Targeted metabolomics identifies accurate CSF metabolite biomarkers [differentiating neuro-Covid from neutrotropic viral infections], 2024, Neu+

SNT Gatchaman

Senior Member (Voting Rights)
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Targeted metabolomics identifies accurate CSF metabolite biomarkers for the differentiation between COVID-19 with neurological involvement and CNS infections with neurotropic viral pathogens
Neu, Frieder; Nay, Sandra; Schuchardt, Sven; Klawonn, Frank; Skripuletz, Thomas; Suehs, Kurt-Wolfram; Pessler, Frank

COVID-19 is primarily considered a respiratory tract infection, but it can also affect the central nervous system (CNS), which can result in long-term sequelae. 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.

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 = 16, comprising acute (n = 13) and post-COVID-19 (n = 3)], (ii) viral meningitis, encephalitis, or myelitis (n = 10) due to herpes simplex virus (n = 2), varicella zoster virus (n = 6), enterovirus (n = 1) and tick-borne encephalitis virus (n = 1), and (iii) aseptic neuroinflammation (meningitis, encephalitis, or myelitis) of unknown etiology (n = 21) as additional disease controls.

Standard CSF parameters indicated absent or low neuroinflammation in neuro-COVID. Indeed, CSF cell count was low in neuro-COVID (median 1 cell/µL, range 0–12) and discriminated it accurately from viral CNS infections (AUC = 0.99) and aseptic neuroinflammation (AUC = 0.98). 32 CSF metabolites passed quality assessment and were included in the analysis. Concentrations of differentially abundant (fold change ≥|1.5|, FDR ≤ 0.05) 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 = 0.09 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 = 1 and 0.93, respectively. Across all samples, TG(20:1_32:3) concentration correlated only moderately with CSF cell count (ρ = 0.65), protein concentration (ρ = 0.64), and Q-albumin (ρ = 0.48), suggesting that its low levels in neuro-COVID CSF are only partially explained by less pronounced neuroinflammation.

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.

Link | PDF (Journal of Translational Medicine) [Open Access]
 
Looks like very good separation between acute/long COVID and the other groups:
IMG_20240704_074207.jpg
Fig. 1 Cerebrospinal fluid (CSF) metabolite populations differ between COVID‑19 with neurological involvement and non‑COVID encephalitis/meningitis/myelitis. Principal component analysis (PCA) was performed based on 32 metabolites (detailed in Table S2) in the comparison between COVID‑19 and viral central nervous system (CNS) infections [dCtrl (viral)] and clinical encephalitis/meningitis/myelitis without pathogen detection [dCtrl (unknown)], respectively. The y‑axis label of A applies also to B and C. A COVID‑19 vs. dCtrl (viral). B COVID‑19 vs. dCtrl (unknown). C dCtrl (viral) vs. dCtrl (unknown). A PCA comprising all three groups is shown as Figure S

IMG_20240704_075128.jpg
Fig. 4 Concentrations of the six best‑validated metabolite biomarkers for the discrimination between COVID‑19 with neurological involvement and dCtrl (viral). They also include the best five for the discrimination between COVID‑19 and dCtrl (unknown). The red dotted line represents optimal cut‑off values in the ROC curve, indicating the optimal trade‑off between sensitivity and specificity, generated by the Youden index method. The y‑axis labels of A and D also apply to B, C, E, F. A TG (20:1_32:2) [triglyceride]; B Cer(d18:1/18:0) [ceramide]; C Cit [citrulline]; D Met [methionine]; E Taurine; F SDMA [symmetrical dimethylarginine]. Significance of between‑group differences is indicated as **p < 0.01, ***p < 0.001, ****p < 0.0001 (Mann–Whitney U‑test)

Would be nice to see healthy controls included in the future.
 
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