Cord blood proteomics identifies biomarkers of early-onset neonatal sepsis

  • 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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article numbere193826
JournalJCI Insight
Volume10
Issue number13
DOIs
StatePublished - Jul 2025

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