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. 2022 Feb 14;13:802228. doi: 10.3389/fphar.2022.802228

Increased Circulating Cytokines Have a Role in COVID-19 Severity and Death With a More Pronounced Effect in Males: A Systematic Review and Meta-Analysis

Huating Hu 1,†, Hudan Pan 1,2,†, Runze Li 1,2, Kancheng He 3, Han Zhang 4, Liang Liu 1,*
PMCID: PMC8883392  PMID: 35237162

Abstract

Background: Coronavirus disease 2019 (COVID-2019), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a worldwide epidemic and claimed millions of lives. Accumulating evidence suggests that cytokines storms are closely associated to COVID-19 severity and death. Here, we aimed to explore the key factors related to COVID-19 severity and death, especially in terms of the male patients and those in western countries.

Methods: To clarify whether inflammatory cytokines have role in COVID-19 severity and death, we systematically searched PubMed, Embase, Cochrane library and Web of Science to identify related studies with the keywords “COVID-19″ and “cytokines”. The data were measured as the mean with 95% confidence interval (CI) by Review Manager 5.3 software. The risk of bias was assessed for each study using appropriate checklists.

Results: We preliminarily screened 13,468 studies from the databases. A total of 77 articles with 13,468 patients were ultimately included in our study. The serum levels of cytokines such as interleukin-6 (IL-6), IL-10, interleukin-2 receptor (IL-2R), tumor necrosis factor (TNF)-α, IL-1β, IL-4, IL-8 and IL-17 were higher in the severity or death group. Notably, we also found that the circulating levels of IL-6, IL-10, IL-2R and TNF-α were significantly different between males and females. The serum levels of IL-6, IL-10, IL-2R and TNF-α were much higher in males than in females, which implies that the increased mortality and severity in males was partly due to the higher level of these cytokines. Moreover, we found that in the severe and non-survivor groups, European patients had elevated levels of IL-6 compared with Asian patients.

Conclusion: These large-scale data demonstrated that the circulating levels of IL-6, IL-10, IL-2R, IL-1β, IL-4, IL-8 and IL-17 are potential risk factors for severity and high mortality in COVID-19. Simultaneously, the upregulation of these cytokines may be driving factors for the sex and region predisposition.

Keywords: COVID-19, cytokines, sex bias, mortality, meta-analysis

Introduction

Coronavirus disease 2019 (COVID-2019), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has raised major public health crises since 2019. Though many patients with COVID-19 present no symptoms or only mild symptoms (including fever, cough, and fatigue), some suffer severe symptoms and may progress to pneumonia, acute respiratory distress syndrome (ARDS), multi organ dysfunction and even death. The severity of COVID-19 is known to be closely correlated to cytokines storms, when the immune system is unable to counteract the virus, cytokine storms in patients may lead to macrophage hyperactivity and further systemic abnormal reactions (Lang et al., 2020; Liang et al., 2020; Makaronidis et al., 2020). However, the characteristics of the cytokine storms in COVID-19 patients have not been fully illustrated.

In death cases, patients with COVID-19 shows a higher risk of mortality in males sex (Griffith et al., 2020). According to the largest sex-disaggregated data from 47 countries, men with COVID-19 have higher morbidity than women with COVID-19 (63.8% men; 36.2% women). In addition, the overall mortality of COVID-19 is more than 2.3 times higher in men than in women (Control and Response, 2020). The discrepancy in COVID-19 outcomes between male and female patients may be attributed to several biological and social factors, especially cytokine storms (Griffith et al., 2020). Moreover, as the Covid-19-related literatures grows increasingly, the racial and ethnic disparities showed that the death and severity rate of Asians are lower than the other population (Tirupathi et al., 2020a; Mackey et al., 2021a).

To this end, we conducted a systematic review and meta-analysis to identify the key factors associated to COVID-19 severity and death, especially in terms of the sex and race bias detected in severe COVID-19 patients.

Methods

Search Strategy

We screened databases (Web of Science, Embase, the Cochrane Library, and PubMed) from December 2019 to June 2021. We also registered on the INPLASY (International Platform of Registered Systematic Review and Meta analysis Protocols platform). The number for our study is INPLASY2021120050. To search for more articles, we also screened related reference lists from relevant studies. The search terms included (“2019 novel coronavirus disease”) OR (“COVID19”) OR (“COVID-19 pandemic”) OR (“SARS-CoV-2 infection”) OR (“COVID-19 virus disease”) OR (“2019 novel coronavirus infection”) OR (“2019-nCoV infection”) OR (“coronavirus disease 2019”) OR (“coronavirus disease-19”) OR (”2019-nCoV disease”) OR (“COVID-19 virus infection”) OR (“cytokines”).

Inclusion and Exclusion Criteria

All the included studies met the following criteria: 1) the types of studies considered for inclusion were prospective or retrospective cohort studies comparing mild groups and severe groups; 2) the circulating levels of cytokines were analyzed before treatment. The exclusion criteria were reviews, studies of interventions other than cytokines, in vitro studies and in vivo animal experiments. To further reduce the accidental error of our study, each analysis of cytokines should contain more than two studies. Only English studies were screened in our study. After screening and collecting the literature, two authors removed duplicate publications by Endnote and independently evaluated each study based on their title and abstract. The symptom criteria are listed as follows.

For the mild group, patients had respiratory symptoms (fever, cough, fatigue, anorexia, headache), without evidence of viral pneumonia or hypoxia.

For the severe group, patients had one or more of the following conditions: respiratory distress, respiratory rate ≥30 times/minute, oxygen saturation (SpO2) ≤93% at rest, arterial partial pressure of oxygen (PaO2)/Fraction of inspiration O2 (FiO2) in arterial blood ≤300 mmHg, >50% lung imaging progress in the short term within 24–48 h, respiratory failure and mechanical ventilation required, shock, combined with other organ failure, and transfer to the intensive care unit (8).

Data Extraction and Quality Assessment

Two authors (Hu & Pan) collected data from the included studies, including the first author, study country, inclusion time, age, sex, sample sizes, mild group/severe group, survivors/non-survivors, study design, and outcomes. Another two authors assessed the quality of the studies using the Newcastle-Ottawa Scale (NOS) and scored points for each included study independently.

Statistical Analysis

Review Manager 5.3 was used to perform all statistical analyses. The mean and standard deviation (SD) were used as measurements across articles. We calculated the sample mean and SD by the sample size and interquartile range (IQR) (Wan et al., 2014; Luo et al., 2018). The circulating levels of cytokines between different groups were collected from the selected articles and analyzed using a random-effects model when I2>50%. The standard Cochran’s Q test and I2 statistics were also used to identify heterogeneity from the included articles. Significant heterogeneity was determined when I2 value > 50% and p-value <0.05.

Results

Large Scale Data From Clinical Reports

A total of 13,468 studies were screened out by the database search. After removing 826 duplicates, we excluded 8452 articles by reading the titles and abstracts of the studies. Then, we read the remaining literature and excluded studies that were not matched to the inclusion and exclusion criteria. There were 77 articles with 13,986 patients ultimately included in this study (Han et al., 2020; Yang et al., 2020; Rutkowska et al., 2021) (Figure 1). The baseline features of all included studies are presented in Table 1.

FIGURE 1.

FIGURE 1

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Literature search and screening process.

TABLE 1.

Basic characteristics of 77 studies included in Meta-analysis.

