Comprehensive Comparison of Sixteen Markers of Biological Aging: Cross-Sectional and Longitudinal Results from the Berlin Aging Study II (BASE-II)

IntroductionThe disproportionate increase in lifespan compared to health span over the past decades results in a growing proportion of life marked by diseases, even if incidence rates are falling in some cases. However, not everyone ages at the same pace and some people remain in good health and preserve physical and cognitive function into old age. To quantify inter-individual differences in the biological aging process, numerous indicators of biological age have been developed. While these markers have often been validated individually, comparisons in the same people are scarce, complicating their evaluation and translation into clinical practice. MethodsIn this study, we analyzed 16 measures of biological aging including epigenetic clocks, proteomics clock, telomere length, and SkinAge, laboratory composite markers (BioAge, Allostatic Load), psychological aging, and Brain Age. These age markers were evaluated cross-sectionally as well as longitudinally in the context of age-associated outcomes covering frailty, mobility, cognitive function, depressive symptoms, autonomy in daily life, nutrition, morbidity, and chronic disease in participants of the Berlin Aging Study II (BASE-II). ResultsLongitudinal data was available for 1,083 participants with a mean age of 68.3 years at baseline (52% women) and an average follow-up period of 7.4 years. Correlation among markers of aging from different domains was low (r[≤]0.31). Allostatic Load Index and DunedinPACE showed the strongest and most consistent cross-sectional and longitudinal associations with age-associated phenotypes, including morbidity, cardiovascular health, and frailty. Both biomarkers individually increased the accuracy of a logistic regression model trained to predict incident cases of Metabolic Syndrome, high cardiovascular risk (Lifess Simple 7) as well as incident frailty (Frieds frailty index) 7.4 years after baseline examination by up to 24 percentage points. ConclusionOur findings support the previously shown distinction between indicators of aging and provide a comprehensive overview of their individual strengths and weaknesses in the context of wide variety of age-associated phenotypes. Furthermore, we show their distinct ability to predict aging-related adverse outcomes and suggest a potential use-case in longitudinal prediction modelling.