{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T16:17:41Z","timestamp":1792167461718,"version":"4.4.0"},"reference-count":47,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["IJMS"],"abstract":"<jats:p>The development of sensitive and specific diagnostic methods for secondary bacterial meningitis remains an urgent challenge in neurosurgical and intensive care units. A combination of various clinical and biochemical parameters, as well as biomarkers and metabolites in cerebrospinal fluid (CSF), can be considered for constructing multivariate diagnostic models. In this study, 96 CSF samples from 53 patients with suspected secondary meningitis were analyzed. The first cohort, consisting of patients with sequelae of severe brain damage, included 7 patients (21 CSF samples) with and 29 patients (56 CSF samples) without secondary bacterial meningitis. The second cohort comprised patients after neurosurgical interventions, including 10 patients (12 CSF samples) with and 7 patients (7 CSF samples) without secondary bacterial meningitis. Combined group 1 with 33 CSF samples from patients with secondary bacterial meningitis and combined group 2 with 63 CSF samples from patients without secondary bacterial meningitis had statistically different cell and biochemical compositions and higher CSF concentrations of biomarkers (interleukin-6 and S100 protein) and lactate-containing aromatic metabolites in group 1. Univariate prognostic models constructed on 4-hydroxyphenyllactic, phenyllactic, and indole-3-lactic acids demonstrated outstanding AUC-ROC of more than 0.91. A multivariate model built on all biomarkers and metabolites resulted in AUC-ROC = 0.94 with a sensitivity of 0.94 and specificity of 0.86, and was found to be the most accurate method for the diagnosis of secondary bacterial meningitis.<\/jats:p>","DOI":"10.3390\/ijms262110522","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T17:38:22Z","timestamp":1761759502000},"page":"10522","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Diagnosis of Secondary Bacterial Meningitis via Aromatic Metabolites and Biomarkers in Cerebrospinal Fluid"],"prefix":"10.3390","volume":"26","author":[{"given":"Petr A.","family":"Meinarovich","sequence":"first","affiliation":[{"name":"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Petrovka Str. 25-2, 107031 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ekaterina A.","family":"Sorokina","sequence":"additional","affiliation":[{"name":"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Petrovka Str. 25-2, 107031 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalia V.","family":"Beloborodova","sequence":"additional","affiliation":[{"name":"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Petrovka Str. 25-2, 107031 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6470-0485","authenticated-orcid":false,"given":"Alisa K.","family":"Pautova","sequence":"additional","affiliation":[{"name":"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Petrovka Str. 25-2, 107031 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2025). WHO Guidelines on Meningitis Diagnosis, Treatment and Care, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e34","DOI":"10.1093\/cid\/ciw861","article-title":"2017 Infectious Diseases Society of America\u2019s Clinical Practice Guidelines for Healthcare-Associated Ventriculitis and Meningitis","volume":"64","author":"Tunkel","year":"2017","journal-title":"Clin. Infect. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"497","DOI":"10.3109\/02688697.2013.771138","article-title":"Conservative Antibiotic Policy in Patients Undergoing Non-Trauma Cranial Surgery Does Not Result in Higher Rates of Postoperative Meningitis: An Audit of Nine Years of Narrow-Spectrum Prophylaxis","volume":"27","author":"Moorthy","year":"2013","journal-title":"Br. J. Neurosurg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.ajic.2008.03.002","article-title":"CDC\/NHSN Surveillance Definition of Health Care\u2013Associated Infection and Criteria for Specific Types of Infections in the Acute Care Setting","volume":"36","author":"Horan","year":"2008","journal-title":"Am. J. Infect. Control"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/j.cmi.2017.05.013","article-title":"Management of Post-Neurosurgical Meningitis: Narrative Review","volume":"23","author":"Hussein","year":"2017","journal-title":"Clin. Microbiol. Infect."