Correlation Between Fecal Microbiota and Corticosteroid Responsiveness in Primary Immune Thrombocytopenia: an...

Corticosteroids (CSs) are the initial therapy for immune thrombocytopenia (ITP); however, their efficacy is not adequately predicted. As a novel biomarker, the composition of the gut microbiota is non-invasively tested and altered in patients with ITP. This study aims to develop a predictive model that leverages gut microbiome data to predict the CS response in patients with ITP within the initial four weeks of treatment. Metagenomic sequencing is performed on fecal samples from 212 patients with ITP, 152 of whom underwent CS treatment and follow-up. Predictive models are trained using six machine-learning algorithms, integrating clinical indices and gut microbiome data. The support vector machine (SVM) algorithm-based model has the highest accuracy (AUC = 0.80). This model utilized a comprehensive feature set that combined clinical data (including sex, age, duration, platelet count, and bleeding scales) with selected microbial species (including Bacteroides ovatus, Bacteroides xylanisolvens, and Parabacteroides gordonii), alpha diversities, KEGG pathways, and microbial modules. This study will provide new ideas for the prediction of clinical CS efficacy, enabling informed decision-making regarding the initiation of CS or personalized treatment in patients with ITP.