Sage Journals: Discover world-class research

Abstract

Objectives:

Disruption in the natural immune reaction due to SARS-CoV-2 infection can initiate a potent cytokine storm among COVID-19 patients. An elevated level of IL-6 and IL-10 during a hyperinflammatory state plays a vital role in increasing the risk of severity and mortality. In this study, we aimed to evaluate the potential of circulating IL-6 and IL-10 levels as biomarkers for detecting the severity and mortality of COVID-19.

Methods:

This study was conducted according to the Cochrane Handbook and PRISMA guidelines. Authorized databases were searched to extract suitable studies using specific search terms. RevMan 5.4 was applied for performing the meta-analysis. Mean differences in IL-6 and IL-10 levels were calculated among COVID-19 patients via a random-effects model. NOS scoring, publication bias and sensitivity analyses were checked to ensure study quality.

Results:

A total of 147 studies were selected, with 31 909 COVID-19 patients under investigation. In the severity analysis, the mean concentration of IL-6 was significantly higher in the severe COVID-19 cases than in the non-severe cases (MD: 19.98; P < .001; 95% CI: 17.56, 22.40). Similar result was observed for IL-10 mean concentration in severe COVID-19 cases (MD: 1.35; P < .001; 95% CI: 0.90, 1.80). In terms of mortality analysis, circulating IL-6 showed sharp elevation in the deceased patients (MD: 42.11; P < .001; 95% CI: 36.86, 47.36). IL-10 mean concentration was higher in the dead patients than in the survived patients (MD: 4.79; P < .001; 95% CI: 2.83, 6.75). Publication bias was not found except for comparing IL-6 levels with disease severity. Sensitivity analysis also reported no significant deviation from the pooled outcomes.

Conclusions:

Elevated levels of circulating IL-6 and IL-10 signifies worsening of COVID-19. To monitor the progression of SARS-CoV-2 infection, IL-6 and IL-10 should be considered as potential biomarkers for severity and mortality detection in COVID-19.

Systematic review registration:

INPLASY registration number: INPLASY202240046.

Keywords

COVID-19 interleukin-6 interleukin-10 meta-analysis cytokine storm

Introduction

Novel coronavirus disease (COVID-19) prevalence was first commenced in 2019 by a highly variating virus “SARS-CoV-2” (severe acute respiratory syndrome coronavirus 2) infection. The infection spread rapidly, turning into a worldwide pandemic that caused nearly 5.6 million death in the last 2 years.^1-3 A number of diagnostic and treatment approaches have been approved in the fight against COVID-19, although a concrete predictor of disease progression is yet to reveal.^4,5 The highly unpredictable nature of this current pandemic has made it difficult to detect the severity of the condition in time. It is crucial to establish a reliable diagnostic marker to follow the pattern of disease development and to halt the process from getting severe, even fatal. Moreover, identifying sensitive and specific biomarkers would create an opportunity to promote stronger preventive and therapeutic strategies.^6-8

The key negative prognostic factor of SARS-CoV-2 infection pathophysiology is cytokine storm, a hyperinflammatory process of cytokine releasing that causes acute systemic reactions. This specific immune reactive condition drives the disease state toward acute respiratory distress syndrome (ARDS). Inflammatory cytokines, more specifically interleukins, are found to be the main mediators involved in the cytokine storm development.⁹ Although rapid innate immune system reaction following SARS-CoV-2 infection is the first-line defense against COVID-19, excessively active immune reaction generates severe complications. During SARS-CoV-2 infection, irreversibly critical damage occurs in the pulmonary system and lung tissues by higher plasma concentrations of circulating interleukins.^9,10 Among the multifunctional proinflammatory cytokines, interleukin-6 (IL-6) and interleukin-10 (IL-10) are suspected to be strongly involved in the COVID-19 related cytokine storm for their potential roles in acute phase immune reactions.^11-13

IL-6, an inflammatory mediator with pleiotropic nature, is highly produced during the initial stage of inflammation and rapidly activates multiple acute phases of inflammatory reactants. In COVID-19 patients, IL-6 is produced in response to antigens from several immune cell types, and a number of clinical investigative studies have reported that serum level of circulating IL-6 was critically higher among the COVID-19 patients from severe to the critical stage.^11,14-16 Another cross-sectional study stated evidence that serum levels of IL-6 above 24.3 pg/ml might be associated with severe pneumonia in COVID-19 patients.¹⁷ IL-10 exerts powerful anti-inflammatory actions that control severe host immune responses toward antigens by preventing multiple functions of T-cells and neutral killer (NK) cells. Again, dysregulation in IL-10 concentration may influence the immune response and severity of SARS-CoV-2 infected patients.¹⁸ In this context, the serum level of IL-10 also showed significant elevation in severe and critical cases of COVID-19, commensurate with IL-6 serum level. Several studies showed evidence that both IL-6 and IL-10 are positively related to the severity and mortality of COVID-19.^8,19-21 According to this evidence, alteration in the normal level of circulating IL-6 and IL-10 can act as potential biomarkers for COVID-19.¹³

Although some previous meta-analyses attempted to evaluate the link between circulating IL-6 and IL-10 levels with severity and mortality of COVID-19, they recommended further investigation with a larger sample size to validate their findings. For this reason, we conducted this updated systematic review and meta-analysis with available literature to reveal the correlation between IL-6 and IL-10 elevation with COVID-19 and the effectiveness of testing serum IL-6 and IL-10 levels as clinical biomarkers.

Methods

The recommendations narrated in the Cochrane Handbook²² and PRISMA (the Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines²³ were followed to conduct this systematic review and meta-analysis. The study is also registered in INPLASY (http://inplasy.com/), and the registration number is INPLASY202240046.

Literature searching strategy

The international scientific authorized databases such as Google Scholar, PubMed, Embase, CNKI, Cochrane Library, and Web of science were used as primary sources to identify and collect the eligible literature. Additional secondary databases were also comprehensively searched to extract more related studies. The specific search terms used for this study were: “COVID-19” OR “SARS-Cov-2”; “interleukin-6 “ OR “IL-6”; and “interleukin-10” OR “IL-10.” The search strategy for each database is enlisted in Supplemental Table 1. All the included literature was selected from December 2019 to December 2021 time period.

Study selection

Two authors individually screened titles and abstracts of the studies from different databases to avoid bias and shortlisted articles with eligibility potentials. The unrelated articles were eliminated from the list after full-text inspection based on the inclusion and exclusion criteria. Any difference of opinions among the authors was resolved via a logical argument with the assistance of the third researcher. The study selection process is outlined in Figure 1.

Figure 1.

Study flow chart representing the selection process of eligible studies.

Inclusion and exclusion criteria

Inclusion criteria: (1) clinical studies, case-control investigations or cohort studies; (2) articles representing severity and mortality in COVID-19 patients; (3) articles providing information on IL-6 and IL-10 level among mild-to-severe COVID-19 patients; (4) articles reporting IL-6 and IL-10 level in the COVID-19 survivors and deceased patients.

Critically ill patients, patients with severe dyspnea, critically low oxygen level, patients under mechanical ventilation, or admitted to the intensive care unit (ICU) were considered severe conditions of COVID-19.

Exclusion criteria: (1) meta-analysis, review articles, letters, or comments; (2) articles written in languages other than English or Chinese; (3) incomplete information required in this meta-analysis; (4) unavailability of full texts.

Quality assessment

The Newcastle-Ottawa Scale (NOS) can determine the quality range of studies by rating them from 0 to 10 stars based on some specific features (Available at http://www.ohri.ca/programs/clinical_epidemiology/oxford.htm.). Articles that scored ⩾6 were considered high-quality ones. All the studies included in this meta-analysis were assessed with NOS for quality evaluation by 2 reviewers independently. Studies that scored less than 6 stars were excluded to maintain the quality range for the present analysis.

Data extraction

Data collection from the enlisted articles was conducted by 2 researchers. Basic information like author name, study period, study location, ethnicity, settings, study design, number of COVID-19 patients, age, mild state, severe to a critical state, number of deceased patients, IL-6, and IL-10 concentrations in patients were extracted.

