NCT04768387

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

An Open-labelled Interventional Study With 25 IBS-M Patients in Which Group 1 (n=14) Followed Six Weeks of AI-based Microbiome Diet and Group 2 (n=11) Followed Standard IBS Diet

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 25 (actual)
• Sponsor: Gazi University · Academic / Other
• Sex: All
• Age: 20 Years – 65 Years
• Healthy volunteers: Accepted

Summary

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.

Conditions

• Irritable Bowel Syndrome Mixed

Interventions

Type	Name	Description
DIETARY_SUPPLEMENT	Personalized microbiome diet	The personalized nutrition model estimates the optimal micronutrient compositions for a required microbiome modulation. In this study, we computed the microbiome modulation needed for an IBS case, based on the IBS-indices generated by the machine learning models. According to that, the baseline microbiome compositions are perturbed randomly with a small probability p. Perturbed profiles are accepted with a probability proportional to the decrease in the IBS-index as suggested by Metropolis sampling. This Monte-Carlo random walk in the microbiome composition space is expected to meet a low IBS-index microbiome composition nearby the baseline microbiome composition of the patient with a minimal modulation. The personalized nutrition model, then, estimates the optimized nutritional composition needed for this individual, expecting to drive the IBS-index to lower values.

Timeline

• Start date: 2020-10-05
• Primary completion: 2020-11-16
• Completion: 2021-01-15
• First posted: 2021-02-24
• Last updated: 2021-02-24

Locations

1 site across 1 country: Turkey (Türkiye)