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

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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.

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References

  1. Piquette-Miller M, Grant DM. The art and science of personalized medicine. Clin Pharmacol Ther 2007; 81(3): 311–315

    Article  PubMed  CAS  Google Scholar 

  2. Lesko LJ. Personalized medicine: elusive dream or imminent reality? Clin Pharmacol Ther 2007; 81(6): 807–816

    Article  PubMed  CAS  Google Scholar 

  3. Hamburg MA, Collins FS. The path to personalized medicine. N Engl J Med 2010; 363(4): 301–304

    Article  PubMed  CAS  Google Scholar 

  4. Meyers DA, Bleecker ER, Holloway JW, Holgate ST. Asthma genetics and personalised medicine. Lancet Respir Med 2014; 2(5): 405–415

    Article  PubMed  Google Scholar 

  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 Gastroenterol 2014; 109(7): 994–1004

    Article  PubMed  CAS  Google Scholar 

  6. Hutchinson L. Personalized cancer medicine: era of promise and progress. Nat Rev Clin Oncol 2011; 8(3): 121

    Article  PubMed  Google Scholar 

  7. Ginsburg GS, McCarthy JJ. Personalized medicine: revolutionizing drug discovery and patient care. Trends Biotechnol 2001; 19(12): 491–496

    Article  PubMed  CAS  Google Scholar 

  8. Pirmohamed M. Personalized pharmacogenomics: predicting efficacy and adverse drug reactions. Annu Rev Genomics Hum Genet 2014 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 Res 2004; 3(2): 179–196

    Article  PubMed  CAS  Google Scholar 

  10. Chung KF. Defining phenotypes in asthma: a step towards personalized medicine. Drugs 2014; 74(7): 719–728

    Article  PubMed  CAS  Google Scholar 

  11. Raciti GA, Nigro C, Longo M, Parrillo L, Miele C, Formisano P, Béguinot F. Personalized medicine and type 2 diabetes: lesson from epigenetics. Epigenomics 2014; 6(2): 229–238

    Article  PubMed  CAS  Google Scholar 

  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 Oncol 2014 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 Genet 2011; 12(1): 56–68

    Article  PubMed  PubMed Central  Google Scholar 

  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

    Google Scholar 

  15. Rzhetsky A, Wajngurt D, Park N, Zheng T. Probing genetic overlap among complex human phenotypes. Proc Natl Acad Sci USA 2007; 104(28): 11694–11699

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  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. Cell 2013; 155(1): 70–80

    Article  PubMed  CAS  Google Scholar 

  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 USA 2008; 105(29): 9880–9885

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  18. van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JA. A text-mining analysis of the human phenome. Eur J Hum Genet 2006; 14(5): 535–542

    Article  PubMed  Google Scholar 

  19. Zhou X, Menche J, Barabási AL, Sharma A. Human symptoms-disease network. Nat Commun 2014; 5: 4212

    PubMed  Google Scholar 

  20. Vidal M, Cusick ME, Barabási AL. Interactome networks and human disease. Cell 2011; 144(6): 986–998

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  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 Med 2013; 2013: 587234

    PubMed  PubMed Central  Google Scholar 

  23. Hu J, Liu B. The basic theory, diagnostic, and therapeutic system of traditional Chinese medicine and the challenges they bring to statistics. Stat Med 2012; 31(7): 602–605

    Article  PubMed  Google Scholar 

  24. Lu AP, Jia HW, Xiao C, Lu QP. Theory of traditional Chinese medicine and therapeutic method of diseases. World J Gastroenterol 2004; 10(13): 1854–1856

    PubMed  Google Scholar 

  25. Xu H, Chen K. Integrative medicine: the experience from China. J Altern Complement Med 2008; 14(1): 3–7

    Article  PubMed  CAS  Google Scholar 

  26. Loscalzo J, Kohane I, Barabasi AL. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol Syst Biol 2007; 3: 124

    Article  PubMed  PubMed Central  Google Scholar 

  27. Liu B, Wang Y. Investigation on the concepts and their relationships among disease, symptoms and syndromes. J Tradit Chin Med 2007; 48: 293–298

    Google Scholar 

  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 Med 2014 (Accepted)

    Google Scholar 

  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 Med 2011; 17(9): 655–662

    Article  PubMed  Google Scholar 

  30. Mirnezami R, Nicholson J, Darzi A. Preparing for precision medicine. N Engl J Med 2012; 366(6): 489–491

    Article  PubMed  Google Scholar 

  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 Psychol 1998; 107(1): 27–37

    Article  PubMed  Google Scholar 

  32. Zhou X, Peng Y, Liu B. Text mining for traditional Chinese medical knowledge discovery: a survey. J Biomed Inform 2010; 43(4): 650–660

    Article  PubMed  Google Scholar 

  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 Med 2012; 2012: 546230

    PubMed  PubMed Central  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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)

    Google Scholar 

  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. Trials 2013; 14(1): 61

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  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)

    CAS  Google Scholar 

  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 Med 2011; 17(4): 307–313

    Article  PubMed  Google Scholar 

  39. Poon J, Luo Z, Zhang RS. Feature representation in the biclustering of symptom-herb relationship in Chinese medicine. Chin J Integr Med 2011; 17(9): 663–668

    Article  PubMed  Google Scholar 

  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 Med 2010; 10(1): 37

    Article  PubMed  PubMed Central  Google Scholar 

  41. Zhou X, Liu B, Wu Z, Feng Y. Integrative mining of traditional Chinese medicine literature and MEDLINE for functional gene networks. Artif Intell Med 2007; 41(2): 87–104

    Article  PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  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 Med 2012; 31(7): 653–660

    Article  PubMed  Google Scholar 

  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 Med 2010; 48(2–3): 139–152

    Article  PubMed  Google Scholar 

  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 Med 2011; 5(2): 163–170

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China

    Xuezhong Zhou & Yubing Li

  2. China Academy of Chinese Medical Sciences, Beijing, 100700, China

    Xuezhong Zhou, Qi Xie & Baoyan Liu

  3. School of Engineering and Informatics, University of Bradford, Bradford, BD7 1DP, UK

    Yonghong Peng

  4. Institute of Basic Theory of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China

    Jingqing Hu

  5. Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China

    Runshun Zhang & Yinghui Wang

  6. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China

    Liyun He, Lijie Jiang, Shiyan Yan & Peng Li

Authors
  1. Xuezhong Zhou
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  2. Yubing Li
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  4. Jingqing Hu
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  5. Runshun Zhang
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  7. Yinghui Wang
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  8. Lijie Jiang
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  9. Shiyan Yan
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  10. Peng Li
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  11. Qi Xie
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  12. Baoyan Liu
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Correspondence to Xuezhong Zhou or Baoyan Liu.

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Zhou, X., Li, Y., Peng, Y. et al. Clinical phenotype network: the underlying mechanism for personalized diagnosis and treatment of traditional Chinese medicine. Front. Med. 8, 337–346 (2014). https://doi.org/10.1007/s11684-014-0349-8

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  • DOI: https://doi.org/10.1007/s11684-014-0349-8

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