AI-Enhanced Gut Brain Axis Profiling for Major Depressive Disorder: Integrating Synthetic Multi-Omics, Deep Learning, and Interpretable Precision Therapeutics

Major depressive disorder (MDD) has been repeatedly linked to disruptions of the gut brain axis (GBA), yet practical decision systems that convert multi-omics patterns into patient-specific guidance remain limited.We present an endto-end, explainable pipeline that learns putative GBA signatures of MDD from synthetic data and translates model attributions into hypothesis-driven nutritional and pharmacological suggestions.We simulated a cohort of N = 1,500 individuals (30% MDD) comprising 200 microbial taxa, 150 metabolites, and 7 clinical features.A regularized dense neural network with class weighting and early stopping was trained and compared with a Random Forest baseline; interpretability was provided by SHAP at global and local levels.On a 20% stratified hold-out test set the deep model achieved Accuracy = 0.98 and AUC = 0.998, with a confusion matrix of [[207, 3], [2, 88]].Feature attributions concentrated on a compact subset of metabolites and taxa consistent with the planted effects in the simulator; RF importances corroborated these signals (e.g., Metabolite_120, 109, 40, 90; Species_198, 197).We further demonstrate a templated mapping from patient-level SHAP profiles to non-clinical recommendations dietary patterns, prebiotic/probiotic directions, and pathway hypotheses involving, for example, kynurenine metabolism, short-chain fatty acids, and bile acids intended to support clinician-led hypothesis generation rather than direct treatment.Because all data are simulated, performance estimates are optimistic and biological interpretations are illustrative.Nonetheless, the approach shows how multi-omics learning coupled with transparent explanations can organize heterogeneous GBA signals into actionable research hypotheses for precision psychiatry.Code and figures are fully reproducible How to cite this paper: de