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Clinical Research and Public HealthImmunologyInfectious disease
Open Access | 
10.1172/jci.insight.193826
1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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1Department of Pediatrics, Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, USA.
3Division of Infectious Diseases, Departments of Medicine and Microbiology-Immunology, and
4Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
5Mass Spectrometry Technology Access Center at McDonnell Genome Institute (MTAC@MGI), Washington University School of Medicine, St. Louis, Missouri, USA.
6Department of Pediatrics, Division of Infectious Diseases, University of Chicago, Chicago, Illinois, USA.
7Department of Medicine, Division of Hematology and Oncology, and
8Department of Preventive Medicine, Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
9Department of Obstetrics and Gynecology, Brown University, Providence, Illinois, USA.
10Department of Pediatrics, Division of Neonatology, UCSD, La Jolla, California, USA.
Address correspondence to: Leena B. Mithal, 225 E. Chicago Ave, Box #20, Chicago, Illinois 60611, USA. Phone: 312.933.9173; Email: lmithal@luriechildrens.org.
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Published June 10, 2025 - More info
Published in Volume 10, Issue 13 on July 8, 2025
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.
Graphical Abstract
Introduction
Early-onset neonatal sepsis (EOS) results in significant morbidity and mortality that disproportionately affects preterm infants (1–5). Clinical management of EOS is complicated by underlying uncertainty in the diagnosis due to undifferentiated symptoms, common risk factors, and lack of timely, reliable diagnostics (6–8). This uncertainty drives early antibiotic use in infants with any suspicion of sepsis. Antibiotics are the most prescribed medication in neonatal intensive care units, with substantial variation by practice, and most antibiotic exposures occur in infants who have negative bacterial cultures (i.e., presumed, culture-negative sepsis) (9, 10). In addition to growing antimicrobial resistance, neonates exposed to antibiotics without proven infection face increased near-term risks of fungal infection, late-onset sepsis, necrotizing enterocolitis, and death (11, 12). Early antibiotic exposure may also cause long-term morbidity, driven by resultant microbiome alterations (dysbiosis) and attendant immunologic and metabolic dysregulation (13, 14). More accurate and timely diagnostics are needed to identify or rule out EOS in neonates to reduce the burden of antibiotic use in this vulnerable population.
Several approaches to improve the rapid, accurate diagnosis of EOS have been devised (15, 16). Molecular pathogen diagnostics and placental pathology are valuable tools; however, they often suffer from delayed time to results, low positive predictive value, and limited availability (17). Laboratory markers of infection, including hematologic indices and markers of inflammation, such as C-reactive protein (CRP) and procalcitonin (18), have poor specificity and can be elevated due to non-infectious causes early in life, potentially leading to overtreatment with antibiotics (19, 20). Vital signs and heart rate variability monitoring algorithms have shown some benefits in triggering evaluations for late-onset sepsis occurring after the first 72 hours of life but not for EOS (21, 22). More holistic tools for risk assessment have also been developed. For example, the Kaiser Permanente Sepsis calculator accounts for various clinical risk factors, including intrapartum antibiotic prophylaxis, duration of rupture of membranes, gestational age (GA), and clinical status. This tool has reduced antibiotic exposure for newborns but can only be applied to infants greater than 34 weeks GA at birth, excluding low-birth-weight or infants who were born earlier who are at highest risk of EOS, EOS-related morbidity and mortality, and from harms of antibiotic overuse (23).
Several studies have investigated specific markers in umbilical cord blood that might improve the diagnosis of EOS. In most cases of EOS, an ascending infection affects the placenta, amniotic fluid, and fetus prior to birth, especially in the setting of spontaneous preterm labor. Umbilical cord blood reflects the intrauterine environment where EOS is seeded and the infant’s state at birth, unaffected by postnatal stressors and physiology. This, along with the ready availability of cord blood, makes it an attractive target for diagnostics. Previous studies by our group have demonstrated elevated levels of CRP, serum amyloid A (SAA), and haptoglobin in culture-proven EOS and in a small subset of presumed sepsis (PS) cases (24). Premature expression of haptoglobin in cord blood was similarly associated with EOS by another group (25). Other studies have implicated potential biomarkers, including presepsin (a soluble CD14 fragment released by myeloid cells during an immunological response) and CD64 (26–28). However, this research was limited to investigating a priori–selected markers and pooled analysis of culture-proven EOS and PS specimens.
Our objective was to use unbiased mass spectrometry proteomics to identify proteins that were differentially abundant in cord blood of infants with EOS compared with infants without EOS. After immunoassay validation, these data were used to develop a machine learning diagnostic model incorporating cord blood biomarkers and clinical factors to accurately identify and rule out EOS in newborn infants.
