Elucidating SARS-CoV-2 Neurotropism: A Comprehensive Mendelian Randomization Study on Cerebrospinal Fluid Biomarkers and their Relevance to COVID-19 Neurological Manifestations

Elucidating SARS-CoV-2 Neurotropism: A Comprehensive Mendelian Randomization Study on Cerebrospinal Fluid Biomarkers and their Relevance to COVID-19 Neurological Manifestations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Elucidating SARS-CoV-2 Neurotropism: A Comprehensive Mendelian Randomization Study on Cerebrospinal Fluid Biomarkers and their Relevance to COVID-19 Neurological Manifestations Ziyan Wu, Honglin Xu, Siyuan Fan, Futai Feng, Zhan Li, Linlin Cheng, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5574959/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Apr, 2025 Read the published version in Virology Journal → Version 1 posted 6 You are reading this latest preprint version Abstract A mendelian randomization (MR) analysis was conducted to investigate whether SARS-CoV-2 invaded the human nervous system. This was confirmed by an increase in biomarkers found in the cerebrospinal fluid (CSF) and plasma of COVID-19 patients. To confirm the neuroinvasive properties of SARS-CoV-2, a series of analyses were conducted utilizing accessible datasets by MR. In addition, external validation was conducted by testing specific proteins in a retrospective cohort study, which included 40 COVID-19 patients with neurological complications and 15 disease controls (DC). Our investigation revealed the hospitalization, severity of COVID-19 increased the area and volume of certain brain regions, but no other significant causal effects were found of brain imaging-derived phenotypes (IDPs) on COVID-19. Notably, the COVID-19 hospitalization significantly increased the area and volume of the left caudal middle frontal gyrus (p_fdr = 0.012; p_fdr = 0.012, respectively). Additionally, COVID-19 severity was linked to the area, volume of the right caudal anterior-cingulate cortex and the volume of the right cuneus cortex (p_fdr = 0.023; p_fdr = 0.025; p_fdr = 0.026, respectively). In the CSF of COVID-19 patients, the median level of CHI3L1 was significantly higher (13677 pg/mL) compared to the DC group (8421 pg/mL, p < 1.00E-04). Similar trends were also found in CSF KLK6 and NGF-β. Additionally, the median NRGN level in plasma was significantly higher in the COVID-19 group (1013.00 pg/mL) compared to the control group (360.00 pg/mL, p = 6.50E-03). A subgroup analysis demonstrated that COVID-19 patients experiencing moderate to critical symptoms exhibited higher levels of GFAP in their CSF compared to those without. Elevated CSF levels of GFAP and S100B were also found in COVID-19 patients with decreased consciousness and comorbidities. This MR analysis provided evidence that SARS-CoV-2 may invade the human nervous system, as indicated by the increased levels of CSF biomarkers CHI3L1, NGF-β, and KLK6 in COVID-19 patients. These findings suggested that neuroinflammation could be a potential mechanism underlying the neurological complications seen in COVID-19 patients. Mendelian randomization COVID-19 neurological complications CSF neuroinflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could lead to coronavirus disease 2019 (COVID-19), which could not only cause respiratory symptoms but could also result in neurological complications[ 1 ]. Approximately 30% of the COVID-19 patients experienced neurologic symptoms, including headache, encephalitis, cerebrovascular disorders, smell or taste impairment, skeletal muscle symptoms[ 2 ]. However, the pathophysiological mechanism behind these COVID-19-related neurological complications remained unclear. To address this issue, we conducted a mendelian randomization (MR) analysis to investigate the potential neuroinvasive capacity of SARS-CoV-2 into the human nervous system. It had been demonstrated in previous research that brain inflammation may be a contributing factor to these neurological symptoms. A number of potential explanations for these manifestations had been put forth, including the direct invasion of SARS-CoV-2 into the brain via retrograde neuronal or the bloodstream, as well as cerebral ischemia and neuronal fusion induced by the virus[ 3 , 4 ]. A considerable number of studies had investigated the distribution of cytokines and other proteins in COVID-19 patients, yet the findings had been inconsistent. In additional, the majority of these studies focused on serum or plasma samples, with a paucity of analysis of conducted on cerebrospinal fluid (CSF) [ 5 – 14 ]. Consequently, we conducted a MR analysis to ascertain whether SARS-CoV-2 invaded the human nervous system. This hypothesis was corroborated by an increase in biomarkers identified in the CSF and plasma of COVID-19 patients during Omicron break in China. Methods Data sources Brain imaging-derived phenotypes (IDPs) data The design of our study was illustrated in Fig. 1 , created by BioGDP.com[ 15 ]. Summary statistics for the genome-wide association study (GWAS) of brain imaging-derived phenotypes (IDPs) were sourced from the Oxford Brain Imaging Genetics (BIG40, https://open.win.ox.ac.uk/ukbiobank/big40/ ), comprising data from 33224 individuals of European ancestry within the UK Biobank [ 16 ]. A total of 587 brain IDPs were retained, as detailed in prior descriptions[ 17 ]. COVID-19 data Summary GWAS statistics for COVID-19 phenotypes (in individuals of European ancestry (EUR)) were retrieved from the COVID-19 Host Genetics Initiative ( https://www.covid19hg.org , Release 7). Our study included three COVID-19 phenotypes: susceptibility, hospitalization, and severity. CSF and plasma protein quantitative trait loci (pQTLs) Data on CSF protein quantitative trait loci (pQTLs) were obtained from a study by Yang et al., which identified several CSF pQTLs [ 18 ]. The plasma pQTLs data were sourced from Zheng et al., who integrated data from five previously published GWAS[ 19 ]. For the primary analysis, multiple testing was controlled by Bonferroni correction with a significance threshold of P < 5.63E-05(0.05/888, 888 representing the number of analyzed pQTLs). For the further validation, a P value threshold of 0.05 was applied. And the plasma pQTLs data retrieved from deCODE Genetics, which generated whole genome sequencing in 49708 Icelanders[ 20 ]. Additional pQTLs for CSF and plasma were obtained from the Online Neurodegenerative Trait Integrative Multi-Omics Explorer (ONTIME, https://ontime.wustl.edu ) for further validation. Only pQTLs meeting the following criteria were included: (i) a genome-wide significant association (p < 5 E-08); (ii) location outside the major histocompatibility complex (MHC) region; (iii) showed independent association [linkage disequilibrium (LD) clumping r 2 < 0.001]. Mendelian randomization analysis Selection of Instrumental variables (IVs) Genetic variants serving as instrumental variables (IVs) were selected according to three principal assumptions: (1) a robust association with the exposure, (2) independence from confounding variables, and (3) influence on the outcome solely via the exposure. IVs were chosen based on genome-wide significance (p < 5E-08) and weak IVs were excluded by calculating the F-statistics (F < 10). To ensure independence, LD testing was performed for each IV (r² 0.8) were utilized. Main methods Multiple complementary methods were applied, including inverse variance-weighted (IVW), MR-Egger, weighted median, simple mode, weighted mode, and Wald ratio methods. The IVW model served as the primary method, while the Wald ratio method was employed when only one SNP was available. Cochran’s Q statistic (p < 0.05 indicating heterogeneity) was used for heterogeneity among IVs. A random-effects model was used in the case of heterogeneity; and a fixed-effects model was used otherwise. Genes exhibiting significant pleiotropy were adjusted, and outlier SNPs were excluded. Leave-one-out tests were performed to evaluate the influence of individual SNPs [ 21 – 25 ]. After applying the false discovery rate (FDR) correction, a significance level of p_fdr < 0.05 was considered statistically significant. Enrollment of patients and disease controls We enrolled 40 COVID-19 patients exhibiting neurological symptoms and 15 disease controls (DC) from Peking Union Medical College Hospital between December 2022 and August 2023. All participants in the external validation were ethnically Chinese and unrelated. Additionally, the data used for MR analysis were derived exclusively from individuals of European ancestry. Importantly, the European GWAS dataset and the Chinese validation cohort were entirely separate populations, ensuring no overlap or interference between the two analyses.COVID-19 diagnoses adhered to the "Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 10)" [ 26 ],with infections confirmed through RT-PCR, serological, or antigen tests. Exclusion criteria included non-neurological COVID-19, cancer, primary neurological diseases, and autoimmune diseases. DCs had cerebral hemorrhage or infarction but no recent SARS-CoV-2 infection. Plasma and cerebrospinal fluid (CSF) samples were collected. However, healthy controls were not included due to the invasive nature of lumbar punctures. The study received approval from the Ethics Committee of Peking Union Medical College Hospital (I-23PJ292), with a waiver for informed consent. Protein Detection in COVID-19 Patients and Disease Controls Protein levels of Tau, pT181 (a phosphorylated form of tau protein), Amyloid beta 1–42, Neurogranin (NRGN), Neurofilament heavy (NF-H), NCAM-1, Kallikrein-6 (KLK6), TDP-43, FGF-21, Glial Fibrillary Acidic Protein (GFAP), UCHL1, S100B, GDNF, CNTF, BDNF, NGF-β, MIG, and CHI3L1 were quantified using the Neuroscience 18-plex Human ProcartaPlex™ Panel (Invitrogen), based on Luminex xMAP technology, following the manufacturer's protocol. Statistical Analysis All statistical analyses were performed using R version 4.3.1 (TwoSampleMR packages), GraphPad Prism version 9, MATLAB R2023a, and Adobe Illustrator 2023. Given the non-normal distribution of the data, results were presented as medians with interquartile ranges (IQR). Differences between the COVID-19 and DC groups were assessed using the Mann-Whitney U-test, with statistical significance set at p < 0.05. Results Bidirectional MR analysis of the effects between IDPs and COVID-19 Figure 2 showed the results of MR analysis investigating