ArticleAnnals of hematology2025
Urinary metabolomics predicts early rituximab response in steroid-nonresponsive pediatric patients with primary immune thrombocytopenia.
Article in Annals of hematology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Plasma metabolomics profile alterations in the onset and progression of paediatric immune thrombocytopenia.British journal of haematology · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
Rituximab (RTX) is an effective therapy for pediatric steroid-nonresponsive immune thrombocytopenia (ITP), yet persistent challenges remain in predicting early therapeutic responses. To address this, we conducted a prospective cohort study analyzing serial urine samples from 37 steroid-nonresponsive ITP patients (17 RTX responders (RTX-R), 20 non-responders (RTX-NR)) and 38 age- and sex-matched controls using untargeted LC-MS metabolomics. We identified 16 pretreatment and 2 posttreatment metabolites distinguishing RTX-R from RTX-NR. Pretreatment metabolic pathway analysis in RTX-R revealed significant enrichment in folate-mediated one-carbon pool, ether lipid metabolism, porphyrin metabolism, and amino acid metabolism (cysteine/methionine, arginine/proline), and purine pathways. Longitudinal clustering analysis demonstrated RTX-induced metabolic changes in purine metabolism, pantothenate and CoA biosynthesis, beta oxidation of fatty acids, and valine, leucine and isoleucine biosynthesis. A machine learning-derived predictive model incorporating seven metabolites (N6-methyl-2’-deoxyadenosine, dopamine 4-sulfate, DL-homocystine, folinic acid, S-adenosylmethioninamine, lysoPA (P-16:0e/0:0), and coproporphyrinogen III) demonstrated robust stratification of patients eligible for RTX before treatment (AUC 0.85 ± 0.13). Complementing this, a two-metabolite (N6-methyl-2’-deoxyadenosine and dehydroepiandrosterone) monitoring model was developed to track treatment responsiveness dynamically (AUC 0.83 ± 0.16). This study elucidates metabolic mechanisms underlying RTX responsiveness and delivers clinically actionable tools to enhance personalized management of pediatric ITP.
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