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Seasonal variation in onset and relapse of IBD and a model to predict the frequency of onset, relapse, and severity of IBD based on artificial neural network

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Abstract

Background

Previous research has yielded conflicting data as to whether the natural history of inflammatory bowel disease follows a seasonal pattern. The purpose of this study was (1) to determine whether the frequency of onset and relapse of inflammatory bowel disease follows a seasonal pattern and (2) to establish a model to predict the frequency of onset, relapse, and severity of inflammatory bowel disease (IBD) with meteorological data based on artificial neural network (ANN).

Method

Patients with diagnosis of ulcerative colitis (UC) or Crohn’s disease (CD) between 2003 and 2011 were investigated according to the occurrence of onset and flares of symptoms. The expected onset or relapse was calculated on a monthly basis over the study period. For artificial neural network (ANN), patients from 2003 to 2010 were assigned as training cohort and patients in 2011 were assigned as validation cohort. Mean square error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the predictive accuracy.

Results

We found no seasonal pattern of onset (P = 0.248) and relapse (P = 0.394) among UC patients. But, the onset (P = 0.015) and relapse (P = 0.004) of CD were associated with seasonal pattern, with a peak in July and August. ANN had average accuracy to predict the frequency of onset (MSE = 0.076, MAPE = 37.58 %) and severity of IBD (MSE = 0.065, MAPE = 42.15 %) but high accuracy in predicting the frequency of relapse of IBD (MSE = 0.009, MAPE = 17.1 %).

Conclusion

The frequency of onset and relapse in IBD showed seasonality only in CD, with a peak in July and August, but not in UC. ANN may have its value in predicting the frequency of relapse among patients with IBD.

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Abbreviations

ANN:

Artificial neural network

M/T:

Medical expense/total expense

MSE:

Mean square error

MAPE:

Mean absolute percentage error

References

  1. Tivon K, Cohen P (1995) Seasonality in duodenal ulcer disease: possible relationship with circannually cycling neurons enclosed in the biological clock. Am J Gastroenterol 90(7):1189–1190

    CAS  PubMed  Google Scholar 

  2. Sears MR, Johnston NW (2007) Understanding the September asthma epidemic. J Allergy Clin Immunol 120(3):526–529. doi:10.1016/j.jaci.2007.05.047

    Article  PubMed  Google Scholar 

  3. Boulay F, Berthier F, Sisteron O, Gendreike Y, Gibelin P (1999) Seasonal variation in chronic heart failure hospitalizations and mortality in France. Circulation 100(3):280–286

    Article  CAS  PubMed  Google Scholar 

  4. Lewis JD, Aberra FN, Lichtenstein GR, Bilker WB, Brensinger C, Strom BL (2004) Seasonal variation in flares of inflammatory bowel disease. Gastroenterology 126(3):665–673. doi:10.1053/j.gastro.2003.12.003

    Article  PubMed  Google Scholar 

  5. Hisamatsu T, Suzuki M, Reinecker HC, Nadeau WJ, McCormick BA, Podolsky DK (2003) CARD15/NOD2 functions as an antibacterial factor in human intestinal epithelial cells. Gastroenterology 124(4):993–1000. doi:10.1053/gast.2003.50153

    Article  CAS  PubMed  Google Scholar 

  6. Singh SB, Davis AS, Taylor GA, Deretic V (2006) Human IRGM induces autophagy to eliminate intracellular mycobacteria. Science 313(5792):1438–1441. doi:10.1126/science.1129577

    Article  CAS  PubMed  Google Scholar 

  7. Hampe J, Franke A, Rosenstiel P, Till A, Teuber M, Huse K, Albrecht M, Mayr G, De La Vega FM, Briggs J, Gunther S, Prescott NJ, Onnie CM, Hasler R, Sipos B, Folsch UR, Lengauer T, Platzer M, Mathew CG, Krawczak M, Schreiber S (2007) A genome-wide association scan of nonsynonymous SNPs identifies a susceptibility variant for Crohn disease in ATG16L1. Nat Genet 39(2):207–211. doi:10.1038/ng1954

    Article  CAS  PubMed  Google Scholar 

  8. Aratari A, Papi C, Galletti B, Angelucci E, Viscido A, D’Ovidio V, Ciaco A, Abdullahi M, Caprilli R (2006) Seasonal variations in onset of symptoms in Crohn’s disease. Dig Liver Dis: Off J Ital Soc Gastroenterol Ital Assoc Study Liver 38(5):319–323. doi:10.1016/j.dld.2005.10.002

    Article  CAS  Google Scholar 

  9. Zeng L, Anderson FH (1996) Seasonal change in the exacerbations of Crohn’s disease. Scand J Gastroenterol 31(1):79–82

    Article  CAS  PubMed  Google Scholar 

  10. Moum B, Aadland E, Ekbom A, Vatn MH (1996) Seasonal variations in the onset of ulcerative colitis. Gut 38(3):376–378

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Auslander JN, Lieberman DA, Sonnenberg A (2005) Lack of seasonal variation in the endoscopic diagnoses of Crohn’s disease and ulcerative colitis. Am J Gastroenterol 100(10):2233–2238. doi:10.1111/j.1572-0241.2005.50127.x

