Abstract
BACKGROUND: Pediatric infection-triggered encephalopathy syndromes (ITES) cause severe neurologic and cognitive deficits, but reliable biomarkers for early diagnosis and improved outcomes are lacking. METHODS: This retrospective study analyzed the clinical characteristics and laboratory data from 48 children with infection-triggered encephalopathy syndromes, using a case-control design. Ultra-high-performance liquid chromatography-tandem mass spectrometry, the Luminex xMAP(®) multiplex assay system, the Cobas(®) 8,000 analyzer, and immunoturbidimetry were utilized to measure blood and urine metabolites, cerebrospinal fluid and plasma cytokines, and cerebrospinal fluid biomarkers and proteins. RESULTS: Initial urinary metabolomic profiling identified 56 differentially abundant metabolites in the infection-triggered encephalopathy syndromes group (50 upregulated, 6 downregulated). Partial least-squares discriminant analysis highlighted 13 metabolites with variable importance in projection scores >1, 12 of which may serve as candidate biomarkers (area under the curve > 0.75; e.g., 3-hydroxybutyrate, fucose). Random Forest modeling prioritized five urinary metabolites: stearate, malate, glucose1, glucose2, and fucose. Similarly, five metabolites, such as C4OH, C14OH(CIL), C18:1OH, C10:2(CIL), and C5DC(CIL)/C16, may serve as potential biomarkers (AUC > 0.75). Cerebrospinal fluid analysis showed elevated interleukin-6 and interleukin-8 levels in the infection-triggered encephalopathy syndromes group (area under the curve > 0.75 each). Clinically, there were significant differences between the ITES group and the control group in terms of Modified Rankin Scale scores, infection status, fever, seizures, and altered consciousness (all p < 0.05). INTERPRETATION: This study identifies a panel of urinary, plasma, and cerebrospinal fluid biomarkers, which provide a thorough molecular profile of infection-triggered encephalopathy syndromes in children. These findings provide a direction for future research on mechanistic studies, early identification, and risk classification.