Probiotics for Preventing Relapse in Ulcerative Colitis: A Systematic Review and Bayesian Network Meta-Analysis

Authors

  • Steven Alvianto School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia https://orcid.org/0000-0003-1783-8531
  • Virly Nanda Muzellina Division of Gastroenterology, Pancreatobiliary, and Digestive Endoscopy, Department of Internal Medicine, Dr. Cipto Mangunkusumo National General Hospital, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
  • Nicolas Daniel Widjanarko School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia
  • Yeong Yeh Lee School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
  • Olivia Wangidjaja School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia

DOI:

https://doi.org/10.24871/2622025130-142

Keywords:

5-ASA, probiotics, probiotics plus 5-ASA, relapse, ulcerative colitis

Abstract

Background: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by periods of relapse and remission. Preventing relapse is critical to improving long-term outcomes. This study aimed to compare the efficacy of probiotics, 5-aminosalicylic acid (5-ASA), probiotics combined with 5-ASA, and placebo in preventing relapse in UC.

Methods: A comprehensive search from PubMed, Cochrane Library, MEDLINE, ProQuest, ScienceDirect, Clinical Trials. gov and Google Scholar databases were conducted. The primary outcome was clinical relapse. A Bayesian random-effects model calculated pooled odds ratios (ORs) with 95% CIs and treatment ranks were assessed using the surface under the cumulative ranking curve (SUCRA).

Results: Of total 552 initial papers, 37 extracted, and 26 were removed due to exclusion criteria. Eleven RCTs involving 1,099 participants were eventually included for analysis. Probiotics combined with 5-ASA had the highest efficacy (OR = 0.23, 95% CI: 0.027–1.09; SUCRA = 71.43), followed by 5-ASA alone (OR = 0.25, 95% CI: 0.035–0.95; SUCRA = 66.90) and probiotics alone (OR = 0.275, 95% CI: 0.059–0.724; SUCRA = 59.69). Placebo ranked lowest (SUCRA = 1.98). The most commonly used probiotics included E. coli Nissle 1917, Lactobacillus GG, and Bifidobacterium species. The most frequently used 5-ASA preparation was mesalazine. Interventions were generally well-tolerated, with no significant adverse events reported.

Conclusion: With the Bayesian NMA, Probiotics plus 5-ASA demonstrates the highest efficacy in preventing relapses in UC. Further research is needed to standardize probiotic regimens and to assess long-term outcomes with the combination approach.

References

McDowell C, Farooq U, Haseeb M. Inflammatory Bowel Disease. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 [cited 2025 Aug 11]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK470312/

Colombel JF, Shin A, Gibson PR. AGA Clinical Practice Update on Functional Gastrointestinal Symptoms in Patients With Inflammatory Bowel Disease: Expert Review. Clin Gastroenterol Hepatol. 2019 Feb;17(3):380-390.e1.

Cosnes J, Gower-Rousseau C, Seksik P, Cortot A. Epidemiology and natural history of inflammatory bowel diseases. Gastroenterology. 2011 May;140(6):1785–94.

Niriella MA, De Silva AP, Dayaratne AHGK, Ariyasinghe MHADP, Navarathne MMN, Peiris RSK, et al. Prevalence of inflammatory bowel disease in two districts of Sri Lanka: a hospital based survey. BMC Gastroenterol. 2010 Mar 19;10:32.

Asakura K, Nishiwaki Y, Inoue N, Hibi T, Watanabe M, Takebayashi T. Prevalence of ulcerative colitis and Crohn’s disease in Japan. J Gastroenterol. 2009;44(7):659–65.

Muzammil MA, Fariha F, Patel T, Sohail R, Kumar M, Khan E, et al. Advancements in Inflammatory Bowel Disease: A Narrative Review of Diagnostics, Management, Epidemiology, Prevalence, Patient Outcomes, Quality of Life, and Clinical Presentation. Cureus. 2023 Jun;15(6):e41120.

