 proteins
Integrative	“Integrative biomarker” 23	Clinical + Epigenetics	clinical measurements and DNAm microarray	EWAS and Elastic-coxph	12 clinical measurements and DNAm levels at 76 CpGs

^*Biomarker selection: Biomarkers were selected based on the following criteria 1) we reviewed biomarkers compiled by Justice et al. 14 ¹⁰, Rutledge et al.²⁴, and Kudryashova et al.²⁵ which, to the best of our knowledge, collectively provide the most comprehensive list of blood-based and omic biomarkers of aging in the geroscience field; 2) we consulted a recent systematic ranking and scoring of biomarkers of aging performed by Hartmann et al. in 2021 to select the most cited biomarkers ²⁶; and 3) we queried the literature using search terms [“epigenetic”, “metabolomic”, “proteomic”, “transcriptomic”], [“mortality”, “death”], and [“validation”] to identify additional omic biomarkers that have been validated in cohort studies. Genomic biomarkers of aging (e.g., genetic variants) were not included in this work, as we focused on biomarkers potentially responsive to interventions. All included biomarkers/studies met the following inclusion criteria: 1) studies that propose new ways to quantify biological aging (predicting aging outcomes better than chronological age ²⁷); 2) biomarkers measured entirely or primarily in blood; 3) biomarkers that are applicable to the general population and not specific to certain subpopulations with specific diseases; 4) biomarkers that underwent validation in an independent cohort (cross-population validation) with at least 10 years of follow-up data on aging outcomes, including mortality (e.g., time to death); and 5) biomarkers whose predictive performance is reported in terms of hazard ratio (HR). As these strict inclusion criteria resulted in omission of numerous studies and biomarkers (see Extended Data Table 3 below for examples).

Abbreviations: SBP: Systolic blood pressure; FEV1: forced expiratory volume at 1 second; FVC: forced vital capacity; CRP: C-reactive protein; ALP: Alkaline phosphatase; WBC: White blood cell count, CN: Creatinine; ALB: Albumin; EWAS: epigenome-wide association studies; EN: Elastic Net; 10F CV: 10 fold cross validation; RFU: relative fluorescence units