Frerichs, Nina MDeianova, NancyEl Manouni El Hassani, SofiaAcharjee, AnimeshQuraishi, Mohammed Nabilde Boode, Willem PCossey, VeerleHulzebos, Christian Vvan Kaam, Anton HKramer, Boris Wd'Haens, Estherde Jonge, Wouter JVijlbrief, Daniel Cvan Weissenbruch, Mirjam MDaulton, EmmaWicaksono, Alfian NCovington, James ABenninga, Marc Ade Boer, Nanne K Hvan Goudoever, Johannes BNiemarkt, Hendrik Jde Meij, Tim G J2024-07-082024-07-082024-05-23Frerichs NM, Deianova N, El Manouni El Hassani S, Acharjee A, Quraishi MN, de Boode WP, Cossey V, Hulzebos CV, van Kaam AH, Kramer BW, d'Haens E, de Jonge WJ, Vijlbrief DC, van Weissenbruch MM, Daulton E, Wicaksono AN, Covington JA, Benninga MA, de Boer NKH, van Goudoever JB, Niemarkt HJ, de Meij TGJ. Fecal microbiota and volatile metabolome pattern alterations precede late-onset meningitis in preterm neonates. J Infect Dis. 2024 May 23:jiae265. doi: 10.1093/infdis/jiae265. Epub ahead of print.1537-661310.1093/infdis/jiae26538781449http://hdl.handle.net/20.500.14200/5048Objective: The fecal microbiota and metabolome are hypothesized to be altered before late-onset neonatal meningitis (LOM), in analogy 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 nine 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 six microbiota features accurately predicts 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<0.05) in the three days pre-LOM (n=92). No single discriminative metabolites were identified by GC-TOF-MS (n=66). Conclusion: 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.en© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.Communicable diseasesArticleThe Journal of infectious diseases