Comprehensive cross-sectional and longitudinal comparison of sixteen markers of biological aging from the Berlin Aging Study II

TL;DR

This study compares 16 aging markers in over 1000 older adults to find which best predict future health issues like frailty and cardiovascular risk.

Contribution

The study provides a comprehensive comparison of 16 biological aging markers and their predictive power for age-related health outcomes.

Findings

Allostatic Load Index and DunedinPACE showed strongest associations with age-related health outcomes.

These two markers improved prediction accuracy of future health risks by up to 24 percentage points.

The study highlights individual strengths and limitations of various aging indicators.

Abstract

The disproportionate increase in lifespan compared to healthspan 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.

In 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).

Here, we analyze longitudinal data from 1083 participants (mean age of 68.3 years at baseline, 52% women) with an average follow-up period of 7.4 years. Allostatic Load Index and DunedinPACE show the strongest and most consistent cross-sectional and longitudinal associations with age-associated phenotypes. Furthermore, both biomarkers individually increase the accuracy of a logistic regression model trained to predict incident cases of Metabolic Syndrome, high cardiovascular risk (Lifes’s Simple 7) as well as incident frailty (Fried’s frailty index) 7.4 years after baseline examination by up to 24 percentage points.

Our 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.

People are living longer, but not everyone ages in the same way. Some people stay healthy well into old age, while others develop health problems earlier. To understand these differences and predict future health risks, various measures of biological aging have been developed, but comparisons of these measures in the same group of people are scarce. In this study, we compared 16 of these measures in more than 1000 older adults over seven years to see which aging markers best predict future health problems. We found that two measures, Allostatic Load and DunedinPACE, stood out as the most reliable for identifying people at risk of future problems such as frailty and cardiovascular risk. These findings could help physicians and scientists better understand aging and improve early detection of health problems in older adults.

Vetter et al. investigate the cross-sectional and longitudinal relationship between 16 markers of ageing and 16 well-established age-associated phenotypes using data the Berlin Aging Study II. The results provide a comprehensive overview of the markers’ individual strengths and limitations and indicate their utility in incident case prediction.

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