Derivation of a metabolic signature associated with bacterial meningitis in infants

Background Diagnosis of bacterial meningitis (BM) is challenging in newborn infants. Presently, biomarkers of BM have limited diagnostic accuracy. Analysis of cerebrospinal fluid (CSF) metabolites may be a useful diagnostic tool in BM. Methods In a nested case-control study, we examined >400 metabolites in CSF of uninfected infants and infants with culture-confirmed BM using gas and liquid chromatography mass spectrometry. Preterm and full-term infants in a Level III or IV Neonatal Intensive Care Unit were prospectively enrolled when evaluated for serious bacterial infection. Results Over 200 CSF metabolites significantly differed in uninfected infants and infants with BM. Using machine learning, we found that as few as 6 metabolites distinguished infants with BM from uninfected infants in this pilot cohort. Further analysis demonstrated three metabolites associated with Group B Streptococcal meningitis. Conclusions We report the first comprehensive metabolic analysis of CSF in infants with BM. In our pilot cohort, we derived a metabolic signature that predicted the presence or absence of BM, irrespective of gestational age, postnatal age, sex, race and ethnicity, presence of neurosurgical hardware, white blood cell count in CSF, and red blood cell contamination in CSF. Metabolic analysis may aid diagnosis of BM and facilitate clinical decision-making in infants. Impact In a pilot cohort, metabolites in cerebrospinal fluid distinguished infants with bacterial meningitis from uninfected infants. We report the first comprehensive metabolic analysis of cerebrospinal fluid in infants with bacterial meningitis. Our findings may be used to improve diagnosis of bacterial meningitis and to offer mechanistic insights into the pathophysiology of bacterial meningitis in infants.