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Borowiak, A., Safranow, K., Sarna, A., and \u0141oniewska, B. (2025). Diagnostic Utility of Procalcitonin and Lactate Determination in Cerebrospinal Fluid for the Diagnosis of Neonatal Meningitis. J. Clin. Med., 14.","DOI":"10.3390\/jcm14020414"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wang, Y., Yang, Y., Kong, Y., and Peng, Y. (2023). The Value of Elevated Cerebrospinal Fluid Lactate Concentrations in Post-Neurosurgical Bacterial Meningitis. BMC Neurol., 23.","DOI":"10.1186\/s12883-023-03428-8"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"59","DOI":"10.3892\/wasj.2024.274","article-title":"Risk Factors for Mortality in Patients with Bacterial Meningitis Following a Neurosurgical Procedure: A Meta-analysis","volume":"6","author":"Suryaningtyas","year":"2024","journal-title":"World Acad. Sci. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1038\/nature24661","article-title":"A Gut Bacterial Pathway Metabolizes Aromatic Amino Acids into Nine Circulating Metabolites","volume":"551","author":"Dodd","year":"2017","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"123008","DOI":"10.1016\/j.jchromb.2021.123008","article-title":"The Quantitative Determination of Indolic Microbial Tryptophan Metabolites in Human and Rodent Samples: A Systematic Review","volume":"1186","author":"Anderson","year":"2021","journal-title":"J. Chromatogr. B"},{"key":"ref_11","unstructured":"Beloborodova, N.V. (2023). Serum Aromatic Microbial Metabolites as Biological Markers in Intensive Care, Springer."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pautova, A.K., Meglei, A.Y., Chernevskaya, E.A., Alexandrova, I.A., and Beloborodova, N.V. (2022). 4-Hydroxyphenyllactic Acid in Cerebrospinal Fluid as a Possible Marker of Post-Neurosurgical Meningitis: Retrospective Study. J. Pers. Med., 12.","DOI":"10.3390\/jpm12030399"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pautova, A.K., Meinarovich, P.A., Zakharchenko, V.E., Sobolev, P.D., Burnakova, N.A., and Beloborodova, N.V. (2025). Differences in the Profile of Aromatic Metabolites in the Corresponding Blood Serum and Cerebrospinal Fluid Samples of Patients with Secondary Bacterial Meningitis. Metabolites, 15.","DOI":"10.3390\/metabo15080527"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sobolev, P.D., Burnakova, N.A., Revelsky, A.I., Zakharchenko, V.E., Beloborodova, N.V., and Pautova, A.K. (2025). A Sensitive Method for the Profiling of Phenyl- and Indole-Containing Metabolites in Blood Serum and Cerebrospinal Fluid Samples of Patients with Severe Brain Damage Using Ultra-High-Pressure Liquid Chromatography-Tandem Mass Spectrometry. J. Pharm. Biomed. Anal., 260.","DOI":"10.1016\/j.jpba.2025.116803"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"430","DOI":"10.11622\/smedj.2021123","article-title":"Diagnostic Value of Cerebrospinal Fluid Levels of D-Lactate, Tumour Necrosis Factor-Alpha and Interleukin-6, -8, and -17 in Suspected Nosocomial Meningitis","volume":"65","author":"Goktas","year":"2024","journal-title":"Singap. Med. J."