the effects of COVID-19 phenotype (susceptibility, hospitalization, and severity) on IDPs. The analysis revealed that COVID-19 hospitalization significantly increased the area and volume of the left caudal middle frontal gyrus (OR = 1.27, 95% CI 1.14–1.42, p_fdr = 0.012; OR = 1.27, 95% CI 1.13–1.42, p_fdr = 0.012, respectively). Additionally, COVID-19 severity was linked to the area, volume of the right caudal anterior-cingulate cortex and the volume of the right cuneus cortex (OR = 1.25, 95% CI 1.12–1.39, p_fdr = 0.023; OR = 1.23, 95% CI 1.11–1.37, p_fdr = 0.025; OR = 1.23, 95% CI 1.10–1.36, p_fdr = 0.026, respectively). No other significant causal effects were found between COVID-19 and IDPs. Screening the proteome for COVID-19 causal proteins Decreased plasma ICAM1 increased the risk of COVID-19 hospitalization (OR = 0.96, 95% CI 0.95–0.98, p = 5.34E-07), COVID-19 severeness (OR = 0.93, 95% CI 0.91–0.95, p = 2.67E-10), respectively. The descend of CSF ICAM1 increased the risk of COVID-19 hospitalization (OR = 0.76, 95% CI 0.68–0.84, p = 5.34E-07), COVID-19 severeness (OR = 0.60, 95% CI 0.51–0.70, p = 2.67E-10), respectively. ONTIME was additional pQTLs for CSF and plasma obtained from the Online Neurodegenerative Trait Integrative Multi-Omics Explorer. The deCODE was plasma pQTLs data retrieved from deCODE Genetics, which generated whole genome sequencing in 49708 Icelanders. Similar causal effects of plasma ICAM1 on COVID-19 hospitalization and severeness had been observed in ONTIME, respectively. Also, similar causal effects of ICAM1 on COVID-19 severeness had been observed in deCODE (Fig. 3 ). In addition, we also extracted the plasma and CSF of available proteins for MR analysis. CHI3L1, kallikrein-6, NGF-β, NRGN and amyloid beta 1–42 were also included as these five proteins were significantly different in our study with detailed results followed. Increased CSF CHI3L1 increased the risk of COVID-19 (OR = 1.01, 95% CI 1.00–1.02, p = 0.034) in ONTIME. And there was causal effect between plasma CHI3L1 and COVID-19 infection (p = 0.042). While no other significant causal effects were depicted. Distribution of special proteins between COVID-19 and DC We analyzed the levels of 18 proteins in CSF and plasma of COVID-19 patients with neurological symptoms, compared to DC. Significant differences were observed in four proteins in the CSF and one in the plasma (Table 1 , Fig. 5). The median level of CHI3L1 in CSF was significantly higher in the COVID-19 (13677 pg/mL, IQR: 11773–16711 pg/mL) compared to DC (8421 pg/mL, IQR: 5960–9510 pg/mL, p < 1.00E-04). Similarly, the median KLK6 level in the CSF was significantly higher in the COVID-19 group (46947 pg/mL, IQR: 39243–57080 pg/mL) than in the DC group (12186 pg/mL, IQR: 12186–12186 pg/mL, p < 1.00E-04). In addition, the median level of NGF-β in CSF was significantly elevated in the COVID-19 (1.20 pg/mL, IQR: 1.32–1.68 pg/mL) compared to the DC (0.42 pg/mL, IQR: 0.00-1.12 pg/mL, p < 1.00E-04). In the plasma, the median NRGN level was also significantly higher in COVID-19 (1013.00 pg/mL, IQR: 243.10–1726.00 pg/mL) than that in the DC (360.00 pg/mL, IQR: 74.94–360.00 pg/mL, p = 6.50E-03). However, the median amyloid beta 1–42 level in CSF was significantly lower in the COVID-19 (43.89 pg/mL, IQR: 18.66–80.75 pg/mL) compared to the DC (83.36 pg/mL, IQR: 31.48–144.80 pg/mL, p = 0.033). Table 1 Detailed information of COVID-19 and DC group Characteristics COVID-19 DC Demographic characteristics Number 40 15 Mean age ± SD 50.70 ± 23.14 33.87 ± 18.52 Male/Female 1.11 2.00 Clinical syndrome of neurological diseases (%) Encephalopathy 18(45.00) -- Guillain-Barre syndrome 5(12.50) -- Myelitis 4(10.00) -- Cerebellar ataxia 3(7.50) -- Encephalitis 2(5.00) -- Epileptic seizures 10(25.00) -- Brainstem encephalitis 2(5.00) -- Acute ischemic stroke 2(5.00) -- Intracranial hypertension 1(2.50) -- Myelopathy 1(2.50) -- Neuropathy 1(2.50) -- Conus medullaris syndrome 1(2.50) -- Clinical manifestation of nervous system (%) Decreased level of consciousness 13(32.50) -- Psychiatric symptoms 8(20.00) -- Epileptic seizures 10(25.00) -- Cognitive decline 6(15.00) -- Ataxia 3(7.50) -- Limb weakness 10(25.00) -- Limb numbness 10(25.00) -- Headache 5(12.50) -- Hyponatremia 6(15.00) -- COVID-19 severity status (%) Mild 27(67.50) -- Moderate 6(15.00) -- Severe 4(10.00) -- Critical 3(7.50) -- Comorbidity (%) Chronic kidney disease 4(10.00) -- Chronic liver disease 3(7.50) -- Coronary heart disease 3(7.5) -- Diabetes 6(15.00) -- Hypertension 12(30.00) -- Lung disease 3(7.5) -- Miocardial infarction 4(10.00) -- Symptoms on admission (%) Fever (temperature ≥ 37.3°C) 32(80.00) -- Cough or Sputum 4(10.00) -- Pharyngalgia 12(30.00) -- Diarrhea 2(5.00) -- Days of onset neurological symptoms after SARS-COV2 infection,median(IQR) 8(3-23.75) -- Laboratory findings on admission,median(IQR) White blood cell count, 10 9 /L 8.91(7.33-12.00) 5.58(5.21–9.75) Total neutrophil count, 10 9 /L 6.30(4.44–9.64) 3.21(2.49–6.02) Total lymphocyte count, 10 9 /L 1.45(0.83–2.47) 1.71(1.48–2.26) Platelets, 10 9 /L 230.50(180.30-282.50) 197.00(160.00-286.00) Hemoglobin, g/L 137.50(121.30–145.00) 146.00(120.00-155.00) Prothrombin time, seconds 12.00(11.43–12.80) 11.70(11.30–12.90) APTT, seconds 26.45(24.20-29.23) 26.90(25.50–30.70) TT, seconds 16.50(15.58–17.18) 16.60(16.10–17.30) Fibrinogen, g/L 3.36(2.73–4.59) 2.66(2.17–4.31) D-dimer, mg/L FEU 0.97(0.31–2.95) 0.15(0.15–0.86) High-sensitivity CRP, mg/L^ 5.60(0.65–35.65) 0.50(0.50–3.26) ESR, mm/h* 15.50(6.00-41.25) 2.00(1.00–11.00) Total cell count in CSF, 10 6 /L 2.50(2.00-65.75) 4.00(1.00-1501.00) White blood cell count in CSF, 10 6 /L 5.50(2.00-5.50) 2.00(0.00–5.00) Mononuclear cell count in CSF, 10 6 /L 1.00(0.00-2.75) 2.00(0.00–5.00) Multiple nuclear cells count in CSF, 10 6 /L 0.00(0.00-1.75) 0.00(0.00–2.00) Glucose in CSF, mmol/L 3.90(3.33–5.23) 3.50(3.30–4.50) Chlorides in CSF, mmol/L 126.00(123.00-129.30) 125.00(124.00-126.00) Protein in CSF, g/L 0.42(0.28–0.93) 0.48(0.41–0.72) IL-6 in CSF, pg/mL & 7.55(2.73-35.00) 3.65(2.18–4.83) IgG synthesis rate # -3.35(-4.85-2.40) -- Abnormal Alb CSF/Alb Serum(%) # 5(26.32) -- Abnormal IgG CSF/IgG Serum (%) @ 16(66.67) -- Positive IgG oligoclonal band in CSF(%) @ 7(29.17) -- Positive IgG oligoclonal band in Serum(%) @ 4(16.67) -- Positive specific IgG oligoclonal band in CSF(%) @ 4(16.67) -- CSF Amyloid beta 1–42 pg/mL 43.89(18.66–80.75) 83.36 (31.48–144.80) CSF CHI3L1 pg/mL 13677 (11773–16711) 8421(5960–9510) CSF KLK6 pg/mL 46947(39243–57080) 12186(12186–12186) CSF NGF-β pg/mL 1.20(1.32–1.68) 0.42(0.00-1.12) Plasma NRGN pg/mL 1013.00(243.10–1726.00) 360.00(74.94–360.00) CSF GFAP in mild COVID-19 pg/mL 2872.35(1308.38-3620.52) 525.66(398.80-1546.02) CSF GFAP in non-mild COVID-19 pg/mL 1064.04(540.20-1805.94) CSF S100B in decreased consciousness COVID-19 pg/mL 7.14(3.10-19.28) 3.46(2.69–6.02) CSF S100B in consciousness COVID-19 pg/mL 3.21(1.90–6.82) CSF NF-H in decreased consciousness COVID-19 pg/mL 75.18(0.00-212.27) 0.00(0.00-377.85) CSF NF-H in consciousness COVID-19 pg/mL 0.00(0.00–0.00) CSF Tau in CNS COVID-19 pg/mL 377.41(135.09-717.61) 143.60(40.27-385.79) CSF Tau in PNS COVID-19 pg/mL 98.18(55.91-130.04) CSF pT181 in COVID-19 with comorbidities pg/mL 9.55(4.34–14.18) 10.56(5.32–16.54) CSF pT181 in COVID-19 without comorbidities pg/mL 5.76(1.19–7.29) Treatments (%) Antiviral therapy 4(10.00) -- Corticosteroids 20(50.00) -- Biological agents 5(12.50) -- Intravenous immunoglobulin 11(27.50) -- Noninvasive mechanical ventilation 13(32.50) -- Invasive mechanical ventilation 7(17.50) -- ECMO 0(0.00) -- *Only 26 COVID-19 patients with ESR, & 20 COVID-19 patients with IL-6 in CSF, # 19 COVID-19 patients with IgG synthesis rate, Alb CSF/Alb Serum, @ 24 COVID-19 patients with IgG CSF/IgG Serum, IgG oligoclonal band in CSF, IgG oligoclonal band in Serum, specific IgG oligoclonal band in CSF were available. ^Only 11 DC with high-sensitivity CRP, *7 DC with ESR, & 6 DC with IL-6 in CSF were available. Subgroup analysis of special proteins between COVID-19 and DC COVID-19 patients in the severe or critical condition group had significantly higher CSF GFAP levels compared to those with milder cases (p = 0.020). Higher GFAP levels were also found in COVID-19 patients who experienced decreased consciousness or had comorbidities, compared to those without these conditions (p = 9.52E-03 and p = 4.74E-03, respectively). Similarly, higher CSF S100B protein levels were found in patients with decreased consciousness or comorbidities (p = 0.021 and p = 0.010, respectively). Patients with decreased consciousness also showed significantly higher levels of NF-H (neurofilament heavy chain) in the CSF compared to those without (p = 7.49E-03). Additionally, COVID-19 patients with comorbidities had higher levels of pT181 in the CSF (p = 0.014). For patients with central nervous system (CNS) involvement, CSF Tau protein levels were significantly elevated compared to those without CNS involvement (p = 1.97E-03). However, the opposite trend was found in patients with weakness or numbness, who showed lower levels of Tau compared to those without these symptoms (p = 0.013 and p = 1.03E-03, respectively). COVID-19 patients with numbness also showed decreased levels of NRGN and NGF-β in the CSF compared to those without numbness (p = 0.015 and p = 0.033, respectively). Discussion We analyzed the distribution of several biomarkers in the CSF and plasma of COVID-19 patients with neurological complications during the Omicron wave in China. Additionally, a bio-informational analysis by MR provided evidence that SARS-CoV-2 could invade the human nervous system. The finding suggested that neuroinflammation could be a key mechanism underlying the neurological complications observed in COVID-19 patients. Our MR results demonstrated a causal relationship between different COVID-19 phenotypes and changes in brain structures. These include alterations in the area and volume of the left caudal middle frontal gyrus, which were associated with cognitive and emotional functions; the area and volume of the right caudal anterior-cingulate cortex, primarily involved in cognitive and emotional processes and the volume of the right cuneus cortex, played a crucial role in visual processing[ 27 ]. A multimodal meta-analysis of neuroimaging studies indicated that COVID-19 can lead to both functional and structural changes in the brain, particularly in