Journal of inflammation research · 2025
Potential Metabolic Markers in the Tongue Coating of Chronic Gastritis Patients for Distinguishing Between Cold Dampness Pattern and Damp Heat Pattern in Traditional Chinese Medicine Diagnosis.
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Lydon-Hassen Kathleen, Jonah Leigh, Mayotte Lisa, Hrabowy Ashley, Graham Bonny, Missens Beverley, Nelson Amanda, Andkhoie Mustafa, Nahachewsky Deana, Yalamanchili Dharma Teja, Gupta Sabyasachi, Ndubuka Nnamdi, Khan Ibrahim, Yacoub Wadieh, Bryson Maggie, Paquette Dana
Аннотация
BACKGROUND: Dampness pattern, a prevalent traditional Chinese medicine (TCM) pattern in chronic gastritis (CG), includes cold dampness (CD) and damp heat (DH) patterns. Tongue coating differences are key diagnostic markers, yet molecular-level analyses are lacking. We applied metabolomics to identify differential metabolites distinguishing these patterns.
METHODS: In this study, the first principal component was analyzed by the OPLS-DA model. The model quality was evaluated by 7-fold cross-validation, and the model validity was evaluated based on R²Y (interpretability of categorical variable Y) and Q² (predictability of the model), and the permutation test was used for further verification. Strict criteria were used for differential metabolite screening. The Euclidean distance matrix of the quantitative values of differential metabolites was calculated, and cluster analysis was performed using the complete linkage method. All pathways mapped by human differential metabolites were retrieved through the KEGG (Kyoto Encyclopedia of Genes and Genomes) Pathway database, and key pathways were screened out by combining enrichment and topological analysis. The spearman algorithm was used to calculate the correlation coefficient and P value matrix. Finally, the effect of the binary classifier was evaluated by drawing the receiver operating characteristic curve (ROC curve) and calculating the area under the curve (AUC), and the combination with the highest AUC was selected as the optimal diagnostic model.
RESULTS: Twenty significant differential metabolites emerged (P<0.05). Pathway analysis highlighted three key pathways, notably glycerophospholipid metabolism involving phosphatidylethanolamine. Phenol-based models showed optimal diagnostic performance (highest AUC).
CONCLUSION: Metabolite profiles significantly differed between CD and DH. Glycerophospholipid metabolism was central, with phosphatidylethanolamine as a key metabolite. Phenol requires further validation as a diagnostic biomarker. These findings advance quantitative diagnosis and mechanistic insights into TCM dampness syndrome in CG.
METHODS: In this study, the first principal component was analyzed by the OPLS-DA model. The model quality was evaluated by 7-fold cross-validation, and the model validity was evaluated based on R²Y (interpretability of categorical variable Y) and Q² (predictability of the model), and the permutation test was used for further verification. Strict criteria were used for differential metabolite screening. The Euclidean distance matrix of the quantitative values of differential metabolites was calculated, and cluster analysis was performed using the complete linkage method. All pathways mapped by human differential metabolites were retrieved through the KEGG (Kyoto Encyclopedia of Genes and Genomes) Pathway database, and key pathways were screened out by combining enrichment and topological analysis. The spearman algorithm was used to calculate the correlation coefficient and P value matrix. Finally, the effect of the binary classifier was evaluated by drawing the receiver operating characteristic curve (ROC curve) and calculating the area under the curve (AUC), and the combination with the highest AUC was selected as the optimal diagnostic model.
RESULTS: Twenty significant differential metabolites emerged (P<0.05). Pathway analysis highlighted three key pathways, notably glycerophospholipid metabolism involving phosphatidylethanolamine. Phenol-based models showed optimal diagnostic performance (highest AUC).
CONCLUSION: Metabolite profiles significantly differed between CD and DH. Glycerophospholipid metabolism was central, with phosphatidylethanolamine as a key metabolite. Phenol requires further validation as a diagnostic biomarker. These findings advance quantitative diagnosis and mechanistic insights into TCM dampness syndrome in CG.