Publication bias assessment

The risk of publication bias was determined by using Review Manager (RevMan 5.4) software for systematic review and meta-analysis. Egger regression test and Begg & Mazumdar test were performed to detect the presence of publication bias. Both the tests were used to verify the significance level of bias among the studies. The presence of asymmetry in the funnel plots also indicates a significant presence of publication bias.

Statistical analysis

The statistical analysis was performed by comparing the concentration level of both IL-6 and IL-10 among the COVID-19 patients according to the disease severity. Patients with mild symptoms or at the recovery stage were considered as the control population and patients with severe or critical conditions were termed as an experimental population. A secondary analysis was also conducted among the survived patients and deceased patients. The control arm showed the IL-6 and IL-10 concentrations among the survived patients, and the experimental arm showed peak concentration (last diagnostic count) levels among the dead patients. The name of the software used to carry out this meta-analysis was RevMan 5.4 from the Cochrane Collaboration, 2020. The unit of concentration measured as pg/ml. We used mean concentration with standard deviation for numerical presentation. The numerical data (mean and SD) was estimated using a validated equation. Estimation of the outcome was pooled as the mean difference with 95% CIs (confidence interval). Two analysis models were used for statistical calculation—the fixed-effects model and the random-effects model. In case of significant heterogeneity (chi-square I² ⩾ 50 and P < .10), DerSimonian-Laird random-effects model was applied and, in the absence of the heterogeneity fixed-effects model (Mantel-Haenszel) was applied (chi-square I² = 50 and P > .10). To evaluate the credibility of the results acquired from this study, we performed sensitivity analysis by omitting the studies one by one with the application of RevMan 5.4.

Results

Study selection

We conducted this meta-analysis on overall 31 909 COVID-19 patients from 147 studies.^{2,4-7,9-18,24-154} Among the recruited patients, 3137 were deceased, and the rest, 28 772 patients, showed mild to severe disease symptoms. The age range of the patients was between 6.25 ± 4.31 and 85.83 ± 7.61 years. All the studies had hospital-based settings; patients under investigation were admitted to the hospitals. No patients under self-quarantine or without hospital admission were included in the study. The recruited studies followed various designs, such as retrospective cohort, prospective cohort, observational cohort, single centered or multicentered cohort, case-control cohort, retrospective observational, prospective observational, prognostic cohort, retrospective longitudinal, non-randomized, cross-sectional observational, and clinical studies. From 147 studies, 107 studies reported IL-6 and IL-10 concentrations in COVID-19 patients and their association with disease severity. On the other hand, 49 studies reported an association of IL-6 and IL-10 serum levels with mortality in COVID-19 patients. Cytokine levels were measured using different biochemical assays—Enzyme-linked immunosorbent assay (ELISA); Electro-chemiluminescent immunoassay (ECLIA); Chemiluminescent immunoassay (CLIA); Online hemodiafiltration (OLHDF), Flow cytometry, Bio plex multiplex immunoassay, Automated immunoassay multiplex array system, and Enzyme-immune assay. The basic information is outlined in Table 1.

Table 1.

Baseline characteristics of the investigative studies reporting blood levels of IL-6 and IL-10 in COVID-19 patients.^{2,4-7,9-18,24-154}.

