Citation Information: JCI Insight. 2025;10(13):e193826. https://doi.org/10.1172/jci.insight.193826.
Abstract
BACKGROUND Symptoms of early-onset neonatal sepsis (EOS) in preterm infants are nonspecific and overlap with normal postnatal physiological adaptations and noninfectious pathologies. This clinical uncertainty and the lack of reliable EOS diagnostics results in liberal use of antibiotics in the first days to weeks of life, leading to increased risk of antibiotic-related morbidities in infants who do not have an invasive infection. METHODS To identify potential biomarkers for EOS in newborn infants, we used unlabeled tandem mass spectrometry proteomics to identify differentially abundant proteins in the umbilical cord blood of infants with and without culture-confirmed EOS. Proteins were then confirmed using immunoassay, and logistic regression and random forest models were built, including both biomarker concentration and clinical variables to predict EOS. RESULTS These data identified 5 proteins that were significantly upregulated in infants with EOS, 3 of which (serum amyloid A, C-reactive protein, and lipopolysaccharide-binding protein) were confirmed using a quantitative immunoassay. The random forest classifier for EOS was applied to a cohort of infants with culture-negative presumed sepsis. Most infants with presumed sepsis were classified as resembling infants in the control group, with low EOS biomarker concentrations.CONCLUSION These results suggest that cord blood biomarker screening may be useful for early stratification of EOS risk among neonates, improving targeted, evidence-based use of antibiotics early in life. FUNDING NIH, Gerber Foundation, Friends of Prentice, Thrasher Research Fund, Ann & Robert H. Lurie Children’s Hospital, and Stanley Manne Children’s Research Institute of Lurie Children’s.
Authors
Leena B. Mithal, Mark E. Becker, Ted Ling-Hu, Young Ah Goo, Sebastian Otero, Aspen Kremer, Surya Pandey, Nicola Lancki, Yawei Li, Yuan Luo, William Grobman, Denise Scholtens, Karen K. Mestan, Patrick C. Seed, Judd F. Hultquist
Demographics and clinical covariates
