Quantitative mapping of gut microbiota feature importance to TCM syndrome elements in clinical T2DM stages.

Introduction: The clinical phenotype of modern type 2 diabetes mellitus (T2DM) has diverged from the classical traditional Chinese medicine (TCM) syndrome of Xiao Ke (wasting-thirst), with obesity replacing emaciation as the dominant presentation and challenging traditional symptom-based syndrome differentiation. The TCM syndrome element system, a quantifiable framework inherently compatible with molecular-level parameters, positions the gut microbiota - causally linked to T2DM, stage-specific in composition, and amenable to targeted intervention - as an ideal molecular anchor for refining syndrome differentiation. However, the quantitative feature importance of specific gut microbes for individual syndrome elements remains undetermined. Methods: We enrolled 154 participants across three T2DM stages (51 pre-diabetes, 53 T2DM, 50 T2DM with complications) for 16S rDNA sequencing, syndrome element assessment, and machine learning. Results: Mendelian randomization identified 21 gut microbiota taxa and functional pathways causally implicated in T2DM in East Asian populations, providing biological priors for subsequent analyses. Support vector machine (SVM) models with SHAP values quantified microbial feature importance for individual syndrome elements, revealing progressive evolution from dampness, qi deficiency, and spleen (pre-diabetes) to dampness, phlegm, heat, and kidney (complications). Discussion: This proof-of-concept study provides the first quantitative mapping of gut microbiota feature importance to TCM syndrome elements, establishing an integrated framework combining MR-based causal prioritization, ML-based feature importance mapping, and FMT-based intervention validation for micro-syndrome differentiation.