Subtyping irritable bowel syndrome using cluster analysis: a systematic review.

Area of Science:

• Gastroenterology
• Computational Biology
• Biostatistics

Background:

• Irritable Bowel Syndrome (IBS) is a prevalent functional gastrointestinal disorder with diverse clinical manifestations.
• Cluster Analysis (CA) is a statistical method used to identify homogeneous subgroups within patient populations.
• Understanding IBS heterogeneity is crucial for developing targeted therapeutic strategies.

Approach:

• A literature review identified studies applying CA to Irritable Bowel Syndrome (IBS) patients.
• Searched major databases (PubMed, Embase, Web of Science, Scopus) for relevant articles.
• Analyzed studies based on variables, participant characteristics, methodology, and findings.

Key Points:

• K-means CA was the most common method (8 studies), followed by Hierarchical CA (4 studies) and Latent Class Analysis (2 studies).
• Studies focused on clinical symptoms (7), anocolorectal functions (4), immunology (2), and microbial composition (1).
• Subgroups showed variations in symptom severity, psychological findings, rectal sensitivity, colonic transit, and microbial profiles.

Conclusions:

• Cluster analysis identifies distinct IBS subgroups based on clinical, functional, and biological factors.
• Methodological heterogeneity in CA application leads to inconsistent subgroup identification.
• Further research is needed to refine CA methodologies for personalized IBS management.