Yoshimatsu Y, Yamada A, Furukawa R, Sono K, Osamura A, Nakamura K, et al. Effectiveness of probiotic therapy for the prevention of relapse in patients with inactive ulcerative colitis. World J Gastroenterol. 2015 May 21;21(19):5985–94.

Mazziotta C, Tognon M, Martini F, Torreggiani E, Rotondo JC. Probiotics Mechanism of Action on Immune Cells and Beneficial Effects on Human Health. Cells. 2023 Jan 2;12(1):184.

Hsu CY, Mustafa MA, Moath Omar T, Taher SG, Ubaid M, Gilmanova NS, et al. Gut instinct: harnessing the power of probiotics to tame pathogenic signaling pathways in ulcerative colitis. Front Med (Lausanne). 2024;11:1396789.

Abraham BP, Quigley EMM. Probiotics in Inflammatory Bowel Disease. Gastroenterol Clin North Am. 2017 Dec;46(4):769–82.

Su GL, Ko CW, Bercik P, Falck-Ytter Y, Sultan S, Weizman AV, et al. AGA Clinical Practice Guidelines on the Role of Probiotics in the Management of Gastrointestinal Disorders. Gastroenterology. 2020 Aug;159(2):697–705.

Bischoff SC, Escher J, Hébuterne X, Kłęk S, Krznaric Z, Schneider S, et al. ESPEN practical guideline: Clinical Nutrition in inflammatory bowel disease. Clin Nutr. 2020 Mar;39(3):632–53.

Guo N, Lv LL. Mechanistic insights into the role of probiotics in modulating immune cells in ulcerative colitis. Immun Inflamm Dis. 2023 Oct;11(10):e1045.

Wirya LE, Schoeck HPG, Simatupang EP. The effect of probiotics on inflammatory bowel disease remission and relapse rate: A systematic review and meta-analysis. J Penyakit Dalam Udayana. 2024 May 5;8(1):12–8.

Kaur L, Gordon M, Baines PA, Iheozor-Ejiofor Z, Sinopoulou V, Akobeng AK. Probiotics for induction of remission in ulcerative colitis. Cochrane Database Syst Rev. 2020 Mar 4;3(3):CD005573.

Iheozor-Ejiofor Z, Kaur L, Gordon M, Baines PA, Sinopoulou V, Akobeng AK. Probiotics for maintenance of remission in ulcerative colitis. Cochrane Database Syst Rev. 2020 Mar 4;3(3):CD007443.

Cochrane Bias. RoB 2: A revised Cochrane risk-of-bias tool for randomized trials [Internet]. [cited 2025 Aug 11]. Available from: https://methods.cochrane.org/bias/resources/rob-2-revised-cochrane-risk-bias-tool-randomized-trials

Izcovich A, Chu DK, Mustafa RA, Guyatt G, Brignardello-Petersen R. A guide and pragmatic considerations for applying GRADE to network meta-analysis. BMJ. 2023 Jun 27;381:e074495.

Owen RK, Bradbury N, Xin Y, Cooper N, Sutton A. MetaInsight: An interactive web-based tool for analyzing, interrogating, and visualizing network meta-analyses using R-shiny and netmeta. Res Synth Methods. 2019 Dec;10(4):569–81.

Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009 Jul 21;6(7):e1000097.

Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions [Internet]. 2nd ed. Chichester (UK): John Wiley & Sons; 2019. Available from: https://training.cochrane.org/handbook

DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986 Sep;7(3):177–88.

Rücker G, Schwarzer G. Reduce dimension or reduce weights? Comparing two approaches to multi-arm studies in network meta-analysis. Stat Med. 2014 Jan 30;33(25):4353–69.

Salanti G. Indirect and mixed-treatment comparison, network, or multiple treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Methods. 2012 Mar;3(2):80–97.

Higgins JP, Jackson D, Barrett JK, Lu G, Ades AE, White IR. Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods. 2012 Mar;3(2):98–110.

Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008 Apr 26;336(7650):924–6.

Guyatt GH, Oxman AD, Kunz R, Vist GE, Falck-Ytter Y, Schünemann HJ, et al. What is "quality of evidence" and why is it important to clinicians? BMJ. 2008 May 3;336(7651):995–8.

