Karakan T.Gundogdu A.Alagözlü H.Ekmen N.Ozgul S.Tunali V.Hora M.Beyazgul D.Nalbantoglu O.U.2024-07-222024-07-22202219490976http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/12964We enrolled consecutive IBS-M patients (n = 25) according to Rome IV criteria. Fecal samples were obtained from all patients twice (pre-and post-intervention) and high-throughput 16S rRNA sequencing was performed. Six weeks of personalized nutrition diet (n = 14) for group 1 and a standard IBS diet (n = 11) for group 2 were followed. AI-based diet was designed based on optimizing a personalized nutritional strategy by an algorithm regarding individual gut microbiome features. The IBS-SSS evaluation for pre- and post-intervention exhibited significant improvement (p < .02 and p < .001 for the standard IBS diet and personalized nutrition groups, respectively). While the IBS-SSS evaluation changed to moderate from severe in 78% (11 out of 14) of the personalized nutrition group, no such change was observed in the standard IBS diet group. A statistically significant increase in the Faecalibacterium genus was observed in the personalized nutrition group (p = .04). Bacteroides and putatively probiotic genus Propionibacterium were increased in the personalized nutrition group. The change (delta) values in IBS-SSS scores (before-after) in personalized nutrition and standard IBS diet groups are significantly higher in the personalized nutrition group. AI-based personalized microbiome modulation through diet significantly improves IBS-related symptoms in patients with IBS-M. Further large-scale, randomized placebo-controlled trials with long-term follow-up (durability) are needed. © 2022 ENBIOSIS Biotechnologies Limited. Published with license by Taylor & Francis Group, LLC.EnglishAll Open Access; Gold Open Access; Green Open AccessArtificial IntelligenceDietGastrointestinal MicrobiomeHumansIrritable Bowel SyndromeRNA, Ribosomal, 16Sprobiotic agentRNA 16SRNA 16SadultArticleartificial intelligencebacterial microbiomeBacteroidesbody massclinical articleClostridiaceaecohort analysiscontrolled studyFaecalibacteriumfecal microbiota transplantationfeces analysisfemalefluorometryfollow upgastrointestinal tractgene sequencehigh throughput screeninghigh throughput sequencinghumanirritable colonmachine learningmalemiddle agedmultivariate analysis of variancepersonalized nutritionpilot studyrank sum testRuminococcaceaetherapy effectartificial intelligencedietintestine floramicrobiologyArticle10.1080/19490976.2022.2138672