Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
Book Review
Brief Report
Case Letter
Case Report
Case Series
Commentary
Current Issue
Editorial
Erratum
Guest Editorial
Images
Images in Neurology
Images in Neuroscience
Images in Neurosciences
Letter to Editor
Letter to the Editor
Letters to Editor
Letters to the Editor
Media and News
None
Notice of Retraction
Obituary
Original Article
Point of View
Position Paper
Review Article
Short Communication
Systematic Review
Systematic Review Article
Technical Note
Techniques in Neurosurgery
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
Book Review
Brief Report
Case Letter
Case Report
Case Series
Commentary
Current Issue
Editorial
Erratum
Guest Editorial
Images
Images in Neurology
Images in Neuroscience
Images in Neurosciences
Letter to Editor
Letter to the Editor
Letters to Editor
Letters to the Editor
Media and News
None
Notice of Retraction
Obituary
Original Article
Point of View
Position Paper
Review Article
Short Communication
Systematic Review
Systematic Review Article
Technical Note
Techniques in Neurosurgery
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
Book Review
Brief Report
Case Letter
Case Report
Case Series
Commentary
Current Issue
Editorial
Erratum
Guest Editorial
Images
Images in Neurology
Images in Neuroscience
Images in Neurosciences
Letter to Editor
Letter to the Editor
Letters to Editor
Letters to the Editor
Media and News
None
Notice of Retraction
Obituary
Original Article
Point of View
Position Paper
Review Article
Short Communication
Systematic Review
Systematic Review Article
Technical Note
Techniques in Neurosurgery
View/Download PDF

Translate this page into:

Original Article
14 (
2
); 224-229
doi:
10.25259/JNRP_6_2022

Associations of the neutrophil/lymphocyte ratio, monocyte/ lymphocyte ratio, and platelet/lymphocyte ratio with COVID-19 disease severity in patients with neurological symptoms: A cross-sectional monocentric study

Department of Neurology, Faculty of Medicine, Kirsehir Ahi Evran University, Kirsehir, Turkey
Department of Clinical Microbiology and Infectious Diseases, Faculty of Medicine, Kirsehir Ahi Evran University, Kirsehir, Turkey
Corresponding author: Asuman Celikbilek, Department of Neurology, Faculty of Medicine, Kirsehir Ahi Evran University, Kirsehir, Turkey. asunebioglu@yahoo.com
Licence
This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

How to cite this article: Sahin BE, Celikbilek A, Kocak Y, Koysuren A, Hizmali1 L. Associations of the neutrophil/lymphocyte ratio, monocyte/ lymphocyte ratio, and platelet/lymphocyte ratio with COVID-19 disease severity in patients with neurological symptoms: A cross-sectional monocentric study. J Neurosci Rural Pract 2023;14:224-9.

Abstract

Objectives:

Data are limited regarding the relationship of neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), and platelet/ lymphocyte ratio (PLR) with neurological symptoms (NS) in COVID-19 patients. This study is the first to assess the utility of the NLR, MLR, and PLR for predicting COVID-19 severity in infected patients with NS.

Materials and Methods:

Consecutive 192 PCR-positive COVID-19 patients with NS were included in this cross-sectional and prospective study. The patients were classified into the non-severe and severe groups. We analyzed routinely complete blood count in these groups in terms of COVID-19 disease severity.

Results:

Advanced age, a higher body mass index, and comorbidities were significantly more common in the severe group (P < 0.001). Among the NS, anosmia (P = 0.001) and memory loss (P = 0.041) were significantly more common in the non-severe group. In the severe group, the lymphocytes and monocyte counts and the hemoglobin level were significantly lower, while the neutrophil count, NLR, and PLR were significantly higher (all P < 0.001). In the multivariate model, advanced age and a higher neutrophil count were independently associated with severe disease (both P < 0.001) but the NLR and PLR were not (both P > 0.05).

Conclusion:

We found positive associations of COVID-19 severity with the NLR and PLR in infected patients with NS. Further research is required to shed more light on the role of neurological involvement in disease prognosis and outcomes.

