Decoding personalized nutritional, microbiome and host patterns impacting clinical and prognostic features in Crohn*s disease

Recruiting

Conditions: Crohns disease
inflammatory bowel disease
10017969

Registration Number: NL-OMON50993

Lead Sponsor: Weizzmann Institute of Science

Brief Summary: Not available

Detailed Description: Not available

Recruitment & Eligibility

Status: Recruiting

Sex: Not specified

Target Recruitment: 38

Inclusion Criteria

1. Children with clinical suspicion for CD.
2. Between 6 and 18 years of age.
3. Naïve to any medical or nutritional intervention.

Exclusion Criteria

1. Chronic treatment with any drug upon enrolment and the se of systemic
antibiotics, probiotics or proton pump inhibitors during 30 days prior to
enrollment.
2. Pregnancy in the last 6 months, breastfeeding.
3. Morbid obesity (BMI > 95th percentile for their age and gender).
4. Following particular dietary regimen/dietitian consultation/participation in
another study.
5. Chronic use of steroids or immunomodulatory medications prior to CD
diagnosis.
6. Any other chronic disease (e.g. HIV, Cushing disease, acromegaly,
hyperthyroidism, etc.), cancer and recent anti-cancer therapy,
neuro-psychiatric disorders, coagulation disorders, celiac disease or any other
chronic GI disorder.
7. Gut-related surgery, including bariatric surgery.
8. Inability of the participant and nuclear family to follow and utilize the
smartphone application.

Study & Design

Study Type: Observational non invasive

Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
<p>1. Collect an unprecedented number of clinical, microbiome, barrier<br /><br>function-related, inflammatory and metabolic measurements from a cohort of<br /><br>newly diagnosed pediatric CD patients followed for a period of 12 months.<br /><br>2. Analyze this big data with an aim to utilize advanced artificial<br /><br>intelligence and machine-learning techniques to correlate multiple dietary,<br /><br>environmental, and microbiome features to disease severity scores, and<br /><br>metabolic (glycemic control) features in these patients.<br /><br>3. Devise individualized machine learning algorithms aimed at harnessing<br /><br>personalized nutritional recommendations to improve individual inflammatory and<br /><br>metabolic features.<br /><br>4. Validate these algorithms in a sub-cohort of newly diagnosed CD patients not<br /><br>involved in the initial machine learning training process.</p><br>

Secondary Outcome Measures