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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.
This study will establish a clinical cohort and a clinical phenotype database of children with congenital diarrhea and enteropathy (CODE), The investigator will mine biomarkers of CODE through multi-omics technology. This study is designed to construct a clinical risk prediction model by combining artificial intelligence technology.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Case | Congenital diarrhea and enteropathy (CODE) patients | ||
| Control | Healthy children |
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| Measure | Description | Time Frame |
|---|---|---|
| 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. | Within approximately 2 years of enrollment |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with chronic diarrhea lasting greater than 2 months or greater than 1 month in patients younger than 2 months of age
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Ying Huang, MD,PHD | Contact | +862164931727 | yhuang815@163.com | |
| Yanqiu Wang, MD | Contact | +862164931727 | 23111240040@m.fudan.edu.cn |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Yanqiu Wang | Recruiting | Shanghai | Shanghai Municipality | 201102 | China |
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| ID | Term |
|---|---|
| D003968 | Diarrhea, Infantile |
| ID | Term |
|---|---|
| D003967 | Diarrhea |
| D012817 | Signs and Symptoms, Digestive |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| Within approximately 2 years of enrollment |
| 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 | Within approximately 30 months of enrollment |