Author Study region Inclusion time Mean age (years) gender Sample sizes Mild group/Severe groups or survival/non-survival groups Study design Outcomes Journal types
Ai-Ping Yang Yang et al. (2020) China N/A 46.4 60% male 93 69/24 retrospective cohort IL-6, IL-10, TNF-α, IL-1β, IL-4, IL-8, IL-17 Normal
Bo Xu Xu et al. (2020a) China 26 Dec 2019 to 1 Mar2020 62 55% male 187 159/28 retrospective observational study IL-6, IL-10, TNF-α, IL-1β Normal
Changcheng Zheng Zheng et al. (2020a) China 15 Feb 2020 60 43.6 male 55 34/21 retrospective observational study IL-6 Normal
Changsong wang Wang et al. (2020a) China N/A 62.9 50% male 45 33/12 retrospective cohort IL-6, IL-10, IL-4 Normal
Chaomin Wu Wu et al. (2020a) China 25 Dec 2019, to 26 Jan 2020 51 43.7% male 201 117/84 retrospective cohort IL-6 Normal
Chuan Qin Qin et al. (2020a) China Jan 10 to 12 Feb 2020 58 52% male 452 166/286 retrospective observational study IL-6, IL-2R, IL-10, TNF-α, IL-8 Normal
Egon Burian Burian et al. (2020) German Mar and April 2020 61.54 35% male 65 37/28 retrospective cohort IL-6 Normal
Fangfang Liu Liu et al. (2020a) China Jan 20 to 23 Feb 2020 48 55.38% male 65 42/23 retrospective cohort IL-6 Normal
Fei Zhou Zhou et al. (2020a) China 29 Dec 2019 to 31 Jan 2020 56 62% male 191 137/54 retrospective cohort IL-6 Normal
Fengqin Zhang Zhang et al. (2020a) China Feb to March 2020 N/A N/A 34 27/7 retrospective observational study IL-6, IL-10, TNF-α, IL-8 Normal
Guang Chen Chen et al. (2020a) China Jan 2–7, 2020 56 81% male 21 10/11 retrospective observational study IL-6, IL-2R, IL-10, TNF-α, IL-8 Normal
Haijun Wang (Wang et al., et al.) China Jan 2 to 5 Feb 2020 49 43.6% male 83 33/50 retrospective cohort IL-6 Normal
Han Huang Han et al. (2020) China Jan 2020 and February 2020 N/A 50% male 102 42/60 retrospective cohort IL-6, IL-10, TNF-α, IL-4 Normal
Hong Huang Huang et al. (2020) China Feb and March 2020 36 46% male 31 27/4 retrospective cohort IL-6, IL-10, TNF-α, IL-2R Normal
Hua Fan Fan et al. (2020) China 30 Dec 2019 to 16 Feb 2020 58.36 67% male 73 47/26 retrospective observational study IL-6 Normal
Huizheng Zhang Zhang et al. (2020b) China N/A N/A 51.2% male 43 29/14 retrospective observational study IL-6, IL-10, TNF-α, IL-17 Preprint
Jia Ma Ma et al. (2020) China 1 Jan 2020 to 30 Mar 2020 62 54.5% male 37 17/20 retrospective observational study IL-6 Normal
Lang Wang Wang et al. (2020b) China Jan 1 to 6 Feb 2020 71 49% male 339 274/65 retrospective observational study IL-6 Normal
Lei Liu Liu et al. (2020b) China N/A 45 62.7% male 51 44/7 retrospective case series IL-6 Preprint
Lucas Quartuccio Quartuccio et al. (2020) Italy N/A 66.5 79% male 24 18/6 retrospective cohort IL-6 Normal
Maria effenberger Effenberger et al. (2020) Austria 26th February to 21st April 2020 60.69 62.5% male 96 81/15 retrospective case series IL-6 Normal
María J. Pérez-Sáez Pérez-Sáez et al. (2020) Spain 18th March 2020 59.3 67.5% male 80 54/26 retrospective case series IL-6 Normal
Mario Fernández‐Ruiz Fernández-Ruiz et al. (2020) Spain 16th March to 27th March 2020 46.8 65.9% male 88 39/49 retrospective cohort IL-6 Normal
Marta Crespo Crespo et al. (2020) Spain Mar to April 2020 71 75% male 16 8/8 Prospective cohort study IL-6 Normal
Miao Luo Luo et al. (2020) China Jan and March 2020 61 51.2% male 1018 817/201 retrospective cohort IL-2R, IL-6, IL-10, TNF-α, IL-8 Normal
Michael Dreher Dreher et al. (2020) German Feb and March 2020 65 66% male 50 26/24 retrospective case series IL-6 Normal
Ming Ni Ni et al. (2020) China 1 to 21 February 2020 60 50% male 27 male 14/female 13 retrospective case series IL-6, IL-10, TNF-α Normal
Paola Toniati Toniati et al. (2020) Italy Mar 9th and 20 Mar 2020 62 88% male 100 77/23 retrospective case series IL-6 Normal
Pingzheng Mo Mo et al. (2020) China Jan 1st to 5 Feb 2020 54 55.5 male 155 70/85 retrospective observational study IL-6 Normal
Qin Lu Qin et al. (2020b) China 26 January 2020 and 5 February 2020 55.2 57.9 male 233 135/98 retrospective cohort IL-6, IL-2R, IL-10, TNF-α Normal
Qiurong Ruan Ruan et al. (2020) China N/A N/A N/A 150 82/68 retrospective observational study IL-6 Normal
Ruirui Wang Wang et al. (2020c) China Jan 20 to 9 Feb 2020 38.7 57% male 125 100/25 retrospective descriptive study IL-6 Normal
Shaohua Li Li et al. (2020a) China 20 Jan 2020, to 20 Mar 2020 48.5 58% male 69 43/26 retrospective cohort IL-6, TNF-α, IL-1β, IL-8 Normal
Susu He He et al. (2020) China Jan 17 to 12 Feb 2020 44.5 53% male 93 60/33 retrospective cohort IL-6. IL-10 Normal
Suxin Wan Wan et al. (2020) China 26 January to 4 February 2020 43.1 53.6% male 123 102/21 retrospective observational study IL-6, IL-10, TNF-α, IL-4, IL-17 Normal
Takahisa Mikami Mikami et al. (2020) United States Mar and April 2020 59 54.5% male 2820 2014/806 retrospective cohort IL-6, TNF-α, IL-8 Normal
Tao Chen Chen et al. (2020b) China 13 January to 12 February 2020 62 62% male 274 161/113 retrospective descriptive study IL-6 IL-2R, IL-10 TNF-α, IL-8 Normal
TAO Liu Liu et al. (2020c) China December 2019 to July 2020 53.9 42.2% male 77 11/66 retrospective cohort IL-6, IL-10 Normal
Tielong Chen Chen et al. (2020c) China 1 Jan 2020, to 10 Feb 2020 54 53.2% male 55 36/19 retrospective case series IL-6 Normal
Tobias Herold Herold et al. (2020) German Feb 29 to 27 Mar 2020 61 70% male 89 57/32 retrospective case series IL-6 Normal
Wenjun Tu Tu et al. (2020) China 3 Jan to 24 February 2020 70 76% male 174 149/25 retrospective case series IL-6 Normal
Xiaohong Yuan Yuan et al. (2020) China Feb 15 to 30 Mar 2020 67 47.9% male 117 61/56 retrospective cohort IL6, IL-10, IL-4 Normal
Xia Xu Xu et al. (2020b) China 3 Feb 2020, to 20 Mar 2020 57 40.91% male 88 47/41 retrospective descriptive study IL-6 IL-2R, TNF-α, IL-8 Normal
Xiong Bei (Xiong et al., 2020) China 21 Mar 2020 66 61.4% male 57 19/38 retrospective case series IL-6 Normal
Yang Liu Liu et al. (2020d) China 22 Jan 2020, to 15 Feb 2020 45 64.4% male 76 46/30 retrospective case series IL-6, IL-2R, IL-10, IL-1β, IL-8 Normal
Yang Xu Xu et al. (2020c) China N/A 57 50.7% male 69 44/25 retrospective cohort IL-6 Preprint
Yang Xu 2 Xu (2020) China N/A N/A N/A 10 8/2 retrospective observational study IL-6 Preprint
Yang Zhao Zhao et al. (2020) China Jan 13 and 4 Mar 2020 58 47.3% male 539 414/125 retrospective observational study IL-6 Normal
Yangjing Xie Xie et al. (2020) China Feb and March 2020 66 43.5% male 62 38/24 retrospective cohort IL-6 Normal
Yanli Wang Wang et al. (2020d) China 25 Jan 2020 and 8 Mar 2020 52 65% male 43 35/8 retrospective observational study IL-6, IL-10, IL-4 Normal
Yaqing Zhou Zhou et al. (2020b) China 28 Jan 2020 to 2 Mar 2020 66 65.9% male 21 8/13 retrospective case series IL-6 Normal
Yi Li Li et al. (2020b) China 28 January 2020, to 12 March 2020 6 56.8% male 125 48/77 retrospective case series IL-6, IL-10, TNF-α, IL-4 Normal
Ying Chi Chi et al. (2020) China N/A 45.21 56% male 66 58/8 retrospective case series IL-6, IL-2R, IL-10, TNF-α, IL-1β, IL-4, IL-8, IL-17 Normal
Yingjie Wu Wu et al. (2020b) China 29 December 2019 to 20 February 2020 61 63.3% male 71 32/39 retrospective case series IL-6, IL-10, TNF-α, IL-4 Normal
Ying Sun Sun et al. (2020) China N/A 47 58.7% male 63 19/44 retrospective case series IL-6 Normal
Yi Zheng Zheng et al. (2020b) China Jan. 22 and Mar. 5, 2020 66 67.6% male 34 19/15 retrospective cohort IL-6, IL-10 Normal
Yong Gao Gao et al. (2020) China 23 Jan 2020 to 2 Feb 2020 44 60.6% male 43 28/15 retrospective case series IL-6 Normal
Zhe Zhu Zhu et al. (2020) China Jan 23 to Feb20, 2020 50.9 36.43% male 127 111/16 retrospective cohort IL-6, IL-10, TNF-α, IL-4 Normal
Zhihua Lv Lv et al. (2020) China 4 Feb 2020 to Feb28, 2020 62 49.4% male 354 115/239 retrospective cohort IL-6, IL-10, TNF-α, IL-4 Normal
Zhilin Zeng Zeng et al. (2020) China 28 Jan 2020, to 12 Feb 2020 62 51.1% male 317 93/224 retrospective cohort IL-6, IL-2R, IL-10, TNF-α Normal
Zhongliang Wang Wang et al. (2020e) China Dec 2019 to February 2020 42 46% male 69 55/14 retrospective cohort IL-6, IL-10, TNF-α, IL-4 Normal
Sophie Hue Hue et al. (2020) France Mar 2020 60 91% male 38 25/13 retrospective cohort IL-6, IL-10 Normal
Elzbieta Kalicinska Kalicińska et al. (2021) Poland Dec 2020 62 52% male 82 51/31, 54/28 Prospective cohort IL-6, TNF-α Normal
Dianming Li Li et al. (2020c) China Mar 2020 56 62.5% male 65 41/24 retrospective cohort IL-6 Normal
Francisco Javier Gil-Etayo Gil-Etayo et al. (2021) Spain Sep 2020 55 67% male 34 28/6 Prospective cohort IL-6, IL-10 Normal
Feng Gao Gao et al. (2021) China Feb 2020 49 42.5% male 121 102/19 retrospective cohort IL-6, IL-10 Normal
Wei Zhu Zhu et al. (2021) China Mar 2020 65 45% male 1106 675/431 retrospective cohort IL-6, IL2R, TNF-α, IL-8 Normal
Zirui Meng (Meng et al. (2021) China Apr 2020 48 53% male 98 71/27 retrospective cohort IL-6, IL-10, TNF-α, IL-8 Normal
Chenze Li Li et al. (2020d) China Apr 2020 63 49.6% male 989 770/219, 141/78 retrospective cohort IL-6, IL-2R, IL-10, TNF-α, IL-8 Normal
Brahim Belaid Belaid et al. (2021) Algeria Apr 2020 59 70.18% male 57 31/26 retrospective cohort IL-6, TNF-α Normal
Rocio Laguna-Goya Laguna-Goya et al. (2020) Spain Apr 2020 52 63.3% male 501 465/36 Prospective cohort IL-6 Normal
Jose J. Guirao Guirao et al. (2020) Spain Apr 2020 65 52% male 50 42/8, 36/14 retrospective cohort IL-6 Normal
Jose Marıa Galvan-Roman Galván-Román et al. (2021) Spain Mar 2020 63 66% male 146 102/44 retrospective cohort IL-6 Normal
Li-Da Chen (hen et al. (2020d) China Mar 2020 52 50% male 94 69/25 retrospective cohort IL-6, IL-2R, TNF-α, IL-8 Normal
Lucía Guillén Guillén et al. (2020) Spain Apr 2020 62 73% male 64 49/15 retrospective cohort IL-6 Normal
Enrico Maria Trecarichi Trecarichi et al. (2020) Italy May 2020 80 57.1% male 48 34/14 retrospective cohort IL-6 Normal
Elzbieta Rutkowska Rutkowska et al. (2021) Poland Jan 2021 56 56% male 38 23/15 retrospective cohort IL-6 Normal
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Studies were published between December 2019 and June 2021. Among the 77 studies, 57 studies were performed in China, eight in Spain, three in Germany, three in Italy, two in Poland, and one each in Austria, the USA, France and Algeria. Seventy-three studies were published in normal journals, and four were published in preprint journals.14 cytokines were reported in these 77 studies, including IL-1β, IL-2, IL-2R, IL-4, IL-5, IL-6, IL-8, IL10, IL-15, IL-17, TNF-α, IFN-γ, MCP-1, and CXCL-10. Review Manager 5.3 was used to calculate and compare the sample mean and SD by the sample size and interquartile range. After removing the cytokines that having no statistical difference in either severe or death group, the cytokines that only contain two articles were also removed. Totally eight cytokines were included in our meta-analysis, containing IL-1β, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-17, and TNF- α. Furthermore, we also screened the cytokines associated with gender or regions of COVID-19 patients. IL-2R, IL-6, IL-10 and TNF-α, which were correlated with gender or regions of COVID-19 patients, were finally presented in this study. All the included studies detected the serum levels of IL-6, while 13 studies focused on IL-2R, 31 studies analyzed IL-10 and 29 studies were related to the serum levels of TNF-α. Five studies analyzed IL-1β, 12 studies analyzed IL-4, 11 studies analyzed IL-8 and IL-17 was studies by four studies. Moreover, five studies analyzed the correlation between genders and cytokines. Fifty-seven and twenty-four studies analyzed the serum levels of cytokines in severity and mortality groups. All 77 studies had NOS quality scores greater than 6, indicating that all these studies have high levels of quality, as shown in Table 2.

