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. 2020 May;55:102763.
doi: 10.1016/j.ebiom.2020.102763. Epub 2020 Apr 18.

Longitudinal Characteristics of Lymphocyte Responses and Cytokine Profiles in the Peripheral Blood of SARS-CoV-2 Infected Patients

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Longitudinal Characteristics of Lymphocyte Responses and Cytokine Profiles in the Peripheral Blood of SARS-CoV-2 Infected Patients

Jing Liu et al. EBioMedicine. .
Free PMC article

Abstract

Background: The dynamic changes of lymphocyte subsets and cytokines profiles of patients with novel coronavirus disease (COVID-19) and their correlation with the disease severity remain unclear.

Methods: Peripheral blood samples were longitudinally collected from 40 confirmed COVID-19 patients and examined for lymphocyte subsets by flow cytometry and cytokine profiles by specific immunoassays.

Findings: Of the 40 COVID-19 patients enrolled, 13 severe cases showed significant and sustained decreases in lymphocyte counts [0·6 (0·6-0·8)] but increases in neutrophil counts [4·7 (3·6-5·8)] than 27 mild cases [1.1 (0·8-1·4); 2·0 (1·5-2·9)]. Further analysis demonstrated significant decreases in the counts of T cells, especially CD8+ T cells, as well as increases in IL-6, IL-10, IL-2 and IFN-γ levels in the peripheral blood in the severe cases compared to those in the mild cases. T cell counts and cytokine levels in severe COVID-19 patients who survived the disease gradually recovered at later time points to levels that were comparable to those of the mild cases. Moreover, the neutrophil-to-lymphocyte ratio (NLR) (AUC=0·93) and neutrophil-to-CD8+ T cell ratio (N8R) (AUC =0·94) were identified as powerful prognostic factors affecting the prognosis for severe COVID-19.

Interpretation: The degree of lymphopenia and a proinflammatory cytokine storm is higher in severe COVID-19 patients than in mild cases, and is associated with the disease severity. N8R and NLR may serve as a useful prognostic factor for early identification of severe COVID-19 cases.

Funding: The National Natural Science Foundation of China, the National Science and Technology Major Project, the Health Commission of Hubei Province, Huazhong University of Science and Technology, and the Medical Faculty of the University of Duisburg-Essen and Stiftung Universitaetsmedizin, Hospital Essen, Germany.

Keywords: COVID-19; Coronavirus; Inflammatory cytokine; Lymphopenia; SARS-CoV-2.

Conflict of interest statement

Declaration of Competing Interest The authors disclose no conflicts of interest.

Figures

Fig. 1
Fig. 1
Kinetic analysis of cell counts of different populations of WBCs in COVID-19 patients. The absolute numbers of total WBCs (a), neutrophils (b), lymphocytes (c) and monocytes (d) in the peripheral blood of mild (blue line) and severe (red line) COVID-19 patients were analyzed at different time points after hospital admission. Error bars, mean ± SD.; Statistics, repeated measures (mixed model) ANOVA. *p<0·05. The upper dotted lines show the upper normal limit of each parameter, and the lower dotted lines show the lower normal limit of each parameter. Number of cases for each time point, M: Mild patients, S: Severe patients. Time≤3d 27(M),13(S); 4-6d 10(M), 1(S); 7-9d 9 (M), 4 (S); 10-12d 6 (M), 3(S); 13-15d 4 (M), 6 (S); ≥16d 2 (M), 7 (S).
Fig. 2
Fig. 2
Kinetic analysis of cell counts of different lymphocyte subsets in COVID-19 patients. The absolute numbers of CD3+ T cells (a), CD8+ T cells (b), CD4+ T cells (c), B cells (d) and NK cells (e) in the peripheral blood of mild (blue line) and severe (red line) COVID-19 patients were analyzed at different time points after hospital admission. Error bars, mean ± SD.; Statistics, repeated measures (mixed model) ANOVA. *p<0·05. Number of cases for each time point, M: Mild patients, S: Severe patients. Time≤3d 27(M),13(S); 4-6d 10(M), 1(S); 7-9d 9 (M), 4 (S); 10-12d 6 (M), 3(S); 13-15d 4 (M), 6 (S); ≥16d 2 (M), 7 (S).
Fig. 3
Fig. 3
Kinetic analysis of the serum levels of inflammatory cytokines in COVID-19 patients. The concentrations of IL-6 (a), IL-10 (b), IL-2 (c), IL-4 (d), TNF-α (e) and IFN-γ (f) in the serum of mild (blue line) and severe (red line) COVID-19 patients were analyzed at different time points after hospital admission. Error bars, mean ± SD.; Statistics, repeated measures (mixed model) ANOVA. *p<0·05. The upper dotted lines show the upper normal limit of each parameter, and the lower dotted lines show the lower normal limit of each parameter. Number of cases for each time point, M: Mild patients, S: Severe patients. Time≤3d 27(M),13(S); 4-6d 10(M), 1(S); 7-9d 9 (M), 4 (S); 10-12d 6 (M), 3(S); 13-15d 4 (M), 6 (S); ≥16d 2 (M), 7 (S).
Fig. 4
Fig. 4
Prognostic factors of severe COVID-19. (a) Principal component analysis was performed by R package “factoextra” to identify correlated variables for distinguishing severe patients from mild COVID-19 patients. Four mostly contributing variables, neutrophil-to-CD8+ T cell ratio (N8R), neutrophil-to-lymphocyte ratio (NLR), neutrophil counts (NE) and White Blood Cells counts (WBC) were identified. (b) ROC curve and AUC were calculated for these 4 selected parameters by using R package “pROC”. The results of this analysis identified N8R with a higher AUC (0·94) than NLR (0·93), NE (0·91) and WBC (0·85).

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