areas such as the temporal lobe, orbital frontal cortex, and the cerebellum. There were also alterations noted in the insula and the limbic system, which are regions critical for emotional regulation and memory [ 28 ]. These findings supported previous research that suggests SARS-CoV-2 could cause nerve damage. This was consistent with reports of COVID-19 patients experiencing neurological symptoms such as headaches, impaired consciousness, acute cerebrovascular events, and epilepsy. The findings suggested that SARS-CoV-2 may invade the nervous system through mechanisms such as: disrupting normal CNS function via cytokine storms; inducing cerebral ischemia or hypoxia, leading to neurological complications. The special proteins could be divided into two categories, one group was the biomarkers of neuroinflammatory or neuro-injury, including CHI3L1,NGF-β,KLK6,NRGN,GFAP,S100B and NF-H; the other group was those of neurodegenerative, for instance: Amyloid beta 1–42, Tau. CHI3L1 was a glycoprotein involved in inflammation, endothelial dysfunction, and tissue remodeling. Within the CNS, CHI3L1 was linked to neuroinflammatory processes and reactive gliosis. CHI3L1 levels were found to be higher in COVID-19 patients compared to controls[ 6 , 8 ]. Additionally, elevated serum CHI3L1 level have been associated with more severe cases of COVID-19 [ 10 , 11 ]. CHI3L1 played a role in the pathogenesis of COVID-19 by increasing the expression of angiotensin-converting enzyme 2 (ACE2), which facilitated viral entry, and the viral spike protein in pulmonary and vascular cells[ 29 ]. However, previous studies primarily focused on serum CHI3L1 in COVID-19 patients, with no research specifically addressing its role in the CSF of COVID-19 patients with neurological complications[ 5 – 14 ]. In our study, we found that CHI3L1 levels in the CSF were significantly higher in COVID-19 patients compared to disease controls (DC). There was also a trend suggesting that CHI3L1 levels were higher in COVID-19 patients with various clinical syndromes, though these differences were not statistically significant. We also observed elevated levels of NGF-β and KLK6 in the CSF of COVID-19 patients. NGF-β was a neuropeptide that contributes to neurogenic inflammation by activating immune cells through proinflammatory cytokines. It had been shown to be effective in inducing both pain and neuroinflammation[ 30 ]. KLK6 was a serine protease that was abundantly expressed in the brain and spinal cord, and its expression in CNS diseases was varied[ 31 ]. NRGN was a regulatory molecule found in the brain, specifically in dendritic spines, where it played a role in synaptic plasticity by linking protein kinase C and calcium/calmodulin signaling pathways[ 32 ]. In our study, NRGN levels were elevated in the plasma of COVID-19 patients compared to DCs. GFAP is a protein released during microglial dysregulation and is involved in tissue healing processes during neuroinflammation and neurodegeneration. Recent studies have suggested that serum GFAP may be a potential biomarker for various neurological complications, particularly during the acute phase of COVID-19 when systemic inflammation was at its peak[ 13 , 33 ]. Our findings supported this, as we observed higher CSF GFAP levels in COVID-19 patients with moderate to severe disease, as well as in those with decreased consciousness or comorbidities. Similarly, S100B was a well-known biomarker used to assess brain damage and blood-brain barrier integrity. It was commonly employed for risk assessment in cases of mild brain injury[ 34 ]. Our study found higher CSF S100B levels in COVID-19 patients with decreased consciousness and comorbidities compared to those without these conditions. Neurofilaments, including NF-H (the heaviest neurofilament), are structural proteins in neurons, and their levels are often elevated in cases of neurodegeneration or injury[ 35 ]. Amyloid beta 1–42, a key protein in the development of Alzheimer's disease, played a critical role in the pathogenesis of the disease. It had been found to bind to various viral proteins, particularly the spike protein of SARS-CoV-2 and the ACE2 receptor. In our study, we observed that the median level of amyloid beta 1–42 in the CSF was significantly lower in COVID-19 patients, consistent with previous research findings[ 36 ]. However, our study had several limitations. First, while we applied multiple robust MR methods to assess different types of pleiotropic effects, we could not fully eliminate the influence of potential confounding factors on the MR results. Second, our analysis relied solely on GWAS of European ancestry populations. Despite the large sample size of this dataset, caution was required when extrapolating these findings to other ethnic groups. Third, our MR analysis did not account for certain factors that influenced COVID-19 hospitalization and severity, such as differences in healthcare systems, treatment protocols, or socioeconomic conditions across countries. Fourth, validation of our findings was limited to a single hospital cohort of 40 COVID-19 patients, which lacked follow-up data and broader demographic diversity. Further large-scale studies with multiethnic cohorts and longitudinal designs were needed to confirm and expand upon these results. Additionally, aside from direct CNS invasion by SARS-CoV-2, research had also shown that brain inflammation and cytokine storms may contribute to neurological symptoms[ 1 ]. The precise mechanisms linking SARS-CoV-2 infection to neurological complications remained unclear. Future experimental studies were necessary to elucidate these pathways. Conclusions In conclusion, MR analysis provided evidence that SARS-CoV-2 may invade the human nervous system. This was validated through the analysis of several biomarkers in the CSF and plasma of COVID-19 patients with neurological complications during the Omicron wave in China. Our findings suggest that neuroinflammation may be a key mechanism underlying these neurological complications. Declarations These authors made equal contributions to this study. Ethics approval and consent to participate: The study received approval from the Ethics Committee of Peking Union Medical College Hospital (I-23PJ292), with a waiver for informed consent. Consent for publication: Not applicable. Availability of data and materials : All data were available from the corresponding author on request. Competing interests : The authors declare no conflicts of interest. Funding: This research was supported by grants from, the National High Level Hospital Clinical Research Funding(2022-PUMCH-B-124,2022-PUMCH-A-007), the National Natural Science Foundation of China Grants (82472348), the National Key Research and Development Program of China (2018YFE0207300), Beijing Natural Science Foundation (M23008), the CAMS Innovation Fund for Medical Sciences (CIFMS: 2021-I2M-C&T-A-002). Authors' contributions s: All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. ZYW and HLX wrote the main manuscript text, SLZ and YZL reviewed the manuscript. Professor. Yongzhe Li had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. References López Lloreda C. COVID's toll on the brain: new clues emerge. Nature. 2024;628:20. Pezzini A, Padovani A. 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Kimura Y, Nakai Y, Shin J, Hara M, Takeda Y, Kubo S, Jeremiah SS, Ino Y, Akiyama T, Moriyama K, et al. Identification of serum prognostic biomarkers of severe COVID-19 using a quantitative proteomic approach. Sci Rep. 2021;11:20638. Abdelhak A, Barba L, Romoli M, Benkert P, Conversi F, D'Anna L, Masvekar RR, Bielekova B, Prudencio M, Petrucelli L, et al. Prognostic performance of blood neurofilament light chain protein in hospitalized COVID-19 patients without major central nervous system manifestations: an individual participant data meta-analysis. J Neurol. 2023;270:3315–28. Domingues KZA, Cobre AF, Lazo REL, Amaral LS, Ferreira LM, Tonin FS, Pontarolo R. Systematic review and evidence gap mapping of biomarkers associated with neurological manifestations in patients with COVID-19. J Neurol. 2024;271:1–23. Almulla AF, Thipakorn Y, Zhou B, Vojdani A, Maes M. Immune activation and immune-associated neurotoxicity in Long-COVID: A systematic review and meta-analysis of 103 studies comprising 58 cytokines/chemokines/growth factors. Brain Behav Immun. 2024;122:75–94. Jiang S, Li H, Zhang L, Mu W, Zhang Y, Chen T, Wu J, Tang H, Zheng S, Liu Y et al. Generic Diagramming Platform (GDP): a comprehensive database of high-quality biomedical graphics. Nucleic Acids Res 2024. Smith SM, Douaud G, Chen W, Hanayik T, Alfaro-Almagro F, Sharp K, Elliott LT. An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat Neurosci. 2021;24:737–45. Guo J, Yu K, Dong S-S, Yao S, Rong Y, Wu H, Zhang K, Jiang F, Chen Y-X, Guo Y, Yang T-L. Mendelian randomization analyses support causal relationships between brain imaging-derived phenotypes and risk of psychiatric disorders. Nat Neurosci. 2022;25:1519–27. Yang C, Farias FHG, Ibanez L, Suhy A, Sadler B, Fernandez MV, Wang F, Bradley JL, Eiffert B, Bahena JA, et al. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat Neurosci. 2021;24:1302–12. Zheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, Gutteridge A, Erola P, Liu Y, Luo S, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet. 2020;52:1122–31. Ferkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, Gunnarsdottir K, Helgason A, Oddsson A, Halldorsson BV, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53:1712–21. Sleiman PM, Grant SF. Mendelian randomization in the era of genomewide association studies. Clin Chem. 2010;56:723–8. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40:304–14. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8. Diagnosis. and treatment protocol of novel coronavirus (version 10),Available at: http://www.nhc.gov.cn/xcs/zhengcwj/202301/bdc1ff75feb94934ae1dade176d30936.shtml . 2023. Amunts K, Zilles K. Architectonic Mapping of the Human Brain beyond Brodmann. Neuron. 