Keywords

COVID-19
Monocyte to lymphocyte ratio
Neurological symptoms
Neutrophil to lymphocyte ratio
Platelet to lymphocyte ratio

INTRODUCTION

The COVID-19 pandemic began at the end of 2019; the disease typically affects the respiratory system but is also associated with multiorgan dysfunction that can involve the central and peripheral nervous systems.[1-3] This was evident even during the early stage of the pandemic.[1,4] The central nervous system (CNS) can be invaded through the bloodstream or retrograde neuronal route. Virus in the blood first infects endothelial cells of the blood-brain barrier or choroid plexus epithelial cells of the blood-cerebrospinal fluid barrier or reaches the CNS in leukocytes. Slow blood flow in the capillary endothelium of the brain microcirculatory system increases viral interaction with the angiotensin-converting enzyme 2 (ACE2) receptor, thus facilitating viral entry into the CNS after capillary endothelial damage.[5,6] Alternatively, the virus may directly infect peripheral olfactory sensory neurons and then progress to the cranial nerves through retrograde axonal transport.[7] Finally, neurons may be indirectly affected by increased cytokine levels attributable to widespread inflammation, an immune response, or hypoxia.[8]

Inflammation triggered by the immune response plays an important role in the progress of infection.[9] When an immune response is activated, the neutrophil and leukocyte counts, neutrophil/lymphocyte ratio (NLR), and monocyte/ lymphocyte ratio (MLR) increase; the platelet/lymphocyte ratio (PLR) decreases in severe cases.[10-15] Few studies have examined the association between these routine complete blood count (CBC) parameters and the development of neurological disease. Flores-Silva et al. reported significantly lower hemoglobin and lymphocyte levels, and significantly higher total neutrophil counts, in COVID-19 patients with neurological findings.[16] In another study, low hemoglobin and platelet levels were associated with COVID-19-related headache, and an increased NLR and decreased platelet count were independent predictors of headache.[17] This study is the first to assess the utility of the NLR, MLR, and PLR for predicting COVID-19 severity in infected patients with neurological symptoms (NS).

MATERIALS AND METHODS

Study population

We enrolled 192 PCR-positive COVID-19 patients with NS treated as outpatients in our pandemic clinic, or hospitalized between January and June 2021. Patients aged <18 years, those with chronic kidney, heart, liver, or malignant disease, and those who were pregnant or morbidly obese were excluded from the study. The study began only after approval from the ethics committee of Kirsehir Ahi Evran University Faculty of Medicine (approval date January 05, 2021; approval no. 2021-01/04). We prospectively collected demographic data, and noted any comorbid diseases or COVID-19-related NS (ageusia and anosmia, dizziness, headache, cranial neuropathy, memory loss, stroke, epilepsy, myopathy, and neuropathic pain). Patients reporting memory loss were evaluated using the Montreal Cognitive Assessment and those with cranial neuropathy, dizziness, epileptic seizures, or stroke underwent detailed neurological examination and cranial tomography. The complaints of the other patients were also noted. Patients were divided into mild, moderate, severe, and critical illness groups. Mild disease was defined as an absence of respiratory symptoms and pulmonary radiological signs, and oxygen saturation (SpO2) ≥96%; moderate disease was defined as the presence of mild respiratory symptoms, radiological evidence of pneumonia, and 93%< SpO2 <96%; severe disease was defined as SpO2 ≤93% requiring oxygen support; and critical disease was defined as a need for intensive care.[18] The patients were then divided into severe and non-severe disease groups, which subsumed critically and severely ill patients, and those with mild and moderate disease, respectively.

Data collection

Blood samples were collected on admission or the 1st day of hospitalization. CBCs were obtained with the LH 750 autoanalyzer (Beckman Coulter, Fullerton, CA, USA). The hemoglobin level, red cell width distribution, white blood cell (WBC), neutrophil, lymphocyte, monocyte, and platelet counts, mean platelet volume, platelet distribution width, and plateletcrit were recorded. The NLR, MLR, and PLR were calculated.

Statistical analysis

All statistical analyses were performed using SPSS for Windows software (version 25.0; IBM Corp., Armonk, NY, USA). The Kolmogorov–Smirnov and Shapiro–Wilks tests were used to verify that the quantitative data were normally distributed. Kurtosis and skewness coefficients were calculated. Data are presented as mean ± standard deviation, median (25–75th percentile), or number (%). The Chi-squared test, Fisher’s exact test, Fisher-Freeman-Halton exact test, and independent t-test were used as appropriate for univariate analyses. Nonnormally distributed variables were compared using the Mann–Whitney U test. To identify risk factors for severe disease, we initially performed univariate logistic regression. Significant variables were included in the multivariate logistic regression analysis. P < 0.05 was considered significant.

