Characteristics of lymphocyte subsets and inflammatory factors in patients with COVID-19

Abstract

Objective

This research aims to examine the involvement of lymphocyte subsets and inflammatory cytokines in the development and progression of COVID-19.

Methods

164 COVID-19 patients were admitted to hospital between December 2022 and January 2023. Based on lung CT scans and whether it is necessary for intensive care unit (ICU) admission, they were categorized into: severe groups (84) and mild disease groups (80). Peripheral blood were also collected from 101 healthy examinees and 164 patients. Flow cytometry (FCM) was used to measure the absolute and relative counts of lymphocyte subsets, while chemiluminescence was used to detect the level of inflammatory cytokines.

Results

The COVID-19 patient group exhibited lower count of lymphocytes subsets than healthy control group. Moreover, COVID-19 patient case presented higher content of cytokines (IL-6, IL-4, IL-8, IL-10, and TNF-α) expression compared to healthy control case. Within the COVID-19 patient group, individuals with severe disease showed lower counts of lymphocytes subsets than the mild disease case. Furthermore, IL-6 levels in severe case were higher than the mild disease patients case. Multi-variate logistic regression analysis confirmed IL-6 (odds ratio: 0.985 [0.977–0.993]), CD3⁺ T cells (odds ratio:1.007 [1.004–1.010]), CD8⁺ T cells (odds ratio:1.016 [1.009–1.023]), and CD19⁺ B cells (odds ratio:1.011 [1.002–1.020]) independently predicted severe progression. ROC curve results indicated AUC for lymphocytes in patients with severe COVID-19 was 0.8686 (0.8112–0.9260), CD3⁺ T cells was 0.8762 (0.8237–0.9287), CD8⁺ T cells was 0.7963 (0.7287–0.8638), CD4⁺ T cells was 0.8600 (0.8036–0.9164), CD19⁺ B cells was 0.7217 (0.6434–0.8001), NK cells was 0.6492 (0.5627–0.7357), age was 0.6699 (0.5877–0.7521), diabetes was 0.5991 (0.5125–0.6857), and IL-6 was 0.7241 (0.6479–0.8003). Furthermore, the ROC curves for different factors (CD3⁺ T cells, age, IL-6) yielded an AUC of 0.9031 (0.8580–0.9483).

Conclusions

The research indicated that COVID-19 patients experience a decrease in lymphocytes subset and an increase in the inflammatory factor IL-6, particularly in the severe case group. As a result, the count of lymphocyte subset (CD3⁺ T cells) and the content of inflammatory cytokine (IL-6) can serve as predictive markers for assessing the severity of COVID-19 and developing treatment plans efficacy.

1. Introduction

In December 2019, China reported a few cases of pneumonia with an unknown cause [1,2]. Since then, the disease has rapidly spread worldwide, posing a great threat to world health security and human health [3,4]. Through genome sequencing of alveolar lavage fluid samples, the novel virus causing this disease was named SARS-CoV-2, and the World Health Organization (WTO) designated the illness occurred by this virus as COVID-19 pneumonia (COVID-19) [5].

Lymphocytes subsets and inflammation cytokines, play a pivotal role in protecting immune system function [[6], [7], [8]]. Following viral infection, there can be variations in the total counts and subsets of lymphocytes, as well as changes in inflammation cytokines, which may be associated with the pathogenic mechanisms of different viruses [9]. Studies have indicated a significant reduction in lymphocyte subsets in COVID-19 patients. However, the specific proportions, absolute counts of these subsets, and their relationship with cytokines in COVID-19 remain unclear [10,11]. The present research aims at elucidating the changes and correlations between lymphocyte subsets and cytokines patients with COVID-19, with the aim of developing new biomarkers and treatment strategies for this disease.

2. Materials and methods

2.1. Clinical blood sample collection

We enrolled 164 COVID-19 patients hospitalized between December 2022 and January 2023. viral RNA load in nasopharyngeal swab samples was detected using the nucleic acid testing kit, according to the instructions provided by the manufacturers, Shanghai BJ Biotechnology Co., Ltd. and Hunan Shengxiang Bioscience Co., Ltd. Additionally, we included 101 patients who underwent physical examinations at our hospital and designated them as the health control group. This study received approval from the ethics committee at Putuo Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China, and informed consent was obtained from all patients.