Study (reference)	Ethnicity	Location	Setting	Design	No. of participants	Age (mean ± SD)	Disease severity	Mortality	Cytokine assay	Biomarker study	NOS rating
Bergantini et al²	Caucasian	Italy	HB	Monocentric retrospective	24	Mild: 62.2 ± 15.6Severe: 65.2 ± 8	Mild: 14Severe: 10	NA	ECLIA	IL-6	7
Burian et al²⁴	Caucasian	Germany	HB	Retrospective cohort	37	61.5 ± 17	Mild: 25Severe: 12	NA	Biochemical assay	IL-6	7
Cai et al²⁵	Asian	China	HB	Retrospective	298	47.17 ± 20.86	Mild: 240Severe: 58	NA	Biochemical assay	IL-6	8
Chang et al²⁶	Asian	China	HB	Retrospective	150	NA	Mild: 93Severe: 57	NA	Biochemical assay	IL-6	8
Chen et al⁴	Asian	China	HB	Retrospective cohort	660	52.33 ± 25.26	NA	Dead: 82Survivors: 578	Biochemical assay	IL-6	8
Chen et al²⁷	Asian	China	HB	Retrospective	21	57 ± 11.93	Mild: 10Severe: 11	NA	CLIA	IL-6, IL-10	8
Chen et al²⁸	Asian	China	HB	Retrospective	172	64	NA	Dead: 87Survivors: 85	Biochemical assay	IL-6	7
Chen et al²⁹	Asian	China	HB	Retrospective	29	NA	Mild: 15Severe: 14	NA	ELISA	IL-6, IL-10	8
Chen et al³⁰	Asian	China	HB	Retrospective observational	94	52.75 ± 16.09	Mild: 69Severe: 25	NA	CLIA	IL-6, IL-10	9
Chen et al³¹	Asian	China	HB	Retrospective cohort	548	56 ± 14.5	Mild: 345Severe: 203	Dead: 103Survivors: 445	Biochemical assay	IL-6	8
Chen et al⁵	Asian	China	HB	Retrospective cohort	274	58.67 ± 19.38	NA	Dead: 113Survivors: 161	Biochemical assay	IL-6, IL-10	7
Chen et al³²	Asian	China	HB	Retrospective	55	55 ± 54.05	NA	Dead: 19Survivors: 36	Biochemical assay	IL-6	6
Chen et al⁹	Asian	China	HB	Retrospective	48	64.6 ± 18.1	Mild: 21Severe: 27	NA	Biochemical assay	IL-6	6
Chen et al¹¹	Asian	China	HB	Retrospective	1453	NA	Mild: 962Severe: 491	NA	ELISA	IL-6	8
Chi et al¹⁰	Asian	China	HB	Retrospective cohort	70	Mild : 42 ± 10.98Severe: 43.24 ± 14.76	Mild: 4Severe: 66	NA	Multiplex biometric immunoassay	IL-6, IL-10	8
Crespo et al³³	Caucasian	Spain	HB	Prospective cohort	16	73.6 ± 4.7	NA	Dead: 8Survivors: 8	Biochemical assay	IL-6	6
De La Flor et al³⁴	Caucasian	Spain	HB	Observational retrospective	10	73.5 ± 9.46	NA	Dead: 3Survivors: 7	OLHDF	IL-6	7
D’Alto et al³⁵	Caucasian	Italy	HB	Prospective	94	Dead: 68 ± 12Survivors: 62 ± 13	NA	Dead: 25Survivors: 69	Biochemical assay	IL-6	7
Ding et al⁶	Asian	China	HB	Prognostic	104	62.82 ± 14.77	Mild: 50Severe: 54	Dead: 16Survivors: 88	Biochemical assay	IL-6	7
Ding et al³⁶	Asian	China	HB	Retrospective	32	Mild: 54.9 ± 11.3Severe: 61.3 ± 17.9Critical: 73.5 ± 12.3	Mild: 11Severe: 21	NA	CLIA	IL-6	8
El-Shabrawy et al¹⁴	African	Egypt	HB	Prognostic cohort	116	Mild : 44.67 ± 42.13Severe: 54.17 ± 54.97	Mild: 99Severe: 17	NA	ELISA	IL-6	8
Fan et al³⁷	Asian	China	HB	Retrospective	73	58.36 ± 14.31	NA	Dead: 47Survivors: 26	Biochemical assay	IL-6	7
Fan et al³⁸	Asian	China	HB	Retrospective observational	101	65.46 ± 9.74	NA	Dead: 101Survivors: 0	Biochemical assay	IL-6	8
Fan et al³⁹	Asian	China	HB	Retrospective longitudinal	21	62.5 ± 12.6	NA	Dead: 4Survivors: 17	Biochemical assay	IL-6	6
Fei et al⁴⁰	Asian	China	HB	Retrospective	72	Mild: 55.7 ± 11.9Severe: 64 ± 16.8	Mild: 52Severe: 20	NA	ELISA	IL-6	7
Fei et al⁴¹	Asian	China	HB	Retrospective cohort	191	56.33 ± 15.69	NA	Dead: 54Survivors: 137	Biochemical assay	IL-6	7
Feng et al⁷	Asian	China	HB	Single-centered, prospective, and observational	114	63.96 ± 13.41	Mild: 94Severe: 20	NA	Biomarkers assay	IL-6, IL-10	7
Gadotti et al¹²	Caucasian	Brazil	HB	Prospective cohort	56	60.33 ± 19.78	NA	Dead: 18Survivors: 38	ELISA	IL-6, IL-10	8
Gan et al⁴²	Asian	China	HB	Retrospective case-control	95	65.67 ± 15.06	NA	Dead: 39Survivors: 56	Biochemical assay	IL-6, IL-10	7
Gao et al⁴³	Asian	China	HB	Retrospective	43	43.74 ± 12.12	Mild: 28Severe: 15	NA	ECLIA	IL-6	8
Gil-Etayo et al⁴⁴	Caucasian	Spain	HB	Prospective observational	34	58.08 ± 23.79	NA	Dead: 6Survivors: 28	Flow cytometry	IL-6	8
Guirao et al¹⁵	Caucasian	Spain	HB	Retrospective cohort	50	Mild: 56.2 ± 2.85Moderate: 65.7 ± 2.05Severe: 64.5 ± 2.26	Mild: 10Severe: 40	Dead: 14Survivor s: 36	ECLIA	IL-6	9
Guner et al⁴⁵	Caucasian	Turkey	HB	Retrospective cohort	222	50.6 ± 16.5	Mild: 172Severe: 50	NA	Biochemical assay	IL-6	8
Han et al¹³	Asian	China	HB	Retrospective cohort	102	Mild: 58.3 ± 12.6Severe: 59.3 ± 14.4Critical:65.1 ± 14.4	Mild: 42Severe: 60	NA	Flow cytometry	IL-6, IL-10	9
Yang et al⁴⁶	Asian	China	HB	Single-centered, retrospective, and observational	94	Dead: 75.8 ± 12.9Survivors: 65.8 ± 10.2	NA	Dead: 13Survivors: 81	Biochemical assay	IL-6, IL-10	9
Henry et al¹⁸	Caucasian	USA	HB	Prospective observational	52	52 ± 20.59	Mild: 36Severe: 16	NA	ELISA	IL-6, IL-10	9
He et al⁴⁷	Asian	China	HB	Retrospective	204	48.33 ± 20.91	Mild: 135Severe: 69	NA	Biochemical assay	IL-6, IL-10	7
He et al⁴⁸	Asian	China	HB	Single-center retrospective	93	47.9 ± 13.2	Mild: 60Severe: 33	NA	Biochemical assay	IL-6, IL-10	7
Herold et al¹⁶	Caucasian	UK	HB	Prospective cohort	89	54.33 ± 49.74	Mild: 57Severe: 32	NA	ELISA	IL-6	8
Holt et al⁴⁹	Caucasian	USA	HB	Retrospective cohort	62	Dead: 74.16 ± 11.4Survivors: 60.3 ± 12.5	NA	Dead: 19Survivors: 43	Biochemical assay	IL-6	6
Hu et al⁵⁰	Asian	China	HB	Retrospective cohort	76	50.47 ± 3.10	Mild: 63Severe: 13	NA	Multiplex biometric immunoassay	IL-6	6
Huang et al⁵¹	Asian	China	HB	Retrospective	64	47.8 ± 18.5	Mild: 43Severe: 21	Dead: 4Survivors: 27	Biochemical assay	IL-6, IL-10	6
Huang et al⁵²	Asian	China	HB	Single-center retrospective	218	62.33 ± 13.43	Mild: 116Severe: 102	NA	ELISA, CLIA	IL-6	7
Huang et al⁵³	Asian	China	HB	Retrospective	83	62 ± 12.31	Mild: 21Severe: 62	NA	Biochemical assay	IL-6	6
Jain et al⁵⁴	Asian	India	HB	Prospective observational	154	Mild: 42.34 ± 6.41Severe: 51.41 ± 9.12	Mild: 91Severe: 63	NA	ELISA	IL-6	7
Ke et al⁵⁵	Asian	China	HB	Single-centered, retrospective case-control	194	63.08 ± 12.88	NA	Dead: 46Survivors: 148	Flow cytometry	IL-6, IL-10	7
Keske et al⁵⁶	Caucasian	Turkey	HB	Retrospective	43	61.67 ± 51.39	NA	Dead: 6Survivors: 37	Biochemical assay	IL-6	6
Kumar et al⁵⁷	Asian	India	HB	Clinical study	386	Dead: 63.4 ± 14Survivors: 48.1 ± 16.3	NA	Dead: 16Survivors: 370	CLIA	IL-6	8
Laguna-Goya et al⁵⁸	Caucasian	Spain	HB	Prospective cohort	454	52 ± 11.9	NA	Dead: 33Survivors: 421	Flow cytometry	IL-6	9