TABLE 2.

Methodological quality of the 77 studies based on the NOS for studies.

First author Study design Selection Comparability Assessment of outcome Total quality scores
Ai-Ping Yang Cohort *** ** ** 7
Bo Xu Cohort *** ** *** 8
Changcheng Zheng Cohort ** ** *** 7
Changsong wang Cohort *** ** *** 8
Chaomin Wu Cohort *** ** ** 7
Chuan Qin Cohort **** ** *** 9
Egon Burian Cohort *** ** ** 7
Fangfang Liu Cohort *** ** ** 7
Fei Zhou Cohort ** ** *** 7
Fengqin Zhang Cohort *** ** *** 8
Guang Chen Cohort *** ** ** 7
Haijun Wang Cohort *** ** ** 7
Han Huang Cohort *** ** *** 8
Hong Huang Cohort **** ** *** 9
Hua Fang Cohort *** ** ** 7
Huizheng Zhang Cohort *** ** *** 8
Jia Ma Cohort *** ** ** 7
Lang Wang Cohort **** ** *** 9
Lei Liu Cohort **** ** *** 9
Lucas Quartuccio Cohort *** ** ** 7
Maria effenberger Cohort *** ** *** 8
María J. Pérez-Sáez Cohort *** ** ** 7
Mario Fernández‐Ruiz Cohort **** ** *** 9
Marta Crespo Cohort **** ** * 7
Miao Luo Cohort *** ** *** 8
Michael Dreher Cohort *** ** *** 8
Ming Ni Cohort *** ** *** 8
Paola Toniati Cohort *** ** ** 7
Pingzheng Mo Cohort **** ** * 7
Qin Lu Cohort **** ** *** 9
Qiurong Ruan Cohort **** ** * 7
Ruirui Wang Cohort ** ** *** 7
Shaohua Li Cohort ** ** *** 7
Sophie Hue Cohort *** ** *** 8
susu He Cohort **** ** * 7
Suxin Wan Cohort *** ** *** 8
Takahisa Mikami Cohort **** ** ** 8
Tao Chen Cohort *** ** *** 8
TAO Liu Cohort *** ** *** 8
Tielong Chen Cohort **** ** * 7
Tobias Herold Cohort **** ** *** 9
Wenjun Tu Cohort *** ** *** 8
Xia Xu Cohort **** ** ** 8
Xiaohong Yuan Cohort *** ** *** 8
Xiong Bei Cohort *** ** *** 8
Yang Liu Cohort ** ** *** 7
Yang Xu Cohort ** ** *** 7
Yang Xu 2 Cohort **** ** *** 9
Yang Zhao Cohort *** ** *** 8
Yangjing Xie Cohort *** ** *** 8
Yanli Wang Cohort *** ** *** 8
Yaqing Zhou Cohort **** ** ** 8
Yi Li Cohort *** ** *** 8
Yi Zheng Cohort *** ** ** 7
Ying Chi Cohort *** ** *** 8
Ying Sun Cohort *** ** *** 8
Yingjie Wu Cohort **** ** *** 9
Yong Gao Cohort *** ** *** 8
Zhe Zhu Cohort ** ** ** 6
Zhihua Lv Cohort **** ** *** 9
Zhilin Zeng Cohort *** ** *** 8
Zhongliang Wang Cohort *** ** *** 8
Elzbieta Kalicinska Cohort **** ** *** 9
Dianming Li Cohort *** ** *** 8
Francisco Javier Gil-Etayo Cohort *** ** *** 8
Feng Gao Cohort *** ** *** 8
Wei Zhu Cohort ** ** *** 7
Zirui Meng Cohort *** ** *** 8
Chenze Li Cohort **** ** *** 9
Brahim Belaid Cohort **** ** ** 8
Rocio Laguna-Goya Cohort **** ** *** 9
Jose J. Guirao Cohort *** ** *** 8
Jose Marıa Galvan-Roman Cohort **** ** *** 9
Li-Da Chen Cohort **** ** * 7
Lucía Guillén Cohort *** ** ** 7
Enrico Maria Trecarichi Cohort *** ** *** 8
Elzbieta Rutkowska Cohort *** ** *** 8
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Proinflammatory Cytokines as the Driving Factor for Severity and High Mortality in COVID-19 Patients