RESULTS

Of the 192 patients, 76 and 116 were classified into the non-severe and severe groups, respectively. The results of group comparisons are shown in [Table 1]. Advanced age, a higher body mass index (BMI), and comorbidities were significantly more common in the severe group (P < 0.001). Among the comorbidities, diabetes mellitus (P < 0.001), hypertension (P < 0.001), coronary artery disease (P = 0.039), and chronic obstructive pulmonary disease (P = 0.040) were significantly more prevalent in the severe group. Among the NS, anosmia (P = 0.001) and memory loss (P = 0.041) were significantly more common in the non-severe group. We found no significant association between the number of NS and disease severity (P = 0.100). In the severe group, the lymphocytes and monocyte counts and the hemoglobin level were significantly lower, while the neutrophil count, NLR, and PLR were significantly higher (all P < 0.001).

Table 1: Demographic and laboratory data (n=192).
Variables Non-severe group (n=116) % Severe group (n=76) P Effect size
Age (years) 37.06±10.72 56.32±11.28 0.000 −1.758
Body mass index (kg/m2) 26.38±4.74 29.06±4.65 0.000 −0.570
Female gender 67 (57.8) 42 (%55.3) 0.733 0.025
Comorbidities 30 (25.9) 45 (61.6) 0.000 0.356
Diabetes mellitus 6 (5.2) 24 (32.9) 0.000 0.369
Hypertension 8 (6.9) 22 (30.1) 0.000 0.310
Coronary artery disease 7 (6.0) 11 (15.1) 0.039 0.150
Chronic obstructive pulmonary disease 6 (5.2) 10 (13.7) 0.040 0.149
Neurological symptoms
Ageusia 72 (62.1) 37 (48.7) 0.067 0.132
Anosmia 71 (61.2) 28 (36.8) 0.001 0.238
Dizziness 14 (12.1) 15 (19.7) 0.147 0.105
Headache 93 (80.2) 61 (80.3) 0.988 0.001
Cranial neuropathy 2 (1.7) 1 (1.3) 0.823 0.016
Memory loss 17 (14.7) 4 (5.3) 0.041 0.147
Stroke 0 (0.0) 1 (1.3) 0.396 0.089
Epilepsy 0 (0.0) 1 (1.3) 0.396 0.089
Myopathy 0 (0.0) 1 (1.3) 0.396 0.089
Neuropathic pain 2 (1.7) 6 (7.9) 0.060 0.151
Complete blood count
White blood cell (103/mm3) 6.39 (4.60–7.60) 6.46 (4.70–9.18) 0.577 −0.249
Neutrophil 3.88±1.86 5.44±3.36 0.000 −0.609
Lymphocyte 1.67±0.81 1.10±0.65 0.000 0.753
Monocyte 0.62±0.29 0.42±0.24 0.000 0.711
Hemoglobin (mg/dL) 14.05±1.69 13.07±1.64 0.000 0.584
Platelet (103/mm3) 226.0 (192.25–261.75) 226.5 (169.0–294.0) 0.767 −0.075
RDW (%) 12.8 (12.3–13.4) 13.1 (12.5–13.7) 0.121 -0.105
MPV (fL) 10.38±0.92 10.29±0.92 0.497 0.100
PCT (%) 0.23 (0.21–0.27) 0.23 (0.18–0.28) 0.515 0.003
PDW (fL) 11.9 (10.8–13.1) 11.4 (10.1–12.8) 0.087 0.214
NLR 2.25 (1.51–3.17) 4.86 (2.40–8.84) 0.000 0.462
MLR 0.31 (0.22–0.60) 0.37 (0.28–0.52) 0.509 0.056
PLR 148.8 (111.8–208.6) 233.5 (141.8–331.8) 0.000 0.410

P<0.05 are considered significant and given as bold

The results of the regression analysis are shown in [Table 2]. In univariate analysis, advanced age, higher BMI, comorbid conditions, anosmia higher neutrophil count, lower lymphocyte and monocyte counts, and higher NLR and PLR were significantly associated with severe disease (all P < 0.001). A 1-unit increase in the NLR and PLR increased the risk of severe disease 1.175- and 1.006-fold, respectively, (both P < 0.001). In the multivariate model, advanced age and a higher neutrophil count were independently associated with severe disease (both P < 0.001) but the NLR and PLR were not (both P > 0.05).

