The Effect of AI-based Microbiome Diet on IBS-M Symptoms

This study was designed as a pilot, open-labelled study. We enrolled consecutive IBS-M patients (n=25, 19 females, 46.06 ± 13.11 years) 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. Patients were divided into two groups based on age, gender and microbiome matched. Six weeks of AI-based microbiome diet (n=14) for group 1 and standard IBS diet (Control group, 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. An algorithm assessing an IBS index score using microbiome composition attempted to design the optimized diets based on modulating microbiome towards the healthy scores. Baseline and post-intervention IBS-SSS (symptom severity scale) scores and fecal microbiome analyses were compared.