Chinese medicine · 2026
NMR-based metabolomics in a clinical cohort: deciphering the metabolic characteristics of gout with the dampness-heat syndrome and elucidate the efficacy of Simiao Pill.
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Hu Yu, Yang Le, Yan Guangli, Sun Ye, Wang Maojie, Kong Ling, Sun Hui, Zhao Xueping, Zhang Xinya, Huang Runyue, Liu Chang, Han Ying, Wang Xijun
Аннотация
BACKGROUND: Gout is an inflammatory arthritis caused by purine metabolism disorders. The gout with the dampness-heat syndrome (GDHS) is a common Traditional Chinese Medicine (TCM) syndrome in this kind of disease, yet its modern scientific basis remains poorly understood. Simiao Pill (SMP), a classic formula in treating GDHS, has an unclear mechanism of action.
METHODS: We conducted a targeted Nuclear Magnetic Resonance (NMR)-based metabolomic analysis on serum and urine samples from 197 GDHS patients and 101 healthy controls. Multiple machine learning algorithms, including support vector machine (SVM), random forest (RF), and least absolute shrinkage and selection operator (LASSO), were employed to identify potential biomarkers for GDHS. The Apriori algorithm was applied to uncover associations between TCM syndrome manifestations and metabolomic biomarkers. A subgroup of 50 GDHS patients received a 4-week SMP treatment, and their metabolomic profiles were compared pre- and post- intervention.
RESULTS: GDHS patients exhibited a significant remodeled metabolome, characterized by disruptions in pyruvate, amino acid metabolism, and energy metabolism. A panel of 12 biomarkers with high diagnostic power was identified. Association rule mining further highlighted triglycerides and glycine as central nodes showing extensive connections to TCM syndromes. SMP intervention significantly reversed the level of 10 biomarkers (e.g., citrate, glycine, lactate), effectively normalizing perturbations in glycolysis/gluconeogenesis, the tricarboxylic acid cycle, and glycine/serine/threonine metabolism, and lipid homeostasis.
CONCLUSION: This real-world clinical study systematically delineates the metabolic features of GDHS, innovatively linking TCM syndromes to specific metabolic disturbances. It confirms that SMP exerts its therapeutic effects through multi-targeted modulation of the metabolic network. This work provides a new scientific paradigm for the study of "disease-syndrome-treatment" in TCM.
METHODS: We conducted a targeted Nuclear Magnetic Resonance (NMR)-based metabolomic analysis on serum and urine samples from 197 GDHS patients and 101 healthy controls. Multiple machine learning algorithms, including support vector machine (SVM), random forest (RF), and least absolute shrinkage and selection operator (LASSO), were employed to identify potential biomarkers for GDHS. The Apriori algorithm was applied to uncover associations between TCM syndrome manifestations and metabolomic biomarkers. A subgroup of 50 GDHS patients received a 4-week SMP treatment, and their metabolomic profiles were compared pre- and post- intervention.
RESULTS: GDHS patients exhibited a significant remodeled metabolome, characterized by disruptions in pyruvate, amino acid metabolism, and energy metabolism. A panel of 12 biomarkers with high diagnostic power was identified. Association rule mining further highlighted triglycerides and glycine as central nodes showing extensive connections to TCM syndromes. SMP intervention significantly reversed the level of 10 biomarkers (e.g., citrate, glycine, lactate), effectively normalizing perturbations in glycolysis/gluconeogenesis, the tricarboxylic acid cycle, and glycine/serine/threonine metabolism, and lipid homeostasis.
CONCLUSION: This real-world clinical study systematically delineates the metabolic features of GDHS, innovatively linking TCM syndromes to specific metabolic disturbances. It confirms that SMP exerts its therapeutic effects through multi-targeted modulation of the metabolic network. This work provides a new scientific paradigm for the study of "disease-syndrome-treatment" in TCM.