TY - JOUR T1 - Gut microbiome identifies risk for colorectal polyps JF - BMJ Open Gastroenterology JO - BMJ Open Gastro DO - 10.1136/bmjgast-2019-000297 VL - 6 IS - 1 SP - e000297 AU - Ezzat Dadkhah AU - Masoumeh Sikaroodi AU - Louis Korman AU - Robert Hardi AU - Jeffrey Baybick AU - David Hanzel AU - Gregory Kuehn AU - Thomas Kuehn AU - Patrick M Gillevet Y1 - 2019/05/01 UR - http://bmjopengastro.bmj.com//content/6/1/e000297.abstract N2 - Objective To 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).Design Presurgery 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.Results We 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.Conclusion Gut 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. ER -