Geroscience clinical trials need biomarker surrogate endpoints for healthspan. Leading candidates are omics-based composites developed from machine learning analysis of aging phenotypes including calendar age, survival, functional capacity, and Pace of Aging. Existing Pace of Aging biomarkers were developed in the Dunedin Longitudinal Study, limiting inference about strengths/weaknesses of the method as distinct from the Study, a unique single-year birth cohort followed through midlife with near-perfect retention and uniform measurement of multi-organ-system function across two decades of follow-up. We adapted our Pace of Aging method for mixed-age cohorts with variable follow-up of organ-function measures and applied it to develop a novel DNA methylation biomarker of Pace of Aging in data from the Framingham Heart Study Offspring Cohort, FraminghamPACE. Validation analyses across four independent cohorts and one clinical trial establish advantages for the Pace of Aging method in developing biomarkers that are both predictive of healthspan and responsive to geroprotective intervention.