    Article  PubMed  Google Scholar 

  12. Zheng MH, Shi KQ, Lin XF, Xiao DD, Chen LL, Liu WY, Fan YC, Chen YP (2013) A model to predict 3-month mortality risk of acute-on-chronic hepatitis B liver failure using artificial neural network. J Viral Hepat 20(4):248–255. doi:10.1111/j.1365-2893.2012.01647.x

    Article  PubMed  Google Scholar 

  13. Liu X, Li NS, Lv LS, Huang JH, Tang H, Chen JX, Ma HJ, Wu XM, Lou TQ (2013) A comparison of the performances of an artificial neural network and a regression model for GFR estimation. Am J Kidney Dis 62(6):1109–1115. doi:10.1053/j.ajkd.2013.07.010

    Article  PubMed  Google Scholar 

  14. Baxt WG (1995) Application of artificial neural networks to clinical medicine. Lancet 346(8983):1135–1138

    Article  CAS  PubMed  Google Scholar 

  15. Wang D, Wang Q, Shan F, Liu B, Lu C (2010) Identification of the risk for liver fibrosis on CHB patients using an artificial neural network based on routine and serum markers. BMC Infect Dis 10:251. doi:10.1186/1471-2334-10-251

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Cross SS, Harrison RF, Kennedy RL (1995) Introduction to neural networks. Lancet 346(8982):1075–1079

    Article  CAS  PubMed  Google Scholar 

  17. Traeger M, Eberhart A, Geldner G, Morin AM, Putzke C, Wulf H, Eberhart LH (2003) Artificial neural networks. Theory and applications in anesthesia, intensive care and emergency medicine. Anaesthesist 52(11):1055–1061

    Article  CAS  PubMed  Google Scholar 

  18. Tu JV (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49(11):1225–1231

    Article  CAS  PubMed  Google Scholar 

  19. Wei CH, Lee Y (2007) Sequential forecast of incident duration using artificial neural network models. Accid Anal Prev 39(5):944–954. doi:10.1016/j.aap.2006.12.017

    Article  PubMed  Google Scholar 

  20. Sellu DP (1986) Seasonal variation in onset of exacerbations of ulcerative proctocolitis. J R Coll Surg Edinb 31(3):158–160

    CAS  PubMed  Google Scholar 

  21. Evans JG, Acheson ED (1965) An epidemiological study of ulcerative colitis and regional enteritis in the Oxford area. Gut 6(4):311–324

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  22. Kangro HO, Chong SK, Hardiman A, Heath RB, Walker-Smith JA (1990) A prospective study of viral and mycoplasma infections in chronic inflammatory bowel disease. Gastroenterology 98(3):549–553

    CAS  PubMed  Google Scholar 

  23. Tysk C, Jarnerot G (1993) Seasonal variation in exacerbations of ulcerative colitis. Scand J Gastroenterol 28(1):95–96

    Article  CAS  PubMed  Google Scholar 

  24. Myszor M, Calam J (1984) Seasonality of ulcerative colitis. Lancet 2(8401):522–523

    Article  CAS  PubMed  Google Scholar 

  25. Don BA, Goldacre MJ (1984) Absence of seasonality in emergency hospital admissions for inflammatory bowel disease. Lancet 2(8412):1156–1157

    Article  CAS  PubMed  Google Scholar 

  26. Sonnenberg A, Jacobsen SJ, Wasserman IH (1994) Periodicity of hospital admissions for inflammatory bowel disease. Am J Gastroenterol 89(6):847–851

    CAS  PubMed  Google Scholar 

  27. Sonnenberg A, McCarty DJ, Jacobsen SJ (1991) Geographic variation of inflammatory bowel disease within the United States. Gastroenterology 100(1):143–149

    CAS  PubMed  Google Scholar 

  28. Yapp TR, Stenson R, Thomas GA, Lawrie BW, Williams GT, Hawthorne AB (2000) Crohn’s disease incidence in Cardiff from 1930: an update for 1991–1995. Eur J Gastroenterol Hepatol 12(8):907–911

    Article  CAS  PubMed  Google Scholar 

  29. Jacobsen BA, Fallingborg J, Rasmussen HH, Nielsen KR, Drewes AM, Puho E, Nielsen GL, Sorensen HT (2006) Increase in incidence and prevalence of inflammatory bowel disease in northern Denmark: a population-based study, 1978–2002. Eur J Gastroenterol Hepatol 18(6):601–606

    Article  PubMed  Google Scholar 

  30. Bai AP, Ouyang Q (2006) Probiotics and inflammatory bowel diseases. Postgrad Med J 82(968):376–382. doi:10.1136/pgmj.2005.040899

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  31. Duchmann R, Kaiser I, Hermann E, Mayet W, Ewe K, Meyer zum Buschenfelde KH (1995) Tolerance exists towards resident intestinal flora but is broken in active inflammatory bowel disease (IBD). Clin Exp Immunol 102(3):448–455

