Association between high or low-quality carbohydrate with depressive symptoms and socioeconomic-dietary factors model based on XGboost algorithm: From NHANES 2007-2018

Background: Depressive symptoms are a serious public mental health problem, and dietary intake is often considered to be associated with depressive symptoms. However, the relationship between the quality of dietary carbohydrates and depressive symptoms remains unclear. Therefore, this study aimed to investigate the relationship between high and low -quality carbohydrates and depressive symptoms and to attempt to construct an integrated model using machine learning to predict depressive symptoms. Methods: A total of 4982 samples from the National Health and Nutrition Examination Survey (NHANES) were included in this study. Carbohydrate intake was assessed by a 24-h dietary review, and depressive symptoms were assessed using the Patient Health Questionnaire -9 (PHQ9). Variance inflation factor (VIF) and Relief -F algorithms were used for variable feature selection. Results: The results of multivariate linear regression showed a negative association between high -quality carbohydrates and depressive symptoms (beta: -0.147, 95 % CI: -0.239, - 0.056, p = 0.002) and a positive association between low -quality carbohydrates and depressive symptoms (beta: 0.018, 95 % CI: 0.007, 0.280, p = 0.001). Subsequently, we used the XGboost model to produce a comprehensive depressive symptom evaluation model and developed a corresponding online tool (http://8.130.128.194:5000/) to evaluate depressive symptoms clinically. Limitations: The cross-sectional study could not yield any conclusions regarding causality, and the model has not been validated with external data. Conclusions: Carbohydrate quality is associated with depressive symptoms, and machine learning models that combine diet with socioeconomic factors can be a tool for predicting depression severity.