Construction and validation of a novel nutrient-based index for risk of aging using an interpretable machine learning...

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.

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 indicating the presence of accelerating age.

In GBCS, of 9512 participants, the mean phenotypic age was 58.8 (standard deviation = 9.2) years. A dietary aging risk index (DARI) was constructed using nutrients and phenotypic age, with median (interquartile range) being 0.03 (0.01, 0.06). During an average follow-up of 16.1 years, after adjusting for twelve potential confounders, higher DARI were associated with older phenotypic age (β = 0.08 years, 95% confidence interval (CI) = 0.06-0.10), higher risks of accelerating age (odds ratio = 1.63, 95% CI = 1.38-1.93) and all-cause mortality (hazards ratio (HR) = 1.10, 95% CI = 1.04-1.17). The association with all-cause mortality was more pronounced in current smoker (HR = 1.29, 95% CI = 1.12-1.50). In NHANES, higher DARI were associated with lower α-Klotho levels (β=-0.020 pg/ml, 95% CI=-0.036 to -0.004).

This study developed and validated a DARI using machine learning methods, offering a comprehensive measure of the impact of multiple nutrients on phenotypic aging. An online tool was created to facilitate its application in population studies.