Characteristics of Lymphocyte Subsets and Cytokine Proles of Patients with Coronavirus Disease 2019

Background: To explore the changes in lymphocyte subsets and cytokine proles in patients with coronavirus disease 2019 (COVID-19) and their relationship with disease severity. Methods: This study included 228 patients with COVID-19 who were treated at Chongqing University Three Gorges Hospital from January 1, 2020 to February 20, 2020. The characteristics of lymphocyte subsets and cytokine proles of severe and mild COVID-19 patients were compared. Of the 228 patients enrolled, 48 were severe patients and 180 were mild patients. Results: Lymphocyte counts, absolute number of total T lymphocytes, CD 4+ T cells, CD 8+ T cells, and total B lymphocytes were signicantly lower in severe patients (0.8×10 9 /L, 424.5×10 6 /L, 266×10 6 /L, 145.5×10 6 /L, 109.5×10 6 /L, respectively) than in mild patients (1.2×10 9 /L, 721×10 6 /L, 439.5×10 6 /L, 281.5×10 6 /L, 135×10 6 /L, respectively). A multivariate logistic regression analysis showed that age, C-reactive protein (CRP) and the neutrophil-to-lymphocyte ratio (NLR) were independent risk factors for developing into severe condition. The lymphocyte subsets decreased and cytokine proles increased more signicantly in severe patients than in mild patients. ACE, angiotensin converting enzyme; APTT, activated partial thromboplastin time; ARDS, acute respiratory distress syndrome; AUC, area under curve; CK, creatine kinase; CKMB, creatine kinase-MB; COVID-19, coronavirus disease 2019; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; DBil, direct bilirubin; ICU, intensive care unit; IFN-γ, interferon-γ; IQR, inter quartile range; MERS, Middle East respiratory syndrome; NLR, neutrophil-to-lymphocyte ratio; PCT, procalcitonin; PT, prothrombin time; ROC, receiver operating characteristic curve; SARS, severe acute respiratory syndrome; TBil, total bilirubin; TNF-α, tumor necrosis factor-α; WBC, white blood cell; IL, interleukin; TNF, tumor necrosis factor; IFN, interferon; L, lymphocyte; Total T, Total T lymphocytes; CD4+, CD 4+ T cells; CD 8+ , CD 8+ T cells; Total B, Total B lymphocytes; NK, natural killer; APTT, activated partial thromboplastin time; OR, odds ratio; CI, condence interval.

Immune disorders and cytokine storm were considered to be the main causes of damage (Fig. 1). Lymphocyte and cytokine changes in peripheral blood have been noted as one of the prominent characteristics in patients with COVID-19. Signi cant lymphocytopenia has been observed during the acute phase of COVID-19, and the degree of lymphocyte decrease and increase in cytokines has seemed to be associated with disease severity [13]. In the present study, the aim is to describe the changes in the peripheral blood lymphocyte subsets and cytokine pro les in patients with COVID-19 and to compare their features between severe and mild patients.

Study Design, Setting, and Population
This was a single-center, retrospective, cohort study. The study was approved by the institutional ethics board of Chongqing Three Gorges Central Hospital, Chongqing, China. The Chongqing Three Gorges Central Hospital is one of the major tertiary teaching hospitals and is responsible for the treatment of COVID-19 assigned by the government. Clinically and laboratory diagnosed patients with COVID-19 admitted to Chongqing Three Gorges Central Hospital from January 1, 2020 to February 20, 2020 were analyzed in this study. Two cohorts were generated according to diagnosis and treatment of pneumonia caused by novel coronavirus infection issued by National Health Commission of the People's Republic of China, severe group and mild group. Mild patients met all following conditions: (1) Epidemiological history, (2) Fever or other respiratory symptoms, Typical CT image abnormities of viral pneumonia, and (4) Positive result of RT-PCR for SARS-CoV-2 RNA. Severe patients additionally met at least one of the following conditions: (1) Shortness of breath, respiratory rate ≥30 times/min, (2) Oxygen saturation (Resting state) ≤93%, or (3) PaO 2 /FiO 2 ≤300mmHg.Written informed consent was waived by the ethics board of the hospital for emerging infectious diseases and oral consent was obtained from the patients.

