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

Main Article Content

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

Abstract

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.

Article Details

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: <https://esmed.org/MRA/mra/article/view/2239>. Date accessed: 28 nov. 2022. doi: https://doi.org/10.18103/mra.v8i11.2239.
Section
Research Articles

References

1. Ogawa E, Furusyo N, Nguyen MH, Tenofovir alafenamide in the treatment of chronic hepatitis B: design, development, and place in therapy. Drug Des Devel Ther 2017; 11:3197-3204.DOI:10.2147/DDDT.S126742
2. Li F, Shao Q, Ji D, Li B, Chen G, Genetic association between CD44 polymorphisms and chronic hepatitis B virus infection in a Chinese Han population. Int J Clin Exp Pathol 2015; 8(9):11675-11679.
3. Chen T, Qian G, Fan C, Sun Y, Wang J, Lu P et al, Qidong hepatitis B virus infection cohort: a 25-year prospective study in high risk area of primary liver cancer. Hepatoma Res 2018; 410. DOI:20517/2394-5079.2017.50
4. Stanaway JD, Flaxman AD, Naghavi M, Fitzmaurice C, Vos T, Abubakar I et al, The global burden of viral hepatitis from 1990 to 2013: findings from the Global Burden of Disease Study 2013. Lancet 2016; 388(10049):1081-1088. DOI:10.1016/S0140-6736(16)30579-7
5. Qi FH, Wang ZX, Cai PP, Zhao L, Gao JJ, Kokudo N et al, Traditional Chinese medicine and related active compounds: a review of their role on hepatitis B virus infection. Drug Discov Ther 2013; 7(6):212-224.
6. Lau DT, Bleibel W, Current status of antiviral therapy for hepatitis B. Therap Adv Gastroenterol 2008; 1(1):61-75. DOI:10.1177/1756283X08093944
7. Hadziyannis SJ, Update on Hepatitis B Virus Infection: Focus on Treatment. J Clin Transl Hepatol 2014; 2(4):285-291. DOI:10.14218/JCTH.2014.00026
8. Dobos GJ, Tan L, Cohen MH, McIntyre M, Bauer R, Li X et al, Are national quality standards for traditional Chinese herbal medicine sufficient? Current governmental regulations for traditional Chinese herbal medicine in certain Western countries and China as the Eastern origin country. Complement Ther Med 2005; 13(3):183-190. DOI:10.1016/j.ctim.2005.06.004
9. Wang L, Zhang L, Feng X, Xing L, Zhang W, Jiang K et al, The Functional Difference of Dendritic Cells in HBeAg Negative Chronic Hepatitis B Patients with Three Different Spleen Deficiency Syndromes and the Therapeutic Evaluation of Chinese Medicine Intervention Based on Syndrome Differentiation. Evid Based Complement Alternat Med 2014; 2014:802402. DOI:10.1155/2014/802402
10. Zhao Y, Peng JH, Li XM, Fu QL, Cui T, Li Q et al, [Diagnostic value of clinical indices in syndrome differentiation of chronic hepatitis B: an exploration based on receiver operating characteristic curves and stepwise discriminant analysis]. Zhong Xi Yi Jie He Xue Bao 2012; 10(12):1382-1387. DOI:10.3736/jcim20121208
11. Lang QB, Zhai DX, Huang F, Chen JG, Zhang YH, Liu Q et al, [Investigation on traditional Chinese medicine syndrome distribution of 4 618 hepatitis B virus infection subjects in Qidong of Jiangsu Province, China]. Zhong Xi Yi Jie He Xue Bao 2012; 10(5):525-531.
12. Hollywood KA, Schmidt K, Takano E, Breitling R, Metabolomics tools for the synthetic biology of natural products. Curr Opin Biotechnol 2018; 54:114-120. DOI:10.1016/j.copbio.2018.02.015
13. van der Greef J, van Wietmarschen H, Schroen J, Wang M, Hankemeier T, Xu G, Systems biology-based diagnostic principles as pillars of the bridge between Chinese and Western medicine. Planta Med 2010; 76(17):2036-2047. DOI:10.1055/s-0030-1250450
14. Siristatidis CS, Sertedaki E, Vaidakis D, Varounis C, Trivella M, Metabolomics for improving pregnancy outcomes in women undergoing assisted reproductive technologies. Cochrane Database Syst Rev 2018; 3:CD011872. DOI:10.1002/14651858.CD011872.pub3
