Body mass index and diet-related inflammation as predictors of sleep disorders: A cross-sectional study.

This study examines diet as a key risk factor for sleep disorders and integrates physiological indicators to develop a machine learning (ML)-based model for targeted public health interventions. Data from 5158 2011 to 2014 National Health and Nutrition Examination Survey (NHANES) participants were analyzed. Dietary, lifestyle, and physiological variables used to build sleep disorder prediction models with random forest, extreme gradient boosting, light gradient boosting machine, and logistic regression. Model interpretability was assessed using Shapley additive explanations (SHAP). Key predictors were further analyzed using progressive modeling and least absolute shrinkage and selection operator (LASSO) regression. All ML models showed acceptable-to-excellent discrimination (area under the receiver operating characteristic curve: 0.744-1.000), with light gradient boosting machine achieving the highest performance (area under the receiver operating characteristic curve  = 1.000). SHAP analysis showed that dietary inflammatory index (DII), body mass index (BMI), and age were positively associated with sleep disorder risk, while mean arterial pressure was negatively associated. In progressively adjusted logistic regression models, BMI was consistently positively associated with sleep disorders (model 3 odds ratio [OR] = 1.065, 95% confidence interval [CI]: 1.050-1.080; P < .001), whereas DII was associated with sleep disorders primarily in less-adjusted models (model 1 OR = 1.099, 95% CI: 1.035-1.168; P = .002; model 2 OR = 1.072, 95% CI: 1.004-1.145; P = .037). To further identify which dietary components driving the DII-related signal were most relevant to sleep disorder risk, we applied LASSO to the nutrient components of DII, which selected iron, carbohydrates, and total fat as the major contributors to the diet-related sleep disorder risk profile. An interpretable ML model based on National Health and Nutrition Examination Survey data demonstrated good discrimination for sleep disorders and consistently highlighted BMI and DII as central correlates. SHAP and LASSO further translated these associations into clinically interpretable dietary signals, including iron, carbohydrate, and total fat intake within the DII framework, supporting screening-oriented risk profiling and prioritization of individuals for further sleep evaluation and targeted nutrition assessment.