Table 2: Univariate and multiple logistic regression analyses to identify the predictors of COVID-19 severity in patients with neurological symptoms.
Variables Univariate Multivariate
OR (95% CI) P OR (95% CI) P
Age (years) 1.157 (1.112–1.204) 0.000 1.284 (1.154–1.429) 0.000
Body mass index (kg/m2) 1.127 (1.056–1.204) 0.000 1.170 (1.002–1.370) 0.051
Female gender 1.107 (0.618–1.983) 0.733 - -
Comorbidities 4.607 (2.457–8.639) 0.000 1.841 (0.292–11.622) 0.516
Neurological symptoms
Ageusia 1.725 (0.960–3.098) 0.068 - -
Anosmia 0.705 (1.489–4.915) 0.001 2.561 (0.539–12.163) 0.237
Dizziness 1.792 (0.809–3.965) 0.150 - -
Headache 1.006 (0.487–2.079) 0.988 - -
Cranial neuropathy 1.316 (0.117–14.769) 0.824 - -
Memory loss 3.091 (0.998–9.575) 0.051 - -
Neuropathic pain 4.886 (0.959–24.880) 0.056 - -
Complete blood count
White blood cell (103/mm3) 1.091 (0.985–1.208) 0.095 - -
Neutrophil 1.266 (1.117–1.434) 0.000 2.152 (1.436–3.226) 0.000
Lymphocyte 0.307 (0.185–0.510) 0.000 1.423 (0.611–3.315) 0.237
Monocyte 0.053 (0.014–0.202) 0.000 25.490 (1.170–555.30) 0.039
Hemoglobin (mg/dL) 0.705 (0.585–1.850) 0.067 - -
Platelet (103/mm3) 1.001 (0.997–1.004) 0.609 - -
RDW (%) 1.089 (0.860–1.379) 0.478 - -
MPV (fL) 0.895 (0.652–1.230) 0.495 - -
PCT (%) 0.964 (0.021–45.096) 0.985 - -
PDW (fL) 0.898 (0.776–1.040) 0.152 - -
NLR 1.175 (1.076–1.283) 0.000 1.011 (0.827–1.237) 0.913
MLR 1.104 (0.866–1.406) 0.425 - -
PLR 1.006 (1.003–1.009) 0.000 1.000 (0.996–1.003) 0.898

NLR: Neutrophil/lymphocyte ratio, MLR: Monocyte/lymphocyte ratio, PLR: Platelet/lymphocyte ratio. P<0.05 are considered significant and given as bold

DISCUSSION

In this study, advanced age, a higher BMI, and comorbidities were significantly more common in the severe group; anosmia and memory loss were significantly more prevalent in the non-severe group; the lymphocyte and monocyte counts and the hemoglobin level were significantly lower, while the neutrophil count, NLR, and PLR were significantly higher, in the severe group; and advanced age and a higher neutrophil count were independent predictors of COVID-19 severity, whereas the NLR and PLR were not.

COVID-19 can have direct and indirect neurological effects. In the former case, the virus achieves “retrograde neuronal access” through the peripheral nerves, spreads hematogenously by directly infecting endothelial cells, and then infiltrates other infected cells. Indirect virus exposure is reflected in marked increases in inflammatory cytokines, hypoxia, and systemic inflammation.[8] Headache, anosmia, ageusia, fatigue, dizziness, and memory loss were the most common NS as in our patients.[19] Anosmia and ageusia were significantly more common in the non-severe group, suggesting that the virus travelled from the nasal cavity to the brain through the cranial nerves.[20] The anosmia and ageusia rates were 19.4–88% in COVID-19-infected patients, who had good prognoses.[21,22] Lechien et al. reported that the olfactory neuroepithelium and bulb limited viral spread, thereby improving prognosis despite local irritation.[21] In this study, anosmia was significantly more common in the non-severe group, as was memory loss. This may be explained by the fact that we did not evaluate intensive care patients, who would be expected to show more cognitive effects. Although the symptoms were not confirmed by neuropsychiatric testing, no patient reported memory loss before infection. The pathophysiology of cognitive impairment remains unclear. The virus may directly affect the brain, or the effects may be non-specific (due to medical intervention, systemic inflammation, assisted ventilation, psychosocial tension,[8] quarantine, etc.). Anxiety, depression, and other mental health problems caused by a lack of social interaction negatively affect cognitive function, and a vicious cycle may develop.[23]

