LAUSR

Aging is a complex process that affects human health and lifespan. While chronological age (CA) is a significant risk factor for many diseases, it does not fully capture biological changes that influence health span. This study explores cognitive measures using the Seoul Neuropsychological Screening Battery and body composition profiles as potential biological age (BA) markers in the older population. Multiple linear regression, principal component analysis (PCA), and the Klemera–Doubal (KDM) methods were used to construct sex-specific BA formulas from 296 healthy individuals (160 women, 136 men, mean age: 70.3 years). The BA formulas were applied to a new cohort of 708 diseased people (376 women, 332 men, mean age: 73.5 years) to generate BAs for each sex. Subsequently, we compared the classification power of CA, BAs, and selected variables when differentiating the healthy group from the comorbidity cohort, with sex stratification. As a result, we found that BAs from PCA and KDM were significantly higher than CA in the diseased group but comparable in the healthy group. BAs from PCA and KDM methods yielded higher classification accuracies than CA alone. Notably, frontal executive domain score and body reactance emerged as two promising markers for aging. These findings suggest that body composition measures and cognitive assessments offer a more accurate reflection of biological health than CA alone. A cohort with a wider age range is needed to generalize these findings.