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.
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.
Individuals under the age of 19.
Patients or guardians who do not consent to participate in the study
Patients( Autoimmune liver diseases (Autoimmune hepatitis, Primary biliary cholangitis, Primary sclerosing cholangitis), Metabolic liver diseases (Non-alcoholic fatty liver disease) Infectious liver diseases (Liver abscess) \- their cohabitant
Case-control
Prospective
Not listed
Chi-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.
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.
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.
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Chungnam National University Hospital
Lead sponsor
Chungnam National University
Collaborator
