Abstract
Biological age (BA) and its residual relative to chronological age are popularly used to quantify individual aging. Although these residuals independently predict age-related health outcomes, conventional BA measures often lack robustness in heterogeneous populations, and their residuals are not directly derivable in clinical practice. To address these limitations, we introduce the Gompertz law-based residual (GOLD-R) framework, a method designed to directly estimate BA residuals and optimized for cross-sectional data. We demonstrated the applicability and robustness of GOLD-R across multiple data types and populations. First, training on DNA methylation data from the EWAS Data Hub, the framework outperformed established epigenetic clocks in predicting mortality in a pan-cancer dataset. Then, applied to UK Biobank proteomics data, GOLD-R generated organismal and organ-specific aging measures that proved more robust than conventional age-prediction approaches in forecasting incident diseases and mortality. Finally, extending the analysis to clinical biomarkers using the NHANES and HRS, we found that GOLD-R residuals, derived from clinical biomarkers, surpassed those from both epigenetic and phenotypic clocks in performance. In summary, our findings establish GOLD-R as a robust algorithm for biological age estimation, providing a practical tool for both research and clinical applications.