To determine whether the circulating levels of inflammatory cytokines are risk factors for severity and mortality of COVID-19 patients, we classified the patients into mild and severe groups. There were 57 studies and 7,807 patients included in this meta-analysis. Compared to patients in the mild group, circulating levels of IL-6 was found to be significantly increased in patients in the severe group (19.76 [16.59, 22.93], p < 0.00001, Supplementary Figure S1). The serum level of IL-6 in the non-surviving group was also significantly elevated compared with that in the surviving group (52.33 [44.16, 60.50], p < 0.00001, Supplementary Figure S2). In addition to IL-6, the serum levels of IL-2R, IL-10, IL-1β, IL-4, IL-8, IL-17 and TNF-α were also elevated in both severe and non-surviving COVID-19 patients (Supplementary Figures S3–S6). Suggesting that the upregulation of these cytokines were correlated with the prognosis of COVID-19 patients.

Alterations of the Distinctive Cytokines Are Related to Sex Bias in COVID-19 Patients

In this meta-analysis, four cytokines were found to be correlated with severity of male COVID-19 patients. Five studies reporting circulating interleukin-6 (IL-6) levels in male (n = 488) and female (n = 509) COVID-19 patients were included. In addition, interleukin-2 receptor (IL-2R), interleukin-10 (IL-10) and tumor necrosis factor α (TNF-α) were also different between male and female patients. Compared to female patients, the expression levels of circulating IL-6 (11.76 [7.56, 15.96], p < 0.000001), IL-2R (85.75 [3.91, 167.59], p = 0.04), IL-10 (1.54 [0.99, 2.08], p < 0.00001) and TNF-α (1.39 [0.81, 1.97], p < 0.00001) were found to be significantly elevated in male patients (Figure 2). Additionally, we conducted a sensitivity analysis to confirm the robustness of the model, and a significant sex gap was detected in circulating levels of IL-6, IL-2R, IL-10 and TNF-α.

FIGURE 2.

FIGURE 2

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Forest plot for the male and female groups. The serum levels of IL-6 levels in the groups of male and female (A). The serum levels of IL-2R levels in the groups of male and female (B). The serum levels of IL-10 levels in the groups of male and female (C). The serum levels of TNF-α levels in the groups of male and female (D).

The Levels of IL-6 Related to Severity and High Mortality in COVID-19 Patients From Different Continents

We further analyzed the correlation between cytokines and continents. We classified the articles into Asia, Europe, Africa and North America groups, and there were 840 European patients, 6,910 Asian patients and 57 African patients in the selected studies. To better interpret the differences between countries, we compared the ages, sex distributions and the severe rate of the included patients in the two territories. Results showed that ages and the proportions of severe or dead patients were comparable, while the male patients in the severe COVID-19 patients in Europe was significantly higher than that in Asia (Supplementary Tables 1, 2). The results of our meta-analysis showed that Asian, European, and African patients with severe COVID-19 had elevated circulating IL-6 levels and the circulating IL-6 levels of European and African was higher than the Asian patients (Figure 3). Notably, we found that there were 997 Asian, 223 European, 19 African and 1007 North American in the analysis of mortality. Among them, all the death patients with COVID-19 had higher IL-6 levels than the survive patients. Moreover, Asian death patients still the have the lowest circulating IL-6 levels than the other continents’ patients (Figure 4). Unlike IL-6, the serum level of IL-10 had the potential to predict the risk of mortality in Asian patients, but it showed no correlation with mortality in European patients (Supplementary Figure S7).

FIGURE 3.

FIGURE 3

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The serum levels of IL-6 in the different continent of mild and severe.

FIGURE 4.

FIGURE 4

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The serum levels of IL-6 in the different continents of the alive and death.

Discussion

The SARS-CoV-2 S protein engages with the host ACE2 receptor and is subsequently cleaved at S1/S2 and S2′ sites by TMPRSS2 protease, which leads to activation of the S2 domain and drives fusion of the viral and host membranes. The secretion of interferon is the first step to start the antiviral program. Alveolar cells are an important part of the epithelial endothelial barrier. After respiratory epithelial cells were first infected by virus, virus infection activates pattern recognition receptors in these cells, triggering the production and release of type I and type III interferons (IFNs) and other proinflammatory mediators (such as cytokines, chemokines and antimicrobial peptides), so as to start the host’s innate and acquired immune response, which further activated the secondary cytokines (such as IL-10, IFN-γ, MCP-1, IL-4, and IL-17) and lead to cytokines storm (Vabret et al., 2020). In the mild patients, immune cells have the ability of eliminating viruses completely and inhibit the them from invading alveoli, which lead to low cytokines in serum (Figure 5). In this study, we identified that the serum levels of IL-6, IL-2R, IL-10, TNF-α, IL-1β, IL-4, IL-8 and IL-17 were significantly elevated in the severe or death cases and probably play crucial roles in the progression of COVID-19. Male sex was identified as a hazard for more severe disease and higher mortality in COVID-19 (Takahashi et al., 2020; Zeng et al., 2020). The recognition of how sex influences COVID-19 outcomes have important significance for clinical management and remission tactics. In this large-scale worldwide meta-analysis, the related cytokines affecting the development of severe disease in male patients were identified and the serum of IL-6, as well as IL-10, IL-2R and TNF-α, in males was obviously higher than that in females.

FIGURE 5.

FIGURE 5

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Increased circulating cytokines affect the development of COVID-19. The SARS-CoV-2 S protein engages with the host ACE2 receptor and is subsequently cleaved at S1/S2 and S2’ sites by TMPRSS2 protease. In the severe patients, COVID-19 invades the alveoli and activates innate immune responses to primary cytokines, such as type I and III IFNs, IL-6 and TNF-α, which further evokes the secondary cytokines and leads to cytokines storm. In the mild patients, immune cells have the ability of eliminating viruses and inhibiting them from invading alveoli, which leads to down-regulate cytokines in serum. 1The cytokines have significant differences between mild and severe groups. 2The cytokines between alive and death groups have significant differences. 3This cytokines have significant differences between male and female groups. 4This cytokines have significant differences between different regions.