15. Ghanbari R, Sumner S, Using Metabolomics to Investigate Biomarkers of Drug Addiction. Trends Mol Med 2018; 24(2):197-205. DOI:10.1016/j.molmed.2017.12.005
16. Antlanger M, Dust T, Reiter T, Bohm A, Lamm WW, Gornicec M et al, Impact of renal impairment on outcomes after autologous stem cell transplantation in multiple myeloma: a multi-center, retrospective cohort study. BMC Cancer 2018; 18(1):1008. DOI:10.1186/s12885-018-4926-0
17. Pasikanti KK, Esuvaranathan K, Hong Y, Ho PC, Mahendran R, Raman Nee Mani L et al, Urinary metabotyping of bladder cancer using two-dimensional gas chromatography time-of-flight mass spectrometry. J Proteome Res 2013; 12(9):3865-3873. DOI:10.1021/pr4000448
18. Huang Z, Lin L, Gao Y, Chen Y, Yan X, Xing J et al, Bladder cancer determination via two urinary metabolites: a biomarker pattern approach. Mol Cell Proteomics 2011; 10(10):M111 007922. DOI:10.1074/mcp.M111.007922
19. Jiang M, Lu C, Zhang C, Yang J, Tan Y, Lu A et al, Syndrome differentiation in modern research of traditional Chinese medicine. J Ethnopharmacol 2012; 140(3):634-642. DOI:10.1016/j.jep.2012.01.033
20. Gu Z, Qi X, Zhai X, Lang Q, Lu J, Ma C et al, Study on TCM Syndrome Differentiation of Primary Liver Cancer Based on the Analysis of Latent Structural Model. Evid Based Complement Alternat Med 2015; 2015:761565. DOI:10.1155/2015/761565
21. Jiansheng L, Haifeng W, Suyun L, Hailong Z, Xueqing Y, Xiaoyun Z et al, Effect of sequential treatment with TCM syndrome differentiation on acute exacerbation of chronic obstructive pulmonary disease and AECOPD risk window. Complement Ther Med 2016; 29:109-115. DOI:10.1016/j.ctim.2016.09.009
22. Ding T, Li Z, Hailemariam T, Mukherjee S, Maxfield FR, Wu MP et al, SMS overexpression and knockdown: impact on cellular sphingomyelin and diacylglycerol metabolism, and cell apoptosis. J Lipid Res 2008; 49(2):376-385. DOI:10.1194/jlr.M700401-JLR200
23. Shakor AB, Taniguchi M, Kitatani K, Hashimoto M, Asano S, Hayashi A et al, Sphingomyelin synthase 1-generated sphingomyelin plays an important role in transferrin trafficking and cell proliferation. J Biol Chem 2011; 286(41):36053-36062. DOI:10.1074/jbc.M111.228593
24. Yano M, Watanabe K, Yamamoto T, Ikeda K, Senokuchi T, Lu M et al, Mitochondrial dysfunction and increased reactive oxygen species impair insulin secretion in sphingomyelin synthase 1-null mice. J Biol Chem 2011; 286(5):3992-4002. DOI:10.1074/jbc.M110.179176
25. Liu J, Huan C, Chakraborty M, Zhang H, Lu D, Kuo MS et al, Macrophage sphingomyelin synthase 2 deficiency decreases atherosclerosis in mice. Circ Res 2009; 105(3):295-303. DOI:10.1161/CIRCRESAHA.109.194613
26. Zhang Y, Dong J, Zhu X, Wang W, Yang Q, The effect of sphingomyelin synthase 2 (SMS2) deficiency on the expression of drug transporters in mouse brain. Biochem Pharmacol 2011; 82(3):287-294. DOI:10.1016/j.bcp.2011.04.009
27. Mitsutake S, Zama K, Yokota H, Yoshida T, Tanaka M, Mitsui M et al, Dynamic modification of sphingomyelin in lipid microdomains controls development of obesity, fatty liver, and type 2 diabetes. J Biol Chem 2011; 286(32):28544-28555. DOI:10.1074/jbc.M111.255646
28. Marathe GK, Pandit C, Lakshmikanth CL, Chaithra VH, Jacob SP, D'Souza CJ, To hydrolyze or not to hydrolyze: the dilemma of platelet-activating factor acetylhydrolase. J Lipid Res 2014; 55(9):1847-1854. DOI:10.1194/jlr.R045492
29. Cekmez Y, Dizdar Gulecoglu M, Ozcan C, Karadeniz L, Kiran G, The utility of maternal mean platelet volume levels for early onset neonatal sepsis prediction of term infants. Ginekol Pol 2017; 88(6):312-314. DOI:10.5603/GP.a2017.0058
30. Gozdas HT, Ince N, Elevated mean platelet volume to platelet ratio predicts advanced fibrosis in chronic hepatitis C. Eur J Gastroenterol Hepatol 2020; 32(4):524-527. DOI:10.1097/MEG.0000000000001599
31. Cho SY, Lee HJ, Park TS, Mean platelet volume in patients with increased gamma-glutamyl transferase. Platelets 2015; 26(3):283-284. DOI:10.3109/09537104.2014.881467