Untargeted Metabolomic Profiling Reveals Lipid Metabolism Dysregulation in Patients With Immune Thrombocytopenia.

Immune thrombocytopenia (ITP) is an acquired autoimmune disorder characterised by immune-mediated platelet destruction, yet its metabolic underpinnings remain incompletely understood. In this study, we applied a non-targeted serum metabolomics approach to characterise metabolic alterations in ITP and to explore candidate diagnostic biomarkers. A total of 37 serum samples were collected, including 20 from patients with ITP and 17 from age- and sex-matched healthy individuals. Global metabolite profiling was performed, followed by quantitative analyses, identification of differentially expressed metabolites, and functional annotation. Machine learning algorithms were then used to construct a diagnostic prediction model based on differential metabolites. Compared with healthy controls, ITP patients exhibited a distinct metabolic signature dominated by alterations in lipids and lipid-like molecules, with 680 metabolites upregulated and 589 downregulated. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed prominent involvement of lipid metabolism, cholesterol metabolism, and unsaturated fatty acid metabolism pathways. Among the differential metabolites, isopimpinellin and oleic acid emerged as highly characteristic of ITP. The prediction model built on these metabolomic features achieved a sensitivity of 78%, a specificity of 100%, and an area under the receiver operating characteristic curve of 0.83, indicating promising diagnostic performance. These findings suggest that dysregulated lipid metabolism is a central feature of ITP and support the potential of serum metabolomics-based machine learning models to aid in the identification of novel biomarkers and to improve early diagnosis of this immune-mediated thrombocytopenia.