Clinical phenotype network: the underlying mechanism for personalized diagnosis and treatment of traditional Chinese medicine

Abstract

Traditional Chinese medicine (TCM) investigates the clinical diagnosis and treatment regularities in a typical schema of personalized medicine, which means that individualized patients with same diseases would obtain distinct diagnosis and optimal treatment from different TCM physicians. This principle has been recognized and adhered by TCM clinical practitioners for thousands of years. However, the underlying mechanisms of TCM personalized medicine are not fully investigated so far and remained unknown. This paper discusses framework of TCM personalized medicine in classic literatures and in real-world clinical settings, and investigates the underlying mechanisms of TCM personalized medicine from the perspectives of network medicine. Based on 246 well-designed outpatient records on insomnia, by evaluating the personal biases of manifestation observation and preferences of herb prescriptions, we noted significant similarities between each herb prescriptions and symptom similarities between each encounters. To investigate the underlying mechanisms of TCM personalized medicine, we constructed a clinical phenotype network (CPN), in which the clinical phenotype entities like symptoms and diagnoses are presented as nodes and the correlation between these entities as links. This CPN is used to investigate the promiscuous boundary of syndromes and the co-occurrence of symptoms. The small-world topological characteristics are noted in the CPN with high clustering structures, which provide insight on the rationality of TCM personalized diagnosis and treatment. The investigation on this network would help us to gain understanding on the underlying mechanism of TCM personalized medicine and would propose a new perspective for the refinement of the TCM individualized clinical skills.

Keywords

personalized medicine / complex network / clinical phenotype network / traditional Chinese medicine

References

[1] Piquette-Miller M, Grant DM. The art and science of personalized medicine. Clin Pharmacol Ther2007; 81(3): 311–315

[2] Lesko LJ. Personalized medicine: elusive dream or imminent reality? Clin Pharmacol Ther2007; 81(6): 807–816

[3] Hamburg MA, Collins FS. The path to personalized medicine. N Engl J Med2010; 363(4): 301–304

[4] Meyers DA, Bleecker ER, Holloway JW, Holgate ST. Asthma genetics and personalised medicine. Lancet Respir Med2014; 2(5): 405–415

[5] Mosli MH, Sandborn WJ, Kim RB, Khanna R, Al-Judaibi B, Feagan BG. Toward a personalized medicine approach to the management of inflammatory bowel disease. Am J Gastroenterol2014; 109(7): 994–1004

[6] Hutchinson L. Personalized cancer medicine: era of promise and progress. Nat Rev Clin Oncol2011; 8(3): 121

[7] Ginsburg GS, McCarthy JJ. Personalized medicine: revolutionizing drug discovery and patient care. Trends Biotechnol2001; 19(12): 491–496

[8] Pirmohamed M. Personalized pharmacogenomics: predicting efficacy and adverse drug reactions. Annu Rev Genomics Hum Genet2014 May 29. [Epub ahead of print]

[9] Weston AD, Hood L. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Proteome Res2004; 3(2): 179–196

[10] Chung KF. Defining phenotypes in asthma: a step towards personalized medicine. Drugs2014; 74(7): 719–728

[11] Raciti GA, Nigro C, Longo M, Parrillo L, Miele C, Formisano P, Béguinot F. Personalized medicine and type 2 diabetes: lesson from epigenetics. Epigenomics2014; 6(2): 229–238

[12] Fraser M, Berlin A, Bristow RG, van der Kwast T. Genomic, pathological, and clinical heterogeneity as drivers of personalized medicine in prostate cancer. Urol Oncol2014 Apr 22. [Epub ahead of print] doi: 10.1016/j.urolonc.2013.10.020

[13] Barabási AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet2011; 12(1): 56–68

[14] Committee on A Framework for Developing A New Taxonomy of Disease. Towards precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: National Academies Press, 2011

[15] Rzhetsky A, Wajngurt D, Park N, Zheng T. Probing genetic overlap among complex human phenotypes. Proc Natl Acad Sci USA2007; 104(28): 11694–11699

[16] Blair DR, Lyttle CS, Mortensen JM, Bearden CF, Jensen AB, Khiabanian H, Melamed R, Rabadan R, Bernstam EV, Brunak S, Jensen LJ, Nicolae D, Shah NH, Grossman RL, Cox NJ, White KP, Rzhetsky A. A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk. Cell2013; 155(1): 70–80

[17] Lee DS, Park J, Kay KA, Christakis NA, Oltvai ZN, Barabási AL. The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad Sci USA2008; 105(29): 9880–9885

[18] van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JA. A text-mining analysis of the human phenome. Eur J Hum Genet2006; 14(5): 535–542

[19] Zhou X, Menche J, Barabási AL, Sharma A. Human symptoms-disease network. Nat Commun2014; 5: 4212

[20] Vidal M, Cusick ME, Barabási AL. Interactome networks and human disease. Cell2011; 144(6): 986–998

[21] Anonymous. The Inner Canon of Emperor Huang, Beijing: Chinese Medical Ancient Books Publishing House, 2003

[22] Jiang L, Liu B, Xie Q, Yang S, He L, Zhang R, Yan S, Zhou X, Liu J. Investigation into the influence of physician for treatment based on syndrome differentiation. Evid Based Complement Alternat Med2013; 2013: 587234

[23] Hu J, Liu B. The basic theory, diagnostic, and therapeutic system of traditional Chinese medicine and the challenges they bring to statistics. Stat Med2012; 31(7): 602–605