Data Collection
Researchers responsible for data collecting were trained before the study began so that they could correctly ll out the case report form and reduce errors. Patient demographic characteristics, such as age, gender, baseline comorbidities, epidemiological history, clinical, and laboratory data, were obtained on the day of admission. Symptoms, vital signs, treatment measures, and radiological data were also collected. The two researchers collected data independently and checked each other's form for mistakes. Clinical outcomes were followed until February 29, 2020.

Statistical Analysis
Categorical variables were described as frequency rates and percentages, and continuous variables were described using the mean, median, and interquartile range (IQR) values. Means for continuous variables were compared using independent group t-tests when the data were normally distributed; otherwise, the Mann-Whitney test was used. For comparisons, a two-sided α of less than 0.05 was considered to be statistically signi cant. A univariate analysis was used to compare the risk factors for developing to critical condition. A multiple logistic regression analysis was used to screen independent risk factors that affect outcomes. The diagnostic values of selected parameters for differentiating severe and mild patients were assessed by the receiver operating characteristic (ROC) and the area under the ROC curve (AUC). Cut-off values were identi ed following the Youden's index of the ROC curve. All of the statistical analyses were conducted using SPSS, Version 17.0.

Baseline Characteristics
A total of 259 patients with con rmed COVID-19 were included, 31 of whom were excluded due to lack of cytokine data. Finally, our cohort consisted of 228 patients (Fig. 2