IL-6, the core factor of “cytokine storm”, plays a pivotal role in the severity and high mortality of COVID-19. It enhances the production of TNF-α and IL-8 by stimulating the differentiation of T follicular helper cells, inhibits antiviral helper T cell 1 (Th1) cell commitment and improves the differentiation of helper T cell 2 (Th2) cells by regulating the circulating of IL-4 and interferon γ (IFN-γ) (Ahmadpoor and Rostaing, 2020; Wu and Yang, 2020). Moreover, elevated levels of IL-6 lead to acute lung injury and suppress the functions of T lymphocytes, macrophages and dendritic cells, which impair the immune system (Zhang et al., 2004). Tocilizumab, an IL-6 antagonist, revealed good capacity in inhibiting inflammation and cytokine storms in COVID-19 and various clinical studies have verified the beneficial effect of IL-6 and its receptor antagonists in treating severe and critical COVID-19 patients (Xu et al., 2020d; Potere et al., 2021). Besides IL-6, TNF-α inhibitor can also reduce lung exudation and inflammatory reactions, it has been used in the treatment of patients with covid-19 patients (Tirupathi et al., 2020b; Mackey et al., 2021b). However, blocking IL-6 and TNF-α inhibitor may not be used to all patients due to its potential adverse events and expensive price (Wang et al., 2020f; Keewan et al., 2021). The identification of which COVID-19 patients are suitable for treatment with IL-6 antagonists and TNF-α inhibitor are meaningful in the clinic. In our study, the cytokines IL-6, IL-10, and TNF-α were significantly upregulated in severe COVID-19 patients, especially in male patients, indicating that IL-6 antagonists and TNF-α inhibitors are more appropriate used in male patients to reduce both severity and mortality rate of COVID-19.

An increasing number of studies have pointed out that there are ethnicity-related differences in cytokines in systemic lupus erythematosus, chronic rhinosinusitis and other autoimmunity diseases (Niewold et al., 2012; Wang et al., 2016; Slight-Webb et al., 2020). We also focused on ethnicity-related differences in cytokines in COVID-19 patients and the results showed that there were lower circulating levels of IL-6 in Asian patients than in European and African patients, suggesting that IL-6 antagonists are recommended to use earlier in western countries.

This study had some limitations. Firstly, the articles that described the differential serum levels of cytokines in males and females were all from China. More clinical experiments should focus on the sex bias of cytokines in COVID-19. Secondly, our meta-analysis mainly investigated studies written in English, which might lead to language bias.

Conclusion

These large-scale data revealed that the serum levels of IL-6, IL-10, IL-2R, TNF-α, IL-1β, IL-4, IL-8, and IL-17 are potential risk factors for severity and high mortality in COVID-19. The IL-6 antagonist and TNF-α inhibitor are likely to be a proper therapeutic strategy to reduce mortality in males with COVID-19 and in Western countries.

Author Contributions

HH wrote the manuscript, HP conceived and designed the study, HH and HP reviewed and revised the manuscript. HH and KH searched the database and extracted the data, HH and RL carried out the Meta-analysis and made figures, HP and HZ revised and sorted out the data, LL designed and performed the final review of the manuscript, all authors contributed to the article and approved the submitted version.

Funding

This work was financially supported by the National Key Research and Development Project of China (2020YFA0708003).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2022.802228/full#supplementary-material