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  32. Ramani S, Banerjee I, Gladstone BP, Sarkar R, Selvapandian D, Le Fevre AM, Jaffar S, Iturriza-Gomara M, Gray JJ, Estes MK, Brown DW, Kang G (2007) Geographic information systems and genotyping in identification of rotavirus G12 infections in residents of an urban slum with subsequent detection in hospitalized children: emergence of G12 genotype in south India. J Clin Microbiol 45(2):432–437. doi:10.1128/jcm.01710-06

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. Cho SH, Kim JH, Kim JC, Shin HH, Kang YH, Lee BK (2006) Surveillance of bacterial pathogens associated with acute diarrheal disease in the Republic of Korea during one year, 2003. J Microbiol 44(3):327–335

    CAS  PubMed  Google Scholar 

  34. Nelson RJ (2004) Seasonal immune function and sickness responses. Trends Immunol 25(4):187–192. doi:10.1016/j.it.2004.02.001

    Article  CAS  PubMed  Google Scholar 

  35. Shirai T, Magara KK, Motohashi S, Yamashita M, Kimura M, Suwazomo Y, Nogawa K, Kuriyama T, Taniguchi M, Nakayama T (2003) TH1-biased immunity induced by exposure to Antarctic winter. J Allergy Clin Immunol 111(6):1353–1360

    Article  CAS  PubMed  Google Scholar 

  36. Linden M, Larson M, Prellner T, Brattsand R, Laitinen LA (1994) Seasonal variation in the function of blood monocytes obtained from healthy nonsmokers, asymptomatic smokers, and smokers with chronic bronchitis. Chronobiol Int 11(4):266–272

    Article  CAS  PubMed  Google Scholar 

  37. MacMurray JP, Barker JP, Armstrong JD, Bozzetti LP, Kuhn IN (1983) Circannual changes in immune function. Life Sci 32(20):2363–2370

    Article  CAS  PubMed  Google Scholar 

  38. Matchock RL, Dorn LD, Susman EJ (2007) Diurnal and seasonal cortisol, testosterone, and DHEA rhythms in boys and girls during puberty. Chronobiol Int 24(5):969–990. doi:10.1080/07420520701649471

    Article  CAS  PubMed  Google Scholar 

  39. Martini GA, Brandes JW (1976) Increased consumption of refined carbohydrates in patients with Crohn’s disease. Klin Wochenschr 54(8):367–371

    Article  CAS  PubMed  Google Scholar 

  40. Mayberry JF, Rhodes J, Newcombe RG (1980) Increased sugar consumption in Crohn’s disease. Digestion 20(5):323–326

    Article  CAS  PubMed  Google Scholar 

  41. Kasper H, Sommer H (1979) Dietary fiber and nutrient intake in Crohn’s disease. Am J Clin Nutr 32(9):1898–1901

    CAS  PubMed  Google Scholar 

  42. Russel MG, Engels LG, Muris JW, Limonard CB, Volovics A, Brummer RJ, Stockbrugger RW (1998) Modern life’ in the epidemiology of inflammatory bowel disease: a case-control study with special emphasis on nutritional factors. Eur J Gastroenterol Hepatol 10(3):243–249

    Article  CAS  PubMed  Google Scholar 

  43. Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43(1):3–31

    Article  CAS  PubMed  Google Scholar 

  44. Manser CN, Paul M, Rogler G, Held L, Frei T (2013) Heat waves, incidence of infectious gastroenteritis, and relapse rates of inflammatory bowel disease: a retrospective controlled observational study. Am J Gastroenterol 108(9):1480–1485. doi:10.1038/ajg.2013.186

    Article  PubMed  Google Scholar 

  45. D’Haens GR, Sartor RB, Silverberg MS, Petersson J, Rutgeerts P (2014) Future directions in inflammatory bowel disease management. J Crohns Colitis. doi:10.1016/j.crohns.2014.02.025

    Google Scholar 

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Acknowledgments

This work was supported by grants from the National Science Foundation of China (No. 81470820 and No. 81370508).

Conflict of interest

The authors declare that they have no competing interests.

Authors’ contribution

Jiang Chen Peng did the statistical work and wrote the paper.

Zhi Hua Ran collected the data and did the statistical work.

Jun Shen designed and checked the paper.

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Authors and Affiliations

  1. State Key Laboratory for Oncogenes and Related Genes, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Renji Hospital, School of Medicine, Shanghai Cancer Institute, Shanghai Institute of Digestive Disease, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai, 200001, China

    Jiang Chen Peng, Zhi Hua Ran & Jun Shen

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  1. Jiang Chen Peng
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  2. Zhi Hua Ran
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Correspondence to Jun Shen.

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Statement

Our study aims to find seasonal variation of IBD frequency in Shanghai and establish artificial neural network to predict the onset and relapse frequency of IBD in Shanghai.

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Peng, J.C., Ran, Z.H. & Shen, J. Seasonal variation in onset and relapse of IBD and a model to predict the frequency of onset, relapse, and severity of IBD based on artificial neural network. Int J Colorectal Dis 30, 1267–1273 (2015). https://doi.org/10.1007/s00384-015-2250-6

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