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In contrast to CNS infections by classic neurotropic viruses, SARS-CoV-2 is usually not detected in cerebrospinal fluid (CSF) from patients with COVID-19 with neurological involvement (neuro-COVID), suggesting fundamental differences in pathogenesis.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>To assess differences in CNS metabolism in neuro-COVID compared to CNS infections with classic neurotropic viruses, we applied a targeted metabolomic analysis of 630 metabolites to CSF from patients with (i) COVID-19 with neurological involvement [n\u2009=\u200916, comprising acute (n\u2009=\u200913) and post-COVID-19 (n\u2009=\u20093)], (ii) viral meningitis, encephalitis, or myelitis (n\u2009=\u200910) due to herpes simplex virus (n\u2009=\u20092), varicella zoster virus (n\u2009=\u20096), enterovirus (n\u2009=\u20091) and tick-borne encephalitis virus (n\u2009=\u20091), and (iii) aseptic neuroinflammation (meningitis, encephalitis, or myelitis) of unknown etiology (n\u2009=\u200921) as additional disease controls.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Standard CSF parameters indicated absent or low neuroinflammation in neuro-COVID. Indeed, CSF cell count was low in neuro-COVID (median 1 cell\/\u00b5L, range 0\u201312) and discriminated it accurately from viral CNS infections (AUC\u2009=\u20090.99) and aseptic neuroinflammation (AUC\u2009=\u20090.98). 32 CSF metabolites passed quality assessment and were included in the analysis. Concentrations of differentially abundant (fold change\u2009\u2265|1.5|, FDR\u2009\u2264\u20090.05) metabolites were both higher (9 and 5 metabolites) and lower (2 metabolites) in neuro-COVID than in the other two groups. Concentrations of citrulline, ceramide (d18:1\/18:0), and methionine were most significantly elevated in neuro-COVID. Remarkably, triglyceride TG(20:1_32:3) was much lower (mean fold change\u2009=\u20090.09 and 0.11) in neuro-COVID than in all viral CNS infections and most aseptic neuroinflammation samples, identifying it as highly accurate biomarker with AUC\u2009=\u20091 and 0.93, respectively. Across all samples, TG(20:1_32:3) concentration correlated only moderately with CSF cell count (\u03c1\u2009=\u20090.65), protein concentration (\u03c1\u2009=\u20090.64), and Q-albumin (\u03c1\u2009=\u20090.48), suggesting that its low levels in neuro-COVID CSF are only partially explained by less pronounced neuroinflammation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The results suggest that CNS metabolite responses in neuro-COVID differ fundamentally from viral CNS infections and aseptic neuroinflammation and may be used to discover accurate diagnostic biomarkers in CSF and to gain insights into differences in pathophysiology between neuro-COVID, viral CNS infections and aseptic neuroinflammation.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12967-024-05422-1","type":"journal-article","created":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T16:09:23Z","timestamp":1720022963000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Targeted metabolomics identifies accurate CSF metabolite biomarkers for the differentiation between COVID-19 with neurological involvement and CNS infections with neurotropic viral pathogens"],"prefix":"10.1186","volume":"22","author":[{"given":"Frieder","family":"Neu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandra","family":"Nay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sven","family":"Schuchardt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Klawonn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Skripuletz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kurt-Wolfram","family":"Suehs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3434-2311","authenticated-orcid":false,"given":"Frank","family":"Pessler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"issue":"1","key":"5422_CR1","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s12941-021-00438-7","volume":"20","author":"SI Mallah","year":"2021","unstructured":"Mallah SI, Ghorab OK, Al-Salmi S, Abdellatif OS, Tharmaratnam T, Iskandar MA, et al. 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