},{"key":"ref_16","first-page":"53","article-title":"Enolases: Limitations for Implementation in Clinical Practice (Critical Review)","volume":"20","author":"Golubev","year":"2024","journal-title":"Gen. Reanimatol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Singh, P., and Ali, S.A. (2022). Multifunctional Role of S100 Protein Family in the Immune System: An Update. Cells, 11.","DOI":"10.3390\/cells11152274"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Meinarovich, P., Pautova, A., Zuev, E., Sorokina, E., Chernevskaya, E., and Beloborodova, N. (2024). An Integrated Approach Based on Clinical Data Combined with Metabolites and Biomarkers for the Assessment of Post-Operative Complications after Cardiac Surgery. J. Clin. Med., 13.","DOI":"10.20944\/preprints202407.1815.v1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"39","DOI":"10.17116\/anaesthesiology202402139","article-title":"Prevention of Nosocomial Pneumonia Recurrence Using a Bacteriophage Cocktail in Intensive Care Unit","volume":"2024","author":"Grechko","year":"2024","journal-title":"Russ. J. Anesthesiol. Reanimatol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Likhvantsev, V.V., Berikashvili, L.B., Yadgarov, M.Y., Yakovlev, A.A., and Kuzovlev, A.N. (2024). The Tri-Steps Model of Critical Conditions in Intensive Care: Introducing a New Paradigm for Chronic Critical Illness. J. Clin. Med., 13.","DOI":"10.3390\/jcm13133683"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"ofy193","DOI":"10.1093\/ofid\/ofy193","article-title":"Cryptococcal Meningitis and Tuberculous Meningitis Co-Infection in HIV-Infected Ugandan Adults","volume":"5","author":"Ellis","year":"2018","journal-title":"Open Forum Infect. Dis."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Widerstr\u00f6m, M. (2016). Significance of Staphylococcus Epidermidis in Health Care-Associated Infections, from Contaminant to Clinically Relevant Pathogen: This Is a Wake-Up Call!. J. Clin. Microbiol., 54.","DOI":"10.1128\/JCM.00743-16"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1503\/jpn.140126","article-title":"Increased Levels of IL-6 in the Cerebrospinal Fluid of Patients with Chronic Schizophrenia\u2014Significance for Activation of the Kynurenine Pathway","volume":"40","author":"Schwieler","year":"2015","journal-title":"J. Psychiatry Neurosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1111\/j.1468-1331.2004.01021.x","article-title":"Cerebrospinal Fluid and Serum Neuron-specific Enolase Concentrations in a Normal Population","volume":"12","author":"Casmiro","year":"2005","journal-title":"Eur. J. Neurol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1515\/cclm.1995.33.10.743","article-title":"Measurement of S-100 Protein in Human Blood and Cerebrospinal Fluid: Analytical Method and Preliminary Clinical Results","volume":"33","author":"Missler","year":"1995","journal-title":"Clin. Chem. Lab. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s11306-020-01757-0","article-title":"Sex and Race Differences of Cerebrospinal Fluid Metabolites in Healthy Individuals","volume":"17","author":"Reavis","year":"2021","journal-title":"Metabolomics"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1086\/425368","article-title":"Practice Guidelines for the Management of Bacterial Meningitis","volume":"39","author":"Tunkel","year":"2004","journal-title":"Clin. Infect. Dis."},{"key":"ref_28","first-page":"1103","article-title":"Cerebrospinal Fluid Analysis","volume":"68","author":"Seehusen","year":"2003","journal-title":"Am. Fam. Physician"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/BF02740665","article-title":"Physiological and Pathological Roles of Interleukin-6 in the Central Nervous System","volume":"15","author":"Gruol","year":"1997","journal-title":"Mol. Neurobiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102520","DOI":"10.1016\/j.ceca.2021.102520","article-title":"Specific Cytokines of Interleukin-6 Family Interact with S100 Proteins","volume":"101","author":"Kazakov","year":"2022","journal-title":"Cell Calcium"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1134\/S106193482470117X","article-title":"Prospects for Monitoring Aromatic and Mitochondrial Metabolites Using Gas Chromatography\u2013Mass Spectrometry during Extracorporeal Blood Purification in Patients with Sepsis","volume":"79","author":"Pautova","year":"2024","journal-title":"J. Anal. Chem."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1097\/JTO.0b013e3181ec173d","article-title":"Receiver Operating Characteristic Curve in Diagnostic Test Assessment","volume":"5","author":"Mandrekar","year":"2010","journal-title":"J. Thorac. Oncol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Alnomasy, S.F., Alotaibi, B.S., Mujamammi, A.H., Hassan, E.A., and Ali, M.E. (2021). Microbial Aspects and Potential Markers for Differentiation between Bacterial and Viral Meningitis among Adult Patients. