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

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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

NL-OMON

Registry ID

NL-OMON50993

Enrollment

Registered

2022-05-27

Start date

2023-07-06

Completion date

Unknown

Last updated

2024-07-15

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

Conditions

Crohns disease inflammatory bowel disease

Interventions

None listed

Eligibility

Age

2 Years to 17 Years

Inclusion criteria

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

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.

Design outcomes

Primary

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

Measure

Time frame

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

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Secondary

Measure	Time frame
-	—

Countries

Netherlands

Outcome results