ArticleBMJ open gastroenterology2019
Gut microbiome identifies risk for colorectal polyps.
Article in BMJ open gastroenterology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05060757 (Microbiome Test for the Detection of Colorectal Polyps), which is not on this map. Cited by 36 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Microbiome Test for the Detection of Colorectal Polyps
Who cites it
36 citing papers in PubMed, 4 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Fecal Microbiome Alterations in Colorectal Cancer: A Systematic Review of Compositional Changes and Microbial Biomarkers.MicrobiologyOpen · 2026Pooled it
- Meta-Analysis of the Gut Microbiome: An African American Representation.International journal of environmental research and public health · 2025Pooled it
- Mediterranean Diet to Prevent the Development of Colon Diseases: A Meta-Analysis of Gut Microbiota Studies.Nutrients · 2021Pooled it
- Identification of microbial markers across populations in early detection of colorectal cancer.Nature communications · 2021Pooled it
- The regional landscape of the human colon culturome in health and cystic fibrosis.Microbiology spectrum · 2026Article
- Impact of gut microbiome on outcomes following endoscopic interventions in gastrointestinal disease.World journal of gastrointestinal endoscopy · 2025Article
- Gut microbiome and nutritional strategies in gastrointestinal cancers: Clinical implications and therapeutic perspectives.World journal of clinical oncology · 2025Review
- Gut microbiome-based machine learning model for early colorectal cancer and adenoma screening.Gut pathogens · 2025Article
- The Regional Landscape of the Human Colon Culturome in Health and Cystic Fibrosis.bioRxiv : the preprint server for biology · 2025Article
- Gut-Microbiome and Nutritional Analysis Reveals Food Intake as a Key Factor in the Incidence and Prevention of Colon Polyps: A Cross-Sectional Study.Health science reports · 2025Article
- Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome.Periodontology 2000 · 2025Review
- Gut Mucosal Microbiome of Patients With Low-Grade Adenomatous Bowel Polyps.Gastro hep advances · 2025Article
- Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis.PloS one · 2025Article
- Gut-microbiota-based ensemble model predicts prognosis of pediatric inflammatory bowel disease.iScience · 2024Article
- Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment.Briefings in bioinformatics · 2024Review
- A review of machine learning methods for cancer characterization from microbiome data.NPJ precision oncology · 2024Review
- Mucosal Microbiome in Patients with Early Bowel Polyps: Inferences from Short-Read and Long-Read 16S rRNA Sequencing.Cancers · 2023Article
- Machine learning-based approaches for cancer prediction using microbiome data.Scientific reports · 2023Article
- Fecal Microbiome Associated with Both Colon Adenomas and Lifetime Colorectal Cancer Risk.Digestive diseases and sciences · 2023Article
- Dysbiosis of a microbiota-immune metasystem in critical illness is associated with nosocomial infections.Nature medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo characterise the gut microbiome in subjects with and without polyps and evaluate the potential of the microbiome as a non-invasive biomarker to screen for risk of colorectal cancer (CRC).
designPresurgery rectal swab, home collected stool, and sigmoid biopsy samples were obtained from 231 subjects undergoing screening or surveillance colonoscopy. 16S rRNA analysis was performed on 552 samples (231 rectal swab, 183 stool, 138 biopsy) and operational taxonomic units (OTU) were identified using UPARSE. Non-parametric statistical methods were used to identify OTUs that were significantly different between subjects with and without polyps. These informative OTUs were then used to build classifiers to predict the presence of polyps using advanced machine learning models.
resultsWe obtained clinical data on 218 subjects (87 females, 131 males) of which 193 were White, 21 African-American, and 4 Asian-American. Colonoscopy detected polyps in 56% of subjects. Modelling of the non-invasive home stool samples resulted in a classification accuracy >75% for Naïve Bayes and Neural Network models using informative OTUs. A naïve holdout analysis performed on home stool samples resulted in an average false negative rate of 11.5% for the Naïve Bayes and Neural Network models, which was reduced to 5% when the two models were combined.
conclusionGut microbiome analysis combined with advanced machine learning represents a promising approach to screen patients for the presence of polyps, with the potential to optimise the use of colonoscopy, reduce morbidity and mortality associated with CRC, and reduce associated healthcare costs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.