2015;88:1086–107. Guo Z, Sun S, Xiao S, Chen G, Chen P, Yang Z, Tang X, Huang L, Wang Y. COVID-19 is associated with changes in brain function and structure: A multimodal meta-analysis of neuroimaging studies. Neurosci Biobehav Rev. 2024;164:105792. Kamle S, Ma B, Lee CM, Schor G, Zhou Y, Lee CG, Elias JA. Host chitinase 3-like-1 is a universal therapeutic target for SARS-CoV-2 viral variants in COVID-19. Elife 2022, 11. Petrella C, Nenna R, Petrarca L, Tarani F, Paparella R, Mancino E, Di Mattia G, Conti MG, Matera L, Bonci E et al. Serum NGF and BDNF in Long-COVID-19 Adolescents: A Pilot Study. Diagnostics (Basel) 2022, 12. Yoon H, Triplet EM, Simon WL, Choi CI, Kleppe LS, De Vita E, Miller AK, Scarisbrick IA. Blocking Kallikrein 6 promotes developmental myelination. Glia. 2022;70:430–50. Peacock WF, Kurek K, Pruc M, Rafique Z, Szarpak L. Neurogranin as a biomarker in differentiating traumatic brain injury: A systematic review and meta-analysis. Am J Emerg Med 2024. Huang Z, Haile K, Gedefaw L, Lau BW, Jin L, Yip SP, Huang CL. Blood Biomarkers as Prognostic Indicators for Neurological Injury in COVID-19 Patients: A Systematic Review and Meta-Analysis. Int J Mol Sci 2023, 24. Niedziela N, Nowak-Kiczmer M, Malciene L, Stasiołek M, Niedziela JT, Czuba ZP, Lis M, Sowa A, Adamczyk-Sowa M. Serum Vitamin D3 as a Potential Biomarker for Neuronal Damage in Smoldering Multiple Sclerosis. Int J Mol Sci 2024, 25. Heiskanen M, Banuelos I, Manninen E, Andrade P, Hämäläinen E, Puhakka N, Pitkänen A. Plasma neurofilament heavy chain is a prognostic biomarker for the development of severe epilepsy after experimental traumatic brain injury. Epilepsia 2024. Ziff OJ, Ashton NJ, Mehta PR, Brown R, Athauda D, Heaney J, Heslegrave AJ, Benedet AL, Blennow K, Checkley AM, et al. Amyloid processing in COVID-19-associated neurological syndromes. J Neurochem. 2022;161:146–57. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Apr, 2025 Read the published version in Virology Journal → Version 1 posted Editorial decision: Accepted 21 Apr, 2025 Reviews received at journal 21 Apr, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Submission checks completed at journal 31 Mar, 2025 First submitted to journal 28 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5574959","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436832321,"identity":"35417bd6-700c-48c2-9072-593533bfdfa4","order_by":0,"name":"Ziyan Wu","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ziyan","middleName":"","lastName":"Wu","suffix":""},{"id":436832322,"identity":"52001eda-8fc4-4d60-bc40-81405c43b0d6","order_by":1,"name":"Honglin Xu","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Honglin","middleName":"","lastName":"Xu","suffix":""},{"id":436832323,"identity":"911aa788-13e4-4f78-8d18-7ae5c8f0abca","order_by":2,"name":"Siyuan Fan","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Siyuan","middleName":"","lastName":"Fan","suffix":""},{"id":436832324,"identity":"3bf846fc-d2cf-405e-a85e-5ea91ceb0024","order_by":3,"name":"Futai Feng","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Futai","middleName":"","lastName":"Feng","suffix":""},{"id":436832325,"identity":"afb11f04-363c-44fe-afcc-0f715c6cf6c1","order_by":4,"name":"Zhan Li","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhan","middleName":"","lastName":"Li","suffix":""},{"id":436832326,"identity":"186ff0a9-77bd-4ffc-b069-3dfcf313b864","order_by":5,"name":"Linlin Cheng","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Cheng","suffix":""},{"id":436832327,"identity":"0df45746-1f6b-405a-b6aa-34ff51efe8f1","order_by":6,"name":"Haolong Li","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haolong","middleName":"","lastName":"Li","suffix":""},{"id":436832328,"identity":"8aefbf20-33d1-4d17-89c2-6c01050ebb21","order_by":7,"name":"Yongmei Liu","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yongmei","middleName":"","lastName":"Liu","suffix":""},{"id":436832329,"identity":"df1de131-74de-4b0d-b317-62925dcc49b2","order_by":8,"name":"Haoting Zhan","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haoting","middleName":"","lastName":"Zhan","suffix":""},{"id":436832330,"identity":"73ad3345-c513-4ea6-ab69-dd1051cd7144","order_by":9,"name":"Xinxin Feng","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinxin","middleName":"","lastName":"Feng","suffix":""},{"id":436832331,"identity":"e975e165-124c-46fc-a6cf-4b9aa44fa88a","order_by":10,"name":"Siyu Wang","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Wang","suffix":""},{"id":436832332,"identity":"d49fdccb-4edb-4e8a-9ee4-13fb89b0e43f","order_by":11,"name":"Shulan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACCQbGAxAWD+ODhAoborQwwLQwGzw4k0aaFjbJh22HCOuQn91jcODnjjuJa9vPHqtIYDvAwN/enYBXi8GdMwYHe888S9x2Ji/tRgLPHQaJM2c34NcikWNwgLftcOK2GzxmNxIkngFFcvFrkZ+RY3DwL1RLQYLBYcJaGG7kGByG2cKQkECEFoMbaQWHZdueGW87k2MskXAgjYegX+RnJG98+Lbtjuy242cMP/78ZyPH395LwGEQcADO4iFGOaqWUTAKRsEoGAUYAABeX1RVfcG24AAAAABJRU5ErkJggg==","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":true,"prefix":"","firstName":"Shulan","middleName":"","lastName":"Zhang","suffix":""},{"id":436832333,"identity":"083d0c0d-20c2-44e4-b710-c15b67425c2f","order_by":12,"name":"Yongzhe Li","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yongzhe","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-12-03 20:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5574959/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5574959/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12985-025-02754-2","type":"published","date":"2025-04-28T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79803744,"identity":"43cb994a-97d3-4a5a-92b3-4c13aa9b6652","added_by":"auto","created_at":"2025-04-03 04:52:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":177879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe overview of our study.\u003c/strong\u003e The identification of casual IDPs and proteins in COVID-19 by mendelian randomization analysis. The validation of biomarkers in in the CSF and plasma of COVID-19 patients.\u003c/p\u003e","description":"","filename":"floatimage124.png","url":"https://assets-eu.researchsquare.com/files/rs-5574959/v1/5ca749d36cbbcc48c500dc8e.png"},{"id":79804237,"identity":"d3daa2b3-a7d3-42ac-b22f-561939560b63","added_by":"auto","created_at":"2025-04-03 05:00:19","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":234570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe results of MR analysis about the effects of COVID-19 phenotype (hospitalization, severity) on IDPs.\u003c/strong\u003e The COVID-19 hospitalization significantly increased the area and volume of the left caudal middle frontal gyrus (OR = 1.27, 95% CI 1.14–1.42, p_fdr= 0.012; OR = 1.27, 95% CI 1.13–1.42, p_fdr= 0.012, respectively). Additionally, COVID-19 severity was linked to the area, volume of the right caudal anterior-cingulate cortex and the volume of the right cuneus cortex (OR = 1.25, 95% CI 1.12–1.39, p_fdr= 0.023; OR = 1.23, 95% CI 1.11–1.37, p_fdr= 0.025; OR = 1.23, 95% CI 1.10–1.36, p_fdr= 0.026, respectively).\u003c/p\u003e","description":"","filename":"floatimage212.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5574959/v1/0af25eeb13b97617daeb5f75.jpeg"},{"id":79804591,"identity":"50e457ba-820f-4cec-b9f5-3fd0d74e54d3","added_by":"auto","created_at":"2025-04-03 05:08:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":487599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR results for plasma and CSF proteins and the risk of COVID-19 phenotype. \u003c/strong\u003eVolcano plots of the MR results for the effects of plasma and CSF proteins on (a) COVID-19\u003cstrong\u003e \u003c/strong\u003ehospitalization and (b) COVID-19severity. Dashed horizontal black line corresponded to \u003cem\u003eP \u003c/em\u003e= 5.63 E-05 (0.05/888). PVE = proportion of variance explained. (c): forest plot of the external validation of the causal relationship between CHI3L1, ICAM-1 and COVID-19 phenotype (susceptibility, hospitalization, and severity).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5574959/v1/ecf3a47ccb764c8e3842be76.png"},{"id":79803751,"identity":"be6d0be4-bd5d-4dbb-bfbc-27db098487c9","added_by":"auto","created_at":"2025-04-03 04:52:19","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":705540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distribution of special proteins between COVID-19 and DC. \u003c/strong\u003e(a): the median amyloid beta 1-42 level in CSF was significantly lower in the COVID-19 (43.89 pg/mL) compared to the DC (83.36 pg/mL, \u003cem\u003ep\u003c/em\u003e=0.033). (b): The median level of CHI3L1 in CSF was significantly higher in the COVID-19 (13677 pg/mL) compared to DC (8421 pg/mL, p\u0026lt;1.00E-04). (c): the median KLK6 level in the CSF was significantly higher in the COVID-19 group (46947 pg/mL) than in the DC group (12186 pg/mL, p\u0026lt;1.00E-04). (d):the median level of NGF-β in CSF was significantly elevated in the COVID-19 (1.20 pg/mL) compared to the DC (0.42 pg/mL, p\u0026lt;1.00E-04). (e):\u003cstrong\u003e \u003c/strong\u003eCOVID-19 patients in the severe or critical condition group had significantly higher CSF GFAP levels compared to those with milder cases (p = 0.020). (f): COVID-19 patients with comorbidities had higher levels of pT181 in the CSF (p = 0.014). (g): higher CSF S100B protein levels were found in patients with decreased consciousness (p = 0.021). (h): for patients with central nervous system (CNS) involvement, CSF Tau protein levels were significantly elevated compared to those without peripheral nervous system (PNS) involvement (p = 1.97E-03). * p:0.01-0.05; ** p:0.001-0.01; ***p: 1.00E-04-0.001, ****p\u0026lt;1.00E-04.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5574959/v1/996d7b548c83f4cedd39de04.jpeg"},{"id":81988040,"identity":"2a9e8cf0-947b-4d60-9df8-acc91aa58b9b","added_by":"auto","created_at":"2025-05-05 16:07:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2560036,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5574959/v1/062776bb-115a-4c76-b31f-71b2bb730dc2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elucidating SARS-CoV-2 Neurotropism: A Comprehensive Mendelian Randomization Study on Cerebrospinal Fluid Biomarkers and their Relevance to COVID-19 Neurological Manifestations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could lead to coronavirus disease 2019 (COVID-19), which could not only cause respiratory symptoms but could also result in neurological complications[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Approximately 30% of the COVID-19 patients experienced neurologic symptoms, including headache, encephalitis, cerebrovascular disorders, smell or taste impairment, skeletal muscle symptoms[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the pathophysiological mechanism behind these COVID-19-related neurological complications remained unclear.