Main inclusion criteria: (1) age over 20 years old, (2) availability experimental data. The enrolled people were assigned to the four study groups based on the Diagnosis and Treatment Plan for Novel Coronavirus Infection, 10th edition, issued by the National Health Commission of the People's Republic of China, during hospitalization or admission. These groups included: (1) mild patients, who met the following conditions: (i) cardinal symptoms of upper respiratory tract infection, for instance dry and itchy throat, swollen and painful throat, cough, fever, etc., (ii) positive SARS-CoV-2 RNA RT-PCR detection results; (2) moderate patients, who exhibited (i) continuous high fever lasting more than three days, or other symptoms like cough and shortness of breath, with oxygen saturation greater than 93% at rest, (ii) and infection pneumonia characteristic observed by CT imaging; (3) severe patients: (i) shortness of breath, (ii) with oxygen saturation less than 93%, (iii) arterial partial pressure of oxygen (PaO2)/oxygen uptake (FiO2) ≤ 300 mmHg (1 mmHg = 0.133 kPa), either or (iv) progressive worsening of clinical manifestation s accompanied by significant progression of internal lung foci by more than 50% within 48 h; (4) critical COVID-19 patients, who experienced (i) respiratory failure, (ii) shock, or (iii) other apparatus exhaustion call for ICU monitoring and treatment. For the purpose of this experiment, all patients with COVID-19 were chopped up into two groups: mild group (comprising mild and moderate patients), severe group (comprising severe and critical patients).

2.2. Data collection

The researchers involved in data collection underwent comprehensive and rigorous training prior to the commencement of the study. This training aimed to ensure their proficiency in accurately organizing the case report forms and minimizing errors. The collected data encompass various aspects, such as basic demographic information (age and gender), complications, clinical symptoms, laboratory examination results, admission and discharge summaries, ICU data, and disease severity assessments.

3. Laboratory examination

3.1. Flow cytometry detection

Blood samples (2 mL) were collected from both the two groups people. Briefly, the counts (cells/μL) and ratio of lymphocytes subsets were measured by six-color flow cytometry reagent (BD Multitest 6 color, # 63533) according to the directions of the kit instructions. The result were counted dependent on BD flow cytometry system (BD Biosciences). The lymphocyte subsets in samples were conducted in strict adherence to the manufacturers' instructions.

3.2. Cytokine detection

The levels of cytokines were quantified dependent on the electrochemiluminescence detection system (C2000, Beijing Hotjing Biotechnology Co., Ltd). The experimental operations were carried out in strict adherence to the reagent kit instructions.

3.3. Statistical analysis

Data calculation were conducted using SPSS 21.0 software. All data is represented by means ± SEM and subjected to t-test. The Frequency and percentage in each category are analyzed using categorical variables and chi square or Fisher's exact test. A p < 0.05 was set as statistical difference. Multiple logistic regression analysis is used to identify independent risk factors related to disease severity Logistic regression analysis, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were employed to evaluate the diagnostic value of distinguishing between mild and severe cases. The cutoff values were identified according to Youden's index of the ROC curve.

4. Results

4.1. Basic characteristics of hospitalized patients with COVID-19

164 COVID-19 patients were admitted for treatment. The median age of the patients was 75.00 years, including 102 males (62.2%) and 62 females (37.8%). The most detected comorbidities in the enrolled individuals were hypertension (54.88%) and diabetes (18.9%). Among the symptoms reported, fever (90.24%), cough (92.07%), as well as shortness of breath (46.95%) were the most common. Additionally, some individuals experienced fatigue (15.24%), chills (12.2%), anorexia (3.05%), etc. (Table 1).

Table 1.

Analysis of clinical characteristics between mild patients and severe patients.

