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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
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT04768387
Enrollment
25
Registered
2021-02-24
Start date
2020-10-05
Completion date
2021-01-15
Last updated
2021-02-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Irritable Bowel Syndrome Mixed

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.

Interventions

DIETARY_SUPPLEMENTPersonalized 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.

Sponsors

ENBIOSIS BIOTECHNOLOGIES
CollaboratorINDUSTRY
TC Erciyes University
CollaboratorOTHER
Gazi University
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
20 Years to 65 Years
Healthy volunteers
Yes

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.

Design outcomes

Primary

MeasureTime frameDescription
IBS-SSS changeChange is measured between the scores pre-intervention and the scores six weeks after the intervention startsChange in IBS-SSS scores according to ROME IV criteria were assessed.

Countries

Turkey (Türkiye)

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026