Obesity is an independent risk factor for the lower respiratory tract infection and mortality (due to severe tissue damage) in COVID-19-infected patients.[24] First, organ damage caused by obesity leads to metabolic syndrome; stress then exacerbates the associated dysfunction. Second, the virus can invade adipose tissue, which increases the vulnerability of the heart and lungs (which express the viral ACE2 receptor). Third, obesity is associated with the high levels of inflammation and an elevated immune response, potentially leading to burnout. Obesity increases abdominal pressure, restricts chest expansion, and renders lung compensation inadequate; obese patients are at increased risk of respiratory failure.[25] A high BMI (>28 kg/m2) and diabetes mellitus are risk factors for severe COVID-19 disease.[26] Several studies reported increased COVID-19-related mortality in hypertensive and older patients.[27,28] Bastug et al. found that intensive care unit patients were older and had more comorbidities than other patients (76.1 vs. 33.1%).[11] The mean age of our severely ill group was 56.32 ± 11.28 years, which was significantly higher than that of the non-severe group (37.06 ± 10.72 years). The oxidant effects of aging and obesity, as well as chronic diseases such as diabetes, increase the severity of COVID-19 disease.[29]

Blood parameters have been extensively studied in patients with severe and non-severe COVID-19 infections; an increased neutrophil count, C-reactive protein (CRP) level and NLR, and a decreased lymphocyte count were indicators of inflammation.[10,30,31] The CRP level is commonly elevated in patients with severe disease, as well as in some mild cases. The CRP level is a marker of infection, but not of COVID-19 disease severity.[31] Lymphocytopenia reflects strong immune system activation even in the early stages of COVID-19 infection and is associated with a poor prognosis.[32] A recent review showed that lymphocytopenia is a component of the cytokine storm associated with severe COVID-19 infection, along with an elevated WBC count and IL-6 level.[31,33-35] In particular, a high NLR was an independent prognostic factor for COVID-19 outcomes and marker of clinical severity.[36] In a Turkish study, the lymphocyte count and hemoglobin level were low, while the leukocyte and neutrophil counts, and NLR, MLR, and PLR were high in critically ill patients transferred to intensive care units.[11] Few studies have evaluated CBC parameters in COVID-19 patients with NS.[16,17] In Mexico City, a high NLR (≥9) in hospitalized patients was an independent predictor of the development of in-hospital neurological complications; the hemoglobin level and lymphocyte count were significantly lower, while the total neutrophil count was significantly higher, in these patients.[16] Hussein et al. found that a low hemoglobin level and platelet count were associated with COVID-19-related headache; an increased NLR and decreased platelet count were independent predictors of headache.[17] Wijeratne et al. noted an increased NLR in a 75-year-old male who developed acute stroke-like symptoms and signs during the course of COVID-19 disease.[37] We also found that the neutrophil count, NLR, and PLR were significantly higher, while the lymphocyte count was lower, in severely ill patients with NS. Elevated NLR and PLR are partly attributable to a low lymphocyte count. However, this does not fully explain the increased NLR; the neutrophil count increase was significant in our patients, even in multivariate analysis. In univariate analysis, a 1-unit increase in the NLR and PLR increased the risk of severe disease 1.175- and 1.006-fold, respectively. However, these increases were not significant in multivariate, possibly due to the small sample size. The hemoglobin level was low in the severe group, as previously reported.[17] As blood samples were collected on admission or the 1st day of hospitalization, this may reflect pre-existing anemia caused by chronic hypertension and/or other comorbidities, rather than COVID-19.

