Fecal Microbiota and Volatile Metabolome Pattern Alterations Precede Late-Onset Meningitis in Preterm Neonates

Background The fecal microbiota and metabolome are hypothesized to be altered before late-onset neonatal meningitis (LOM), analogous to late-onset sepsis (LOS). The present study aimed to identify fecal microbiota composition and volatile metabolomics preceding LOM. Methods Cases and gestational age-matched controls were selected from a prospective, longitudinal preterm cohort study (born <30 weeks' gestation) at 9 neonatal intensive care units. The microbial composition (16S rRNA sequencing) and volatile metabolome (gas chromatography-ion mobility spectrometry [GC-IMS] and GC-time-of-flight-mass spectrometry [GC-TOF-MS]) were analyzed in fecal samples 1-10 days pre-LOM. Results Of 1397 included infants, 21 were diagnosed with LOM (1.5%), and 19 with concomitant LOS (90%). Random forest classification and MaAsLin2 analysis found similar microbiota features contribute to the discrimination of fecal pre-LOM samples versus controls. A random forest model based on 6 microbiota features accurately predicted LOM 1-3 days before diagnosis with an area under the curve (AUC) of 0.88 (n = 147). Pattern recognition analysis by GC-IMS revealed an AUC of 0.70-0.76 (P <. 05) in the 3 days pre-LOM (n = 92). No single discriminative metabolites were identified by GC-TOF-MS (n = 66). Conclusions Infants with LOM could be accurately discriminated from controls based on preclinical microbiota composition, while alterations in the volatile metabolome were moderately associated with preclinical LOM.