Characteristic	Total (n = 164)	Mild patients (n = 80)	Severe patients (n = 84)	P value
Sociodemographic
Age, Median (IQR), years	75.00(18.00)	71.50(16.75)	79.50(16.75)	<0.001
Male, n(%)	102(62.20)	46(57.50)	56(66.67)	0.226
Clinical medical disease
Hypertension, n(%)	90(54.88)	40(50.00)	50(59.52)	0.221
Diabetes, n(%)	31(18.90)	7(8.75)	24(28.57)	0.001
Cancer, n(%)	3(1.83)	1(1.25)	2(2.38)	1.000
Others, n(%)	85(51.83)	33(41.25)	52(61.90)	0.008
Symptom on hospital admission
Fever, n(%)	148(90.24)	71(88.75)	77(91.67)	0.529
Cough, n(%)	151(92.07)	75(93.75)	76(90.47)	0.438
Breath shortness, n(%)	77(46.95)	35(43.75)	42(50.00)	0.423
Fatigue, n(%)	25(15.24)	11(13.75)	14(16.67)	0.603
Chills, n(%)	20(12.20)	7(8.75)	13(15.48)	0.188
Anorexia, n(%)	5(3.05)	0(0.00)	5(5.95)	0.078
Diarrhea, n(%)	0(0.00)	0(0.00)	0(0.00)	1.000
Others, n(%)	4(2.44)	1(1.25)	3(3.57)	0.648

4.2. Alteration of lymphocyte subsets in patients

Firstly, we conducted a statistical analysis on changes in patient lymphocyte subsets. Compared to the healthy individuals, individuals with COVID-19 exhibited significantly decreased levels of CD3⁺ T, CD4⁺ T, CD8⁺ T cells (all P < 0.001, as depicted in Fig. 1A–C,E), CD19⁺ B cells (P < 0.001, Fig. 1H), NK cells (P < 0.001, Fig. 1J), and total lymphocytes (P < 0.001, Fig. 1L). Furthermore, the ratio of lymphocytes in patients case was significantly lower in CD3⁺ cells (P < 0.001 Fig. 1B), CD4⁺ cells (P = 0.03, Fig. 1D), CD8⁺ cells (P < 0.001, Fig. 1F), but significantly increased in NK cells (P < 0.001, Fig. 1K) and CD4+/CD8+ ratio (P < 0.001, Fig. 1G). However, the ratio of CD19⁺ B cells did not show statistical differences(P > 0.05, Fig. 1I).

Fig. 1.

Analysis of lymphocyte subsets between COVID-19 group (CP) and healthy group (HC). ***, P < 0 0.001; *, P < 0 0.05; ns, no statistical differences.

4.3. Correlation between lymphocyte subsets and the progression of COVID-19

In comparison to mild case, individuals with severe COVID-19 displayed significantly decrease in the total count of CD3⁺ (P < 0.001, Fig. 2A), CD4⁺ (P < 0.001, Fig. 2C), CD8⁺ T cells (P < 0.001, Fig. 2E), CD19⁺ B cells (P < 0.001, Fig. 2H), NK cells (P < 0.001, Fig. 2J), and lymphocytes (P < 0.001, Fig. 2L). Furthermore, the ratio of lymphocytes in severe case was significantly lower in CD3⁺ cells (P < 0.001 Fig. 2B), CD4⁺ cells (P < 0.001, Fig. 2D), but significantly increased in NK cells (P < 0.001, Fig. 2K). However, no statistical differences were shown in the CD4+/CD8+ ratio (P = 0.37, Fig. 2D), CD8⁺ ratio (P = 0.11, Fig. 2F), and CD19⁺ B ratio (P = 0.13, Fig. 2I).

Fig. 2.

Analysis of lymphocyte subsets in patients with mild and severe COVID-19. ***, P < 0 0.001; ns, no statistical differences.

4.4. Detection of changes in cytokine levels

We measure the levels of cytokine in COVID-19 individuals using the chemiluminescence method. In comparison to the healthy individuals, individuals with COVID-19 exhibited significantly higher levels of IL-4 (P = 0.04, Fig. 3C), IL-6 (P < 0.001, Fig. 3D), IL-8 (P < 0.001, Fig. 3E), IL-10 (P < 0.001, Fig. 3F), and TNF-α (P < 0.001, Fig. 3G). However, no statistically difference was observed in the levels of IL-1β (P = 0.07, Fig. 3A) and IL-2 (P = 0.31, Fig. 3B).

Fig. 3.