This study had some limitations. First, it was a relatively small single-center study. Second, a cross-sectional design was used so causality cannot be inferred. Furthermore, we measured laboratory parameters only once; obtaining more measurements in a longitudinal study might aid prediction of COVID-19 severity. Third, we lacked data on inflammatory markers (such as interleukins). Fourth, neither magnetic resonance imaging nor electrophysiological tests were performed because infection control measures were in place.

CONCLUSION

Similar to prior reports, we found positive associations of COVID-19 severity with the NLR and PLR in infected patients with NS. Although these markers are not ideal, they are readily available and may assist the identification of patients at increased risk of severe COVID-19. Further longitudinal studies including larger cohorts are needed to shed more light on the role of neurological involvement in disease prognosis and outcomes.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent.

Conflicts of interest

There are no conflicts of interest.

Financial support and sponsorship

Nil.

References

  1. , , , , , , et al. Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China. JAMA Neurol. 2020;77:683-90.
    [CrossRef] [PubMed] [Google Scholar]
  2. , , , . Evidence of the COVID-19 virus targeting the CNS: Tissue distribution, host-virus interaction, and proposed neurotropic mechanisms. ACS Chem Neurosci. 2020;11:995-8.
    [CrossRef] [PubMed] [Google Scholar]
  3. , , , , , . Non-COVID neurological emergencies: A silent killer going unnoticed during COVID-19 pandemic. J Neurosci Rural Pract. 2021;12:461-9.
    [CrossRef] [PubMed] [Google Scholar]
  4. , , , . Neurological involvement in COVID-19 patients: A narrative review. J Neurosci Rural Pract. 2020;11:526-9.
    [CrossRef] [PubMed] [Google Scholar]
  5. . Neurologic complications of coronavirus infections. Neurology. 2020;94:809-10.
    [CrossRef] [PubMed] [Google Scholar]
  6. , , , , , , et al. Neurological and musculoskeletal features of COVID-19: A systematic review and meta-analysis. Front Neurol. 2020;11:687.
    [CrossRef] [PubMed] [Google Scholar]
  7. , , , , . Neuroinvasion and inflammation in viral central nervous system infections. Mediators Inflamm. 2016;2016:8562805.
    [CrossRef] [PubMed] [Google Scholar]
  8. , , , , , , et al. Neuroinvasion and inflammation in viral central nervous system infections. Mediators. 2021;10:3441.
    [CrossRef] [PubMed] [Google Scholar]
  9. , , , , , , et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020;382:727-33.
    [CrossRef] [PubMed] [Google Scholar]
  10. , , , , , , et al. Can we predict the severity of coronavirus disease 2019 with a routine blood test? Pol Arch Intern Med. 2020;130:400-6.
    [CrossRef] [Google Scholar]
  11. , , , , , , et al. Clinical and laboratory features of COVID-19: Predictors of severe prognosis. Int Immunopharmacol. 2020;88:106950.
    [CrossRef] [PubMed] [Google Scholar]
  12. , , , , , , et al. Evaluation of hematological parameters as an indicator of disease severity in Covid-19 patients: Pakistan's experience. J Clin Lab Anal. 2021;35:e23809.
    [CrossRef] [PubMed] [Google Scholar]
  13. , , , , . Associations between hematological parameters and disease severity in patients with SARS-CoV-2 infection. J Clin Lab Anal. 2021;35:e23604.
    [CrossRef] [Google Scholar]
  14. , , , , , , et al. Clinical features and potential risk factors for discerning the critical cases and predicting the outcome of patients with COVID-19. J Clin Lab Anal. 2020;34:e23547.
    [CrossRef] [Google Scholar]
  15. , , , , , . Peripheral blood inflammatory markers in predicting prognosis in patients with COVID-19. Some differences with influenza A. J Clin Lab Anal. 2021;35:e23657.
    [CrossRef] [Google Scholar]
  16. , , , , , , et al. Neurologic manifestations in hospitalized patients with COVID-19 in Mexico city. PLoS One. 2021;16:e0247433.
    [CrossRef] [PubMed] [Google Scholar]