\u003c/p\u003e \u003cp\u003eTo address this issue, we conducted a mendelian randomization (MR) analysis to investigate the potential neuroinvasive capacity of SARS-CoV-2 into the human nervous system. It had been demonstrated in previous research that brain inflammation may be a contributing factor to these neurological symptoms. A number of potential explanations for these manifestations had been put forth, including the direct invasion of SARS-CoV-2 into the brain via retrograde neuronal or the bloodstream, as well as cerebral ischemia and neuronal fusion induced by the virus[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA considerable number of studies had investigated the distribution of cytokines and other proteins in COVID-19 patients, yet the findings had been inconsistent. In additional, the majority of these studies focused on serum or plasma samples, with a paucity of analysis of conducted on cerebrospinal fluid (CSF) [\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Consequently, we conducted a MR analysis to ascertain whether SARS-CoV-2 invaded the human nervous system. This hypothesis was corroborated by an increase in biomarkers identified in the CSF and plasma of COVID-19 patients during Omicron break in China.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eBrain imaging-derived phenotypes (IDPs) data\u003c/h2\u003e \u003cp\u003eThe design of our study was illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, created by BioGDP.com[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Summary statistics for the genome-wide association study (GWAS) of brain imaging-derived phenotypes (IDPs) were sourced from the Oxford Brain Imaging Genetics (BIG40, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://open.win.ox.ac.uk/ukbiobank/big40/\u003c/span\u003e\u003cspan address=\"https://open.win.ox.ac.uk/ukbiobank/big40/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), comprising data from 33224 individuals of European ancestry within the UK Biobank [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A total of 587 brain IDPs were retained, as detailed in prior descriptions[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eCOVID-19 data\u003c/h3\u003e\n\u003cp\u003eSummary GWAS statistics for COVID-19 phenotypes (in individuals of European ancestry (EUR)) were retrieved from the COVID-19 Host Genetics Initiative (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.covid19hg.org\u003c/span\u003e\u003cspan address=\"https://www.covid19hg.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Release 7). Our study included three COVID-19 phenotypes: susceptibility, hospitalization, and severity.\u003c/p\u003e\n\u003ch3\u003eCSF and plasma protein quantitative trait loci (pQTLs)\u003c/h3\u003e\n\u003cp\u003eData on CSF protein quantitative trait loci (pQTLs) were obtained from a study by Yang et al., which identified several CSF pQTLs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The plasma pQTLs data were sourced from Zheng et al., who integrated data from five previously published GWAS[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For the primary analysis, multiple testing was controlled by Bonferroni correction with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5.63E-05(0.05/888, 888 representing the number of analyzed pQTLs). For the further validation, a P value threshold of 0.05 was applied. And the plasma pQTLs data retrieved from deCODE Genetics, which generated whole genome sequencing in 49708 Icelanders[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additional pQTLs for CSF and plasma were obtained from the Online Neurodegenerative Trait Integrative Multi-Omics Explorer (ONTIME, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ontime.wustl.edu\u003c/span\u003e\u003cspan address=\"https://ontime.wustl.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for further validation. Only pQTLs meeting the following criteria were included: (i) a genome-wide significant association (p\u0026thinsp;\u0026lt;\u0026thinsp;5 E-08); (ii) location outside the major histocompatibility complex (MHC) region; (iii) showed independent association [linkage disequilibrium (LD) clumping r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001].\u003c/p\u003e\n\u003ch3\u003eMendelian randomization analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSelection of Instrumental variables (IVs)\u003c/h2\u003e \u003cp\u003eGenetic variants serving as instrumental variables (IVs) were selected according to three principal assumptions: (1) a robust association with the exposure, (2) independence from confounding variables, and (3) influence on the outcome solely via the exposure. IVs were chosen based on genome-wide significance (p\u0026thinsp;\u0026lt;\u0026thinsp;5E-08) and weak IVs were excluded by calculating the F-statistics (F\u0026thinsp;\u0026lt;\u0026thinsp;10). To ensure independence, LD testing was performed for each IV (r\u0026sup2; \u0026lt; 0.001) using European ancestry samples from the 1000 Genomes Project. In cases, proxy single nucleotide polymorphisms (SNPs) with high LD (r\u0026sup2; \u0026gt; 0.8) were utilized.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMain methods\u003c/h3\u003e\n\u003cp\u003eMultiple complementary methods were applied, including inverse variance-weighted (IVW), MR-Egger, weighted median, simple mode, weighted mode, and Wald ratio methods. The IVW model served as the primary method, while the Wald ratio method was employed when only one SNP was available. Cochran\u0026rsquo;s Q statistic (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating heterogeneity) was used for heterogeneity among IVs. A random-effects model was used in the case of heterogeneity; and a fixed-effects model was used otherwise. Genes exhibiting significant pleiotropy were adjusted, and outlier SNPs were excluded. Leave-one-out tests were performed to evaluate the influence of individual SNPs [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. After applying the false discovery rate (FDR) correction, a significance level of p_fdr\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003ch3\u003eEnrollment of patients and disease controls\u003c/h3\u003e\n\u003cp\u003eWe enrolled 40 COVID-19 patients exhibiting neurological symptoms and 15 disease controls (DC) from Peking Union Medical College Hospital between December 2022 and August 2023. All participants in the external validation were ethnically Chinese and unrelated. Additionally, the data used for MR analysis were derived exclusively from individuals of European ancestry. Importantly, the European GWAS dataset and the Chinese validation cohort were entirely separate populations, ensuring no overlap or interference between the two analyses.COVID-19 diagnoses adhered to the \"Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 10)\" [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e],with infections confirmed through RT-PCR, serological, or antigen tests. Exclusion criteria included non-neurological COVID-19, cancer, primary neurological diseases, and autoimmune diseases. DCs had cerebral hemorrhage or infarction but no recent SARS-CoV-2 infection. Plasma and cerebrospinal fluid (CSF) samples were collected. However, healthy controls were not included due to the invasive nature of lumbar punctures. The study received approval from the Ethics Committee of Peking Union Medical College Hospital (I-23PJ292), with a waiver for informed consent.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eProtein Detection in COVID-19 Patients and Disease Controls\u003c/h2\u003e \u003cp\u003eProtein levels of Tau, pT181 (a phosphorylated form of tau protein), Amyloid beta 1\u0026ndash;42, Neurogranin (NRGN), Neurofilament heavy (NF-H), NCAM-1, Kallikrein-6 (KLK6), TDP-43, FGF-21, Glial Fibrillary Acidic Protein (GFAP), UCHL1, S100B, GDNF, CNTF, BDNF, NGF-β, MIG, and CHI3L1 were quantified using the Neuroscience 18-plex Human ProcartaPlex\u0026trade; Panel (Invitrogen), based on Luminex xMAP technology, following the manufacturer's protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R version 4.3.1 (TwoSampleMR packages), GraphPad Prism version 9, MATLAB R2023a, and Adobe Illustrator 2023. Given the non-normal distribution of the data, results were presented as medians with interquartile ranges (IQR). Differences between the COVID-19 and DC groups were assessed using the Mann-Whitney U-test, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBidirectional MR analysis of the effects between IDPs and COVID-19\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the results of MR analysis investigating the effects of COVID-19 phenotype (susceptibility, hospitalization, and severity) on IDPs. The analysis revealed that COVID-19 hospitalization significantly increased the area and volume of the left caudal middle frontal gyrus (OR\u0026thinsp;=\u0026thinsp;1.27, 95% CI 1.14\u0026ndash;1.42, p_fdr\u0026thinsp;=\u0026thinsp;0.012; OR\u0026thinsp;=\u0026thinsp;1.27, 95% CI 1.13\u0026ndash;1.42, p_fdr\u0026thinsp;=\u0026thinsp;0.012, respectively). Additionally, COVID-19 severity was linked to the area, volume of the right caudal anterior-cingulate cortex and the volume of the right cuneus cortex (OR\u0026thinsp;=\u0026thinsp;1.25, 95% CI 1.12\u0026ndash;1.39, p_fdr\u0026thinsp;=\u0026thinsp;0.023; OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI 1.11\u0026ndash;1.37, p_fdr\u0026thinsp;=\u0026thinsp;0.025; OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI 1.10\u0026ndash;1.36, p_fdr\u0026thinsp;=\u0026thinsp;0.026, respectively). No other significant causal effects were found between COVID-19 and IDPs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eScreening the proteome for COVID-19 causal proteins\u003c/h2\u003e \u003cp\u003eDecreased plasma ICAM1 increased the risk of COVID-19 hospitalization (OR\u0026thinsp;=\u0026thinsp;0.96, 95% CI 0.95\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;5.34E-07), COVID-19 severeness (OR\u0026thinsp;=\u0026thinsp;0.93, 95% CI 0.91\u0026ndash;0.95, p\u0026thinsp;=\u0026thinsp;2.67E-10), respectively. The descend of CSF ICAM1 increased the risk of COVID-19 hospitalization (OR\u0026thinsp;=\u0026thinsp;0.76, 95% CI 0.68\u0026ndash;0.84, p\u0026thinsp;=\u0026thinsp;5.34E-07), COVID-19 severeness (OR\u0026thinsp;=\u0026thinsp;0.60, 95% CI 