A generalized approach for estimating the pace of aging.

BACKGROUND: Quantifying the rate of aging is essential for understanding age-related physiological decline and predicting late-life outcomes. The Pace of Aging (PoA) framework addresses this by modeling longitudinal changes across multiple biomarkers. However, the original Dunedin PoA assigns equal weight to all biomarkers and was derived from a young cohort with limited mortality follow-up, which restricts its applicability in older populations and in settings where biomarker relevance is uncertain. METHODS: We developed a weighted Pace of Aging (wPoA) using data from the Swedish Adoption/Twin Study of Aging (SATSA), a longitudinal cohort of older adults with up to nine waves of biomarker data collected over three decades. Using mixed effects models, we estimated individual random intercepts and slopes for 10 biomarkers and assigned weights to these random effects based on their association with time-to-mortality. This mortality-informed weighting distinguishes the wPoA from the original Dunedin PoA. RESULTS: We found that wPoA was positively correlated with established biological age predictors and more strongly correlated with low-dimensional predictors such as Functional Aging Index (FAA) and the Frailty Index (FI). Modest correlations across all considered predictors suggest that each measure captures distinct aspects of biological aging. CONCLUSIONS: In conclusion, the wPoA framework introduces a data-driven weighting of random effects and is particularly well-suited for aging cohorts and in settings where marker relevance is uncertain a priori, such as high-dimensional or omics-based contexts. This work provides a foundation for future single-timepoint surrogates that can also be used in settings lacking rich longitudinal data.