Phenoage-adjusted indicators enhance prognostic prediction

Background: With advancing age, body composition, anthropometric indicators and laboratory biomarkers undergo slow yet significant physiological alterations. This study aimed to explore the feasibility of adjusting these indicators using phenotypic age and to assess their effectiveness in predicting mortality risk in cancer patients and the general population. Methods: The participant pool for this study was sourced from two databases with a specific focus on nutrition, specifically the Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC) and the National Health and Nutrition Examination Survey (NHANES). Body composition, anthropometric, and laboratory indicators were recorded accordingly. The integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to compare the predictive abilities of different metrics for prognosis. The KM curve and COX regression analysis were employed to describe the associations between the phenoage-adjusted indicators and prognosis. Results: In the INSCOC study, a total of 2853 cancer patients were included. In the NHANES study, a total of 4477 participants were included. Phenoage was significantly negatively correlated with muscle mass and hand grip strength (HGS). Phenoage-adjusted indicators demonstrated higher specificity and sensitivity in predicting mortality in both cancer patients and the general population. Compared to traditional definitions, the adjusted indicators, such as high-density lipoprotein (HDL)/phenoage and HGS/phenoage, can more effectively distinguish between different prognostic groups. In the NHANES, HDL/phenoage was associated with both all-cause and cardiovascular disease-specific mortality. Conclusion: This study confirmed the feasibility and effectiveness of using phenoage to adjust body composition, anthropometric, and laboratory indicators. The adjusted indicators more accurately reflect an individual’s health status. Body function assessment and disease diagnosis should be adjusted by biological and not only chronological age in the future.