Association of depressive symptoms with systemic immune-inflammation index and platelet parameters among survivors of myocardial infarction: a cross-sectional NHANES study enhanced by machine learning and SHAP analysis

Background Inflammation is implicated in the elevated risk of depressive disorder following myocardial infarction (MI), with platelets serving as a key link between thrombosis, inflammation, and depression. Although the systemic immune-inflammation index (SII), platelet count (PLT), and mean platelet volume (MPV) are readily accessible hematological parameters, their associations with post-MI depressive symptoms remain underexplored. This study investigates these relationships in MI survivors, augmented by machine learning (ML) and SHapley Additive exPlanations (SHAP) analysis for enhanced predictive insights. Methods This cross-sectional study utilized data from 1,352 adults with self-reported MI history in the National Health and Nutrition Examination Survey (NHANES) 2009–2020. Multivariable logistic regression, subgroup analyses, dose-response curves, and sensitivity analyses were conducted to assess independent associations between depressive symptoms (Patient Health Questionnaire-9 score ≥ 5) and log2-transformed SII, PLT, and MPV. Additionally, 14 supervised ML algorithms were benchmarked using 5-fold cross-validation to predict depressive symptoms, with SHAP applied to the top-performing model for feature interpretability. Results In multivariable regression, log2SII (OR = 1.22, 95% CI = 1.06–1.41, P = 0.0069) and log2PLT (OR = 1.78, 95% CI = 1.30–2.43, P = 0.0003) showed positive associations with depressive symptoms, while log2MPV did not (OR = 0.53, 95% CI = 0.24–1.17, P = 0.1150). Subgroup analyses revealed robust associations for log2SII except in females and BMI < 25 kg/m² groups, with no significant interactions for log2PLT or log2MPV across demographics or comorbidities. Dose-response curves indicated positive correlations with log2SII and log2PLT, and an inverted U-shaped relationship with log2MPV (inflection point: 3.04). Sensitivity analysis confirmed that the results of this study were robust either after using the methods of multiple imputation or excluding stroke participants. We also observed a strong association of log2SII and log2PLT with trouble sleeping, feeling tired and poor appetite, of log2MPV with poor appetite. ML benchmarking identified Random Forest as optimal (AUC = 0.779, R² = 0.229, RMSE = 0.424), outperforming other models. SHAP analysis ranked age (15.8% impact) and log2PLT as the top predictors, with higher values of these factors being associated with an increased likelihood of depressive symptoms, thereby reinforcing the interactions between inflammation and platelets. Conclusions In a nationally representative U.S. sample, elevated log2SII and log2PLT are independently associated with depressive symptoms in MI survivors, with an inverted U-shaped curve for log2MPV. ML and SHAP integration corroborates and refines the predictive insights gleaned from traditional regression, highlighting age and platelet dynamics as key drivers, and supports targeted screening in vulnerable subgroups like obese or middle aged MI survivors. Clinical trial number Not applicable.