Analysis of cytokines in COVID-19 (CP) patients and healthy people (HC). ***, P < 0.001; *, P < 0 0.05; ns, no statistically difference.

4.5. Correlation between inflammatory cytokine and the progression of COVID-19

In comparison to mild individuals, severe COVID-19 individuals exhibited a higher level of IL-6 in inflammatory cytokines (P < 0.001) (Figure D). However, no statistically difference was observed in the levels of cytokines (IL-1β (P = 0.15), Fig. 4A, IL-2 (P = 0.99), Fig. 4B, IL-4 (P = 0.54), Fig. 4C, IL-8 (P = 0.95), Fig. 4E, IL-10 (P = 0.94), Fig. 4F, and TNF-α (P = 0.24), Fig. 4G).

Fig. 4.

Cytokine levels between mild group and severe group in COVID-19. ***, P < 0 0.001; ns, no statistically difference.

4.6. ROC curve was used to assess the independent predictive factors

In conclusion, in view of the significantly different changes in lymphocyte subsets and IL-6 in patients with COVID-19, multiple logistic regression and ROC curves were plotted to identify independent predictors of aggravation, considering factors such as age, diabetes, lymphocyte subsets, and IL-6 cytokine. The results revealed that IL-6 (odds ratio: 0.985 [0.977–0.993]), CD3⁺ T cells (odds ratio: 1.007 [1.004–1.010]), CD8⁺ T cells (odds ratio: 1.016 [1.009–1.023]), and CD19⁺ B cells (odds ratio: 1.011 [1.002–1.020]) are risk factors for COVID-19 infection aggravation (Table 2).

Table 2.

Multivariate logistic regression was used to predict COVID-19 infection aggravation.

	β	OR	95% CI	P value
IL-6	−0.015	0.985	0.977–0.993	＜0.001
CD3+ cells	0.007	1.007	1.004–1.010	＜0.001
CD8+ cells	0.016	1.016	1.009–1.023	＜0.001
CD19+ cells	0.011	1.011	1.002–1.020	0.020
Constant	−6.739	0.001		＜0.001

Subsequently, we conducted ROC curve to estimate the predictive accuracy of lymphocyte subsets and inflammatory cytokines in determining the progression of COVID-19. The analysis revealed that the AUC for lymphocytes in severe COVID-19 patients was 0.8686 (0.8112–0.9260), (Table 3 and Fig. 5A),CD3+ T cells was 0.8762 (0.8237–0.9287), (Table 3), CD8⁺ T cells was 0.7963 (0.7287–0.8638), (Table 3), CD4⁺ T cells was 0.8600 (0.8036–0.9164), (Table 3), CD19⁺ B cells was 0.7217 (0.6434–0.8001), (Table 3), NK cells was 0.6492 (0.5627–0.7357), (Table 3), IL-6 was 0.7241 (0.6479–0.8003), (Table 3 and Fig. 5B), and age was 0.6699 (0.5877–0.7521), diabetes was 0.5991 (0.5125–0.6857), (Table 3 and Fig. 5C). The ROC result also exhibited that the AUC for different factors (CD3⁺ T cell, age, and IL-6) was 0.9031 (0.8580–0.9483) (Table 3 and Fig. 5D).

Table 3.

ROC curve for predicting COVID-19 infection aggravation.

Predictor	AUC	P value	95% CI	Cutoff value	Jordan index	Sensitvity
Age	0.670	＜0.001	0.588–0.752	＞88.50	0.132	0.515
Diabetes	0.599	0.03	0.512–0.686	＞0.50	0.198	0.913
Lymphocyte	0.869	＜0.001	0.811–0.926	＜600.90	0.371	0.690
CD3+ cells	0.876	＜0.001	0.824–0.929	＜357.00	0.379	0.687
CD4+ cells	0.860	＜0.001	0.804–0.916	＜204.00	0.362	0.686
CD8+ cells	0.796	＜0.001	0.729–0.864	＜99.39	0.298	0.653
CD19+ cells	0.722	＜0.001	0.643–0.800	＜41.56	0.223	0.616
NK cells	0.649	＜0.001	0.563–0.736	＜49.14	0.150	0.576
IL-6	0.724	＜0.001	0.648–0.800	＞66.91	0.225	0.615
Combined predictor	0.903	＜0.001	0.858–0.948	＜0.295	0.698	0.708

Fig. 5.

