Investigating the association between the food inflammation scores of individuals and stroke in adults: an extreme gradient boosting machine learning model interpreted with shapley additive explanations.

Abstract

Background: Chronic systemic inflammation is a pivotal modifiable risk factor for stroke. The food-based Food Inflammation Index (FII) offers a novel approach to assess dietary inflammatory potential, yet the association between its derivative, the Food Inflammation Scores of Individuals (FISI), and stroke prevalence remains to be elucidated.

Methods: This study analyzed a cohort of 19,681 adults from the NHANES (2007-2018) database. The FISI-stroke association was assessed using multivariable logistic regression and machine learning models (XGBoost), interpreted via SHAP analysis.

Results: Higher FISI scores were positively associated with increased stroke prevalence in a dose-dependent manner. Specifically, a one-unit rise in FISI34, FISI26-USDA, and FISI26-CHINA corresponded to 7%, 18%, and 22% higher stroke odds, respectively. XGBoost modeling identified FISI34 as a key predictor, corroborating regression findings.

Conclusions: This study establishes a robust link between higher FISI, derived from the FII, and stroke risk. The FII framework surpasses nutrient-based indices by providing personalized, actionable, food-specific guidance for stroke prevention through anti-inflammatory diets.