Advancements in the study of gut microbiome in disease diagnosis

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported — no quantitative pooled effect size is provided in this narrative review

• Primary Outcome: Gut microbiome dysbiosis is commonly observed across multiple diseases; machine learning diagnostic models show variable performance depending on cohort, sequencing platform, data preprocessing, and algorithmic choice

• Nnt Or Sensitivity: No diagnostic accuracy statistics (sensitivity, specificity, AUC, LR+, LR−) are reported in the abstract; individual study performance metrics are referenced qualitatively only

• Confidence Interval: Not reported

Clinical Application

Gut microbiome profiling via 16S rRNA sequencing or shotgun metagenomics is technically feasible in research settings but is not yet standardised for routine clinical diagnostics. Lack of harmonised protocols, reference databases, and validated clinical thresholds limits immediate implementation. Machine learning model deployment requires substantial informatics infrastructure not available in most clinical settings. Gut microbiome-based diagnostics are not currently listed on the Medicare Benefits Schedule (MBS) in Australia, and no gut microbiome diagnostic test has received TGA regulatory approval for clinical use as of the knowledge cutoff. The RACGP does not currently endorse microbiome-based testing for disease diagnosis in primary care. Australian research groups (e.g., through the Microbiome Research Centre, UNSW Sydney) are active in this field, but translation to PBS-subsidised or TGA-approved clinical tools remains a future aspiration. Clinicians should be cautious of direct-to-consumer microbiome testing marketed in Australia, which lacks regulatory oversight and clinical validation. Potentially applicable to adult patients across a range of conditions where gut microbiome dysbiosis has been implicated (e.g., inflammatory bowel disease, colorectal cancer, metabolic syndrome, neuropsychiatric conditions); however, the review does not specify which diseases or patient subgroups have the strongest evidence base for clinical translation

Abstract

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.

References

1. 1.Liu, K., Huang, Z., & Liu, Q. (2026). Advancements in the study of gut microbiome in disease diagnosis. Antonie van Leeuwenhoek. https://doi.org/10.1152/physiolgenomics.00082.2014 [Note: DOI as supplied by submitter — bibliographic inconsistency flagged; DOI string corresponds to American Physiological Society / Physiological Genomics, not Antonie van Leeuwenhoek / Springer. Independent verification required before citation.]