  17. , , , , , , et al. Relative frequency and risk factors of COVID-19 related headache in a sample of egyptian population: A hospital-based study. Pain Med. 2021;22:2092-9.
    [CrossRef] [PubMed] [Google Scholar]
  18. , , , , , , et al. Serum CCL17 level becomes a predictive marker to distinguish between mild/moderate and severe/critical disease in patients with COVID-19. Gene. 2021;766:145145.
    [CrossRef] [PubMed] [Google Scholar]
  19. , , , , , , et al. The European academy of neurology COVID-19 registry (ENERGY): An international instrument for surveillance of neurological complications in patients with COVID-19. Eur J Neurol. 2021;28:3303-23.
    [CrossRef] [PubMed] [Google Scholar]
  20. , , , , , , et al. Human coronaviruses and other respiratory viruses: Underestimated opportunistic pathogens of the central nervous system? Viruses. 2019;12:14.
    [CrossRef] [PubMed] [Google Scholar]
  21. , , , , , , et al. Epidemiological, otolaryngological, olfactory and gustatory outcomes according to the severity of COVID-19: A study of 2579 patients. Eur Arch Otorhinolaryngol. 2021;278:2851-9.
    [CrossRef] [Google Scholar]
  22. , , , , . Potential pathogenesis of ageusia and anosmia in COVID-19 patients. Int Forum Allergy Rhinol. 2020;10:1103-4.
    [CrossRef] [PubMed] [Google Scholar]
  23. , , , . Cognitive and mental health changes and their vulnerability factors related to COVID-19 lockdown in Italy. PLoS One. 2021;16:e0246204.
    [CrossRef] [PubMed] [Google Scholar]
  24. , , , , , , et al. Obesity is associated with higher risk of intensive care unit admission and death in influenza A (H1N1) patients: A systematic review and meta-analysis. Obes Rev. 2011;12:653-9.
    [CrossRef] [PubMed] [Google Scholar]
  25. , , . Obesity aggravates COVID-19: A systematic review and meta-analysis. J Med Virol. 2021;93:257-61.
    [CrossRef] [PubMed] [Google Scholar]
  26. , , , , , , et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497-506.
    [CrossRef] [Google Scholar]
  27. , , , , , , et al. Estimates of the severity of coronavirus disease 2019: A model-based analysis. Lancet Infect Dis. 2020;20:669-77.
    [CrossRef] [Google Scholar]
  28. , . Obezite, COVID-19 pandemisinde risk faktörü müdür? Turk Klin J Health Sci. 2020;5:313-22.
    [CrossRef] [Google Scholar]
  29. , , , , , , et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: A nationwide analysis. Eur Respir J. 2020;55:2000547.
    [CrossRef] [PubMed] [Google Scholar]
  30. . Neutrophil-to-lymphocyte ratio and lymphocyte-to-C-reactive protein ratio in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis. J Med Virol. 2020;92:1733-4.
    [CrossRef] [PubMed] [Google Scholar]
  31. , , , , , , et al. Clinical, laboratory and radiological characteristics and outcomes of novel coronavirus (SARS-CoV-2) infection in humans: A systematic review and series of meta-analyses. PLoS One. 2020;15:e0239235.
    [CrossRef] [PubMed] [Google Scholar]
  32. , , , , , , et al. Lymphopenia predicts disease severity of COVID-19: A descriptive and predictive study. Signal Transduct Target Ther. 2020;5:33.
    [CrossRef] [PubMed] [Google Scholar]
  33. , , , , . Cytokine release syndrome in severe COVID-19: Interleukin-6 receptor antagonist tocilizumab may be the key to reduce mortality. Int J Antimicrob Agents. 2020;55:105954.
    [CrossRef] [PubMed] [Google Scholar]
  34. , , , , , , et al. Characterization of cytokine/chemokine profiles of severe acute respiratory syndrome. Am J Respir Crit Care Med. 2005;171:850-7.
    [CrossRef] [PubMed] [Google Scholar]
  35. , , , , . Human immunopathogenesis of severe acute respiratory syndrome (SARS) Virus Res. 2008;133:13-9.
    [CrossRef] [PubMed] [Google Scholar]
  36. , , , . The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients. Int Immunopharmacol. 2020;84:106504.
    [CrossRef] [PubMed] [Google Scholar]
  37. , , , , . First Australian case of good recovery of a COVID-19 patient with severe neurological symptoms post prolonged hospitalization. Cureus. 2020;12:e10366.
    [CrossRef] [PubMed] [Google Scholar]
Show Sections