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0251518"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1097\/MEJ.0000000000000721","article-title":"Validation of the Bacterial Meningitis Score in Adults Consulting at an Emergency Department: A Retrospective Multicentric Study","volume":"27","author":"Delannoy","year":"2020","journal-title":"Eur. J. Emerg. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1177\/0004563215589381","article-title":"Interleukin-6 in Cerebrospinal Fluid as a Biomarker of Acute Meningitis","volume":"53","author":"Prieto","year":"2016","journal-title":"Ann. Clin. Biochem. Int. J. Lab. Med."},{"key":"ref_36","first-page":"7059","article-title":"Diagnostic Performance of Interleukin-6 and Interleukin-8 for Bacterial Meningitis: A Meta-Analysis","volume":"8","author":"Yao","year":"2015","journal-title":"Int. J. Clin. Exp. Med."},{"key":"ref_37","first-page":"371","article-title":"Diagnostic Value of Serum Procalcitonin, CSF Neutrophil-to-Lymphocyte Ratio, and CSF Lactate in Pediatric Bacterial Meningoencephalitis","volume":"27","author":"Nugrahanto","year":"2024","journal-title":"Ann. Indian. Acad. Neurol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Victor, A., Medeiros Santos, D.A., Nery, E.K., Mori, D.P., de Carvalho Lucas, P.C., Cammarota, D., Florez Montero, G.L., Barcellos Filho, F.N., Frugis Yu, A.L., and Carvalhanas, T.R.M.P. (2025). Improving Meningitis Surveillance and Diagnosis with Machine Learning: Insights from S\u00e3o Paulo. PLoS Digit. Health, 4.","DOI":"10.1371\/journal.pdig.0000925"},{"key":"ref_39","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array Programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"ref_41","unstructured":"Seabold, S., and Perktold, J. (July, January 28). Statsmodels: Econometric and Statistical Modeling with Python. Proceedings of the SciPy 2010, Austin, TX, USA."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1080\/10543406.2016.1265542","article-title":"Wald Tests for Variance-Adjusted Equivalence Assessment with Normal Endpoints","volume":"27","author":"Chen","year":"2017","journal-title":"J. Biopharm. Stat."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"25","DOI":"10.4097\/kja.21209","article-title":"Receiver Operating Characteristic Curve: Overview and Practical Use for Clinicians","volume":"75","author":"Nahm","year":"2022","journal-title":"Korean J. Anesthesiol."},{"key":"ref_44","unstructured":"Raschka, S. (2018). Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"421","DOI":"10.5414\/CPP46421","article-title":"Random Effects Models in Clinical Research","volume":"46","author":"Cleophas","year":"2008","journal-title":"Int. J. Clin. Pharmacol. Ther."},{"key":"ref_46","first-page":"e20230137","article-title":"Mixed-Effects Model: A Useful Statistical Tool for Longitudinal and Cluster Studies","volume":"49","author":"Silveira","year":"2023","journal-title":"J. Bras. Pneumol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e70056","DOI":"10.1111\/cts.70056","article-title":"Practical Guide to SHAP Analysis: Explaining Supervised Machine Learning Model Predictions in Drug Development","volume":"17","author":"Schmitt","year":"2024","journal-title":"Clin. Transl. Sci."}],"updated-by":[{"DOI":"10.3390\/ijms262411856","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000}}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/21\/10522\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T09:34:43Z","timestamp":1765272883000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/21\/10522"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,29]]},"references-count":47,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["ijms262110522"],"URL":"https:\/\/doi.org\/10.3390\/ijms262110522","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,29]]}}}