Construction and validation of a novel nutrient-based index for risk of aging using an interpretable machine learning framework: results from two population-based studies.

ABSTRACT:

Background

Population aging is becoming increasingly prominent. Although various dietary factors have been associated with aging in older people, no dietary score specifically related to phenotypic aging has yet been developed.

Methods

We used data from the Guangzhou Biobank Cohort Study (GBCS) and National Health and Nutrition Examination Survey (NHANES). Interpretable machine learning framework including adaptive elastic-net (AENET), eXtreme Gradient Boosting (XGBoost), and Random Survival Forests (RSF) analysis combined with Shapley additive explanations (SHAP) were used to construct and validate a dietary index related to aging. Accelerated age is defined as the residual from a linear regression of phenotypic age on chronological age, with values greater than 0 indicatin

SUBMITTER: Li RQ 

PROVIDER: S-EPMC12751845 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature