Gompertz law based biological age (GOLD BioAge): a simple and practical measurement of biological aging to capture morbidity and mortality risks

Abstract Biological age reflects actual aging and overall health, but current aging clocks are often complex and difficult to interpret, limiting their clinical application. In this study, we introduced a Gompertz law-based biological age (GOLD BioAge) model that simplified aging assessment. We estimated GOLD BioAge using clinical biomarkers and found significant associations of the difference from chronological age (BioAgeDiff) with risks of morbidity and mortality in NHANES. Moreover, we developed GOLD ProtAge and MetAge using proteomics and metabolomics data, which outperformed the clinical-only model in predicting mortality and chronic disease risks in UK Biobank. Benchmark analysis illustrated that our models exceeded common aging clocks in predicting mortality across diverse age groups in both NHANES and UK Biobank. The results demonstrated that the GOLD BioAge algorithm effectively applied to both clinical and omics data, showing excellent performance in predicting age-related outcomes. Additionally, we created a simplified version called the Light BioAge, which used three biomarkers for aging assessment. The Light model reliably captured mortality risks in three validation cohorts (CHARLS, RuLAS, CLHLS). It significantly predicted the onset of frailty, stratified frail individuals, and collectively identified individuals at high risk of mortality. In summary, the algorithm of GOLD BioAge could provide a valuable framework for aging assessment in public health and clinical practice. Highlights The algorithm of Gompertz law based biological age (GOLD BioAge) was proposed to construct biological aging clocks with convenient and interpretable calculations, which had better performance in predicting mortality risks. Our approach was applicable to proteomics and metabolomics, yielding ProtAge and MetAge with great clinical prospect to improve accuracy of aging assessment and prevent age-related diseases. The Light BioAge, a simplified version, was developed using age and three biomarkers, and it independently predicted mortality in three cohorts. The Light BioAgeDiff significantly predicted the onset of frailty, stratified frail individuals, and collectively identified individuals at high risk of mortality.