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Completed

NCT Number: NCT04768387

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

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.

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Key information

Conditions

Irritable Bowel Syndrome Mixed

Age range

20 year–65 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Gazi University

Ankara, Turkey (Türkiye)

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Personalized microbiome diet

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.

Other names: ARTIFICIAL INTELLIGENCE BASED PERSONALIZED DIET

Primary outcomes

  1. IBS-SSS change

    Time frame: Change is measured between the scores pre-intervention and the scores six weeks after the intervention starts

    Change in IBS-SSS scores according to ROME IV criteria were assessed.

Sponsors and collaborators

Lead sponsor

Gazi University

Other

Collaborators

  • ENBIOSIS BIOTECHNOLOGIES
  • TC Erciyes University

Registry information

Official study title

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

Important dates

Study start
2020
Primary completion
2020
Study completion
2021
First posted
Feb 24, 2021
Registry last updated
Feb 24, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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