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References

  1. Ahmadpoor P., Rostaing L. (2020). Why the Immune System Fails to Mount an Adaptive Immune Response to a COVID-19 Infection. Transpl. Int. 33 (7), 824–825. 10.1111/tri.13611 [DOI] [PubMed] [Google Scholar]
  2. Belaid B., Lamara Mahammad L., Mihi B., Rahali S., Djidjeli A., Larab Z., et al. (2021). T Cell Counts and IL-6 Concentration in Blood of North African COVID-19 Patients Are Two Independent Prognostic Factors for Severe Disease and Death. J. Leukoc. Biol. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Burian E., Jungmann F., Kaissis G. A., Lohöfer F. K., Spinner C. D., Lahmer T., et al. (2020). Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort. J. Clin. Med. 9 (5). 10.3390/jcm9051514 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Chen G., Wu D., Guo W., Cao Y., Huang D., Wang H., et al. (2020). Clinical and Immunological Features of Severe and Moderate Coronavirus Disease 2019. J. Clin. Invest. 130 (5), 2620–2629. 10.1172/JCI137244 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Chen L. D., Zhang Z. Y., Wei X. J., Cai Y. Q., Yao W. Z., Wang M. H., et al. (2020). Association between Cytokine Profiles and Lung Injury in COVID-19 Pneumonia. Respir. Res. 21 (1), 201. 10.1186/s12931-020-01465-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chen T., Dai Z., Mo P., Li X., Ma Z., Song S., et al. (2020). Clinical Characteristics and Outcomes of Older Patients with Coronavirus Disease 2019 (COVID-19) in Wuhan, China: A Single-Centered, Retrospective Study. J. Gerontol. A. Biol. Sci. Med. Sci. 75 (9), 1788–1795. 10.1093/gerona/glaa089 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chen T., Wu D., Chen H., Yan W., Yang D., Chen G., et al. (2020). Clinical Characteristics of 113 Deceased Patients with Coronavirus Disease 2019: Retrospective Study. BMJ 368, m1091. 10.1136/bmj.m1091 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chi Y., Ge Y., Wu B., Zhang W., Wu T., Wen T., et al. (2020). Serum Cytokine and Chemokine Profile in Relation to the Severity of Coronavirus Disease 2019 in China. J. Infect. Dis. 222 (5), 746–754. 10.1093/infdis/jiaa363 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Control C. C. D., Response P. E. W. G. N. E. (2020). The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19) in China. Zhonghua Liu Xing Bing Xue Za Zhi 41 (2), 145–151. 10.3760/cma.j.issn.0254-6450.2020.02.003 [DOI] [PubMed] [Google Scholar]
  10. Crespo M., Pérez-Sáez M. J., Redondo-Pachón D., Llinàs-Mallol L., Montero M. M., Villar-García J., et al. (2020). COVID-19 in Elderly Kidney Transplant Recipients. Am. J. Transpl. 20 (10), 2883–2889. 10.1111/ajt.16096 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Dreher M., Kersten A., Bickenbach J., Balfanz P., Hartmann B., Cornelissen C., et al. (2020). The Characteristics of 50 Hospitalized COVID-19 Patients with and without ARDS. Dtsch Arztebl Int. 117 (16), 271–278. 10.3238/arztebl.2020.0271 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Effenberger M., Grander C., Grabherr F., Griesmacher A., Ploner T., Hartig F., et al. (2020). Systemic Inflammation as Fuel for Acute Liver Injury in COVID-19. Digestive and Liver Disease : Official Journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Fan H., Zhang L., Huang B., Zhu M., Zhou Y., Zhang H., et al. (2020). Cardiac Injuries in Patients with Coronavirus Disease 2019: Not to Be Ignored. Int. J. Infect. Dis. 96, 294–297. official publication of the International Society for Infectious Diseases. 10.1016/j.ijid.2020.05.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Fernández-Ruiz M., López-Medrano F., Pérez-Jacoiste Asín M., Maestro de la Calle G., Bueno H., Caro-Teller J., et al. (2020). Tocilizumab for the Treatment of Adult Patients with Severe COVID-19 Pneumonia: A Single-center Cohort Study. J. Med. Virol. 93 (2), 831–842. 10.1002/jmv.26308 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Galván-Román J., Rodríguez-García S., Roy-Vallejo E., Marcos-Jiménez A., Sánchez-Alonso S., Fernández-Díaz C., et al. (2021). IL-6 Serum Levels Predict Severity and Response to Tocilizumab in COVID-19: An Observational Study. J. Allergy Clin. Immunol. 147 (1), 72–80. e8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gao F., Zheng K. I., Yan H. D., Sun Q. F., Pan K. H., Wang T. Y., et al. (2021). Association and Interaction between Serum Interleukin-6 Levels and Metabolic Dysfunction-Associated Fatty Liver Disease in Patients with Severe Coronavirus Disease 2019. Front. Endocrinol. (Lausanne) 12, 604100. 10.3389/fendo.2021.604100 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Gao Y., Li T., Han M., Li X., Wu D., Xu Y., et al. (2020). Diagnostic Utility of Clinical Laboratory Data Determinations for Patients with the Severe COVID-19. J. Med. Virol. 92 (7), 791–796. 10.1002/jmv.25770 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gil-Etayo F. J., Suàrez-Fernández P., Cabrera-Marante O., Arroyo D., Garcinuño S., Naranjo L., et al. (2021). T-helper Cell Subset Response Is a Determining Factor in COVID-19 Progression. Front Cel Infect Microbiol 11, 624483. 10.3389/fcimb.2021.624483 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Griffith D. M., Sharma G., Holliday C. S., Enyia O. K., Valliere M., Semlow A. R., et al. (2020). Men and COVID-19: A Biopsychosocial Approach to Understanding Sex Differences in Mortality and Recommendations for Practice and Policy Interventions. Prev. Chronic Dis. 17, E63. 10.5888/pcd17.200247 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Guillén L., Padilla S., Fernández M., Agulló V., García J., Telenti G., et al. (2020). Preemptive Interleukin-6 Blockade in Patients with COVID-19. Sci. Rep. 10 (1), 16826. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Guirao J. J., Cabrera C. M., Jiménez N., Rincón L., Urra J. M. (2020). High Serum IL-6 Values Increase the Risk of Mortality and the Severity of Pneumonia in Patients Diagnosed with COVID-19. Mol. Immunol. 128, 64–68. 10.1016/j.molimm.2020.10.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Han H., Ma Q., Li C., Liu R., Zhao L., Wang W., et al. (2020). Profiling Serum Cytokines in COVID-19 Patients Reveals IL-6 and IL-10 Are Disease Severity Predictors. Emerg. Microbes Infect. 9 (1), 1123–1130. 10.1080/22221751.2020.1770129 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. He S., Zhou C., Lu D., Yang H., Xu H., Wu G., et al. (2020). Relationship between Chest CT Manifestations and Immune Response in COVID-19 Patients. Int. J. Infect. Dis. 98, 125–129. 10.1016/j.ijid.2020.06.059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Herold T., Jurinovic V., Arnreich C., Lipworth B. J., Hellmuth J. C., von Bergwelt-Baildon M., et al. (2020). Elevated Levels of IL-6 and CRP Predict the Need for Mechanical Ventilation in COVID-19. J. Allergy Clin. Immunol. 146 (1), 128–e4. e4. 10.1016/j.jaci.2020.05.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Huang H., Zhang M., Chen C., Zhang H., Wei Y., Tian J., et al. (2020). Clinical Characteristics of COVID-19 in Patients with Preexisting ILD: A Retrospective Study in a Single center in Wuhan, China. J. Med. Virol. 92 (11), 2742–2750. 10.1002/jmv.26174 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hue S., Beldi-Ferchiou A., Bendib I., Surenaud M., Fourati S., Frapard T., et al. (2020). Uncontrolled Innate and Impaired Adaptive Immune Responses in Patients with COVID-19 Acute Respiratory Distress Syndrome. Am. J. Respir. Crit. Care Med. 202 (11), 1509–1519. 10.1164/rccm.202005-1885OC [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Kalicińska E., Szymczak D., Zińczuk A., Adamik B., Smiechowicz J., Skalec T., et al. (2021). Immunosuppression as a Hallmark of Critical COVID-19: Prospective Study. Cells 10 (6). 10.3390/cells10061293 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Keewan E., Beg S., Naser S. A. (2021). Anti-TNF-α Agents Modulate SARS-CoV-2 Receptors and Increase the Risk of Infection through Notch-1 Signaling. Front. Immunol. 12, 641295. 10.3389/fimmu.2021.641295 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Laguna-Goya R., Utrero-Rico A., Talayero P., Lasa-Lazaro M., Ramirez-Fernandez A., Naranjo L., et al. (2020). IL-6-based Mortality Risk Model for Hospitalized Patients with COVID-19. J. Allergy Clin. Immunol. 146 (4), 799–e9. e9. 10.1016/j.jaci.2020.07.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lang F. M., Lee K. M., Teijaro J. R., Becher B., Hamilton J. A. (2020). GM-CSF-based Treatments in COVID-19: Reconciling Opposing Therapeutic Approaches. Nat. Rev. Immunol. 20 (8), 507–514. 10.1038/s41577-020-0357-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Li C., Jiang J., Wang F., Zhou N., Veronese G., Moslehi J. J., et al. (2020). Longitudinal Correlation of Biomarkers of Cardiac Injury, Inflammation, and Coagulation to Outcome in Hospitalized COVID-19 Patients. J. Mol. Cel Cardiol 147, 74–87. 10.1016/j.yjmcc.2020.08.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Li D., Liu C., Liu J., Hu J., Yang Y., Zhou Y. (2020). Analysis of Risk Factors for 24 Patients with COVID-19 Developing from Moderate to Severe Condition. Front. Cel Infect Microbiol 10, 548582. 10.3389/fcimb.2020.548582 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Li S., Jiang L., Li X., Lin F., Wang Y., Li B., et al. (2020). Clinical and Pathological Investigation of Patients with Severe COVID-19. JCI insight 5 (12). 10.1172/jci.insight.138070 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Li Y., Deng W., Xiong H., Li H., Chen Z., Nie Y., et al. (2020). Immune-related Factors Associated with Pneumonia in 127 Children with Coronavirus Disease 2019 in Wuhan. Pediatr. Pulmonol 55 (9), 2354–2360. 10.1002/ppul.24907 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Liang W. H., Guan W. J., Li C. C., Li Y. M., Liang H. R., Zhao Y., et al. (2020). Clinical Characteristics and Outcomes of Hospitalised Patients with COVID-19 Treated in Hubei (Epicentre) and outside Hubei (Non-epicentre): a Nationwide Analysis of China. Eur. Respir. J. 55 (6). 10.1183/13993003.00562-2020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Liu F., Ji C., Luo J., Wu W., Zhang J., Zhong Z., et al. (2020). Clinical Characteristics and Corticosteroids Application of Different Clinical Types in Patients with corona Virus Disease 2019. Sci. Rep. 10 (1), 13689. 10.1038/s41598-020-70387-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Liu L., Gao J-Y., Hu W-m., Zhang X-x., Guo L., Liu C-q., et al. (2020). Clinical Characteristics of 51 Patients Discharged from Hospital with COVID-19 in Chongqing, China. medRxiv; 2020. 02.20.20025536. [Google Scholar]
