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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p> Graphical Abstract <p></p>

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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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月18日

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  • Zhiwen Yan,
  • Kang Luo,
  • Qinghuan Yang,
  • Xiaoqing Liu,
  • Huan Zhao,
  • Yuan Gao,
  • Qiang Zhang,
  • Jun Mu

摘要

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.

Graphical Abstract