ROC analysis of different factors in severe group. A ROC analysis of lymphocyte subsets. B ROC analysis of IL-6. C ROC analysis of clinic characteristic (age and diabetes). IL-6D ROC curves of different factors (CD3+T, age, IL-6). ***, P < 0 0.001; **, P < 0.01; *, P < 0 0.05; ns, no statistically difference.

5. Discussion

COVID-19 belongs to the β Coronavirus family. In infected individuals, these coronaviruses can trigger a sustained cytokine and chemokine response known as a cytokine storm, which is it related to increased incidence and mortality of immune disorders [12,13]. The host's immune response, which includes both intrinsic and adaptive immunity, have a crucial role in controlling and resolving SARS-CoV-2 viral infections [14, 15]. The intrinsic immune system identifies viral invasions by recognizing molecular models and is the main host defense mechanism for suppressing viral invasions, as well as coordinating and accelerating the evolution of adaptive immunity [16]. Pattern recognition receptors (PRRs) reply to invading pathogens, triggering the activation of inflammation. This can lead to the production of cytokines that inhibit and reduce viral infection [17]. Intrinsic immune responses typically restrict viral entry, viral translation, viral replication, and viral assembly. Damage-associated molecular patterns (DAMPs) can also stimulate the migration of other immune cells, like monocytes, T cells. Releasing pro-inflammatory cytokines can further increase inflammation. In a sensitive immune system, an elevation in viral load and progression of inflammatory response can result in a cytokine storm. Severe patients may experience elevated content of cytokines, which can ultimately cause multi-organ damage and septic shock. Disease severity and defense are also influenced by the roles of T cells and B cells. Lymphocytes play a vital role in keeping immune balance in the body. Similar to other infectious diseases, viral infections can destroy the body's lymphocyte subsets [18]. Lymphocyte subsets cells contribute to cytoxic responses and humoral immunity against viral infections. Therefore, detecting the alterations of lymphocyte subsets and inflammatory cytokines in patients with novel coronavirus can provide significant insights into the mechanism of COVID-19 destroying the human immune system.

In our research, we performed a analysis of data from mild and severe COVID-19 patients. We specifically focused on examining lymphocyte subsets and cytokine in these individuals. Our findings indicate that the severe case had a higher percentage of older individuals than the mild group. Furthermore, a larger number of severe individuals had pre-existing chronic diseases, like diabetes and hypertension. These results suggest that elderly individuals, particularly those with underlying medical conditions, probably more susceptible to developing severe forms of COVID-19. These results align with others studies [[19], [20], [21]] that have reported similar associations.

Cellular immunity plays a crucial role in combating viral infections, and accumulating evidence suggests that lymphocytes and their subsets are instrumental in maintaining immune system stability and respiratory function [22,23]. During the acute phase of diseases like SARS and MERS, a significant reduction in lymphocyte counts, particularly CD3⁺ and CD4⁺ T cells, has been observed. The degree of T lymphocyte reduction has been closely correlated with disease severity [24,25]. It is important to note that viral infections can disrupt the balance of lymphocyte subsets, although the mechanisms underlying these changes may vary [26]. Regarding COVID-19, several studies have reported a common observation of decreased absolute lymphocyte counts, particularly in severe/critical patients [1,27,28]. Specifically, we found that the CD3+T cell counts in the severe case were lower than that the mild case. CD3⁺ T cells play a important role in clearing viral infections by secreting perforin, granzymes, and interferon. Similarly, CD4⁺ T cells contribute to the co-stimulation of CD8⁺ T cells and CD19⁺ B cells, which are vital in the defense against viral infections [29].