Guyatt GH, Oxman AD, Kunz R, Brozek J, Alonso-Coello P, Rind D, et al. GRADE guidelines 6. Rating the quality of evidence--imprecision. J Clin Epidemiol. 2011 Dec;64(12):1283–93.

Guyatt GH, Oxman AD, Kunz R, Woodcock J, Brozek J, Helfand M, et al. GRADE guidelines: 8. Rating the quality of evidence--indirectness. J Clin Epidemiol. 2011 Dec;64(12):1303–10.

Guyatt GH, Oxman AD, Sultan S, Glasziou P, Akl EA, Alonso-Coello P, et al. GRADE guidelines: 9. Rating up the quality of evidence. J Clin Epidemiol. 2011 Dec;64(12):1311–6.

Guyatt GH, Oxman AD, Sultan S, Brozek J, Glasziou P, Alonso-Coello P, et al. GRADE guidelines: 11. Making an overall rating of confidence in effect estimates for a single outcome and for all outcomes. J Clin Epidemiol. 2013 Feb;66(2):151–7.

Guyatt GH, Oxman AD, Sultan S, Brozek J, Glasziou P, Akl EA, et al. GRADE guidelines: 12. Preparing summary of findings tables--binary outcomes. J Clin Epidemiol. 2013 Feb;66(2):158–72.

Guyatt GH, Oxman AD, Sultan S, Brozek J, Glasziou P, Akl EA, et al. GRADE guidelines: 13. Preparing summary of findings tables--continuous outcomes. J Clin Epidemiol. 2013 Feb;66(2):173–83.

Schünemann HJ, Brożek J, Guyatt G, Oxman A, editors. GRADE Handbook for Grading Quality of Evidence and Strength of Recommendations. Updated October 2013. The GRADE Working Group, 2013. Available from: https://gdt.gradepro.org/app/handbook/handbook.html

Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019 Aug 28;366:l4898.

IntHout J, Ioannidis JPA, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian–Laird method. BMC Med Res Methodol. 2014 Feb 18;14:25.

Rücker G, Schwarzer G, Carpenter JR, Schumacher M. Undue reliance on I² in assessing heterogeneity may mislead. BMC Med Res Methodol. 2008 Dec 17;8:79.

Huedo-Medina TB, Sánchez-Meca J, Marín-Martínez F, Botella J. Assessing heterogeneity in meta-analysis: Q statistic or I² index? Psychol Methods. 2006 Jun;11(2):193–206.

Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997 Sep 13;315(7109):629–34.

Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994 Dec;50(4):1088–101.

Duval S, Tweedie R. Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000 Jun;56(2):455–63.

Sterne JA, Egger M, Moher D, editors. Addressing Reporting Biases. In: Higgins JPT, Green S, editors. Cochrane Handbook for Systematic Reviews of Interventions [Internet]. Chichester (UK): John Wiley & Sons; 2008 [updated Mar 2011]. Available from: https://handbook-5-1.cochrane.org/chapter_10

Thompson SG, Higgins JP. How should meta-regression analyses be undertaken and interpreted? Stat Med. 2002 Apr 30;21(11):1559–73.

Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002 Jun 15;21(11):1539–58.

Borenstein M, Hedges LV, Higgins JP, Rothstein HR. Introduction to Meta-Analysis. Chichester (UK): John Wiley & Sons; 2009.

Schwarzer G, Carpenter JR, Rücker G. Meta-Analysis with R. Cham: Springer International Publishing; 2015.

Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3):1–48.

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Published

2025-08-15

How to Cite

Alvianto, S., Muzellina, V. N., Widjanarko, N. D., Lee, Y. Y., & Wangidjaja, O. (2025). Probiotics for Preventing Relapse in Ulcerative Colitis: A Systematic Review and Bayesian Network Meta-Analysis. The Indonesian Journal of Gastroenterology, Hepatology, and Digestive Endoscopy, 26(2), 130–142. https://doi.org/10.24871/2622025130-142