ArticleJournal of inflammation research2025
Plasma Bile Acid Profiling Reveals Novel Early Diagnostic Biomarkers for Pediatric Sepsis.
Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
The trial behind it
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
3 citing papers in PubMed.
- Short-Chain Fatty Acids: A Key Modulator of Sepsis-Associated Acute Respiratory Distress Syndrome.Journal of inflammation research · 2026Review
- Gut barrier-microbiota crosstalk in sepsis: from pathogenesis to potential therapies.Frontiers in immunology · 2026Review
- Bile acid dysregulation in sepsis: mechanisms, clinical implications, and future perspectives.Frontiers in cellular and infection microbiology · 2026Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Sepsis is a life-threatening condition, and early detection remains a challenge. While bile acids have been implicated in various diseases, their role as biomarkers for sepsis is underexplored. This study aims to identify metabolites associated with sepsis and assess the potential of bile acids for early diagnosis and prognosis of pediatric sepsis. Methods: We enrolled 100 participants in the discovery phase and 141 participants in the validation phase. Non-targeted liquid chromatography-mass spectrometry (LC-MS) metabolomics analyses were performed to identify differential metabolites between sepsis patients and healthy controls. Targeted quantitative analysis of 12 plasma bile acids (BA) was conducted to assess their concentrations. Machine learning algorithms, including Recursive Feature Elimination (RFE), Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Support Vector Machine-RFE (SVM-RFE), and Gradient Boosting Decision Tree (GBDT), were employed to identify key BAs for sepsis diagnosis. Results: Untargeted metabolomics revealed bile acid pathway dysregulation, validated by targeted quantification showing elevated primary bile acids-cholic acid (CA), glycocholic acid (GCA), taurocholic acid (TCA), glycochenodeoxycholic acid (GCDCA), taurochenodeoxycholic acid (TCDCA)-and taurine-conjugated secondary bile acid TDCA ( Conclusion: Our results highlight the potential of bile acid profiling as a diagnostic and prognostic biomarker for pediatric sepsis. These findings suggest a path toward early intervention, improving patient outcomes by enabling timely detection and treatment.
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Registered trials
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