Unravelling the planetary health diet index-depression nexus: utilisation of network and machine learning approaches

Adherence to the planetary health diet index (PHDI) is associated with health benefits, but its relationship with depression, particularly the roles of dietary components and symptoms, remains poorly understood. We aimed to (1) compare PHDI component interrelationships between individuals with and without depression; (2) identify PHDI components most predictive of depression using machine learning and (3) identify bridge nodes connecting PHDI components with depressive symptoms. This cross-sectional study analysed data from 25 542 adults in the National Health and Nutrition Examination Survey 2005-2018. The PHDI was calculated from two 24-h dietary recalls. Depression was assessed with the Patient Health Questionnaire-9 with scores ≥10 indicating depression. We employed network analysis to compare dietary structures, an extreme gradient boosting (XGBoost) model interpreted using shapley additive explanations (SHAP) values to identify predictors and bridge network analysis to identify bridge nodes connecting the PHDI and depressive symptom communities. The PHDI network in the non-depressed group was more densely connected than in the depressed group. SHAP analysis of the XGBoost model identified non-starchy vegetables (PHDI2) and added sugars (PHDI14) as the most important predictors, with higher component scores associated with a lower predicted probability of depression. In the bridge network analysis, appetite problems (PHQ5) and depressed mood (PHQ2) emerged as the strongest bridge nodes between the PHDI and depressive symptom communities. A structured, plant-forward dietary pattern was associated with lower depression levels, with appetite problems and depressed mood identified as the bridge nodes between the PHDI and depressive symptom communities.