Pediatric infection-triggered encephalopathy syndromes: a multidimensional biomarker analysis

Pediatric infection-triggered encephalopathy syndromes (ITES) cause severe neurologic and cognitive deficits, but reliable biomarkers for early diagnosis and improved outcomes are lacking.

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.

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).

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.