Abstract
BACKGROUND: Research has revealed potential links between specific dietary habits and accelerated aging. However, most studies focus only on singular diets or lack ethnic diversity.</p>
OBJECTIVE: This study aimed to investigate the associations between 5 dietary indices and the risk of accelerated aging and develop an interpretable machine learning (ML) model for accelerated aging prediction.</p>
METHODS: We explored associations between dietary indices and the risk of accelerated aging using data from the US National Health and Nutrition Examination Survey (NHANES) and the UK Biobank. A weighted linear regression analysis was used to determine whether accelerated aging was linked to dietary habits, and the covariates were gradually adjusted to ensure that the association was stable. Nonlinear correlations were explored using restricted cubic spline curves. In addition, multiple ML algorithms were used to build predictive models of accelerated aging risk.</p>
RESULTS: Except for the Dietary Inflammation Index (β=0.35, 95% CI 0.23-0.74), the other 4 dietary indices (Alternative Healthy Eating Index, Alternative Mediterranean Diet, Healthy Eating Index-2020, and Dietary Approaches to Stop Hypertension) were negatively associated with the risk of accelerated aging in NHANES participants. Similar results were observed in UK Biobank participants. Nine ML algorithms were used to develop risk prediction models, among which the gradient boosting decision tree model showed the best overall performance. A web-based prediction platform was developed and made publicly available.</p>
CONCLUSIONS: Significant associations between accelerated aging and dietary indices were observed. High compliance with the Dietary Inflammation Index had a promoting effect on accelerated aging, while high compliance with the Alternative Healthy Eating Index, Alternative Mediterranean Diet, Healthy Eating Index-2020, and Dietary Approaches to Stop Hypertension showed varying degrees of protection against accelerated aging.</p>