This study will establish a clinical cohort of children with congenital diarrhea and enteropathy (CODE), mine biomarkers of CODE through multi-omics technology and construct a clinical risk prediction model.
Patients with chronic diarrhea lasting greater than 2 months or greater than 1 month in patients younger than 2 months of age
Patients with consent from parents or legal guardians
Chronic diarrhea caused by specific infections, i.e. CMV, Clostridioides difficile
Chronic diarrhea with necrotizing enterocolitis, short bowel syndrome
Functional diarrhea
Patients with poor compliance
Patients with chronic diarrhea lasting greater than 2 months or greater than 1 month in patients younger than 2 months of age
Case-control
Cross-sectional
Not listed
Clinical phenotype of congenital diarrhea and enteropathy in China
Describe the clinical phenotype(Birth status, family history, clinical features of diarrhea, laboratory examination, endoscopic and histological evaluation results, growth and development indicators, previous treatment and effect were collected) of congenital diarrhea and enteropathy in China,We will use our own mobile application or to collect the relevant data, which will be filled in by the parents of the child.
Biomarkers of congenital diarrhea and enteropathy with diagnostic value through microbiome, metabolome and proteome features
Plasma and stool were collected from patients and healthy control children for multi-omics screening to identify biomarkers, of which differential expression were mined through proteome(olink), microbiome(metagenomic sequencing) and metabolome( untargeted metabolomics)，relevant statistical analyses were performed using non-parametric tests, such as the Wilcoxon signed-rank test.
Cinical risk prediction model for congenital diarrhea and enteropathy built by artificial intelligence and machine learning
Using artificial intelligence and machine learning to construct predictive models for congenital diarrhea and enteropathy by combining children's clinical phenotypes and multi-omics results，such as the random forest model
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