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

Study Overview
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Brief Summary
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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.

Official Title
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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

Conditions
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Irritable Bowel Syndrome Mixed

Intervention / Treatment
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* Dietary Supplement: Personalized microbiome diet


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Diagnosed with IBS by a medical doctor. BMI between 18.5-39.9 kg/m2 No hospitalization in the last 12 months. No antibiotics use in the last 6 months. No cancer diagnosis by a medical doctor. No chronic complex diseases including diabetes and hypertension. Exclusion Criteria: Not being diagnosed with IBS. Having a diagnosed chronic disease. Having a diagnosed mental or psychiatric disorder . Having endocrinal disorders. Being pregnant. Antibiotics use in the last 6 months. Hospitalization history in the last 12 months. Drug use. Being morbid obese.

Ages Eligible for Study
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Minimum Age: 20 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Personalized microbiome diet<br>Six weeks of AI-based microbiome diet was introduced. | Dietary Supplement: Personalized microbiome diet<br>* 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.<br>* Other names: ARTIFICIAL INTELLIGENCE BASED PERSONALIZED DIET;|
| Active Comparator: Standard IBS diet<br>Six weeks of standard IBS diet was introduced. | Dietary Supplement: Personalized microbiome diet<br>* 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.<br>* Other names: ARTIFICIAL INTELLIGENCE BASED PERSONALIZED DIET;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| IBS-SSS change | Change in IBS-SSS scores according to ROME IV criteria were assessed. | Change is measured between the scores pre-intervention and the scores six weeks after the intervention starts |