Multimodal Fitting of Damp-Heat Pouring Downward Syndrome Gout Models: Integrating Clinical Features and Serum Metabolic Profiles into Rat Models

Abstract Objective: To address the critical gap in traditional gout animal models that fail to recapitulate the traditional Chinese medicine (TCM) syndrome of Damp-Heat Pouring Downward Syndrome (DHPDS), this study aims to establish a disease-syndrome integrated model that aligns with TCM theory. Materials and Methods: Six DHPDS gout rat models were developed by integrating endogenous dampness-heat induction (high-fat diet, ethanol, and ginger extract) and exogenous pathogenic stimulation (artificial climate chamber), followed by phenotypic, biochemical, and histopathological evaluations. Serum metabolomic profiling was performed using ultra-high-performance liquid chromatography-Q/Orbitrap/LTQ MS. Non-negative matrix factorization (NMF) distilled clinical topic features from data of 92 patients with DHPDS gout. Model fitting analysis employed a dual-module framework: NMF-derived feature coefficients were converted to weighted phenotypic scores, and metabolomic congruence analysis was used to evaluate biomarker overlap between animal models and clinical cohorts. Results: Comprehensive evaluations (phenotypic, biochemical, and histopathological) confirmed the successful establishment of DHPDS gout rat models. Metabolomic profiling detected 31, 37, 38, 38, 46, and 42 differentially expressed metabolites in models 1–6, primarily linked to gout-related pathways: purine/pyrimidine metabolism, amino acid homeostasis, lipid metabolism, and arachidonic acid metabolism. Clinical topic features of DHPDS gout included high uric acid, high low-density lipoprotein, high triglyceride, diet reduction, and high creatinine. Model 5 demonstrated superior congruence with clinical DHPDS gout features, achieving the highest composite fitting score (172/200). Conclusions: This study established six DHPDS gout animal models guided by TCM theory. Through multimodal fitting analysis combining clinical features with metabolomic profiling, Model 5 was identified as the most clinically representative. The multimodal fitting framework establishes a novel paradigm for precision modeling of TCM syndromes.