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.
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.
Study Type
OBSERVATIONAL
Enrollment
3,000
Chungnam National University Hospita
Daejeon, Jung-gu, South Korea
RECRUITINGChi-square test
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.
Time frame: Before treatment, 3~12months after treatment
t-test
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.
Time frame: Before treatment, 3~12months after treatment
Pearson's coefficient
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.
Time frame: Before treatment, 3~12months after treatment
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