Li et al⁵⁹	Asian	China	HB	Retrospective	476	61.33 ± 14.09	NA	Dead: 183Survivors: 293	Biochemical assay	IL-6, IL-10	7
Li et al⁶⁰	Asian	China	HB	Retrospective cohort	102	57.33 ± 18.8	NA	Dead: 15Survivors: 87	Biochemical assay	IL-6, IL-10	6
Li et al⁶¹	Asian	China	HB	Retrospective	1449	55 ± 17.81	NA	Dead: 122Survivors: 1327	Biochemical assay	IL-6, IL-10	6
Li et al⁶²	Asian	China	HB	Single-center, retrospective	215	Mild: 42.67 ± 14.96Severe: 49.5 ± 39.57	Mild: 159Severe: 56	NA	Flow cytometry	IL-6, IL-10	7
Liu et al⁶³	Asian	China	HB	Multicenter, Retrospective cohort	2044	61 ± 14.09	Mild: 1087Severe: 957	NA	CLIA	IL-6, IL-10	8
Liu et al⁶⁴	Asian	China	HB	Retrospective	50	54 ± 20.86	Mild: 24Severe: 26	NA	Biochemical assay	IL-6, IL-10	6
Liu et al⁶⁵	Asian	China	HB	Single-center, retrospective	51	43.33 ± 12.97	Mild: 44Severe: 7	NA	Biochemical assay	IL-6	6
Liu et al⁶⁶	Asian	China	HB	Retrospective cohort	255	60 ± 50.7	Mild: 214Severe: 41	NA	Biochemical assay	IL-6, IL-10	7
Liu et al⁶⁷	Asian	China	HB	Retrospective cohort	80	55 ± 43.3	Mild: 11Severe: 69	NA	ELISA	IL-6, IL-10	8
Liu et al⁶⁸	Asian	China	HB	Retrospective cohort	124	NA	Mild: 37Severe: 87	NA	Biochemical assay	IL-6	7
Liu et al⁷⁰	Asian	China	HB	Retrospective cohort	101	62.33 ± 17.3	Mild: 47Severe: 54	NA	Biochemical assay	IL-6, IL-10	7
Liu et al⁷¹	Asian	China	HB	Retrospective	76	47 ± 45.36	Mild: 30Severe: 46	NA	Biochemical assay	IL-6, IL-10	6
Liu et al⁷²	Asian	China	HB	Single-center, retrospective	67	Mild: 46 ± 22.79Severe: 64.77 ± 11.79Critical: 64 ± 11.21	Mild: 10Severe: 57	NA	Flow cytometry	IL-6, IL-10	6
Luo et al⁷³	Asian	China	HB	Multicenter, Retrospective	1018	59.67 ± 14.85	NA	Dead: 201Survivors: 817	CLIA	IL-6, IL-10	9
Lu et al⁷⁴	Asian	China	HB	Single-center, retrospective	121	6.25 ± 4.31	Mild: 101Severe: 20	NA	Flow cytometry	IL-6, IL-10	9
Lv et al⁷⁵	Asian	China	HB	Retrospective cohort	354	58.33 ± 49.87	Mild: 115Severe: 239	NA	Biochemical assay	IL-6, IL-10	7
Ma et al⁷⁶	Asian	China	HB	Single-center, retrospective	37	63.67 ± 8.48	Mild: 17Severe: 20	NA	Biochemical assay	IL-6	6
Ma et al⁷⁷	Asian	China	HB	Retrospective cohort	84	50.93 ± 15.24	Mild: 64Severe: 20	NA	Flow cytometry	IL-6	7
Maeda et al⁷⁸	Caucasian	USA	HB	Single-center retrospective cohort	224	63 ± 17	Mild: 167Severe: 57	NA	Biochemical assay	IL-6	8
Mandel et al⁷⁹	Caucasian	Israel	HB	Prospective non-randomized cohort	71	62 ± 13.8	NA	Dead: 12Survivors: 59	ELISA	IL-6	8
McElvaney et al⁸⁰	Caucasian	Ireland	HB	Retrospective	40	55.5 ± 17.7	Mild: 20Severe: 20	NA	ELISA	IL-6, IL-10	8
Merza et al⁸¹	Caucasian	Iraq	HB	Retrospective	56	Mild: 35.7Severe: 51.75	Mild: 41Severe: 15	NA	ELISA	IL-6, IL-10	8
Mikami et al⁸²	Caucasian	USA	HB	Multicenter Retrospective cohort	6493	58 ± 21.5	Mild: 2785Severe: 3708	Dead: 806Survivors: 2014	Biochemical assay	IL-6	7
Mo et al⁸³	Asian	China	HB	Single-center, retrospective	155	54 ± 17.96	Mild: 70Severe: 85	NA	Biochemical assay	IL-6	6
Morisson et al⁸⁴	Caucasian	USA	HB	Retrospective observational cohort	81	64.33 ± 9.81	NA	Dead: 35Survivors: 46	Biochemical assay	IL-6	6
Myhre et al⁸⁵	Caucasian	Norway	HB	Prospective observational	123	Dead: 64.3 ± 10.7Survivors: 57.8 ± 16.3	NA	Dead: 35Survivors: 88	ECLIA	IL-6	7
Nie et al⁸⁶	Asian	China	HB	Retrospective	97	43 ± 22.58	Mild: 72Severe: 25	NA	Biochemical assay	IL-6, IL-10	6
Pandolfi et al⁸⁷	Caucasian	Italy	HB	Prospective cohort	33	Mild: 62.67 ± 8.05Severe: 58 ± 11.33	Mild: 5Severe: 28	NA	ELISA	IL-6	8
Qin et al⁸⁸	Asian	China	HB	Single-center, retrospective	452	57.33 ± 14.87	Mild: 166Severe: 286	NA	Flow cytometry	IL-6, IL-10	8
Quartuccio et al⁸⁹	Caucasian	Italy	HB	Retrospective	24	Dead: 68.8 ± 9.4Survivors: 65.8 ± 8.2	NA	Dead: 6Survivors: 18	ECLIA	IL-6	7
Quiroga et al⁹⁰	Caucasian	Spain	HB	Single-centered, prospective, and observational	16	72 ± 15	NA	Dead: 4Survivor s: 12	Enzyme-immune assay	IL-6	7
Rastrelli et al⁹¹	Caucasian	Italy	HB	Retrospective cohort	31	Mild: 61.5 ± 9.14Severe: 62.83 ± 48.15Dead: 73 ± 27.85	Mild: 21Severe: 10	NA	ECLIA	IL-6	8
Ruan et al⁹²	Asian	China	HB	Multicenter Retrospective cohort	150	Dead: 54.33 ± 49.99Survivors: 58.3 ± 27.9	NA	Dead: 68Survivors: 82	Biochemical assay	IL-6	6
Sabaka et al⁹³	Caucasian	Slovakia	HB	Retrospective	45	Mild: 80.33 ± 10.98Severe: 85.83 ± 7.61	Mild: 26Severe: 19	NA	ECLIA	IL-6	8
Sarfaraz et al⁹⁴	Asian	Pakistan	HB	Prospective cohort	170	Dead: 61 ± 12.57Survivors: 53 ± 13	NA	Dead: 67Survivors: 103	Biochemical assay	IL-6	6
Sarhan et al⁹⁵	African	Egypt	HB	Retrospective	203	58.67 ± 23.89	Mild: 26Severe: 19	NA	ELISA	IL-6	8
Shi et al⁹⁶	Asian	China	HB	Multicenter Retrospective cohort	Zhao et al¹⁴⁸	50.58 ± 10.67	Mild: 119Severe: 29	NA	Biochemical assay	IL-6, IL-10	9
Shi et al⁹⁷	Asian	China	HB	Retrospective	87	56.67 ± 49.75	Mild: 51Severe: 36	NA	Biochemical assay	IL-6	6
Shi et al⁹⁸	Asian	China	HB	Prospective observational	45	Mild: 40.23 ± 12.61Severe: 59.35 ± 18.07Critical: 66.9 ± 17.01	Mild: 13Severe: 32	NA	Biochemical assay	IL-6, IL-10	7
Simioli et al⁹⁹	Caucasian	Italy	HB	Single-center case-control	29	64 ± 22.5	Mild: 11Severe: 18	NA	Biochemical assay	IL-6	7
Song et al¹⁰⁰	Asian	China	HB	Retrospective	73	Mild: 48 ± 17.1Severe: 55.93 ± 12.51	Mild: 31Severe: 42	NA	Biochemical assay	IL-6, IL-10	6
Song et al¹⁰¹	Asian	China	HB	Single-center, retrospective cohort	1172	59 ± 14.84	Mild: 881Severe: 291	NA	Biochemical assay	IL-6, IL-10	7
Song et al¹⁰²	Asian	China	HB	Cross sectional observational	41	40.83 ± 12.68	Mild: 29Severe: 12	NA	Flow cytometry	IL-6	8
Sun et al¹⁰³	Asian	China	HB	Retrospective	244	Dead: 72 ± 9Survivors: 67.67 ± 6	NA	Dead: 121Survivors: 123	Biochemical assay	IL-6	7
Sun et al¹⁰⁴	Asian	China	HB	Prospective observational cohort	99	Mild: 52 ± 15.28Severe: 70.83 ± 14.88	Mild: 49Severe: 50	NA	Biochemical assay	IL-6, IL-10	7
Sun et al¹⁰⁵	Asian	China	HB	Prospective cohort	63	45 ± 62.21	Mild: 44Severe: 19	NA	Biochemical assay	IL-6	6
Taha et al¹⁰⁶	African	Egypt	HB	Observational cohort	85	54 ± 17.35	Mild: 46Severe: 39	Dead: 21Survivors: 64	ELISA	IL-6	8
Tang et al¹⁰⁷	Asian	China	HB	Prospective	120	58 ± 15.76	Mild: 60Severe: 60	NA	Flow cytometry	IL-6, IL-10	9
Tian et al¹⁰⁸	Asian	China	HB	Multicenter, retrospective, cohort	751	63.33 ± 8.91	Mild: 84Severe: 148	NA	CLIA	IL-6, IL-10	9