0.51\u0026ndash;0.70, p\u0026thinsp;=\u0026thinsp;2.67E-10), respectively. ONTIME was additional pQTLs for CSF and plasma obtained from the Online Neurodegenerative Trait Integrative Multi-Omics Explorer. The deCODE was plasma pQTLs data retrieved from deCODE Genetics, which generated whole genome sequencing in 49708 Icelanders. Similar causal effects of plasma ICAM1 on COVID-19 hospitalization and severeness had been observed in ONTIME, respectively. Also, similar causal effects of ICAM1 on COVID-19 severeness had been observed in deCODE (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, we also extracted the plasma and CSF of available proteins for MR analysis. CHI3L1, kallikrein-6, NGF-β, NRGN and amyloid beta 1\u0026ndash;42 were also included as these five proteins were significantly different in our study with detailed results followed. Increased CSF CHI3L1 increased the risk of COVID-19 (OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI 1.00\u0026ndash;1.02, p\u0026thinsp;=\u0026thinsp;0.034) in ONTIME. And there was causal effect between plasma CHI3L1 and COVID-19 infection (p\u0026thinsp;=\u0026thinsp;0.042). While no other significant causal effects were depicted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of special proteins between COVID-19 and DC\u003c/h2\u003e \u003cp\u003eWe analyzed the levels of 18 proteins in CSF and plasma of COVID-19 patients with neurological symptoms, compared to DC. Significant differences were observed in four proteins in the CSF and one in the plasma (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;5). The median level of CHI3L1 in CSF was significantly higher in the COVID-19 (13677 pg/mL, IQR: 11773\u0026ndash;16711 pg/mL) compared to DC (8421 pg/mL, IQR: 5960\u0026ndash;9510 pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;1.00E-04). Similarly, the median KLK6 level in the CSF was significantly higher in the COVID-19 group (46947 pg/mL, IQR: 39243\u0026ndash;57080 pg/mL) than in the DC group (12186 pg/mL, IQR: 12186\u0026ndash;12186 pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;1.00E-04). In addition, the median level of NGF-β in CSF was significantly elevated in the COVID-19 (1.20 pg/mL, IQR: 1.32\u0026ndash;1.68 pg/mL) compared to the DC (0.42 pg/mL, IQR: 0.00-1.12 pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;1.00E-04). In the plasma, the median NRGN level was also significantly higher in COVID-19 (1013.00 pg/mL, IQR: 243.10\u0026ndash;1726.00 pg/mL) than that in the DC (360.00 pg/mL, IQR: 74.94\u0026ndash;360.00 pg/mL, p\u0026thinsp;=\u0026thinsp;6.50E-03). However, the median amyloid beta 1\u0026ndash;42 level in CSF was significantly lower in the COVID-19 (43.89 pg/mL, IQR: 18.66\u0026ndash;80.75 pg/mL) compared to the DC (83.36 pg/mL, IQR: 31.48\u0026ndash;144.80 pg/mL, p\u0026thinsp;=\u0026thinsp;0.033).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed information of COVID-19 and DC group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.70\u0026thinsp;\u0026plusmn;\u0026thinsp;23.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.87\u0026thinsp;\u0026plusmn;\u0026thinsp;18.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale/Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical syndrome of neurological diseases (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEncephalopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuillain-Barre syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(12.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyelitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebellar ataxia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEncephalitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpileptic seizures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrainstem encephalitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute ischemic stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntracranial hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyelopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConus medullaris syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical manifestation of nervous system (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecreased level of consciousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(32.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychiatric symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpileptic seizures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive decline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtaxia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimb weakness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimb numbness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(12.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyponatremia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19 severity status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(67.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptoms on admission (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever (temperature\u0026thinsp;\u0026ge;\u0026thinsp;37.3\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(80.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough or Sputum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePharyngalgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiarrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays of onset neurological symptoms after SARS-COV2 infection,median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(3-23.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory findings on admission,median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.91(7.33-12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.58(5.21\u0026ndash;9.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal neutrophil count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.30(4.44\u0026ndash;9.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.21(2.49\u0026ndash;6.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lymphocyte count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45(0.83\u0026ndash;2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.71(1.48\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230.50(180.30-282.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197.00(160.00-286.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.50(121.30\u0026ndash;145.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146.00(120.00-155.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProthrombin time, seconds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.00(11.43\u0026ndash;12.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.70(11.30\u0026ndash;12.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT, seconds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.45(24.20-29.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.90(25.50\u0026ndash;30.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT, seconds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.50(15.58\u0026ndash;17.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.60(16.10\u0026ndash;17.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrinogen, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.36(2.73\u0026ndash;4.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.66(2.17\u0026ndash;4.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer, mg/L FEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97(0.31\u0026ndash;2.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15(0.15\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-sensitivity CRP, mg/L^\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.60(0.65\u0026ndash;35.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50(0.50\u0026ndash;3.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR, mm/h*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.50(6.00-41.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00(1.00\u0026ndash;11.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cell count in CSF, 10\u003csup\u003e6\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.50(2.00-65.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00(1.00-1501.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count in CSF, 10\u003csup\u003e6\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.50(2.00-5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00(0.00\u0026ndash;5.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMononuclear cell count in CSF, 10\u003csup\u003e6\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00(0.00-2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00(0.00\u0026ndash;5.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple nuclear cells count in CSF, 10\u003csup\u003e6\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00(0.00-1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.00\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose in CSF, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.90(3.33\u0026ndash;5.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.50(3.30\u0026ndash;4.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorides in CSF, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.00(123.00-129.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.00(124.00-126.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein in CSF, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42(0.28\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48(0.41\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 in CSF, pg/mL\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.55(2.73-35.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65(2.18\u0026ndash;4.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgG synthesis rate\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.35(-4.85-2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal Alb CSF/Alb Serum(%)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(26.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal IgG CSF/IgG Serum (%)\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive IgG oligoclonal band in CSF(%)\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(29.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive IgG oligoclonal band in Serum(%)\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive specific IgG oligoclonal band in CSF(%)\u003csup\u003e@\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF Amyloid beta 1\u0026ndash;42 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.89(18.66\u0026ndash;80.