  38. Liu T., Zhang J., Yang Y., Ma H., Li Z., Zhang J., et al. (2020). The Role of Interleukin-6 in Monitoring Severe Case of Coronavirus Disease 2019. EMBO Mol. Med. 12 (7), e12421. 10.15252/emmm.202012421 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Liu Y., Liao W., Wan L., Xiang T., Zhang W. (2020). Correlation between Relative Nasopharyngeal Virus RNA Load and Lymphocyte Count Disease Severity in Patients with COVID-19. Viral Immunol. [DOI] [PubMed] [Google Scholar]
  40. Luo D., Wan X., Liu J., Tong T. (2018). Optimally Estimating the Sample Mean from the Sample Size, Median, Mid-range, And/or Mid-quartile Range. Stat. Methods Med. Res. 27 (6), 1785–1805. 10.1177/0962280216669183 [DOI] [PubMed] [Google Scholar]
  41. Luo M., Liu J., Jiang W., Yue S., Wei S. (2020). IL-6 Combined with CD8+ T Cell Count Early Predict In-Hospital Mortality for Patients with COVID-19. JCI Insight 5 (13). 10.1172/jci.insight.139024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lv Z., Cheng S., Le J., Huang J., Feng L., Zhang B., et al. (2020). Clinical Characteristics and Co-infections of 354 Hospitalized Patients with COVID-19 in Wuhan, China: a Retrospective Cohort Study. Microbes Infect. 22, 195–199. 10.1016/j.micinf.2020.05.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Ma J., Yin J., Qian Y., Wu Y. (2020). Clinical Characteristics and Prognosis in Cancer Patients with COVID-19: A Single center's Retrospective Study. J. Infect. 81 (2), 318–356. 10.1016/j.jinf.2020.04.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Mackey K., Ayers C. K., Kondo K. K., Saha S., Advani S. M., Young S., et al. (2021). Racial and Ethnic Disparities in COVID-19-Related Infections, Hospitalizations, and Deaths : A Systematic Review. Ann. Intern. Med. 174 (3), 362–373. 10.7326/M20-6306 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Mackey K., Ayers C. K., Kondo K. K., Saha S., Advani S. M., Young S., et al. (2021). Racial and Ethnic Disparities in COVID-19-Related Infections, Hospitalizations, and Deaths. Ann. Intern. Med. 174 (3), 362–373. 10.7326/m20-6306 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Makaronidis J., Mok J., Balogun N., Magee C. G., Omar R. Z., Carnemolla A., et al. (2020). Seroprevalence of SARS-CoV-2 Antibodies in People with an Acute Loss in Their Sense of Smell And/or Taste in a Community-Based Population in London, UK: An Observational Cohort Study. Plos Med. 17 (10), e1003358. 10.1371/journal.pmed.1003358 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Meng Z., Wang M., Zhao Z., Zhou Y., Wu Y., Guo S., et al. (2021). Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China. Front. Med. (Lausanne) 8, 663145. 10.3389/fmed.2021.663145 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Mikami T., Miyashita H., Yamada T., Harrington M., Siau E. (2020). Risk Factors for Mortality in Patients with COVID-19 in New York City. J. Gen. Intern. Med. 36 (1), 17–26. 10.1007/s11606-020-05983-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Mo P., Xing Y., Xiao Y., Deng L., Zhao Q., Wang H., et al. (2020). Clinical Characteristics of Refractory COVID-19 Pneumonia in Wuhan, China. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Ni M., Tian F. B., Xiang D. D., Yu B. (2020). Characteristics of Inflammatory Factors and Lymphocyte Subsets in Patients with Severe COVID-19. J. Med. Virol. 92 (11), 2600–2606. 10.1002/jmv.26070 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Niewold T. B., Kelly J. A., Kariuki S. N., Franek B. S., Kumar A. A., Kaufman K. M., et al. (2012). IRF5 Haplotypes Demonstrate Diverse Serological Associations Which Predict Serum Interferon Alpha Activity and Explain the Majority of the Genetic Association with Systemic Lupus Erythematosus. Ann. Rheum. Dis. 71 (3), 463–468. 10.1136/annrheumdis-2011-200463 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Pérez-Sáez M., Blasco M., Redondo-Pachón D., Ventura-Aguiar P., Bada-Bosch T., Pérez-Flores I., et al. (2020). Use of Tocilizumab in Kidney Transplant Recipients with COVID-19. Am. J. Transplant. : official J. Am. Soc. Transplant. Am. Soc. Transpl. Surgeons. 20 (11), 3182–3190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Potere N., Di Nisio M., Cibelli D., Scurti R., Frattari A., Porreca E., et al. (2021). Interleukin-6 Receptor Blockade with Subcutaneous Tocilizumab in Severe COVID-19 Pneumonia and Hyperinflammation: a Case-Control Study. Ann. Rheum. Dis. 80 (2), 1–2. 10.1136/annrheumdis-2020-218243 [DOI] [PubMed] [Google Scholar]
  54. Qin C., Zhou L., Hu Z., Zhang S., Yang S., Tao Y., et al. (2020). Dysregulation of Immune Response in Patients with Coronavirus 2019 (COVID-19) in Wuhan, China. Clin. Infect. Dis. 71 (15), 762–768. 10.1093/cid/ciaa248 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Qin L., Li X., Shi J., Yu M., Wang K., Tao Y., et al. (2020). Gendered Effects on Inflammation Reaction and Outcome of COVID-19 Patients in Wuhan. J. Med. Virol. 92 (11), 2684–2692. 10.1002/jmv.26137 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Quartuccio L., Sonaglia A., Pecori D., Peghin M., Fabris M., Tascini C., et al. (2020). Higher Levels of IL-6 Early after Tocilizumab Distinguish Survivors from Nonsurvivors in COVID-19 Pneumonia: A Possible Indication for Deeper Targeting of IL-6. J. Med. Virol. 92 (11), 2852–2856. 10.1002/jmv.26149 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Ruan Q., Yang K., Wang W., Jiang L., Song J. (2020). Clinical Predictors of Mortality Due to COVID-19 Based on an Analysis of Data of 150 Patients from Wuhan, China. Intensive Care Med. 46 (5), 846–848. 10.1007/s00134-020-05991-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Rutkowska E., Kwiecień I., Żabicka M., Maliborski A., Raniszewska A., Kłos K., et al. (2021). Cytokines and Leukocytes Subpopulations Profile in SARS-CoV-2 Patients Depending on the CT Score Severity. Viruses 13 (5). 10.3390/v13050880 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Slight-Webb S., Smith M., Bylinska A., Macwana S., Guthridge C., Lu R., et al. (2020). Autoantibody-positive Healthy Individuals with Lower Lupus Risk Display a Unique Immune Endotype. J. Allergy Clin. Immunol. 146 (6), 1419–1433. 10.1016/j.jaci.2020.04.047 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Sun Y., Dong Y., Wang L., Xie H., Li B., Chang C., et al. (2020). Characteristics and Prognostic Factors of Disease Severity in Patients with COVID-19: The Beijing Experience. J. Autoimmun. 112, 102473. 10.1016/j.jaut.2020.102473 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Takahashi T., Ellingson M. K., Wong P., Israelow B., Lucas C., Klein J., et al. (2020). Sex Differences in Immune Responses that Underlie COVID-19 Disease Outcomes. Nature 588 (7837), 315–320. 10.1038/s41586-020-2700-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Tirupathi R., Muradova V., Shekhar R., Salim S. A., Al-Tawfiq J. A., Palabindala V. (2020). COVID-19 Disparity Among Racial and Ethnic Minorities in the US: A Cross Sectional Analysis. Trav. Med Infect Dis 38, 101904. 10.1016/j.tmaid.2020.101904 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Tirupathi R., Muradova V., Shekhar R., Salim S. A., Al-Tawfiq J. A., Palabindala V., et al. (2020). COVID-19 Disparity Among Racial and Ethnic Minorities in the US: A Cross Sectional Analysis. Trav. Med Infect Dis 38, 101904. 10.1016/j.tmaid.2020.101904 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Toniati P., Piva S., Cattalini M., Garrafa E., Regola F., Castelli F., et al. (2020). Tocilizumab for the Treatment of Severe COVID-19 Pneumonia with Hyperinflammatory Syndrome and Acute Respiratory Failure: A Single center Study of 100 Patients in Brescia, Italy. Autoimmun. Rev. 19 (7), 102568. 10.1016/j.autrev.2020.102568 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Trecarichi E. M., Mazzitelli M., Serapide F., Pelle M. C., Tassone B., Arrighi E., et al. (2020). Clinical Characteristics and Predictors of Mortality Associated with COVID-19 in Elderly Patients from a Long-Term Care Facility. Sci. Rep. 10 (1), 20834. 10.1038/s41598-020-77641-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Tu W. J., Cao J., Yu L., Hu X., Liu Q. (2020). Clinicolaboratory Study of 25 Fatal Cases of COVID-19 in Wuhan. Intensive Care Med. 46 (6), 1117–1120. 