Toniati et al¹⁰⁹	Caucasian	Italy	HB	Single-centered, prospective	100	63.33 ± 10.53	Mild: 77Severe: 23	NA	Biochemical assay	IL-6	6
Tu et al¹¹⁰	Asian	China	HB	Single-center, retrospective cohort	174	Dead: 71.33 ± 12.58Survivors: 50 ± 18.71	NA	Dead: 25Survivors: 149	Biochemical assay	IL-6	7
Vultaggio et al¹⁷	Caucasian	Italy	HB	Retrospective observational cohort	208	65.7 ± 15	Mild: 145Severe: 63	NA	ELISA	IL-6	8
Wan et al¹¹¹	Asian	China	HB	Prospective	66	Mild: 43.05 ± 13.12Severe: 61.29 ± 15.55	Mild: 45Severe: 21	NA	Flow cytometry	IL-6, IL-10	8
Wang et al¹¹²	Asian	China	HB	Retrospective	28	68.6 ± 9	Mild: 14Severe: 14	NA	Biochemical assay	IL-6, IL-10	6
Wang et al¹¹³	Asian	China	HB	Multicenter, retrospective	165	45.67 ± 11.97	Mild: 115Severe: 50	NA	Biochemical assay	IL-6	7
Wang et al¹¹⁴	Asian	China	HB	Retrospective	339	70 ± 8.19	NA	Dead: 65Survivors: 274	Biochemical assay	IL-6	6
Wang et al¹¹⁵	Asian	China	HB	Single-center, retrospective, descriptive	125	38.76 ± 13.799	Mild: 100Severe: 25	NA	Biochemical assay	IL-6	6
Wang et al¹¹⁶	Asian	China	HB	Retrospective case-control	43	Mild: 43.05 ± 13.12Severe: 61.29 ± 15.55	Mild: 35Severe: 8	NA	Flow cytometry	IL-6. IL-10	8
Wang et al¹¹⁷	Asian	China	HB	Retrospective cohort	43	46.33 ± 20.44	Mild: 36Severe: 7	NA	Biochemical assay	IL-6, IL-10	7
Wang et al¹¹⁸	Asian	China	HB	Retrospective	59	67.4 ± 11.3	NA	Dead: 41Survivors: 18	CLIA	IL-6, IL-10	6
Webb et al¹¹⁹	Caucasian	USA	HB	Prospective observational cohort	72	55.67 ± 18.62	Mild: 5Severe: 67	NA	Biochemical assay	IL-6	6
Wei et al¹²⁰	Asian	China	HB	Retrospective	252	64.8 ± 13.3	Mild: 131Severe: 98	NA	CLIA	IL-6, IL-10	9
Wu et al¹²¹	Asian	China	HB	Retrospective cohort	201	51.33 ± 12.69	Mild: 117Severe: 84	Dead: 44Survivor s: 40	Biochemical assay	IL-6	6
Wu et al¹²²	Asian	China	HB	Single-center, retrospective cohort	Zhao et al¹⁴⁸	75 ± 78.61	Mild: 60Severe: 88	NA	Biochemical assay	IL-6, IL-10	6
Wu et al¹²³	Asian	China	HB	Retrospective	71	57 ± 21.19	Mild: 32Severe: 39	NA	Flow cytometry	IL-6, IL-10	8
Xiao et al¹²⁴	Asian	China	HB	Retrospective	143	NA	Mild: 107Severe: 36	NA	Biochemical assay	IL-6	6
Xie et al¹²⁵	Asian	China	HB	Retrospective	29	64.1 ± 14.95	Mild: 22Severe: 7	NA	Biochemical assay	IL-6	7
Xu et al¹²⁶	Asian	China	HB	Single-centered, retrospective observational	187	60.5 ± 16.81	Mild: 80Severe: 107	Dead: 28Survivors: 117	Biochemical assay	IL-6, IL-10	7
Xu et al¹²⁷	Asian	China	HB	Multicenter Retrospective observational	324	63.2 ± 14.5	Mild: 177Severe: 147	NA	Biochemical assay	IL-6	8
Xu et al¹²⁸	Asian	China	HB	Retrospective	155	Mild: 39.84 ± 15.09Severe: 50.97 ± 13.55	Mild: 125Severe: 30	NA	Biochemical assay	IL-6	6
Xu et al⁶⁹	Asian	China	HB	Single-center, retrospective cohort	88	57.11 ± 15.39	Mild: 47Severe: 41	NA	Biochemical assay	IL-6	6
Xu et al¹²⁹	Asian	China	HB	Multicenter Retrospective	69	56.33 ± 19.69	Mild: 44Severe: 25	NA	Flow cytometry	IL-6	9
Yan et al¹³⁰	Asian	China	HB	Single-centered, retrospective observational	48	69.4 ± 9.9	NA	Dead: 39Survivors: 9	Biochemical assay	IL-6	6
Yang et al¹³¹	Asian	China	HB	Single-center, retrospective	93	46.4 ± 17.6	Mild: 69Severe: 24	NA	Flow cytometry	IL-6, IL-10	8
Yang et al¹³²	Asian	China	HB	Retrospective	76	NA	Mild: 42Severe: 34	NA	Biochemical assay	IL-6, IL-10	6
Yang et al¹³³	Asian	China	HB	Retrospective observational	55	44 ± 15.23	Mild: 21Severe: 34	NA	Biochemical assay	IL-6	6
Yang et al¹³⁴	Asian	China	HB	Retrospective case-control	45	32 ± 29.87	Mild: 23Severe: 22	NA	Flow cytometry	IL-6	8
Yuan et al¹³⁵	Asian	China	HB	Retrospective	189	60.33 ± 14.94	Mild: 102Severe: 87	NA	Biochemical assay	IL-6	6
Yuan et al¹³⁶	Asian	China	HB	Retrospective	117	64.67 ± 10.51	Mild: 53Severe: 54	NA	Biochemical assay	IL-6, IL-10	6
Zeng et al¹³⁷	Asian	China	HB	Retrospective	49	Mild: 46 ± 19Severe: 60 ± 16Critical: 68 ± 20	Mild: 28Severe: 21	NA	Biochemical assay	IL-6, IL-10	6
Zeng et al¹³⁸	Asian	China	HB	Retrospective	317	61 ± 14.15	Mild: 93Severe: 224	NA	CLIA	IL-6, IL-10	8
Zhang et al¹³⁹	Asian	China	HB	Retrospective	222	61 ± 12.69	Mild: 81Severe: 67	NA	Automated immunoassay multiplex array system	IL-6, IL-10	8
Zhang et al¹⁴⁰	Asian	China	HB	Retrospective	82	72.5 ± 11.32	NA	Dead: 82Survivors: 0	Automated immunoassay multiplex array system	IL-6	8
Zhang et al¹⁴¹	Asian	China	HB	Retrospective case-series	98	63.9 ± 1.4	NA	Dead: 36Survivors: 62	Biochemical assay	IL-6	7
Zhang et al¹⁴²	Asian	China	HB	Retrospective	43	Mild: 44.4 ± 15.9Severe: 61.9 ± 9.4	Mild: 29Severe: 14	NA	ELISA	IL-6, IL-10	8
Zhang et al¹⁴³	Asian	China	HB	Single-center, retrospective	111	42.33 ± 18.78	Mild: 93Severe: 18	Dead: 18Survivors: 93	Biochemical assay	IL-6, IL-10	7
Zhang et al¹⁴⁴	Asian	China	HB	Retrospective	134	60.78 ± 12.98	Mild: 33Severe: 101	Dead: 101Survivors: 33	Biochemical assay	IL-6	6
Zhang et al¹⁴⁵	Asian	China	HB	Single-center retrospective observational	74	63.33 ± 12.10	Mild: 47Severe: 27	NA	Biochemical assay	IL-6	7
Zhang et al¹⁴⁶	Asian	China	HB	Retrospective	38	Dead: 37.7 ± 8.2Survivors: 35.8 ± 4.1	NA	Dead: 18Survivors: 20	Biochemical assay	IL-6	6
Zhang et al¹⁴⁷	Asian	China	HB	Retrospective	326	51.33 ± 54.36	Mild: 28Severe: 293	NA	Flow cytometry	IL-6	8
Zhao et al¹⁴⁹	Asian	China	HB	Single-center, retrospective	172	64.33 ± 10.47	Mild: 112Severe: 60	NA	Biochemical assay	IL-6, IL-10	7
Zhao et al¹⁵⁰	Asian	China	HB	Prospective	71	49.33 ± 19.67	Mild: 53Severe: 18	NA	Bio plex multiplex immunoassay	IL-6, IL-10	8
Zheng et al¹⁵⁰	Asian	China	HB	Single-center, Retrospective cohort	96	54.7 ± 15.43	Mild: 22Severe: 74	NA	Biochemical assay	IL-6, IL-10	7
Zheng et al¹⁵¹	Asian	China	HB	Retrospective	34	66.67 ± 13.93	Mild: 19Severe: 15	NA	ELISA	IL-6, IL-10	8
Zhou et al⁴¹	Asian	China	HB	Multicenter, retrospective, cohort	191	56.33 ± 15.69	NA	Dead: 54Survivors: 137	Biochemical assay	IL-6	7
Zhou et al¹⁵²	Asian	China	HB	Single-center, retrospective	21	66.10 ± 13.94	Mild: 8Severe: 13	NA	Automatic biochemical analyzer	IL-6	9
Zhu et al¹⁵³	Asian	China	HB	Retrospective	127	50.90 ± 15.26	Mild: 111Severe: 16	NA	Flow cytometry	IL-6, IL-10	8
Zou et al¹⁵⁴	Asian	China	HB	Retrospective	121	63.83 ± 12.38	Mild: 69Severe: 52	Dead: 14Survivors: 107	Biochemical assay	IL-6, IL-10	7

