Disease Informatics: Host factors simplified

1. Disease Informatics: Host factors simplified Rajendra P Deolankar oonnatie@yahoo.com Laughter is the best medicine Prerequisite http://www.pitt.edu/~super1/lecture/lec25371/index.htm http://www.pitt.edu/~super1/lecture/lec25381/001.htm

2. Modern man is genetically same as Paleolithic man But lives in the artifact world And hence “gene X artifact” is an important subject matter for disease study Artifacts http://news.nationalgeographic.com/news/2004/09/images/040910_awastack.jpg

3. Confounders Age, Sex, Socioeconomic status, Caste etc are not causes of disease but could be pointers to molecules and mechanisms causing disease

4. Organization of disease system Mere set of organs is not organism System cannot work without organization To manage a system effectively, you might focus on the interactions of the parts rather than their behavior taken separately. Russell L. Ackoff

5. Personality of disease Disease has a personality and associated factors are its organs Associated factors are mostly but not necessarily component causes (CCs)

6. Disease Causal Mechanism (DCM) Summarily, Mere set of CCs  DCM CCs: Component causes Conceptual scheme of ageing as the accumulation of component causes throughout life Ageing starts with the accumulation of component causes A–E. The presence of these five component causes completes sufficient cause I, resulting in effect I, e.g. unsteadiness. In the following period, the addition of component causes F–H completes sufficient cause II, resulting in effect II, e.g. a gait disorder. The further accumulation of component causes I and J completes sufficient cause III, resulting in effect III, e.g. death (see also the description of the example). http://www.biomedcentral.com/content/pdf/1471-2318-3-7.pdf

7. Teamwork P + X  PX Where P and X are CCs and PX is interaction / teamwork amongst CCs CCs: Component causes

8. PX  DCM Disease is outcome of DCM where factors work in team Teamwork PX predominate over individual factors P + X DCM: Disease causal mechanism PX: Interaction / teamwork amongst CCs

9. Disease and DCMs DCM is regarded as sufficient cause For a given disease, there could be several sufficient causes Three sufficient causes of disease. http://www.ajph.org/cgi/content/full/95/S1/S144/F1

10. Rothman and Greenland Proposed model Prof. Sander Greenland PROF. Kenneth J Rothman A model of causation that describes causes in terms of sufficient causes and their component causes illuminates important principles such as multicausality, the dependence of the strength of component causes on the prevalence of complementary component causes, and interaction between component causes.

11. Innate α (Host X Environment) Immunity Specificα (Host X pathogen) Paraspecificα (Innate X immunogen) Disease resistance Host is a common factor

12. Infectious diseases Pathogen (P) must work together with some CC (X) to compose DCM P is also CC CC: Component cause DCM: Disease causal mechanism

13. What is X? We have seen that, in an infectious diseases, Pathogen (P) must work together with some CC (X) to compose DCM X could be described as complex interaction of host/ environmental factors

14. DCM centric Disease Definitions DCM could be P1X OR P2X OR … PmX Secretory Diarrhoea may be associated with E coli toxin (P1X) OR Vibrio cholerae 01 toxin (P2X) OR … NSP4 of rotavirus (PmX)

15. CC centric disease definitions DCM could also be PX1 OR PX2 OR ... PXn Dengue virus (P) could be associated with fever (PX1), hemorrhagic fever (PX2) OR … Shock syndrome (PXn) X1≠ X2 ≠ Xn http://www.wordinfo.info/words/index/info/view_unit/1/?letter=B&spage=3 Crude classifications and false generalizations are the curse of organized life.  George Bernard Shaw

16. Disease definition for Total disease burden in a locality DCM could be: P1X OR P2X OR … PmX OR P1X1 OR P1X2 OR … P1Xn OR … P2X1 OR P2X2 OR … P2Xn OR … ………………………PmXn

17. Taking the wicket of a key player helps in winning the cricket match CC in CC centric disease definitions is regarded as a key player It is easy to detect CC It is easy to plan strategy against CC Information on DCM is complex and difficult to compile

18. Several captains!!! Almost no players This could be a limitation of CC centric disease definitions To fight Secretory Diarrhoea, solution based on E coli target may not work against rotavirus. Ultimately several solutions are required to fight the disease.

19. Team may win even after captains fails Biologicals and chemicals prepared to fight against the disease May fail May produce complications May emerge into new disease Increase the expenditure on public health

20. Description of a human in Indian Medicine Self = Somatic body (Annamaya kosha) + Vitality (Pranamaya kosha) + Mind (Manomaya kosha) + Intellect (Vidnyanamaya kosha) + Bliss (Anandamaya kosha) Human body computer = Intellect (Central processing unit) + Self / Ego (Software) + Memory (Free space, Floppy/ Hard disk) + Mind/ senses (Program) + Life history (Data)

21. Principals in DCM in Ancient Indian Medicine Errors in 4 C’s: Catch Control Carry on and Chuck

22. 1st C Error: In catching from nature: food, water, air, sunlight etc Outcome: Slip in right body composition Solution: Balancing Dosha Dosha≡ Body composition

23. 2nd C Error: In controlling the body network Outcome: Slip in right response to the stimulus Solution: Appropriation of Dushya Dushya ≡ response to stimulus

24. 3rd C Error: In carrying on the routine Outcome: Deviation from optimum basal metabolic rate Solution: Regularising Agni Agni ≡ Basal Metabolic Rate

