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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT03589183
Other study ID # IBD library_2018
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date April 1, 2018
Est. completion date March 31, 2028

Study information

Verified date March 2023
Source Kyunghee University Medical Center
Contact Chang Kyun Lee, M.D.,Ph.D.
Phone 82-2-958-8149
Email changkyun.lee@khu.ac.kr
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Inflammatory bowel disease (IBD) is a chronic inflammatory condition for gastrointestinal tract. There have been numerous studies to reveal dysbiosis of fecal/mucosal microbiome composition in IBD patients but actual trend of dysbiosis is strikingly different among patient's ethnicity. In this background, the investigators have composed a prospective cohort of Korean IBD patients in a large academic referral IBD center. Using an integrated multi-omics bioinformatic analysis, the investigators aim to explore gut microbial signatures along with distinct clinical/genetic features, and their potential interplay in patients with IBD.


Description:

This prospective cohort study aims to build a gut microbiome library for Korean IBD patients, and also aims to explore gut microbial signatures along with distinct clinical/genetic features and their potential interplay using the investigator's multi-omics analysis platform. After signing the informed consent, various biological samples (fecal, mucosal and blood samples) as well as comprehensive clinical data (including psychometric evaluations using patient survey) will be obtained from patients periodically. The multi-omics investigations will comprise: metagenomics (16s RNA sequencing and whole metagenomic sequecing), metabolomics, human genomics (genome-wide SNP data, whole exome and genome sequencing), transcriptomics, proteomics, epigenetics and single-cell multi-omics analysis. In vitro and in vivo study using fecal samples will be conducted. In brief, after extracting the DNA from blood samples, whole genome sequencing will be performed and data will be compared with the previously reported variances. Novel variances or incidence of specific variances will be measured. Fecal sample will be collected and microbiome composition of each study subjects will be analyzed. Fecal microbial diversity will be measured from sequencing data of 16S ribosomal RNA which is highly preserved area of genetic information amongst microorganisms. Colonic mucosal sample will be collected when routine endoscopic surveillance is planned. Sample will be collected from diseased and normal mucosal altogether for analysis. Microbial diversity will be measured from 16S RNA sequencing data of the samples. The multi-omics data will be analyzed with comprehensive clinical metadata using an integrated bioinformatics analysis platform. Clinical data include demographics, disease characteristics and variouis patient-reported outomes data (including health-related quality of life, anxiety, depression and others). Patient-reported outcomes data will be collected using validated questionnaires periodically. Data from basic analysis will be re-explore in terms of inter-individual difference, difference between disease characteristics (such as disease phenotype, type of medical treatment and serologic markers), difference between psychosocial characteristics (such as the presence of anxiety or depression and quality of life) and etc. In addition, data from basic analysis will be used as the reference for the Korean IBD patient and it will be compared with other data obtained from the "different ethnicity" or "healthy" Korean population. In conclusion, this long term, large-scale prospective study will provide a platform for studying a field of translational medicine to reveal hidden signatures of gut microbiome underlying IBD and to identify potential biological markers in IBD.


Recruitment information / eligibility

Status Recruiting
Enrollment 1500
Est. completion date March 31, 2028
Est. primary completion date March 31, 2028
Accepts healthy volunteers No
Gender All
Age group N/A and older
Eligibility Inclusion Criteria: - Patients with established diagnosis of inflammatory bowel disease, including ulcerative colitis and Crohn disease Exclusion Criteria: - Person with history of using antibiotics or probiotics within previous 2 weeks.

Study Design


Locations

Country Name City State
Korea, Republic of Kyung Hee University Hospital Seoul

Sponsors (1)

Lead Sponsor Collaborator
Kyunghee University Medical Center

Country where clinical trial is conducted

Korea, Republic of, 

References & Publications (4)

Huang H, Vangay P, McKinlay CE, Knights D. Multi-omics analysis of inflammatory bowel disease. Immunol Lett. 2014 Dec;162(2 Pt A):62-8. doi: 10.1016/j.imlet.2014.07.014. Epub 2014 Aug 15. — View Citation

Nishida A, Inoue R, Inatomi O, Bamba S, Naito Y, Andoh A. Gut microbiota in the pathogenesis of inflammatory bowel disease. Clin J Gastroenterol. 2018 Feb;11(1):1-10. doi: 10.1007/s12328-017-0813-5. Epub 2017 Dec 29. — View Citation

Thia KT, Loftus EV Jr, Sandborn WJ, Yang SK. An update on the epidemiology of inflammatory bowel disease in Asia. Am J Gastroenterol. 2008 Dec;103(12):3167-82. doi: 10.1111/j.1572-0241.2008.02158.x. — View Citation

Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007 Jun 7;447(7145):661-78. doi: 10.1038/nature05911. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary Composition and diversity of gut microbiome Fecal and/or intestinal mucosal samples are obtained from enrolled subjects for metagenomic sequencing. After extracting fecal or mucosal DNA, microbial composition and diversity are measured from 16sRNA high-throuput sequencing or Shotgun method. Data are compared across different disease phenotypes, such as types of disease (ulcrerative colitis versus Crohn's disease), type of treatment and the presence of psychological disorders (anxiety/depression). Up to 10 years
Secondary Taxonomic profiling of gut microbiome Fecal and/or intestinal mucosal samples are obtained from enrolled subjects for metagenomic sequencing. After extracting fecal or mucosal DNA, taxonomic profiles associated with inflammatory bowel diseases and different disease phenotypes are analyzed from 16sRNA high-throuput sequencing or Shotgun method. Up to 10 years
Secondary Finding of single nucleotide polymorphisms (SNPs) Blood samples are obatined from enrolled subjects for genome-wide microarray. After extracting DNA, SNPs related to inflammatory bowel diseases and different disease phenotypes are explored (Genome-wide association study [GWAS] statistical significance threshold, P < 5.00*E-08). Up to 10 years
Secondary Finding of serologic biomarkers Blood samples are obatined from enrolled subjects for proteomic analysis. Serologic biomakers implicating in inflammatory bowel diseases and disease phenotypes are explored. Up to 10 years
Secondary Correlation between host genotyping and gut microbiome Blood samples are obatined from enrolled subjects for genome-wide microarray. After extracting DNA, SNPs related to inflammatory bowel diseases and different disease phenotypes are explored (Genome-wide association study [GWAS] statistical significance threshold, P < 5.00*E-08). Fecal and/or intestinal mucosal samples are obtained from enrolled subjects for metagenomic sequencing. After extracting fecal or mucosal DNA, taxonomic profiles associated with inflammatory bowel diseases and different disease phenotypes are analyzed from 16sRNA high-throuput sequencing or Shotgun method. Taxonomic composition of gut microbiome are compared according to the sequecing data of host genomes . Up to 10 years
Secondary Liquid biopsy biosignatures assessed by single cell RNA-Seq Blood and intestinal mucosal biopsy samples are obtained from enrolled subjects, particularly those treated with biological drugs or small molecues for single cell analysis (RNA-Seq). Data obtained from single cell analysis will be compared across different time frame (for example, before versus after specific treatment) and across differnt disease phenotypes. Up to 10 years
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