ResultsIdentification of EOS biomarkers in cord blood. Table 1 displays the characteristics of the cohort across sepsis groups. Among the EOS cohort, known risk factors for EOS development were overrepresented. Infants with EOS had lower GAs as compared with infants in the control cohort (mean, 30.7 ± 3.3 weeks vs. 33.6 ± 4.5 weeks; 0% >37 weeks, 0% EOS vs. 27% controls) and were less frequently female (36% female vs. 49% female). Chorioamnionitis (43% vs. 0.7%), prolonged rupture of membranes (PROM) (64% vs. 11.3%), and vaginal delivery (71% vs. 51%) were likewise more common in patients with EOS compared with individuals acting as controls. The PS cohort resembled the EOS cohort with a greater frequency of prematurity, chorioamnionitis, and PROM than in the control group. Confirmed pathogens in blood culture included E. coli (n = 8), Streptococcus agalactiae (n = 2), Klebsiella oxytoca (n = 1), Proteus mirabilis (n = 1), Haemophilus influenzae (n = 1), and Listeria monocytogenes (n = 1).
Table 1Demographics and clinical covariates
To assess protein abundance, we performed liquid chromatography with tandem mass spectrometry (LC-MS/MS) on sera isolated from frozen, banked blood specimens from our control and EOS cohorts (Figure 1A). Spectra were analyzed using MaxQuant for label-free quantification (LFQ) of protein abundance (29). Peptides corresponding to 437 proteins were detected. After batch normalization, we visualized the protein abundance of the 255 most commonly detected proteins (present in >20% of specimens) across all 164 specimens using a hierarchically clustered heatmap. For heatmap visualization, missing values were imputed separately for EOS and control specimens either by iterative imputation for proteins in >70% or by sampling from the lowest decile of protein abundance for proteins in <70% of specimens. When visualized using hierarchical clustering, there were no clear changes in the plasma proteome that clustered by GA, sex, or specimen type (i.e., EOS or control) (Figure 1B). Clustering with naive imputation of zeros for all missing values revealed similar results (Supplemental Figure 1; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.193826DS1).
Figure 1Proteomic identification of differentially abundant proteins in early-onset sepsis cord blood. (A) Diagram of workflow. (B) Hierarchically clustered heatmap of protein abundance in cord blood for EOS and control specimens. Specimens are clustered by gestational age category, sex, and sample type. Missing values were imputed. (C) Plot of mean abundance of proteins in EOS and control specimens. Black points were significant by Mann-Whitney U test with Benjamini-Hochberg FDR adjustment (P < 0.05).
These data suggest that any changes in the plasma proteome during EOS would be driven by a small number of proteins as opposed to global restructuring. To assess per protein changes in abundance, we plotted the mean abundance of each of the 255 most commonly detected proteins in our control specimens versus our EOS specimens (Figure 1C). Five proteins were significantly enriched in the plasma of infants with culture-confirmed EOS: CRP, lipopolysaccharide-binding protein (LBP), SAA1, leucine-rich α-2-glycoprotein 1 (LRG1), and serine proteinase inhibitor A3 (SERPINA3) (Figure 1C). These are all acute-phase reactant proteins that are upregulated in the plasma in response to inflammation. A sensitivity analysis was performed repeating differential abundance analysis between EOS specimens and GA-matched control specimens (excluding the full-term group, ≥37 weeks gestation). Results were consistent, with the same 5 proteins significantly elevated in the EOS specimens (Supplemental Figure 2A). Due to the small number of female infants with EOS (n = 4), statistical power was not adequate to detect differences of plausible magnitude for each sex.
As there were many fewer EOS specimens compared with control specimens, proteins found exclusively or predominantly in EOS cord blood could be excluded by our removal of proteins present in <20% of samples (n = 182 proteins). As these proteins might be superior predictors of EOS, we also assessed differential presence and absence of these less frequently detected proteins. Of the 182 proteins detected in fewer than 20% of specimens, only 3 were more often found in EOS specimens: SAA2, which was observed in 71% of EOS specimens but 2% of control specimens; haptoglobin/haptoglobin-related protein, which was observed in 43% of EOS specimens but 17% of control specimens; and lipocalin-2 (LCN2), which was observed in 36% of EOS specimens but 9% of control specimens. Notably, these proteins are also all acute-phase reactants, with SAA2 sequence and function largely redundant with SAA1. Given their rarity in the control specimens, we could not statistically compare their abundance across cohorts, so we focused on the 5 acute-phase reactants identified above.
