Insomnia is one of the most common sleep disorders. Traditionally, its pathophysiology has been interpreted mainly from the perspective of the central nervous system (CNS). However, accumulating evidence suggests that the microbiota–gut–brain axis (MGBA), a bidirectional communication network linking the gut and the CNS, may play an important role in the development, maintenance, and treatment of insomnia. This review summarizes the major signaling pathways of the MGBA and discusses its potential mechanisms in insomnia. Current evidence indicates that gut microbiota and their metabolites may influence sleep–wake homeostasis through neural, immune, endocrine, and circadian pathways. At the same time, insomnia-related stress responses, immune imbalance, and lifestyle disturbances may in turn affect the gut microbiota, thereby forming a bidirectional regulatory network. Animal and clinical studies further support a close association between gut microbial dysbiosis and insomnia. In addition, this review systematically summarizes factors that may affect the MGBA, including diet, lifestyle, psychosocial stress, medications, and medical exposures. On this basis, MGBA-targeted interventions, such as dietary modification, prebiotics and probiotics, lifestyle interventions, fecal microbiota transplantation, and natural medicines, may provide promising new strategies for the prevention and treatment of insomnia. Nevertheless, the current evidence still relies largely on animal studies and cross-sectional research, and further longitudinal studies and high-quality interventional trials are needed to clarify causality, long-term efficacy, and standardized therapeutic approaches.

BACKGROUND Health problems associated with energy metabolism imbalance have received increasing attention. Our team was the first to propose the concept of gastrointestinal heat retention syndrome (GHRS). Triggered by high-calorie diets and excessive energy intake, GHRS is strongly associated with recurrent respiratory infections and other pediatric conditions in children. Existing research on GHRS has primarily focused on risk factor analyses, and no interpretable exploratory self-assessment model for estimating prevalence probability is currently available for routine household use. OBJECTIVE This study aims to develop and validate an interpretable machine learning model to help caregivers conduct exploratory self-assessments of the probability that children meet the criteria for pediatric GHRS and to provide a practical assessment tool for household use. METHODS This study conducted a questionnaire survey of kindergarten children in Longgang District, Shenzhen, China. Samples with missing information on GHRS-related symptoms and signs were excluded. Independent correlates were identified using univariate logistic analysis, collinearity testing, Least Absolute Shrinkage and Selection Operator regression, and multivariable logistic regression. After handling missing data and preprocessing, the dataset was randomly divided into training and test sets in an 8:2 ratio. SMOTETomek (Synthetic Minority Oversampling Technique Tomek Links) was included as a conservative resampling step in the training pipeline, and optimal features were selected using a combination of Pearson correlation analysis and recursive feature elimination. In addition, a Random Forest (RF) sensitivity analysis without SMOTETomek was performed, and the minimum required sample size was estimated according to the events per variable (EPV) rule. Seven machine learning models were developed and evaluated in terms of discrimination, calibration, and clinical utility. The SHAP (Shapley Additive Explanations) method was used to interpret the optimal model, which was subsequently deployed online using the Streamlit framework. RESULTS A total of 120,198 questionnaires were collected, of which 108,447 were deemed valid and included in the analysis. The ratio of GHRS-positive to GHRS-negative cases was nearly balanced. The study identified 59 independent correlates of GHRS, including 10 protective factors and 49 risk factors. After data cleaning, a complete-case dataset of 49,798 participants was obtained, exceeding the minimum sample size requirement of 3747 cases based on the EPV rule. During internal validation, the RF model demonstrated acceptable discriminatory performance, stable calibration, and high net benefit in decision curve analysis, and was therefore selected as the primary analytical model. SHAP analysis identified 5 key predictive features. The resulting online tool collects information through 75 single-choice questions and automatically provides an estimated probability of GHRS together with lifestyle recommendations. CONCLUSIONS Using cross-sectional data, this study developed and validated an interpretable model that enables caregivers to perform exploratory self-assessments of the probability that children meet the study-specific GHRS scale criteria. As a household self-screening tool, the model helps caregivers estimate this probability based on readily obtainable information and model-generated risk estimates.

Background: Health problems associated with energy metabolism imbalance have received increasing attention. Our team was the first to propose the concept of gastrointestinal heat retention syndrome (GHRS). Triggered by high-calorie diets and excessive energy intake, GHRS is strongly associated with recurrent respiratory infections and other pediatric conditions in children. Existing research on GHRS has primarily focused on risk factor analyses, and no interpretable exploratory self-assessment model for estimating prevalence probability is currently available for routine household use. Objective: This study aims to develop and validate an interpretable machine learning model to help caregivers conduct exploratory self-assessments of the probability that children meet the criteria for pediatric GHRS and to provide a practical assessment tool for household use. Methods: This study conducted a questionnaire survey of kindergarten children in Longgang District, Shenzhen, China. Samples with missing information on GHRS-related symptoms and signs were excluded. Independent correlates were identified using univariate logistic analysis, collinearity testing, Least Absolute Shrinkage and Selection Operator regression, and multivariable logistic regression. After handling missing data and preprocessing, the dataset was randomly divided into training and test sets in an 8:2 ratio. SMOTETomek (Synthetic Minority Oversampling Technique Tomek Links) was included as a conservative resampling step in the training pipeline, and optimal features were selected using a combination of Pearson correlation analysis and recursive feature elimination. In addition, a Random Forest (RF) sensitivity analysis without SMOTETomek was performed, and the minimum required sample size was estimated according to the events per variable (EPV) rule. Seven machine learning models were developed and evaluated in terms of discrimination, calibration, and clinical utility. The SHAP (Shapley Additive Explanations) method was used to interpret the optimal model, which was subsequently deployed online using the Streamlit framework. Results: A total of 120,198 questionnaires were collected, of which 108,447 were deemed valid and included in the analysis. The ratio of GHRS-positive to GHRS-negative cases was nearly balanced. The study identified 59 independent correlates of GHRS, including 10 protective factors and 49 risk factors. After data cleaning, a complete-case dataset of 49,798 participants was obtained, exceeding the minimum sample size requirement of 3747 cases based on the EPV rule. During internal validation, the RF model demonstrated acceptable discriminatory performance, stable calibration, and high net benefit in decision curve analysis, and was therefore selected as the primary analytical model. SHAP analysis identified 5 key predictive features. The resulting online tool collects information through 75 single-choice questions and automatically provides an estimated probability of GHRS together with lifestyle recommendations. Conclusions: Using cross-sectional data, this study developed and validated an interpretable model that enables caregivers to perform exploratory self-assessments of the probability that children meet the study-specific GHRS scale criteria. As a household self-screening tool, the model helps caregivers estimate this probability based on readily obtainable information and model-generated risk estimates.