25. 4th C Error: In chucking the waste Outcome: A body that is not free from dysbiotics and morbid substances Solution: Elimination of Ama Prof. Stig Bengmark Ama ≡ products of dysbiosis Human microbial organs Gut associated microbiota organ Vagina associated microbiota organ Skin associated microbiota organ

26. Disease triad described in ancient Indian medicine Disease triad is working together of Host factors (Adhyatmic), Environmental factors (Adhidaivik) and Agents: physical, chemical or biological (Adhibhautik) To give outcome as disease (Vyadhi)

27. Associations with disease outcome CCs work together to give disease outcomes that can be observed at a particular time, at a particular place or in a particular person Time (Season: Kala-bala), Place (Daiva- bala) and Person (Prakruti, described separately)

28. Details observed in Person 1. Genetically predisposed / metabolically imprinted (Aadi- bala pravritta) 2. Congenital (Janma-bala) 3. Imbalance of body composition (Dosha-bala) 4. Metabolic activity (Vata, Pitta and Kapha) 5. Trauma (Sanghata-bala) and 6. Age, sex, socioeconomic status etc (Svabhava-bala) Thrifty genes http://www.bmj.com/cgi/content/full/328/7447/1070 Prenatal adaptations http://www.bmj.com/cgi/content/full/328/7447/1070

29. Host body phenotypic characterization Density Somatotype State of matter Composition Motility Shape etc

30. Density of body Variation in dosha resemble density of the body (Vata-light, Kapha-heavy; Pitta-neither heavy nor light)

31. Somatotype of the body Ectomorph (Vata), Endomorph (Kapha) and Mesomorph (Pitta) Mesomorph Ectomorph Endomorph http://www.innerexplorations.com/catpsy/t1c4.htm

32. State of body matter Gas (Vata) Solid (Kapha) and Liquid (Pitta)

33. Body composition Low muscle (Vata) Fatty and muscular (Kapha) and In between i.e. lean mass dominated (Pitta)

34. Body motility High (Vata) Low (Kapha) and Medium (Pitta)

35. Body shape Linear (Vata) Hour glass (Pitta) and Apple, pear or rectangular (Kapha)

36. Tridosha in ancient medicine Vata, Pitta and Kapha of a person are called as Tridosha in the ancient Indian medicine None of the body characterization criteria described in the earlier slides singly can describe the tridosha Tridosha is complex Quantifying tridosha; Rajni Joshi method http://www.liebertonline.com/doi/abs/10.1089/acm.2004.10.879?cookieSet=1&journalCode=acm

37. Dosha assessment Dosha assessment may vary depending upon the skill level of the vaidya (Doctor)

38. What we can learn from ancient science Parameters for measurement of characteristics of a person could be many and hence data could be quite huge Dimensionality of data can be reduced if similar parameters are grouped together

39. How to reduce dimensionality of data? Techniques in multivariate statistics Computer databases and software

40. Example of database preparation Prepare a multi-dimension data set using all possible criteria on a representative population (Density, somatotype, body shape (digitalize), composition, IQ, EQ etc)

41. Example of application of multivariate statistics Cluster the individuals by applying some algorithm of multivariate statistics The individuals having similar characteristic will fall in one group Thus the population will be divided in a few groups (G1, G2…Gk)

42. Example of application of multivariate statistics Perform Principal Component Analysis (Statistical analysis) on each group (G1, G2…Gk) The number of variables now would be 2 to 3. The three components expected could be similar to Vata, Pitta and Kapha in order of their importance This order will vary in each group (cluster) http://content.digitalwell.washington.edu/msr/external_release_talks_12_05_2005/13651/lecture.htm

43. Example of prediction model List which variables are closely associated with Principal Components See how Principal Components look like in real life How the Principal Components can be predicted? (e.g. least square technique)

44. Prepare a model for component balance Understand host with fewer parameters (Principal components) Estimate the Prakruti (constitution of a person) Estimate the Vikruti (Loss of harmony in constitution) Stress and disturbed sleep are such factors which could contribute spontaneously to the DCM

45. The procedure described is based on phenotypic characterization The preventive strategies could be described as: Nutriphenomics Pharmacophenomics etc Preventive strategies Now, human genomic data is also available The preventive strategies are: Nutrigenomics Pharmacogenomics etc

46. Ancient Indian methods to tackle total disease burden in a locality Seasonal lifestyle goals (Rutucharya) Diurnal lifestyle goals (Dinacharya)

47. How to implement goals Control by risk groups (Vrata) Transformation of patients (Vaikalya) Festivals for everybody (Sana) Message of Rishi Panchami Vrata Reduce artifacts from your lifestyle

48. Make up of Vrata,Vaikalya and Sana Functional foods Nutraceuticals Exercises and Spiritual practices Dr V Prakash

49. Importance of Indian Medicine for Disease Informatics Genetic and lifestyle variables of a host could be described in fewer words (e.g. Vata, Pitta and Kapha) to understand events in Disease Causal Chains (DiCC) A great help in drawing DiCC Disease informatics for setting up Disease definition, drawing Disease Causal Chain / Web, marking Risk Events, Backend and Frontend Events, and Health Problem Solutions http://bmj.bmjjournals.com/cgi/eletters/331/7516/566#134452

50. Thanks Points in Indian Medicine is outcome of discussion with Dr. Mandar Akkalkotkar Statistics guru is Dr. Sham J Amdekar