AUTHOR=Wang Xinqiong , Xiao Yuan , Xu Xu , Guo Li , Yu Yi , Li Na , Xu Chundi 

TITLE=Characteristics of Fecal Microbiota and Machine Learning Strategy for Fecal Invasive Biomarkers in Pediatric Inflammatory Bowel Disease

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2021.711884

DOI=10.3389/fcimb.2021.711884

ISSN=2235-2988

ABSTRACT=Background: The diagnosis and treatment of pediatric Inflammatory Bowel Disease (PIBD) was challenging due to the complexity of the disease and lack of disease specific biomarkers. The novel machine learning (ML) technique may be a useful tool to provide a new route for the identification of early biomarkers for the diagnosis of PIBD.
Methods: Sixty-six treatment naive PIBDs and 27 healthy controls were enrolled as an exploration cohort. Fecal microbiome profiling using 16S rRNA gene sequencing was performed. The correlation between microbiota and inflammatory and nutritional markers was evaluated using Spearman’s correlation. A random forest model was used to set up a ML approach for the diagnosis of PIBD using 1902 markers. A validation cohort including 14 PIBD and 48 Irritable Bowel Syndrome (IBS) was enrolled to further evaluate the sensitivity and accuracy of the model.
Result: Compared with healthy subjects, PIBD patients showed a significant lower diversity of gut microbiome. The increased Escherichia-Shigella and Enterococcus were positively correlated with inflammatory markers, and negatively correlated with nutrition markers, which indicated more severe disease. A diagnostic ML model was successfully setup for differential diagnosis of PIBD integrating the top 11 OTUs. This diagnostic model showed outstanding performance differentiating IBD from IBS in an independent validation cohort.
Conclusion: The diagnosis penal based on the ML of gut microbiome may be a favorable tool for the precise diagnosis and treatment of PIBD. And the study of the relationship between disease status and microbiome was an effective way to clarify the pathogenesis of PIBD.