Laboratory Findings
There were obvious differences in the laboratory ndings between the severe and mild patients.  (Table 2).   [14]. Thus, it is of great signi cance to study the laboratory data and the clinical development of the disease in order to guide management.
In this study, the clinical manifestations and laboratory data of severe and mild patients with COVID-19 were compared. In addition, the characteristics of lymphocyte subsets and cytokine pro les of peripheral blood in the enrolled patients were analyzed. It was found that most of severe patients were older than mild patients.
In addition, the severe group had more patients with basic diseases than the mild group, which means that older patients, in particular those with basic diseases, such as hypertension and diabetes, may be more likely to develop severe COVID-19. These ndings are consistent with several previous studies [13,15]. Fever, cough, and expectoration were found to be the most common symptoms. However, the above symptoms did not appear in some patients. In addition, some patients had symptoms in the digestive system or nervous system only.
In terms of laboratory ndings, a majority of COVID-19 patients suffered from lymphocytopenia. This study showed that lymphocytopenia occurred in 97.9% of severe patients and 80% of mild patients. Speci cally, lymphocyte counts, absolute number of total T lymphocytes, CD4+T cells, CD8+T cells, and total B lymphocytes were signi cantly lower in severe patients than mild patients. The decrease in lymphocyte and lymphocyte subsets was related to the severity of the disease. Cellular immunity is an important part of the human immune system in a viral infection. It has been con rmed that cell-mediated speci c cellular immunity plays a crucial role in the process of virus elimination and killing [16].
Increasing evidence suggests that lymphocytes play a crucial role in airway diseases [17,18]. Previous studies have shown that a marked lymphocytopenia also occurred in a majority of the patients during the acute phase of severe acute respiratory syndrome (SARS) and middle east respiratory syndrome (MERS), with CD4+ and CD8+T cell subsets particularly affected. In addition, the degree of decrease in the T lymphocytes was found to be associated with disease severity [19][20][21][22][23]. However, the mechanisms by which they cause lymphocyte changes were different. Previous data and the results of this study found that there was a remarkable decrease in lymphocyte subsets in COVID-19 patients, especially in severe patients.
Currently, the pathophysiological mechanism of lymphocyte decline in COVID-19 patients remains unclear and further investigations are required. Although there is insu cient knowledge to understand the mechanism, cellular immunode ciency may be one of the primary immunopathologic changes in COVID-19 patients.
Early studies have documented that cytokine storms, also known as in ammatory storms, have occurred in a large number of patients with COVID-19. In SARS patients, increased amounts of proin ammatory cytokines in the serum, such as IL1B, IL6, IL12, IFN-γ, IP10, and MCP1, were observed and were considered to be related to the pulmonary inflammation and extensive lung damage, and even multiple organ failure [24].
Study showed that MERS patients also had increased concentrations of proinflammatory cytokines (IFN-γ, TNF-α, IL15, and IL17) [25]. Recent data have indicated that patients with COVID-19 also had high concentrations of serum cytokine pro les, such as TNF-α, IL-1, IL-6, and IFN-γ [13,15]. In clinical work, it was found that the course of disease and lung lesions progressed rapidly, and even multiple organ failure developed in a short time in some patients with a high concentration of cytokines. In this study, it was noted that patients typically had increased concentrations of serum IL-6. Moreover, concentrations of serum IL-6 was signi cantly higher in severe patients than in mild patients, and this agreed with the concept of a cytokine storm [26]. However, IL-4, IL-10, IL-17, TNF-α, and IFN-γ were all nearly in the normal range. Although the exact mechanism of changes in the cytokines remains to be elucidated, a higher concentration of serum cytokine seems to be associated with poor outcomes. Therefore, monitoring the changes in cytokines is of certain signi cance for the early detection and management of critically ill patients.
Early screening of critically ill patients may improve clinical outcomes. In the current study, the clinical and laboratory features of patients with COVID-19 were explored. The enrolled patients were divided into two cohorts based on disease severity. Baseline characteristics, clinical presentation, and laboratory data were compared between severe and mild patients. A multivariate logistic regression analysis and a ROC curve analysis were further performed. In addition, AUC and cutoff values were calculated. It was found that patients with CRP > 32.9 mg/L, NLR > 2.9, and age > 53.5 years tended to develop into a severe condition. CRP is an acute-phase reactant that increases in the circulation in response to a variety of in ammatory stimuli. CRP has traditionally been considered a biomarker of bacterial infection. However, evidence is needed to distinguish bacterial and viral infections with CRP. A study indicated that COPD patients with lower plasma CRP and IL-6 levels had lower grade systemic in ammation and better physical activity [23]. A systematic review suggested that the average CRP levels upon diagnosis were signi cantly higher in patients who developed severe H1N1 influenza compared to their counterparts with a no severe disease.
Furthermore, levels of CRP have been associated with the degree of H1N1 severity [27]. It was noted in this study that a majority of patients with COVID-19 had increased levels of CRP. In this study, CRP was signi cantly higher in severe patients than mild patients, which indicated that CRP may be associated with disease severity. The results of this study showed that CRP was the most signi cant factor that affected the incidence of severe illness, and it had a signi cant predictive value. The AUC of CRP was 0.90, and the cutoff value was 32.9 (Speci city: 83.1%, Sensitivity: 85.4%). A recent study showed that the NLR was the most useful prognostic factor that affected the prognosis for severe patients with COVID-19. In this study, NLR was signi cantly higher in severe group than mild group. NRP can also be considered as one of the warning indexes for critically ill patients. Our study revealed that patients with a CRP > 32.9 mg/L, NLR > 2.9, and age > 53.5 years tended to develop into the severe condition.
In summary, the characteristics of lymphocyte subsets and cytokine pro les between severe and mild patients with COVID-19 were compared in this study. On the basis of the results, risk factors for patients developing a severe condition were identi ed. This is helpful to identify high-risk patients as early as possible and to provide intensive monitoring and treatment, ultimately to reduce the mortality rate of COVID-19 patients.
This study has several potential limitations. First, the retrospective single-center design leads to missing data and unavoidable biases. However, researchers responsible for data collection were trained before the study began so that they could correctly ll out the case report form and reduce errors. In addition, two researchers collected data independently and checked each other's forms for mistakes so as to minimize the bias as much as possible. Second, data were not collected continuously during the patients' hospitalization, and as a consequence, the trend of these clinical and laboratory indicators could not be described.
Fortunately, all of the data was recorded in our electronic medical record system, and we will extract and collect parameters needed for further study in the future.

Conclusions
The degree of lymphocyte subsets decreased and cytokine pro les increased in severe patients as compared to mild patients. The CRP, NLR, and age may serve as powerful prognostic factors for the early identi cation of severe patients. Figure 1 Immune disorders and cytokine storm were considered to be the main causes of damage    The ROC curve for predicting disease severity using CRP, NLR, and age