Protease Regulation and Impact of Sodium as Mechanisms of Inflammation in IBD

Overview

The study looks at how eating salt affects gut health in people with Crohn's disease. The aim of the study is to find out whether eating more salt increases the breakdown of proteins in the gut and if this makes inflammation and symptoms worse. By studying the link between salt, gut bacteria and inflammation, the study hopes to improve diet advice for people with Crohn's disease. This research may help find specific foods that affect the disease and lead to better, more personalized nutrition plans.

Detailed description

This longitudinal exploratory study has two related phases:

Phase 1 will enroll 300 Crohn's disease (CD) patients from McMaster's IBD and IBD Nutrition Clinics to assess dietary sodium intake and fecal proteolytic activity in relation to disease activity (active/inactive). Participants will be followed annually for 2 years, forming a registry. At baseline and each follow-up visit, data collected will include: demographics, BMI, CDAI, diet (1-week food recall via Keenoa and 6-month sodium questionnaire), blood (sodium, metabolomics, CRP), stool (microbiota, metabolomics, fecal calprotectin, proteolytic activity), and spot urine (sodium, potassium, creatinine, metabolomics). Extra visits will occur if a disease flare happens.

Phase 2 will involve 80 participants from Phase 1 (40 high-sodium diet \[HSD\], 40 low-sodium diet \[LSD\]), selected based on sodium intake. Over three visits (baseline, week 1, week 2), participants will provide fecal samples for microbiota and metabolomics analysis, and blood/urine samples (baseline and week 2). Colonoscopy with biopsies and video will be performed at week 2. Diet will be closely tracked using Keenoa to assess intake of sodium, carbs, UPF, fibre, and calories. After Phase 2, participants return to Phase 1 follow-up.

Primary outcome measures

• Dietary Sodium Intake [Time frame: Phase 1: baseline (Year 0); Year 1; and Year 2. Phase 2: baseline (Week 0); Week 1; and Week 2.]

Secondary outcome measures (4)

• Proteolytic Activity in Stool and Colonic Biopsies [Time frame: Stool: Phase 1 - baseline (Year 0); Year 1; and Year 2. Biopsy: Phase 2 - Week 2]
• Crohn's Disease Activity Index (CDAI) [Time frame: Phase 1: baseline (Year 0); Year 1; and Year 2. Phase 2: baseline (Week 0); and Week 2.]
• Mucosal Permeability [Time frame: Phase 2: Week 2]
• Patient Subgroup Characterization Based on Metabolic and Microbial Profiles [Time frame: Phase 1: baseline (Year 0); Year 1; and Year 2. Phase 2: baseline (Week 0); Week 1; and Week 2.]

Eligibility criteria

Inclusion criteria

• 18 and 70 years of age
• Crohn's disease diagnosis
• Willing and able to sign written informed consent prior to study entry
• Able to comply with the study procedures, in the opinion of the investigator

Exclusion criteria

• Antibiotics, antibacterial agents, or probiotics, currently, or within the last 8 weeks
• Alcohol or drug abuse
• Pregnancy
• Concurrent systemic disease and/or laboratory abnormalities considered by investigators to be a risk or that could interfere with data collection

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

Canada · 2 centers

• McMaster University — Hamilton
• McMaster University Medical Centre — Hamilton

Publications

• Pang Z, Lu Y, Zhou G, Hui F, Xu L, Viau C, Spigelman AF, MacDonald PE, Wishart DS, Li S, Xia J. MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Res. 2024 Jul 5;52(W1):W398-W406. doi: 10.1093/nar/gkae253. PMID 38587201
• Caminero A, Herran AR, Nistal E, Perez-Andres J, Vaquero L, Vivas S, Ruiz de Morales JM, Albillos SM, Casqueiro J. Diversity of the cultivable human gut microbiome involved in gluten metabolism: isolation of microorganisms with potential interest for coeliac disease. FEMS Microbiol Ecol. 2014 May;88(2):309-19. doi: 10.1111/1574-6941.12295. Epub 2014 Mar 3. PMID 24499426
• Whelan FJ, Waddell B, Syed SA, Shekarriz S, Rabin HR, Parkins MD, Surette MG. Culture-enriched metagenomic sequencing enables in-depth profiling of the cystic fibrosis lung microbiota. Nat Microbiol. 2020 Feb;5(2):379-390. doi: 10.1038/s41564-019-0643-y. Epub 2020 Jan 20. PMID 31959969
• Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi: 10.1186/s13059-014-0550-8. PMID 25516281
• Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods. 2013 Dec;10(12):1200-2. doi: 10.1038/nmeth.2658. Epub 2013 Sep 29. PMID 24076764
• Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015 Jan;12(1):59-60. doi: 10.1038/nmeth.3176. Epub 2014 Nov 17. PMID 25402007
• Bushnell B, Rood J, Singer E. BBMerge - Accurate paired shotgun read merging via overlap. PLoS One. 2017 Oct 26;12(10):e0185056. doi: 10.1371/journal.pone.0185056. eCollection 2017. PMID 29073143
• Kopylova E, Noe L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012 Dec 15;28(24):3211-7. doi: 10.1093/bioinformatics/bts611. Epub 2012 Oct 15. PMID 23071270

Identifiers

NCT: NCT07169123 · HiREB-18816

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