Syndrome differentiation of chronic hepatitis B patients by integrating routine clinical laboratory and plasma metabolomics analysis data

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Peng Gao Xin Huang Aijun Sun Jinlong Guo Hangyu Gong Sichao Li Lu Xing


Purpose: Traditional Chinese Medicines (TCMs) has been officially approved for chronic hepatitis B infection treatment in China. Correct syndrome differentiation is the key prerequisite for rational prescribing TCMs. Unfortunately, TCM physicians have scarcities of objective measures for syndrome differentiation and their diagnosis exclusively relies on individual experience. This study aimed to find out some objective parameters to aid syndrome differentiation.

Methods: Three commonly encountered clinical syndromes named accumulated dampness-heat syndrome (ADHS), spleen deficiency with liver depression (SDLD) and blood stasis vessel obstruction syndrome (BSVO) were selected.  64 qualified patients with definite syndromes were enrolled and their plasma amino acids and lipids were profiled by metabolomics analysis. Additionally, their routine clinical laboratory parameters were also collected.

Results: Through orthogonal partial least squares-discriminant analysis, the three syndromes could be properly differentiated. The differential parameters between every two syndromes were screened out. Most of them were of the small molecular metabolites. A distinct difference was found between ADHS and the other two syndromes. BSVO and SDLD showed relatively less discrepancy. Furthermore, it was deduced that sphingomyelin metabolism might dominate ADHS phenotype, and platelet functions might affect SDLD phenotype to some extent.

Conclusions: These findings provided primary evidence for the objective classification of varied syndromes of chronic hepatitis B patients and proved that metabolomics analysis might be a valuable tool to aid syndrome differentiation in some circumstances.

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How to Cite
GAO, Peng et al. Syndrome differentiation of chronic hepatitis B patients by integrating routine clinical laboratory and plasma metabolomics analysis data. Medical Research Archives, [S.l.], v. 8, n. 11, dec. 2020. ISSN 2375-1924. Available at: <>. Date accessed: 28 nov. 2022. doi:
Research Articles


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