EOS cord blood samples contain higher levels of acute-phase reactant proteins. Principal component analysis (PCA) of the protein abundance data for CRP, LBP, SAA1, LRG1, and SERPIN3A resulted in a clear separation of most of the EOS and control specimens, with axes accounting for 84% of the variance (Figure 2A). Hierarchical clustering by the abundance of these 5 proteins likewise shows a clear EOS cluster, though with a few control specimens interspersed (Figure 2B). Protein abundance for all 5 proteins was significantly different between the EOS and control specimens (Figure 2C). Results were consistent, showing significantly different protein abundance when analysis included or excluded full-term GA infants (Supplemental Figure 2B). The distributions for CRP overlapped more than the other proteins. These data suggest that the measurement of one or more of these putative biomarkers in cord blood might be sufficient to differentiate healthy infants from those with EOS.
Figure 2Details of differentially abundant proteins. (A) PCA and (B) clustered heatmap of EOS and control specimen values for the 5 differentially abundant proteins. For B, missing protein abundance values were imputed. (C) Distribution of protein abundance in EOS and control specimens for each protein. Box plots show median and interquartile range. Whiskers extend to the last point within 1.5× interquartile range of the box. Bee swarms show individual samples. Comparisons were significant by Mann-Whitney U test with Benjamini-Hochberg FDR adjustment.
Quantitative immunoassays confirm elevated CRP, SAA, and LBP in EOS cord blood specimens. Though useful for target discovery, shotgun proteomic data cannot be used to rule out the presence of a protein in a specimen, nor is it a clinically feasible approach. Therefore, we next sought to validate these findings using commercially available, quantitative immunoassay kits to detect CRP, SAA1/SAA2, and LBP (Figure 3A). Only preterm controls (n = 105) were included for better comparison to our infants with EOS (all <37 weeks). Once again, the concentrations of the 3 biomarkers were significantly elevated in the EOS compared with the control specimens, though for each biomarker 3 EOS specimens had lower concentrations more comparable to the control samples (Figure 3B and Supplemental Figure 3). PCA of these data demonstrated that though most of the EOS specimens formed a distinct cluster, 3 EOS specimens clustered with the control specimens (Figure 3C). These 3 infants had different GAs at birth (24 5/7, 33 5/7, and 34 3/7), and blood cultures yielded typical septic pathogens (E. coli, E. coli, and K. oxytoca, respectively) (Table 2). Notably, however, these 3 infants had the longest intervals between birth and a positive blood culture (drawn at 65, 55, and 62 hours of life, respectively). The two infants with E. coli had a negative blood culture in the first day of life, whereas the infant with K. oxytoca developed emesis and increased abdominal girth on day 3 of life with an abdominal radiograph concerning for pneumatosis, prompting a septic workup. The other 11 infants with EOS had a positive blood culture that was drawn within the first few hours of life.
Figure 3Quantitative multiplex immunoassay detection of potential EOS biomarkers. (A) Diagram of experimental procedure. (B) Distribution of protein concentration in mg/mL in EOS and control specimens for each protein. Box plots show median and interquartile range. Whiskers extend to the last point within 1.5× interquartile range of the box. Bee swarms show individual samples. Comparisons were significant by Mann-Whitney U test with Benjamini-Hochberg FDR adjustment. (C) PCA of EOS and control specimen protein abundance based on MSD data.
Table 2Clinical characteristics of EOS cases
Predictive modeling of EOS. To assess the predictive value of these putative biomarkers versus currently referenced clinical variables, we made a series of small logistic regression models containing sex, GA, and either a single biomarker or single clinical variable utilizing immunoassay results (Table 3). Clinical variables analyzed included plasma protein concentration, multiple gestations, chorioamnionitis, preeclampsia, PROM, route of delivery, labor, and a delivery sum score factoring in both the delivery route and if the mother went into labor. To improve the predictive power for in utero–derived EOS, the 3 outlier infants were excluded from modeling. The logistic model containing a biomarker had the lowest Akaike information criteria (AIC) than those containing a clinical variable, with the best-performing single clinical variable being the presence of clinical chorioamnionitis. We next explored multivariate logistic regression models. Most clinical variables as operationalized have a small cardinality and are colinear with at least one other variable, posing a challenge for meaningful model selection and interpretation of variable importance; we thus selected single clinical variables from correlated variable sets and evaluated model performance of these pruned formulae (Supplemental Figure 4 and Table 3). Model performance for the best performing set of clinical variables with increasing representation of biomarkers (none, SAA only, or all 3) showed both improved classification performance and reduced AIC with the addition of SAA. However, the best performing models had recall of 0.82, indicating that a substantial fraction of authentic EOS cases would be missed were these models used for prediction.
Table 3Logistic regression of EOS risk
To employ a modeling strategy more robust to the small sample size and variable selection challenges of our dataset, we produced random forest (RF) models with or without the biomarker concentrations. As with the logistic models, RF models were built initially with and subsequently without the 3 outlier infants identified in PCA analysis (Figure 3 and Table 2) to focus on diagnosis of EOS present at
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