We 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. GRAPHICAL ABSTRACT

INTRODUCTION: Personalized management strategies are pivotal in addressing irritable bowel syndrome (IBS). This multicenter randomized controlled trial focuses on comparing the efficacy of a microbiome-based artificial intelligence-assisted personalized diet (PD) with a low-fermentable oligosaccharides, disaccharides, monosaccharides, and polyols diet (FODMAP) for IBS management. METHODS: One hundred twenty-one patients participated, with 70 assigned to the PD group and 51 to the FODMAP diet group. IBS subtypes, demographics, symptom severity (IBS-SSS), anxiety, depression, and quality of life (IBS-QOL) were evaluated. Both interventions spanned 6 weeks. The trial's primary outcome was the within-individual difference in IBS-SSS compared between intervention groups. RESULTS: For the primary outcome, there was a change in IBS-SSS of −112.7 for those in the PD group vs −99.9 for those in the FODMAP diet group (P = 0.29). Significant improvement occurred in IBS-SSS scores (P < 0.001), frequency (P < 0.001), abdominal distension (P < 0.001), and life interference (P < 0.001) in both groups. In addition, there were significant improvements in anxiety levels and IBS-QOL scores for both groups (P < 0.001). Importantly, PD was effective in reducing IBS SSS scores across all IBS subtypes IBS-Constipation (IBS-C; P < 0.001), IBS-Diarrhea (IBS-D; P = 0.01), and IBS-Mixed (IBS-M; P < 0.001) while FODMAP diet exhibited comparable improvements in IBS-C (P = 0.004) and IBS-M (P < 0.001). PD intervention significantly improved IBS-QOL scores for all subtypes (IBS-C [P < 0.001], IBS-D [P < 0.001], and IBS-M [P = 0.008]) while the FODMAP diet did so for the IBS-C (P = 0.004) and IBS-D (P = 0.022). Notably, PD intervention led to significant microbiome diversity shifts (P < 0.05) and taxa alterations compared with FODMAP diet. DISCUSSION: The artificial intelligence-assisted PD emerges as a promising approach for comprehensive IBS management. With its ability to address individual variation, the PD approach demonstrates significant symptom relief, enhanced QOL, and notable diversity shifts in the gut microbiome, making it a valuable strategy in the evolving landscape of IBS care.