Abbreviations: CLIA, chemiluminescent immunoassay; ECLIA, electro-chemiluminescent immunoassay; ELISA, enzyme-linked immunosorbent assay; HB, hospital based; NA, not available; NOS, Newcastle Ottawa Scale; OLHDF, online hemodiafiltration.

Association of IL-6 with the severity and mortality of COVID-19

We assessed 107 studies to verify fluctuation in serum IL-6 concentration among COVID-19 patients in response to disease severity. Comparatively, elderly patients showed severe to critical symptoms than younger patients, according to selected studies. The mean difference in serum IL-6 level was 19.98 higher in the severe patients than in the mild category patients. IL-6 level showed significant elevation in the severe COVID-19 cases (MD: 19.98; P < .001; 95% CI: 17.56, 22.40).

To evaluate the impact of IL-6 level on the mortality of COVID-19 patients, 49 studies of 147 included studies were assessed. The result showed that deceased COVID-19 patients had 42.11 times higher mean concentration than survived patients. IL-6 level was significantly increased in the dead patients (MD: 42.11; P < .001; 95% CI: 36.86, 47.36), and the fluctuation was highly noticeable (Table 2, Figures 2 and 3).

Table 2.

Effect of elevated IL-6 and IL-10 levels on disease severity and mortality in COVID-19 patients.

Interleukin	Covariates	Test of association			Test of heterogeneity			Publication bias (P-value)
Interleukin	Covariates	Mean difference	95% CI	P-value	Model	P-value	I² (%)	Egger’s test	Begg-Mazumdar’s test
IL-6	Severity	19.98	17.56, 22.40	<.001	Random	<.001	97	0.005	0.023
IL-6	Mortality	42.11	36.86, 47.36	<.001	Random	<.001	98	0.718	0.716
IL-10	Severity	1.35	0.90, 1.80	<.001	Random	<.001	91	0.091	0.455
IL-10	Mortality	4.79	2.83, 6.75	<.001	Random	<.001	81	0.669	1.00

Figure 2.

Forest plots showing IL-6 and IL-10 levels in COVID-19 patients based on disease severity index: (a) IL-6 levels in severe and non-severe COVID-19 cases and (b) IL-10 levels in severe and non-severe COVID-19 cases.

Figure 3.

Forest plots showing IL-6 and IL-10 levels in COVID-19 patients based on mortality index: (a) IL-6 levels in dead and survivors COVID-19 cases and (b) IL-10 levels in dead and survivors COVID-19 cases.

Association of IL-10 with the severity and mortality of COVID-19

Fifty-two studies were assessed to identify fluctuation in serum IL-10 concentration according to disease severity among COVID-19 patients. The mean difference in serum IL-10 level was 1.35 between severe and mild COVID-19 patients. IL-10 level showed significantly high concentration in the severe COVID-19 cases (MD: 1.35; P < .001; 95% CI: 0.90, 1.80).

To evaluate the impact of elevated IL-10 level on the mortality of COVID-19 patients, 12 studies of 147 included studies were assessed. The outcome showed that dead COVID-19 patients had an increased IL-10 to a mean concentration of 4.79 than survived patients. IL-10 level was significantly increased in the deceased patients (MD: 4.79; P < .001; 95% CI: 2.83, 6.75), and the fluctuation indicated that IL-10 might be associated with mortality in COVID-19 (Table 2, Figures 2 and 3).

Sensitivity analysis and publication bias

No visual asymmetry was observed during analyzing funnel plots indicating the absence of publication bias (Figure 4). Egger’s regression test showed a significant outcome in IL-6 versus COVID-19 severity model (P = .005). Other analyses did not show any significant publication bias (IL-6 vs COVID-19 mortality: P = .652; IL-10 vs COVID-19 severity: P = .091; IL-10 vs COVID-19 mortality: P = .669). Begg-Mazumdar’s test also showed similar results (IL-6 vs COVID-19 severity: P = .023; IL-6 vs COVID-19 mortality: P = .730; IL-10 vs COVID-19 severity: P = .455; IL-10 vs COVID-19 mortality: P = 1.00). The results are shown in Table 2. Sensitivity analysis was performed by excluding the studies one by one to verify the stability of the final outcome. The final outcome was stable, and none of the studies interfered with the core results (not shown).

Figure 4.

Funnel plots for publication bias analysis in different meta-analysis models: (a) IL-6 and severity of COVID-19, (b) IL-10 and severity of COVID-19, (c) IL-6 and mortality of COVID-19, and (d) IL-10 and mortality of COVID-19.

Discussion

The wave of COVID-19 is still ongoing, with full rhythm failing a number of attempts to control this pandemic situation. Most of the COVID-19 cases remain mild, and patients get their recovery from the fully active natural immune system, but 14% of patients face severe symptoms that lead to ARDS, septic shock and multiple organ failure.¹³ The ultimate outcome of severe SARS-CoV-2 infection becomes life-threatening, which is undeniable. Moreover, the rate of survival is minimal in severe to critical cases. The burden of emergency COVID-19 cases is uprising drastically worldwide. SARS-CoV-2 infection has become a threat to human race, and researchers are still struggling to improve this overwhelming situation. Early detection of the severe stage of infection could cease the disease progression toward the critical stage. Checkpoint of severity will also reduce the risk of mortality from COVID-19.⁸

In our study, we have accumulated a number of evidence suggesting that cytokine storms developed during SARS-CoV-2 infection intensify the damage rapidly. Elderly patients, children, or patients with a previous disease condition with a weak immunity system mostly show a severe immune reaction. Anti-inflammatory treatments could not instantly reduce the sharp elevation of cytokines in the human body. As a result, the consequences of acute tissue damage and critical lung inflammation become challenging to control.¹⁵⁵ IL-6 and IL-10 show a significant elevation in COVID-19 patients with mild conditions, and the concentration sharply increases manyfold when the condition gets worsens. These 2 biomarkers should be observed as a primary prognostic indicator in COVID-19 patients to understand the disease state.^155,156

Efficient immune activity is essential in the fight against any infection, although overproduction and unnecessary activation of active immune cells may cause much more irreversible damage than the actual infection. In COVID-19 cases, cytokine storm in the risk population reduces the lung capacity by flooding lung surfaces with inflammatory cells. The oversensitive immune activity becomes ineffective and fills the air sacks of the lungs with fluid limiting their oxygen uptake ratio, which leads to inevitable deaths.^157-159 IL-6 and IL-10 are major pleiotropic interleukins involved in potent inflammatory reactions observed in human body during any infection. Among these 2 cytokines, IL-6 helps to conduct acute phase immune reactions by recruiting immune cells in the infected area. But the excess level of IL-6 is responsible for anaphylactic shock or cytokine storm. This phenomenon will cause additional damage rather than wiping out infectious agents. On the other hand, IL-10 is responsible for maintaining homeostatic balance in the immune system by exerting anti-inflammatory actions. Human immune system can control or inhibit severe inflammation itself when the body starts healing by a homeostatic mechanism. Both IL-6 and IL-10 are closely involved in COVID-19 pathogenesis.^160-162 IL-6 is one of the critical inflammatory mediators in patients severely suffering from COVID-19. The level of IL-6 is elevated in these subjects and has been considered an important choice for COVID-19 targeting. Therapeutic agents (eg, sarilumab, tocilizumab) that suppress the IL-6 signaling mechanism have been reported to be effective against COVID-19.^163-165