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.36 (31.48\u0026ndash;144.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF CHI3L1 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13677 (11773\u0026ndash;16711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8421(5960\u0026ndash;9510)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF KLK6 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46947(39243\u0026ndash;57080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12186(12186\u0026ndash;12186)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF NGF-β pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20(1.32\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42(0.00-1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma NRGN pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1013.00(243.10\u0026ndash;1726.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360.00(74.94\u0026ndash;360.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF GFAP in mild COVID-19 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2872.35(1308.38-3620.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e525.66(398.80-1546.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF GFAP in non-mild COVID-19 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1064.04(540.20-1805.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF S100B in decreased consciousness COVID-19 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.14(3.10-19.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.46(2.69\u0026ndash;6.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF S100B in consciousness COVID-19 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.21(1.90\u0026ndash;6.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF NF-H in decreased consciousness COVID-19 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.18(0.00-212.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.00(0.00-377.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF NF-H in consciousness COVID-19 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00(0.00\u0026ndash;0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF Tau in CNS COVID-19 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377.41(135.09-717.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e143.60(40.27-385.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF Tau in PNS COVID-19 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.18(55.91-130.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF pT181 in COVID-19 with comorbidities pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.55(4.34\u0026ndash;14.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10.56(5.32\u0026ndash;16.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF pT181 in COVID-19 without comorbidities pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.76(1.19\u0026ndash;7.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatments (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiviral therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(12.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntravenous immunoglobulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(27.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoninvasive mechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(32.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasive mechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(17.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e*Only 26 COVID-19 patients with ESR, \u003csup\u003e\u0026amp;\u003c/sup\u003e20 COVID-19 patients with IL-6 in CSF, \u003csup\u003e#\u003c/sup\u003e19 COVID-19 patients with IgG synthesis rate, Alb CSF/Alb Serum, \u003csup\u003e@\u003c/sup\u003e24 COVID-19 patients with IgG CSF/IgG Serum, IgG oligoclonal band in CSF, IgG oligoclonal band in Serum, specific IgG oligoclonal band in CSF were available. ^Only 11 DC with high-sensitivity CRP, *7 DC with ESR, \u003csup\u003e\u0026amp;\u003c/sup\u003e6 DC with IL-6 in CSF were available.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis of special proteins between COVID-19 and DC\u003c/h2\u003e \u003cp\u003eCOVID-19 patients in the severe or critical condition group had significantly higher CSF GFAP levels compared to those with milder cases (p\u0026thinsp;=\u0026thinsp;0.020). Higher GFAP levels were also found in COVID-19 patients who experienced decreased consciousness or had comorbidities, compared to those without these conditions (p\u0026thinsp;=\u0026thinsp;9.52E-03 and p\u0026thinsp;=\u0026thinsp;4.74E-03, respectively). Similarly, higher CSF S100B protein levels were found in patients with decreased consciousness or comorbidities (p\u0026thinsp;=\u0026thinsp;0.021 and p\u0026thinsp;=\u0026thinsp;0.010, respectively). Patients with decreased consciousness also showed significantly higher levels of NF-H (neurofilament heavy chain) in the CSF compared to those without (p\u0026thinsp;=\u0026thinsp;7.49E-03). Additionally, COVID-19 patients with comorbidities had higher levels of pT181 in the CSF (p\u0026thinsp;=\u0026thinsp;0.014). For patients with central nervous system (CNS) involvement, CSF Tau protein levels were significantly elevated compared to those without CNS involvement (p\u0026thinsp;=\u0026thinsp;1.97E-03). However, the opposite trend was found in patients with weakness or numbness, who showed lower levels of Tau compared to those without these symptoms (p\u0026thinsp;=\u0026thinsp;0.013 and p\u0026thinsp;=\u0026thinsp;1.03E-03, respectively). COVID-19 patients with numbness also showed decreased levels of NRGN and NGF-β in the CSF compared to those without numbness (p\u0026thinsp;=\u0026thinsp;0.015 and p\u0026thinsp;=\u0026thinsp;0.033, respectively).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe analyzed the distribution of several biomarkers in the CSF and plasma of COVID-19 patients with neurological complications during the Omicron wave in China. Additionally, a bio-informational analysis by MR provided evidence that SARS-CoV-2 could invade the human nervous system. The finding suggested that neuroinflammation could be a key mechanism underlying the neurological complications observed in COVID-19 patients.\u003c/p\u003e \u003cp\u003eOur MR results demonstrated a causal relationship between different COVID-19 phenotypes and changes in brain structures. These include alterations in the area and volume of the left caudal middle frontal gyrus, which were associated with cognitive and emotional functions; the area and volume of the right caudal anterior-cingulate cortex, primarily involved in cognitive and emotional processes and the volume of the right cuneus cortex, played a crucial role in visual processing[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A multimodal meta-analysis of neuroimaging studies indicated that COVID-19 can lead to both functional and structural changes in the brain, particularly in areas such as the temporal lobe, orbital frontal cortex, and the cerebellum. There were also alterations noted in the insula and the limbic system, which are regions critical for emotional regulation and memory [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These findings supported previous research that suggests SARS-CoV-2 could cause nerve damage. This was consistent with reports of COVID-19 patients experiencing neurological symptoms such as headaches, impaired consciousness, acute cerebrovascular events, and epilepsy. The findings suggested that SARS-CoV-2 may invade the nervous system through mechanisms such as: disrupting normal CNS function via cytokine storms; inducing cerebral ischemia or hypoxia, leading to neurological complications.\u003c/p\u003e \u003cp\u003eThe special proteins could be divided into two categories, one group was the biomarkers of neuroinflammatory or neuro-injury, including CHI3L1,NGF-β,KLK6,NRGN,GFAP,S100B and NF-H; the other group was those of neurodegenerative, for instance: Amyloid beta 1\u0026ndash;42, Tau. CHI3L1 was a glycoprotein involved in inflammation, endothelial dysfunction, and tissue remodeling. Within the CNS, CHI3L1 was linked to neuroinflammatory processes and reactive gliosis. CHI3L1 levels were found to be higher in COVID-19 patients compared to controls[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, elevated serum CHI3L1 level have been associated with more severe cases of COVID-19 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. CHI3L1 played a role in the pathogenesis of COVID-19 by increasing the expression of angiotensin-converting enzyme 2 (ACE2), which facilitated viral entry, and the viral spike protein in pulmonary and vascular cells[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, previous studies primarily focused on serum CHI3L1 in COVID-19 patients, with no research specifically addressing its role in the CSF of COVID-19 patients with neurological complications[\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In our study, we found that CHI3L1 levels in the CSF were significantly higher in COVID-19 patients compared to disease controls (DC). There was also a trend suggesting that CHI3L1 levels were higher in COVID-19 patients with various clinical syndromes, though these differences were not statistically significant. We also observed elevated levels of NGF-β and KLK6 in the CSF of COVID-19 patients. NGF-β was a neuropeptide that contributes to neurogenic inflammation by activating immune cells through proinflammatory cytokines. It had been shown to be effective in inducing both pain and neuroinflammation[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. KLK6 was a serine protease that was abundantly expressed in the brain and spinal cord, and its expression in CNS diseases was varied[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. NRGN was a regulatory molecule found in the brain, specifically in dendritic spines, where it played a role in synaptic plasticity by linking protein kinase C and calcium/calmodulin signaling pathways[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In our study, NRGN levels were elevated in the plasma of COVID-19 patients compared to DCs. GFAP is a protein released during microglial dysregulation and is involved in tissue healing processes during neuroinflammation and neurodegeneration. Recent studies have suggested that serum GFAP may be a potential biomarker for various