Advancements in the study of gut microbiome in disease diagnosis.

This review summarizes disease-associated changes in gut microbial composition and evaluates the diagnostic performance of models constructed with different machine-learning algorithms. The review seeks to answer questions related to the relationship between the human gut microbiome and disease progression, how different machine learning algorithms affect disease diagnosis using gut microbiome data, and how disease-specific microbial communities impact diagnostic models. Multiple studies report that gut microbiome dysbiosis is commonly observed in many diseases, though patterns vary between conditions and cohorts. Large-scale computational analyses are increasingly applied to identify microbial signatures and to build diagnostic models; however, model performance often depends on data source, preprocessing and choice of algorithm. Overall, evidence indicates disease-associated shifts in gut microbial composition, and that diagnostic model accuracy is sensitive to cohort, sequencing and modeling choices. While certain taxa recur across studies for some diseases, heterogeneity between cohorts limits immediate clinical translation; thus, harmonized study designs and external validation are required. Future work should prioritize reproducible multi-cohort analyses, transparent reporting (e.g., PRISMA for reviews) and prospective validation before clinical deployment.