ArticleThe Journal of infectious diseases2025
Fecal Microbiota and Volatile Metabolome Pattern Alterations Precede Late-Onset Meningitis in Preterm Neonates.
Article in The Journal of infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Longitudinal fecal amino acid profiles in extremely preterm infants: early-life determinants and associations with late-onset sepsis.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- The gut microbiota shapes the human and murine breath volatilome.Cell metabolism · 2026Article
- Longitudinal analysis of fecal tryptophan metabolites and microbiome composition in very preterm infants: impact of birth mode and feeding type.Gut microbes · 2025Article
- Consequences of host-microbiome interactions in preterm infants.Infection and immunity · 2025Review
- Oops, the Microbes Did It Again: Gut Dysbiosis Precedes Late-Onset Meningitis.The Journal of infectious diseases · 2025Article
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22 authors.
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Abstract
backgroundThe 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.
methodsCases 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.
resultsOf 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).
conclusionsInfants 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.
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