neurological complications, particularly during the acute phase of COVID-19 when systemic inflammation was at its peak[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our findings supported this, as we observed higher CSF GFAP levels in COVID-19 patients with moderate to severe disease, as well as in those with decreased consciousness or comorbidities. Similarly, S100B was a well-known biomarker used to assess brain damage and blood-brain barrier integrity. It was commonly employed for risk assessment in cases of mild brain injury[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our study found higher CSF S100B levels in COVID-19 patients with decreased consciousness and comorbidities compared to those without these conditions. Neurofilaments, including NF-H (the heaviest neurofilament), are structural proteins in neurons, and their levels are often elevated in cases of neurodegeneration or injury[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Amyloid beta 1\u0026ndash;42, a key protein in the development of Alzheimer's disease, played a critical role in the pathogenesis of the disease. It had been found to bind to various viral proteins, particularly the spike protein of SARS-CoV-2 and the ACE2 receptor. In our study, we observed that the median level of amyloid beta 1\u0026ndash;42 in the CSF was significantly lower in COVID-19 patients, consistent with previous research findings[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, our study had several limitations. First, while we applied multiple robust MR methods to assess different types of pleiotropic effects, we could not fully eliminate the influence of potential confounding factors on the MR results. Second, our analysis relied solely on GWAS of European ancestry populations. Despite the large sample size of this dataset, caution was required when extrapolating these findings to other ethnic groups. Third, our MR analysis did not account for certain factors that influenced COVID-19 hospitalization and severity, such as differences in healthcare systems, treatment protocols, or socioeconomic conditions across countries. Fourth, validation of our findings was limited to a single hospital cohort of 40 COVID-19 patients, which lacked follow-up data and broader demographic diversity. Further large-scale studies with multiethnic cohorts and longitudinal designs were needed to confirm and expand upon these results. Additionally, aside from direct CNS invasion by SARS-CoV-2, research had also shown that brain inflammation and cytokine storms may contribute to neurological symptoms[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The precise mechanisms linking SARS-CoV-2 infection to neurological complications remained unclear. Future experimental studies were necessary to elucidate these pathways.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, MR analysis provided evidence that SARS-CoV-2 may invade the human nervous system. This was validated through the analysis of several biomarkers in the CSF and plasma of COVID-19 patients with neurological complications during the Omicron wave in China. Our findings suggest that neuroinflammation may be a key mechanism underlying these neurological complications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThese authors made equal contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e The study received approval from the Ethics Committee of Peking Union Medical College Hospital (I-23PJ292), with a waiver for informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eAll data were available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was supported by grants from, the National High Level Hospital Clinical Research Funding(2022-PUMCH-B-124,2022-PUMCH-A-007), the National Natural Science Foundation of China Grants (82472348), the National Key Research and Development Program of China (2018YFE0207300), Beijing Natural Science Foundation (M23008), the CAMS Innovation Fund for Medical Sciences (CIFMS: 2021-I2M-C\u0026amp;T-A-002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;s:\u0026nbsp;\u003c/strong\u003eAll authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. ZYW and HLX wrote the main manuscript text, SLZ and YZL reviewed the manuscript. Professor. Yongzhe Li had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eL\u0026oacute;pez Lloreda C. COVID's toll on the brain: new clues emerge. Nature. 2024;628:20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePezzini A, Padovani A. Lifting the mask on neurological manifestations of COVID-19. Nat Rev Neurol. 2020;16:636\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePang Z, Tang A, He Y, Fan J, Yang Q, Tong Y, Fan H. Neurological complications caused by SARS-CoV-2. Clin Microbiol Rev 2024:e0013124.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeinhardt J, Streit S, Dittmayer C, Manitius RV, Radbruch H, Heppner FL. The neurobiology of SARS-CoV-2 infection. 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Plasma neurofilament heavy chain is a prognostic biomarker for the development of severe epilepsy after experimental traumatic brain injury. \u003cem\u003eEpilepsia\u003c/em\u003e 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZiff OJ, Ashton NJ, Mehta PR, Brown R, Athauda D, Heaney J, Heslegrave AJ, Benedet AL, Blennow K, Checkley AM, et al. Amyloid processing in COVID-19-associated neurological syndromes. J Neurochem. 2022;161:146\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"virology-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"virj","sideBox":"Learn more about [Virology Journal](http://virologyj.biomedcentral.com/)","snPcode":"12985","submissionUrl":"https://submission.nature.com/new-submission/12985/3","title":"Virology Journal","twitterHandle":"@VirologyJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mendelian randomization, COVID-19, neurological complications, CSF, neuroinflammation","lastPublishedDoi":"10.21203/rs.3.rs-5574959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5574959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA mendelian randomization (MR) analysis was conducted to investigate whether SARS-CoV-2 invaded the human nervous system. This was confirmed by an increase in biomarkers found in the cerebrospinal fluid (CSF) and plasma of COVID-19 patients.\u003c/p\u003e \u003cp\u003eTo confirm the neuroinvasive properties of SARS-CoV-2, a series of analyses were conducted utilizing accessible datasets by MR. In addition, external validation was conducted by testing specific proteins in a retrospective cohort study, which included 40 COVID-19 patients with neurological complications and 15 disease controls (DC).\u003c/p\u003e \u003cp\u003eOur investigation revealed the hospitalization, severity of COVID-19 increased the area and volume of certain brain regions, but no other significant causal effects were found of brain imaging-derived phenotypes (IDPs) on COVID-19. Notably, the COVID-19 hospitalization significantly increased the area and volume of the left caudal middle frontal gyrus (p_fdr\u0026thinsp;=\u0026thinsp;0.012; p_fdr\u0026thinsp;=\u0026thinsp;0.012, respectively). Additionally, COVID-19 severity was linked to the area, volume of the right caudal anterior-cingulate cortex and the volume of the right cuneus cortex (p_fdr\u0026thinsp;=\u0026thinsp;0.023; p_fdr\u0026thinsp;=\u0026thinsp;0.025; p_fdr\u0026thinsp;=\u0026thinsp;0.026, respectively).\u003c/p\u003e \u003cp\u003eIn the CSF of COVID-19 patients, the median level of CHI3L1 was significantly higher (13677 pg/mL) compared to the DC group (8421 pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;1.00E-04). Similar trends were also found in CSF KLK6 and NGF-β. Additionally, the median NRGN level in plasma was significantly higher in the COVID-19 group (1013.00 pg/mL) compared to the control group (360.00 pg/mL, p\u0026thinsp;=\u0026thinsp;6.50E-03). A subgroup analysis demonstrated that COVID-19 patients experiencing moderate to critical symptoms exhibited higher levels of GFAP in their CSF compared to those without. Elevated CSF levels of GFAP and S100B were also found in COVID-19 patients with decreased consciousness and comorbidities.\u003c/p\u003e \u003cp\u003eThis MR analysis provided evidence that SARS-CoV-2 may invade the human nervous system, as indicated by the increased levels of CSF biomarkers CHI3L1, NGF-β, and KLK6 in COVID-19 patients. These findings suggested that neuroinflammation could be a potential mechanism underlying the neurological complications seen in COVID-19 patients.\u003c/p\u003e","manuscriptTitle":"Elucidating SARS-CoV-2 Neurotropism: A Comprehensive Mendelian Randomization Study on Cerebrospinal Fluid Biomarkers and their Relevance to COVID-19 Neurological Manifestations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 04:52:14","doi":"10.21203/rs.3.rs-5574959/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-21T21:06:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-21T21:04:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7644337671670239664995301445868267353","date":"2025-04-21T21:01:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T10:42:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-31T23:10:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Virology Journal","date":"2025-03-28T12:57:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"virology-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"virj","sideBox":"Learn more about [Virology Journal](http://virologyj.biomedcentral.com/)","snPcode":"12985","submissionUrl":"https://submission.nature.com/new-submission/12985/3","title":"Virology Journal","twitterHandle":"@VirologyJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b31ea02e-29cd-4237-a23e-a82177de859e","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T16:04:38+00:00","versionOfRecord":{"articleIdentity":"rs-5574959","link":"https://doi.org/10.1186/s12985-025-02754-2","journal":{"identity":"virology-journal","isVorOnly":false,"title":"Virology Journal"},"publishedOn":"2025-04-28 15:57:11","publishedOnDateReadable":"April 28th, 2025"},"versionCreatedAt":"2025-04-03 04:52:14","video":"","vorDoi":"10.1186/s12985-025-02754-2","vorDoiUrl":"https://doi.org/10.1186/s12985-025-02754-2","workflowStages":[]},"version":"v1","identity":"rs-5574959","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5574959","identity":"rs-5574959","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}