Various studies have identified CD4⁺, CD8⁺ T cells as robust predictors of severity and clinical outcome of COVID-19 [[30], [31], [32]]. However, in this study, we observe no statistical difference in the ratio of these CD4⁺, CD8⁺ T cells or CD4+/CD8+ T cell between severe and mild groups. This suggests that CD4⁺ T and CD8⁺ T lymphocytes are decreased in both the two groups. As CD3⁺ T cells represents total T cells, monitoring CD3⁺ T cells is likely a more appropriate and beneficial biomarker for evaluating the severity and mortality of COVID-19. Understanding these immune status differences can provide insights into the pathological mechanism and clinical appearances of COVID-19 [29]. However, the specific cause underlying lymphopenia in patients with COVID-19 is still unclear and requires further research. Some studies have demonstrated many COVID-19 sufferers experience cytokine storms or inflammatory storms. In SARS patients, increased levels of proinflammatory factors were observed in the serum, such as IL-10, IL-1β, IL-6, and TNF-αwere related to lung inflammation [33]. Recent data also indicate elevated levels of cytokine in COVID-19 patients, like IL-1β, IL-6, IFN-γ [[34], [35], [36]]. Clinical observations have revealed rapid disease progression and lung pathology in patients with high cytokine concentrations, leading to multiple organ failure. In our study, we observed the concentration of IL-6 were elevated in COVID-19 individuals serum. Furthermore, the severe group exhibited significantly higher serum IL-6 concentrations compared to the mild group, consistent with the concept of a cytokine storm [37]. However, no significant differences in IL-2, IL-1β, IL-4, IL-8, IL-10, and TNF-α between the severe and mild patients. The precise mechanisms underlying cytokine changes in COVID-19 patients require further investigation. Monitoring cytokine changes holds importance for the early diagnosis and prevention of severe cases. It is crucial to identify individuals who are sensitive to developing severe COVID-19 disease. Timely monitoring disease severity contributes to informed clinical decision-making [38]. Early identification of severe COVID-19 patients through screening can significantly improve clinical outcomes. Therefore, it is crucial to identify potential predictors of disease severity and outcomes to find individuals who require care, such as ICU admission, special care, and other special treatment. Our study focuses on COVID-19 patients and categorizes them into two cases based on disease progression: mild and severe. The clinicopathological characteristics, laboratory data of enrolled individuals were analyzed. Multiple logistic regression, ROC curve analysis were conducted, AUC and cut-off values were calculated. The study revealed that CD3+T cells and IL-6 were the key factors to predict the progress and development of COVID-19. The AUC of 0.8762 for CD3⁺ T cells and 0.7241 for IL-6. The combined predictors, including age, CD3⁺ T cells, and IL-6, exhibited an AUC of 0.9031.

In conclusion, this study compared the conduction of lymphocyte subsets and the inflammatory cytokines in COVID-19 patients varying severity. The results provide valuable viewpoints into predictive factors for the progress and development of COVID-19, facilitating early intervention and monitoring, and ultimately reducing COVID-19 mortality rates.

6. Conclusions

The lymphocyte subsets and serum inflammatory cytokines changes have demonstrated a strong correlation with 2019 coronavirus disease (COVID-19). In comparison to mild COVID-19 patients, severe cases exhibited a decrease in lymphocyte subset count and an increase in serum IL-6 levels. Notably, the alterations of CD3+T cells and IL-6 can be regarded as independent factor of the development of COVID-19 in the early stage. These findings have great significance in exploring the disease mechanism and severity of COVID-19, besides, for the development of treatment strategies and novel biological markers for the disease.

Funding

This work was supported by the Shanghai Science and Technology Plan Project General Project 23ZR1456400 (Z.X. Chen), Shanghai Municipal Commission of Health and Health Clinical Subject Youth Project 20214Y0502 (Z.X. Chen).

Ethics approval and consent to patient

This study was reviewed and approved by the Ethics Committee of The Pu Tuo Hospital of Shanghai university of Chinese medicine, with the approval number: [PTEC-R-2023-28(Y)-1]. All participants/patients provided informed consent to participate in the study.

Data availability statement

All data during this study are included in this published article. Requests to the data should be directed to the corresponding author.

CRediT authorship contribution statement

Zixi Chen: Writing – review & editing, Writing – original draft, Software, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation. Jinpeng Li: Software. Jin Zheng: Methodology. Fenfen Xiang: Methodology. Xiaoxiao Li: Methodology. Mengzhe Zhang: Methodology. Xiangdong Kang: Investigation. Rong Wu: Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.