Liver-gut Axis Study Through Identification of Liver Disease-specific Microbiome

Exploration of Liver-gut Axis Through Identification of Liver Disease-specific Microbiome

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06519162

Acronym

LGAS

Enrollment

3000

Registered

2024-07-25

Start date

2024-07-08

Completion date

2029-07-08

Last updated

2026-03-20

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

Conditions

Autoimmune Hepatitis, Liver Abscess, Non-Alcoholic Fatty Liver Disease, Primary Biliary Cirrhosis, Primary Sclerosing Cholangitis

Keywords

Liver Diseases, Microbiota, Immune System

Brief summary

In this study, we aim to identify gut microbiomes specific to patients with chronic refractory liver disease and to conduct a gut-liver axis study on the pathogenesis and disease progression.

Detailed description

1. The investigators will recruit study participants aged 19 and older who have been diagnosed with autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, non-alcoholic steatohepatitis, or liver abscess, along with their household members, and who have agreed to participate in this study at Chungnam National University Hospital. 2. The investigators collect and analyze blood and stool samples from the patients and their household members 3. Sampling Method: Non-Probability Samples 4. Study Population: Group of autoimmune hepatitis: patients with definite autoimmune hepatitis or their healthy households for controls Group of primary biliary cholangitis: patients with definite primary biliary cholangitis or their healthy households for controls Group of primary sclerosing cholangitis: patients with definite primary sclerosing cholangitis or their healthy households for controls Group of non-alcoholic steatohepatitis: patients with definite non-alcoholic steatohepatitis or their healthy households for controls Group of liver abscess: patients with definite liver abscess or their healthy households for controls.

Interventions

None listed

Study design

Observational model

CASE_CONTROL

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

19 Years to No maximum

Healthy volunteers

Yes

Inclusion criteria

* Adults aged 19 years and older diagnosed with autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, non-alcoholic fatty liver disease, or liver abscess, who have consented to participate in this study at Chungnam National University Hospital. * Adults aged 19 years and older, who are cohabitants of patients diagnosed with autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, non-alcoholic fatty liver disease, or liver abscess, and have consented to participate in this study at Chungnam National University Hospital. B. Exclusion

Exclusion criteria

* Individuals under the age of 19. * Patients or guardians who do not consent to participate in the study

Design outcomes

Primary

Measure	Time frame	Description
Chi-square test	Before treatment, 3~12months after treatment	Using the Galaxy platform, perform taxonomic profiling followed by LDA(Linear Discriminant Analysis ) analysis. Group by primary diagnosis and derive the LDA(Linear Discriminant Analysis) Effect Size, or utilize MaAsLin2(Microbiome Multivariable Association with Linear Models 2) to identify genera/species showing significant compositional changes between diseases.
t-test	Before treatment, 3~12months after treatment	Using the Galaxy platform, perform taxonomic profiling followed by LDA(Linear Discriminant Analysis) analysis. Group by primary diagnosis and derive the LDA(Linear Discriminant Analysis) Effect Size, or utilize MaAsLin2(Microbiome Multivariable Association with Linear Models 2) to identify genera/species showing significant compositional changes between diseases.
Pearson's coefficient	Before treatment, 3~12months after treatment	Using the Galaxy platform, perform taxonomic profiling followed by LDA(Linear Discriminant Analysis) analysis. Group by primary diagnosis and derive the LDA(Linear Discriminant Analysis) Effect Size, or utilize MaAsLin2(Microbiome Multivariable Association with Linear Models 2) to identify genera/species showing significant compositional changes between diseases.

Countries

South Korea

Contacts

CONTACTSoon Ok Kim

dona3734@cnuh.co.kr82-42-280-6717

CONTACTHyuksoo Eun

hyuksoo@cnuh.co.kr82-42-280-7418

PRINCIPAL_INVESTIGATORHyuksoo Eun

Chungnam National University Hospital

Outcome results