10.1007/s00134-020-06023-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Vabret N., Britton G. J., Gruber C., Hegde S., Kim J., Kuksin M., et al. (2020). Immunology of COVID-19: Current State of the Science. Immunity 52 (6), 910–941. 10.1016/j.immuni.2020.05.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Wan S., Yi Q., Fan S., Lv J., Zhang X., Guo L., et al. (2020). Relationships Among Lymphocyte Subsets, Cytokines, and the Pulmonary Inflammation index in Coronavirus (COVID-19) Infected Patients. Br. J. Haematol. 189 (3), 428–437. 10.1111/bjh.16659 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Wan X., Wang W., Liu J., Tong T. (2014). Estimating the Sample Mean and Standard Deviation from the Sample Size, Median, Range And/or Interquartile Range. BMC Med. Res. Methodol. 14, 135. 10.1186/1471-2288-14-135 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Wang C., Fei D., Li X., Zhao M., Yu K. (2020). IL-6 May Be a Good Biomarker for Earlier Detection of COVID-19 Progression. Intensive Care Med. 46 (7), 1475–1476. 10.1007/s00134-020-06065-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Wang H., Luo S., Shen Y., Li M., Zhang Z., Dong Y., et al. Multiple Enzyme Release, Inflammation Storm and Hypercoagulability AreProminent IndicatorsFor Disease ProgressionIn COVID-19: A Multi-Centered, CorrelationStudy with CT Imaging Score. Social Science Electronic Publishing. [Google Scholar]
  72. Wang L., He W., Yu X., Hu D., Bao M., Liu H., et al. (2020). Coronavirus Disease 2019 in Elderly Patients: Characteristics and Prognostic Factors Based on 4-week Follow-Up. J. Infect. 80 (6), 639–645. 10.1016/j.jinf.2020.03.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Wang R., Pan M., Zhang X., Han M., Fan X., Zhao F., et al. (2020). Epidemiological and Clinical Features of 125 Hospitalized Patients with COVID-19 in Fuyang, Anhui, China. Int. J. Infect. Dis. 95, 421–428. official publication of the International Society for Infectious Diseases. 10.1016/j.ijid.2020.03.070 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Wang X., Zhang N., Bo M., Holtappels G., Zheng M., Lou H., et al. (2016). Diversity of TH Cytokine Profiles in Patients with Chronic Rhinosinusitis: A multicenter Study in Europe, Asia, and Oceania. J. Allergy Clin. Immunol. 138 (5), 1344–1353. 10.1016/j.jaci.2016.05.041 [DOI] [PubMed] [Google Scholar]
  75. Wang Y., Mao Q., Zhou X. (2020). Does Tocilizumab Have a Magical Therapeutic Effect on COVID-19 Patients without Obvious Adverse Reactions. Proc. Natl. Acad. Sci. U S A. 117 (49), 30896–30897. 10.1073/pnas.2009961117 [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Wang Y., Zhu F., Wang C., Wu J., Liu J., Chen X., et al. (2020). Children Hospitalized with Severe COVID-19 in Wuhan. Pediatr. Infect. Dis. J. 39 (7), e91–e4. 10.1097/INF.0000000000002739 [DOI] [PubMed] [Google Scholar]
  77. Wang Z., Yang B., Li Q., Wen L., Zhang R. (2020). Clinical Features of 69 Cases with Coronavirus Disease 2019 in Wuhan, China. Clin. Infect. Dis. 71 (15), 769–777. 10.1093/cid/ciaa272 [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Wu C., Chen X., Cai Y., Xia J., Zhou X., Xu S., et al. (2020). Risk Factors Associated with Acute Respiratory Distress Syndrome and Death in Patients with Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern. Med. 180 (7), 934–943. 10.1001/jamainternmed.2020.0994 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Wu D., Yang X. O. (2020). TH17 Responses in Cytokine Storm of COVID-19: An Emerging Target of JAK2 Inhibitor Fedratinib. J. Microbiol. Immunol. Infect. 53 (3), 368–370. 10.1016/j.jmii.2020.03.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Wu Y., Huang X., Sun J., Xie T., Lei Y., Muhammad J., et al. (2020). Clinical Characteristics and Immune Injury Mechanisms in 71 Patients with COVID-19. mSphere 5 (4). 10.1128/mSphere.00362-20 [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Xie Y., You Q., Wu C., Cao S., Qu G., Yan X., et al. (2020). Impact of Cardiovascular Disease on Clinical Characteristics and Outcomes of Coronavirus Disease 2019 (COVID-19). Circ. J. 84 (8), 1277–1283. 10.1253/circj.CJ-20-0348 [DOI] [PubMed] [Google Scholar]
  82. Xiong B., Liu T., Luo P., Wei Y., Zhou Y., Liu M., et al. (2020). Prominent Hypercoagulability Associated with Inflammatory State Among Cancer Patients with SARS-CoV-2 Infection. Front. Oncol. 10, 1345. 10.3389/fonc.2020.01345 [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Xu B., Fan C. Y., Wang A. L., Zou Y. L., Yu Y. H., He C., et al. (2020). Suppressed T Cell-Mediated Immunity in Patients with COVID-19: A Clinical Retrospective Study in Wuhan, China. J. Infect. 81 (1), e51–e60. 10.1016/j.jinf.2020.04.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Xu X., Han M., Li T., Sun W., Wang D., Fu B., et al. (2020). Effective Treatment of Severe COVID-19 Patients with Tocilizumab. Proc. Natl. Acad. Sci. U S A. 117 (20), 10970–10975. 10.1073/pnas.2005615117 [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Xu X., Yu M. Q., Shen Q., Wang L. Z., Yan R. D., Zhang M. Y., et al. (2020). Analysis of Inflammatory Parameters and Disease Severity for 88 Hospitalized COVID-19 Patients in Wuhan, China. Int. J. Med. Sci. 17 (13), 2052–2062. 10.7150/ijms.47935 [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Xu Y. (2020). Dynamic Profile of Severe or Critical COVID-19 Cases. medRxiv, 2020. 03.18.20038513. [Google Scholar]
  87. Xu Y., Li Y-r., Zeng Q., Lu Z-b., Li Y-z., Wu W., et al. (2020). Clinical Characteristics of SARS-CoV-2 Pneumonia Compared to Controls in Chinese Han Population. medRxiv; 2020. 03.08.20031658. [Google Scholar]
  88. Yang A. P., Li H. M., Tao W. Q., Yang X. J., Wang M., Yang W. J., et al. (2020). Infection with SARS-CoV-2 Causes Abnormal Laboratory Results of Multiple Organs in Patients. Aging (Albany NY) 12 (11), 10059–10069. 10.18632/aging.103255 [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Yuan X., Huang W., Ye B., Chen C., Huang R., Wu F., et al. (2020). Changes of Hematological and Immunological Parameters in COVID-19 Patients. Int. J. Hematol. 112 (4), 553–559. 10.1007/s12185-020-02930-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Zeng Z., Yu H., Chen H., Qi W., Chen L., Chen G., et al. (2020). Longitudinal Changes of Inflammatory Parameters and Their Correlation with Disease Severity and Outcomes in Patients with COVID-19 from Wuhan, China. Crit. Care 24 (1), 525. 10.1186/s13054-020-03255-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Zhang F., Xiong Y., Wei Y., Hu Y., Wang F., Li G., et al. (2020). Obesity Predisposes to the Risk of Higher Mortality in Young COVID-19 Patients. J. Med. Virol. 92 (11), 2536–2542. 10.1002/jmv.26039 [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Zhang H., Wang X., Fu Z., Luo M., Zhang Z., Zhang K., et al. (2020). Potential Factors for Prediction of Disease Severity of COVID-19 Patients. medRxiv. 03.20.20039818. [Google Scholar]
  93. Zhang Y., Li J., Zhan Y., Wu L., Yu X., Zhang W., et al. (2004). Analysis of Serum Cytokines in Patients with Severe Acute Respiratory Syndrome. Infect. Immun. 72 (8), 4410–4415. 10.1128/IAI.72.8.4410-4415.2004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Zhao Y., Nie H. X., Hu K., Wu X. J., Zhang Y. T., Wang M. M., et al. (2020). Abnormal Immunity of Non-survivors with COVID-19: Predictors for Mortality. Infect. Dis. Poverty 9 (1), 108. 10.1186/s40249-020-00723-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Zheng C., Wang J., Guo H., Lu Z., Ma Y., Zhu Y., et al. (2020). Risk-adapted Treatment Strategy for COVID-19 Patients. Int. J. Infect. Dis. 94, 74–77. official publication of the International Society for Infectious Diseases. 10.1016/j.ijid.2020.03.047 [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Zheng Y., Sun L. J., Xu M., Pan J., Zhang Y. T., Fang X. L., et al. (2020). Clinical Characteristics of 34 COVID-19 Patients Admitted to Intensive Care Unit in Hangzhou, China. J. Zhejiang Univ. Sci. B 21 (5), 378–387. 10.1631/jzus.B2000174 [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Zhou F., Yu T., Du R., Fan G., Liu Y., Liu Z., et al. (2020). Clinical Course and Risk Factors for Mortality of Adult Inpatients with COVID-19 in Wuhan, China: a Retrospective Cohort Study. Lancet 395 (10229), 1054–1062. 10.1016/S0140-6736(20)30566-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  98. Zhou Y., Han T., Chen J., Hou C., Hua L., He S., et al. (2020). Clinical and Autoimmune Characteristics of Severe and Critical Cases of COVID-19. Clin. Transl Sci. 13 (6), 1077–1086. 10.1111/cts.12805 [DOI] [PMC free article] [PubMed] [Google Scholar]
  99. Zhu W., Zhang H., Li Y., Ding Z., Liu Z., Ruan Y., et al. (2021). Optimizing Management to Reduce the Mortality of COVID-19: Experience from a Designated Hospital for Severely and Critically Ill Patients in China. Front. Med. (Lausanne) 8, 582764. 10.3389/fmed.2021.582764 [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Zhu Z., Cai T., Fan L., Lou K., Hua X., Huang Z., et al. (2020). Clinical Value of Immune-Inflammatory Parameters to Assess the Severity of Coronavirus Disease 2019. Int. J. Infect. Dis. 95, 332–339. official publication of the International Society for Infectious Diseases. 10.1016/j.ijid.2020.04.041 [DOI] [PMC free article] [PubMed] [Google Scholar]

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