What question did this study set out to answer?

This research aims to investigate the metabolic characteristics of gout with dampness-heat syndrome and evaluate the effectiveness of Simiao Pill.

January 23, 2026Open Access

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

Key Points

This research aims to investigate the metabolic characteristics of gout with dampness-heat syndrome and evaluate the effectiveness of Simiao Pill.
Conducted an NMR-based metabolomic analysis on serum and urine samples.
Employed machine learning algorithms like SVM, RF, and LASSO to identify biomarkers.
Applied the Apriori algorithm to find associations between TCM manifestations and metabolites.
Comparative analysis of metabolomic profiles of 50 GDHS patients pre- and post-SMP treatment.
GDHS patients showed a significant alteration in metabolome related to pyruvate and amino acid metabolism.
Identified a panel of 12 biomarkers with high diagnostic capacity.
Triglycerides and glycine were highlighted as key links to TCM syndrome manifestations.
SMP treatment normalized 10 biomarkers, correcting disturbances in critical metabolic pathways.

Abstract

Abstract 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. Graphical Abstract

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