ARTIFICIAL INTELLIGENCE BASED PERSONALIZED DIET: A PILOT CLINICAL STUDY FOR IBS

Abstract

Background and aims: Certain diets often used to manage functional gastrointestinal symptoms in patients with irritable bowel syndrome (IBS). Personalized diet-induced microbiome modulation is being preferred method for symptom improvement in IBS. Although personalized nutritional therapies targeting gut microbiota using artificial intelligence (AI) promises a great potential, this approach has not been studied in patients with IBS. Therefore, in this study we investigated the efficacy of AI-based personalized microbiome diet in patients with IBS-Mix (M). Methods: This study was designed as a pilot, open-labelled study. We enrolled consecutive IBS-M patients (n=25, 19 females, 46.06 (std. 13.11 years) according to Rome IV criteria. Fecal samples were obtained from all patients twice (pre- and post-intervention) and high-througput 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. Results: The IBS-SSS evaluation for both pre- and post-intervention exhibited significant improvement (p<0.02 and p<0.001 for the control and intervention groups, respectively). While the IBS-SSS evaluation changed to moderate from severe in 82% (14 out of 17) of the intervention group, no such change was observed in the control group. After 6-weeks of intervention, a major shift in microbiota profiles in terms of alfa- or beta-diversity was not observed in both groups. A trend of decrease in Ruminococcaceae family for the intervention group was observed (p=0.17). A statistically significant increase in Faecalibacterium genus was observed in the intervention group (p = 0.04). Bacteroides and putatively probiotic genus Propionibacterium were increased in the intervention group, however Prevotella was increased in the control group. The change (delta) values in IBS-SSS scores (beforeafter) intervention and control groups are significantly higher in the intervention group. Conclusion: 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.