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

NCT ID: NCT04768387

Last Updated: 2021-02-24

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 25 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2020-10-05
• Study Completion Date: 2021-01-15

Brief 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

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

Personalized microbiome diet

Six weeks of AI-based microbiome diet was introduced.

Group Type: EXPERIMENTAL

Personalized microbiome diet

Intervention Type: DIETARY_SUPPLEMENT

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.

Standard IBS diet

Six weeks of standard IBS diet was introduced.

Group Type: ACTIVE_COMPARATOR

Personalized microbiome diet

Intervention Type: DIETARY_SUPPLEMENT

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.

Interventions

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.

Intervention Type: DIETARY_SUPPLEMENT

Other Intervention Names

ARTIFICIAL INTELLIGENCE BASED PERSONALIZED DIET

Eligibility Criteria

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.

• Minimum Eligible Age: 20 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

ENBIOSIS BIOTECHNOLOGIES

INDUSTRY

Sponsor Role: collaborator

TC Erciyes University

OTHER

Sponsor Role: collaborator

Gazi University

OTHER

Sponsor Role: lead

Responsible Party

Tarkan Karakan

Professor Doctor of Gastroenterology

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Gazi University

Ankara, , Turkey (Türkiye)

Countries

Turkey (Türkiye)

Other Identifiers

EnbiosisIBS

Identifier Type: -

Identifier Source: org_study_id