Many studies attempted to find out immune-inflammatory predictors for disease severity in COVID-19. A recent systematic review and meta-analysis that included 19 studies with 3115 participants found that IL-6 and IL-10 level was higher in the severe COVID-19 cases than in the non-severe cases.¹⁶⁶ Another study performed on 24 articles with 6212 participants recommended both IL-6 and IL-10 as potential biomarkers for COVID-19 severity and mortality.¹⁵⁵ Bao et al¹⁶⁷ reported that severe patients had increased levels of IL-6 (1.93-fold) and IL-10 (1.55-fold) serum concentration in a study involving 35 articles (5912 patients). Zawawi et al¹⁶⁸ showed that both the interleukins are associated with the severity of COVID-19 in their recent meta-analysis. In another network meta-analysis with 71 eligible studies involving 8647 patients, a rise in the IL-6 and IL-10 count was observed with worsening of the COVID-19 infection.¹⁶⁹ Other studies with a limited sample size also conducted a similar assessment and reported similar findings.^8,19-21 The evidence from these studies was sub-optimal and significant heterogeneity was observed due to the limited sample size. To create valid evidence that sharp elevation in IL-6 and IL-10 levels should be considered as a checkpoint of COVID-19 severity and mortality, we carried out this large-scale updated meta-analysis.

The findings from our present meta-analysis revealed that the mean IL-6 and IL-10 serum level was significantly higher in the COVID-19 patients. Severe category patients faced a sharp increase in the serum level compared to non-severe category patients. A similar result was also observed in the case of mortality. The deceased patients showed abnormally high serum concentrations of IL-6 and IL-10 than the survived patients. Moreover, the concentration of serum interleukins in the dead patients was significantly higher than the severe cases of COVID-19.

The current meta-analysis had some drawbacks that should be mentioned. Most of the included studies were retrospective cohort studies with smaller sample sizes. As the number of studies was huge, some detailed and basic information like—sex, treatment, duration of infection, smoking habit, and body mass index (BMI) could not be added to the meta-analysis. The presence of heterogeneity was another limitation of the study. The heterogeneity may be due to the different ethnic groups, sample size variation, different interventions to treat the symptoms of COVID-19 and variation in the inclusion and exclusion criteria for mild and severe groups selection. In spite of the limitations, our study is methodologically strong. According to our understanding, this is the most comprehensive and updated systematic review and meta-analysis on the association between circulating levels of IL-6 and IL-10 and the severity and mortality of COVID-19.

Conclusion

In summary, this investigative meta-analysis confirmed that sharp elevation in serum IL-6 and IL-10 worsens COVID-19 clinical outcomes. IL-6 and IL-10 are associated with the severity and mortality of COVID-19. The circulating level of both interleukins can act as potential biomarkers for the disease severity and mortality in SARS-CoV-2 infected patients.

Supplemental Material

sj-docx-1-bmi-10.1177_11772719221106600 – Supplemental material for Elevated Levels of Pleiotropic Interleukin-6 (IL-6) and Interleukin-10 (IL-10) are Critically Involved With the Severity and Mortality of COVID-19: An Updated Longitudinal Meta-Analysis and Systematic Review on 147 Studies

Supplemental material, sj-docx-1-bmi-10.1177_11772719221106600 for Elevated Levels of Pleiotropic Interleukin-6 (IL-6) and Interleukin-10 (IL-10) are Critically Involved With the Severity and Mortality of COVID-19: An Updated Longitudinal Meta-Analysis and Systematic Review on 147 Studies by Sarah Jafrin, Md. Abdul Aziz and Mohammad Safiqul Islam in Biomarker Insights

Footnotes

Acknowledgements

This meta-analysis was supported and assisted by the Department of Pharmacy, Noakhali Science and Technology University, Sonapur, Noakhali.

Funding:

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests:

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author Contributions

MSI conceptualized this meta-analysis; SJ and MAA wrote the primary draft; MSI carried out the statistical analyses; MSI critically reviewed and revised the manuscript; Before submission, all authors read and approved the final version of the manuscript.

Data Availability Statement

All data generated or analyzed during the present meta-analysis are available from the corresponding author on reasonable request.

ORCID iD

Mohammad Safiqul Islam

Supplemental Material

Supplemental material for this article is available online.

References

Guan

, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382:1708-1720.

Bergantini

Bargagli

d’Alessandro

, et al. Prognostic bioindicators in severe COVID-19 patients. Cytokine. 2021;141:155455.

WHO. COVID-19 Weekly Epidemiological Update. World Health Organization; 2021. Accessed December 20, 2021. https://www.who.int/publications/m/item/covid-19-weekly-epidemiological-update

Chen

Sun

Wang

Clinical characteristics and risk factors for mortality among inpatients with COVID-19 in Wuhan, China. Clin Transl Med. 2020;10:e40.

Chen

, et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. BMJ. 2020;368:m1091.

Ding

Zhang

, et al. Prognostic role and diagnostic power of seven indicators in COVID-19 patients. Front Med. 2021;8:733274.

Feng

, et al. Clinical characteristics and short-term outcomes of severe patients with COVID-19 in Wuhan, China. Front Med. 2020;7:491.

Henry

de Oliveira

MHS

Benoit

Plebani

Lippi

Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chem Lab Med. 2020;58:1021-1028.

Chen

Zhao

, et al. Detectable serum severe acute respiratory syndrome coronavirus 2 viral load (RNAemia) is closely correlated with drastically elevated interleukin 6 level in critically ill patients with coronavirus disease 2019. Clin Infect Dis. 2020;71:1937-1942.

10.

Chi

, et al. Serum cytokine and chemokine profile in relation to the severity of coronavirus disease 2019 in China. J Infect Dis. 2020;222:746-754.

11.

Chen

Zhou

Chen

, et al. Consecutive monitoring of interleukin-6 is needed for COVID-19 patients. Virol Sin. 2021;36:1093-1096.

12.

Gadotti

de Castro Deus

Telles

, et al. IFN-γ is an independent risk factor associated with mortality in patients with moderate and severe COVID-19 infection. Virus Res. 2020;289:198171.

13.

Han

, et al. Profiling serum cytokines in COVID-19 patients reveals IL-6 and IL-10 are disease severity predictors. Emerg Microbes Infect. 2020;9:1123-1130.

14.

El-Shabrawy

Alsadik

El-Shafei

, et al. Interleukin-6 and C-reactive protein/albumin ratio as predictors of COVID-19 severity and mortality. Egypt J Bronchol. 2021;15:5.

15.

Guirao

Cabrera

Jiménez

Rincón

Urra

JM.

High serum IL-6 values increase the risk of mortality and the severity of pneumonia in patients diagnosed with COVID-19. Mol Immunol. 2020;128:64-68.

16.

Herold

Jurinovic

Arnreich

, et al. Elevated levels of IL-6 and CRP predict the need for mechanical ventilation in COVID-19. J Allergy Clin Immunol. 2020;146:128-136.e4.

17.

Vultaggio

Vivarelli

Virgili

, et al. Prompt predicting of early clinical deterioration of moderate-to-severe COVID-19 patients: usefulness of a combined score using IL-6 in a preliminary study. J Allergy Clin Immunol Pract. 2020;8:2575-2581.e2.

18.

Henry

Benoit

Vikse

, et al. (Covid-19). Clin Chem Lab Med. 2020;59:599-607.

19.

Mulchandani

Lyngdoh

Kakkar

AK.

Deciphering the COVID-19 cytokine storm: systematic review and meta-analysis. Eur J Clin Invest. 2021;51:e13429.

20.