[24] Lu AP, Jia HW, Xiao C, Lu QP. Theory of traditional Chinese medicine and therapeutic method of diseases. World J Gastroenterol2004; 10(13): 1854–1856

[25] Xu H, Chen K. Integrative medicine: the experience from China. J Altern Complement Med2008; 14(1): 3–7

[26] Loscalzo J, Kohane I, Barabasi AL. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol Syst Biol2007; 3: 124

[27] Liu B, Wang Y. Investigation on the concepts and their relationships among disease, symptoms and syndromes. J Tradit Chin Med2007; 48: 293–298

[28] Li Y, Zhou X, Zhang R, Wang Y, Peng Y, Hu J, Xie Q, Xue Y, Xu L, Liu X, Liu B. Complex network approach for investigating the herb-symptom correspondence phenomenon and analyzing the TCM clinical herb-symptom association knowledge. BMC Complement Altern Med2014 (Accepted)

[29] Zhou XZ, Zhang RS, Shah J, Rajgor D, Wang YH, Pietrobon R, Liu BY, Chen J, Zhu JG, Gao RL. Patterns of herbal combination for the treatment of insomnia commonly employed by highly experienced Chinese medicine physicians. Chin J Integr Med2011; 17(9): 655–662

[30] Mirnezami R, Nicholson J, Darzi A. Preparing for precision medicine. N Engl J Med2012; 366(6): 489–491

[31] O’Connor TG, McGuire S, Reiss D, Hetherington EM, Plomin R. Co-occurrence of depressive symptoms and antisocial behavior in adolescence: a common genetic liability. J Abnorm Psychol1998; 107(1): 27–37

[32] Zhou X, Peng Y, Liu B. Text mining for traditional Chinese medical knowledge discovery: a survey. J Biomed Inform2010; 43(4): 650–660

[33] Chen J, Lu P, Zuo X, Shi Q, Zhao H, Luo L, Yi J, Zheng C, Yang Y, Wang W. Clinical data mining of phenotypic network in angina pectoris of coronary heart disease. Evid Based Complement Alternat Med2012; 2012: 546230

[34] Zhao Y, Zhang NL, Wang T, Wang Q. Discovering symptom co-occurrence patterns from 604 cases of depressive patient data using latent tree models. J Altern Complement Med 2014; 20(4): 265–271

[35] Liu B, Chen S, Zhou X, Ni Q, He L. The principle of patient classification for type 2 diabetes based on symptoms. Beijing J Tradit Chin Med (Beijing Zhong Yi)2009; 28: 267–269 (in Chinese)

[36] Yan S, Zhang R, Zhou X, Li P, He L, Liu B. Exploring effective core drug patterns in primary insomnia treatment with Chinese herbal medicine: study protocol for a randomized controlled trial. Trials2013; 14(1): 61

[37] Zhang L, Wang, J, Wang Y. Research on FANG-ZHENG Correspondence. China J Tradit Chin Med Pharm (Zhonghua Zhong Yi Yao Za Zhi)2005; 20: 8–10 (in Chinese)

[38] Zhang XP, Zhou XZ, Huang HK, Feng Q, Chen SB, Liu BY. Topic model for Chinese medicine diagnosis and prescription regularities analysis: case on diabetes. Chin J Integr Med2011; 17(4): 307–313

[39] Poon J, Luo Z, Zhang RS. Feature representation in the biclustering of symptom-herb relationship in Chinese medicine. Chin J Integr Med2011; 17(9): 663–668

[40] Liu GP, Li GZ, Wang YL, Wang YQ. Modelling of inquiry diagnosis for coronary heart disease in Traditional Chinese Medicine by using multi-label learning. BMC Complement Altern Med2010; 10(1): 37

[41] Zhou X, Liu B, Wu Z, Feng Y. Integrative mining of traditional Chinese medicine literature and MEDLINE for functional gene networks. Artif Intell Med2007; 41(2): 87–104

[42] Su SB, Jia W, Lu A, Li S. Evidence-Based ZHENG: A Traditional Chinese Medicine Syndrome 2013. Evid Based Complement Alternat Med2014; 2014: 484201

[43] Li S, Fan TP, Jia W, Lu A, Zhang W. Network pharmacology in traditional chinese medicine. Evid Based Complement Alternat Med2014; 2014: 138460

[44] Liu B, Zhou X, Wang Y, Hu J, He L, Zhang R, Chen S, Guo Y. Data processing and analysis in real-world traditional Chinese medicine clinical data: challenges and approaches. Stat Med2012; 31(7): 653–660

[45] Zhou X, Chen S, Liu B, Zhang R, Wang Y, Li P, Guo Y, Zhang H, Gao Z, Yan X. Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artif Intell Med2010; 48(2–3): 139–152

[46] Liu B, Zhang Y, Hu J, He L, Zhou X. Thinking and practice of accelerating transformation of traditional Chinese medicine from experience medicine to evidence-based medicine. Front Med2011; 5(2): 163–170

Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grant Nos. 61105055, 81230086), National Basic Research Program of China (973 Program, 2014CB542903), National High Technology Research and Development Program of China (863 Program, 2012AA02A609), National Key Technology R&D Program (2013BAI02B01, 2013BAI13B04, 2013BAH06F03), National S&T Major Project of China (2012ZX09503-001-003) and the Fundamental Research Funds for the Central Universities.

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg