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Published on: September 22, 2019
Modifiable Factors Influencing Disease Flares in Inflammatory Bowel Disease: A Literature Overview of Lifestyle,
Lola J M Koppelman1, Aroha A Oyugi1, P W Jeroen Maljaars1
1Department of Gastroenterology and Hepatology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands.
Abstract:
Background: A significant concern for patients with Inflammatory Bowel Disease (IBD) is predicting and managing disease flares. While healthcare providers rely on biomarkers, providing conclusive patient advice remains challenging. This review explores the role of lifestyle, psychological health, and environmental exposures in the prediction and management of IBD flares. Methods: This review followed PRISMA guidelines (2020). A structured search was conducted in PubMed for articles published between 2012 and 2024, using free and Medical Subject Heading (MeSH) terms for predicting factors in IBD. Inclusion criteria included studies reporting primary data on modifiable clinical or environmental predictors of IBD relapse, excluding studies on post-operative investigations, treatment cessation, and pediatric or pregnant populations. The Mixed Method Appraisal Tool (MMAT) was used to assess the quality of the studies. Results: Out of 2287 identified citations, 58 articles were included. Several modifiable factors influencing disease flares were identified, including psychological stress, sleep disturbances, smoking, and nutrition. Poor sleep quality and mental health were linked to increased flare risks, while smoking was associated with higher relapse rates in Crohn's disease. Environmental exposures, such as heat waves and high-altitude regions, also contributed. Predictive models integrating clinical, lifestyle, and psychological factors showed promising accuracy but require further refinement. Limitations of this review include the potential for publication bias, variability in flare definitions, and limited sample sizes Conclusions: Key predictors of IBD flares include dietary factors, psychological stress, poor sleep quality, and pharmacological influences. Personalized approaches integrating these predictors can optimize disease control and improve patient outcomes.
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