Background There is divergence in the rate at which people age. The concept of biological age is postulated to cap‑ ture this variability, and hence to better represent an individual’s true global... Show moreBackground There is divergence in the rate at which people age. The concept of biological age is postulated to cap‑ ture this variability, and hence to better represent an individual’s true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the diference between chronological and predicted biological age is informative of one’s chronological age-independent aging divergence ∆. Methods We investigated the statistical assumptions underlying the most popular cross-sectional biological age pre‑ dictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold. Results The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging’s association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a crosssectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random. Conclusions Cross-sectional methods for predicting biological age commonly use the untestable identical-associa‑ tion assumption, which previous literature in the feld had never explicitly acknowledged. These methods have inher‑ ent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors Show less
