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Updated: Jul 25, 2025
Author Spotlight: Obtaining High-Quality CSF and Blood Samples for Epilepsy Biomarker Discovery
Published on: September 1, 2023
Metabolomics of cerebrospinal fluid reveals prognostic biomarkers in pediatric status epilepticus
Tianqi Wang1, Chunpei Li1, Yu Ma1
1Department of Neurology, National Children's Medical Center, Children's Hospital of Fudan University, Shanghai, China.
Insights
This study identified key metabolites in cerebrospinal fluid to predict outcomes in pediatric patients with status epilepticus (SE). A model using glutamyl-glutamine and 3-iodothyronamine shows high accuracy for SE prognosis.
Area of Science:
- Neurology
- Metabolomics
- Pediatric Emergency Medicine
Background:
- Status epilepticus (SE) is a frequent neurological emergency in children.
- Identifying prognostic biomarkers is crucial for managing SE outcomes.
Purpose of the Study:
- To screen for prognostic biomarkers of SE in cerebrospinal fluid (CSF) using metabolomics.
- To develop a predictive model for SE prognosis in pediatric patients.
Main Methods:
- Compared CSF metabolomics profiles between poor and good outcome groups in children with SE using UPLC-QTOF-MS.
- Utilized Mann-Whitney U test, Benjamini-Hochberg correction, and partial least squares discriminant analysis (PLS-DA).
Main Results:
- A PLS-DA model showed significant metabolic differences between outcome groups (Q²=0.798).
- Identified 49 prognosis-related metabolites; 20 showed high predictive value (AUC ≥ 80%).
- A logistic regression model combining glutamyl-glutamine and 3-iodothyronamine achieved high predictive accuracy (AUC=0.976, sensitivity=0.863, specificity=0.956).
Conclusions:
- Highlighted metabolomic disturbances in pediatric SE CSF.
- Identified glutamyl-glutamine and 3-iodothyronamine as potential prognostic biomarkers.
- Established a highly predictive model for SE prognosis in children.
Aims:
Status epilepticus (SE) is the most common neurological emergency in pediatric patients. This study aimed to screen for prognostic biomarkers of SE in the cerebrospinal fluid (CSF) using metabolomics.
Methods:
Ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS) was conducted to identify prognostic biomarkers in CSF metabolomics by comparing the poor outcome group (N = 13) with the good outcome group (N = 15) of children with SE. Differentially expressed metabolites were identified using Mann-Whitney U test corrected by Benjamini-Hochberg and partial least squares discriminant analysis (PLS-DA).
Results:
The PLS-DA model identified and validated significant metabolic differences between the poor and good outcome groups of children with SE (PLS-DA with R2 Y = 0.992 and Q2 = 0.798). A total of 49 prognosis-related metabolites were identified. Of these metabolites, 20 including glutamyl-glutamine, 3-iodothyronamine, and L-fucose had an area under the curve (AUC) ≥ 80% in prognostic prediction of SE. The logistic regression model combining glutamyl-glutamine and 3-iodothyronamine produced an AUC value of 0.976, with a sensitivity of 0.863 and specificity of 0.956. Pathway analysis revealed that dysregulation of the citrate cycle (TCA) and arginine biosynthesis may contribute to poor SE prognosis.
Conclusions:
This study highlighted the prognosis-related metabolomic disturbances in the CSF of children with SE and identified potential prognostic biomarkers. A prognostic prediction model combining glutamyl-glutamine and 3-iodothyronamine with high predictive value was established.
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