Leisman

Ronner

Pinotti

, et al. Cytokine elevation in severe and critical COVID-19: a rapid systematic review, meta-analysis, and comparison with other inflammatory syndromes. Lancet Respir Med. 2020;8:1233-1244.

21.

Coomes

Haghbayan

Interleukin-6 in covid-19: a systematic review and meta-analysis. Rev Med Virol. 2020;30:1-9.

22.

Higgins

JPT

Thomas

Chandler

, et al., eds. Cochrane Handbook for Systematic Reviews of Interventions Version 6.0 (Updated July 2019). Cochrane; 2019. https://training.cochrane.org/handbook/current

23.

Moher

Liberati

Tetzlaff

Altman

; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6:e1000097.

24.

Burian

Jungmann

Kaissis

, et al. Intensive care risk estimation in COVID-19 pneumonia based on clinical and imaging parameters: experiences from the Munich cohort. J Clin Med. 2020;9:1514.

25.

Cai

Huang

, et al. COVID-19 in a designated infectious diseases hospital outside Hubei Province, China. Allergy. 2020;75:1742-1752.

26.

Chang

Yang

Wang

Liao

, et al. Clinical significance of serum hs-CRP, IL-16, and PCT in diagnosis and prognosis of patients with COVID-19. Drugs Clin. 2020;35:417-420.

27.

Chen

Guo

, et al. Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Investig. 2020;130:2620-2629.

28.

Chen

Wang

Bao

Jin

Simplified immune-dysregulation index: a novel marker predicts 28-day mortality of intensive care patients with COVID-19. Intensive Care Med. 2020;46:1645-1647.

29.

Chen

Huiguo

Wei

, et al. Analysis of clinical features of 29 patients with 2019 novel coronavirus pneumonia. Zhonghua Jie He He Hu Xi Za Zhi. 2020;43:203-208.

30.

Chen

Zhang

Wei

, et al. Association between cytokine profiles and lung injury in COVID-19 pneumonia. Respir Res. 2020;21:201.

31.

Chen

Sang

Jiang

, et al. Longitudinal hematologic and immunologic variations associated with the progression of COVID-19 patients in China. J Allergy Clin Immunol. 2020;146:89-100.

32.

Chen

Dai

, et al. 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. 2020;75:1788-1795.

33.

Crespo

Pérez-Sáez

Redondo-Pachón

, et al. COVID-19 in elderly kidney transplant recipients. Am J Transplant. 2020;20:2883-2889.

34.

De La Flor

Valga

Marschall

, et al. Targeting cytokine storm in COVID-19: a role of online hemodiafiltration with asymmetric cellulose triacetate in maintenance hemodialysis patients-a report of 10 cases. Case Rep Nephrol. 2021;2021:5575928.

35.

D’Alto

Marra

Severino

, et al. Right ventricular-arterial uncoupling independently predicts survival in COVID-19 ARDS. Crit Care. 2020;24:670.

36.

Ding

Zhang

Huang

YZ.

Correlation analysis of the severity and clinical prognosis of 32 cases of patients with COVID-19. Respir Med. 2020;167:105981.

37.

Fan

Zhang

Huang

, et al. Cardiac injuries in patients with coronavirus disease 2019: not to be ignored. Internet J Infect Dis. 2020;96:294-297.

38.

Zhang

Huang

Xia

, et al. Retrospective analysis of clinical features in 134 coronavirus disease 2019 cases. Epidemiol Infect. 2020;148:e199.

39.

Fan

Wang

, et al. Letter to the editor: Low-density lipoprotein is a potential predictor of poor prognosis in patients with coronavirus disease 2019. Metabolism. 2020;107:154243.

40.

Fei

Tong

Tao

Wang

Value of neutrophil-to-lymphocyte ratio in the classification diagnosis of coronavirus disease 2019. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2020;32:554-558.

41.

Zhou

, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395:1054-1062.

42.

Gan

Yang

Leucocyte subsets effectively predict the clinical outcome of patients with COVID-19 pneumonia: a retrospective case-control study. Front Public Health. 2020;8:299.

43.

Gao

Han

, et al. Diagnostic utility of clinical laboratory data determinations for patients with the severe COVID-19. J Med Virol. 2020;92:791-796.

44.

Gil-Etayo

Suàrez-Fernández

Cabrera-Marante

, et al. T-Helper cell subset response is a determining factor in COVID-19 progression. Front Cell Infect Microbiol. 2021;11:624483.

45.

Güner

Hasanoğlu

Kayaaslan

, et al. COVID-19 experience of the major pandemic response center in the capital: results of the pandemic’s first month in Turkey. Turk J Med Sci. 2020;50:1801-1809.

46.

Yang

Zhang

Ling

QG.

Risks factors for death among COVID-19 patients combined with hypertension, coronary heart disease or diabetes. Beijing Da Xue Xue Bao. 2020;52:420-424.

47.

Zhang

, et al. The clinical course and its correlated immune status in COVID-19 pneumonia. J Clin Virol. 2020;127:104361.

48.

Zhou

, et al. Relationship between chest CT manifestations and immune response in COVID-19 patients. Internet J Infect Dis. 2020;98:125-129.

49.

Holt

Batra

Murthi

, et al. Lack of tocilizumab effect on mortality in COVID19 patients. Sci Rep. 2020;10:17100.

50.

Yin

, et al. Lower circulating interferon-gamma is a risk factor for lung fibrosis in COVID-19 patients. Front Immunol. 2020;11:585647.

51.

Huang

Song

, et al. Predictors of coronavirus disease 2019 severity: a retrospective study of 64 cases. Jpn J Infect Dis. 2021;74:54-60.

52.

Huang

Luo

, et al. The inflammatory factors associated with disease severity to predict COVID-19 progression. J Immunol. 2021;206:1597-1608.

53.

Huang

Chen

, et al. Predictive value of laboratory tests on severity of newly hospitalized patients with COVID-19. Chin J Lab Med. 2020;43:973-977.

54.

Jain

Chaurasia

Sengar

Singh

Mahor

Narain

Analysis of vitamin D level among asymptomatic and critically ill COVID-19 patients and its correlation with inflammatory markers. Sci Rep. 2020;10:20191.

55.

Yue

Zeng

Yang

Clinical characteristics of confirmed and clinically diagnosed patients with 2019 novel coronavirus pneumonia: a single-center, retrospective, case-control study. Med Clin. 2020;155:327-334.

56.

Keske

Tekin

Sait

, et al. Appropriate use of tocilizumab in COVID-19 infection. Internet J Infect Dis. 2020;99:338-343.

57.

Kumar

Mittal

Gopalakrishnan

Garg

Misra

Effect of plasma glucose at admission on COVID-19 mortality: experience from a tertiary hospital. Endocr Connect. 2021;10:589-598.

58.

Laguna-Goya

Utrero-Rico

Talayero

, et al. IL-6-based mortality risk model for hospitalized patients with COVID-19. J Allergy Clin Immunol. 2020;146:799-807.e9.

59.

Jiang

Wang

, et al. Longitudinal correlation of biomarkers of cardiac injury, inflammation, and coagulation to outcome in hospitalized COVID-19 patients. J Mol Cell Cardiol. 2020;147:74-87.

60.

Chen

, et al. Predictors of fatality including radiographic findings in adults with COVID-19. Respir Res. 2020;21:146.

61.

Cao

Chen

, et al. Hematological features of persons with COVID-19. Leukemia. 2020;34:2163-2172.

62.

Liu

, et al. Immune characteristics distinguish patients with severe disease associated with SARS-CoV-2. Immunol Res. 2020;68:398-404.

63.

Liu

Cui

Zeng

, et al. Risk factors for developing into critical COVID-19 patients in Wuhan, China: a multicenter, retrospective, cohort study. EClinicalMedicine. 2020;25:100471.

64.

Liu

Zheng

Cai

, et al. Neutrophil-to-lymphocyte ratio, a critical predictor for assessment of disease severity in patients with COVID-19. Int J Lab Hematol. 2021;43:329-335.

65.

Liu

Gao

, et al. Clinical characteristics of 51 patients discharged from hospital with COVID-19 in Chongqing, China. MedRxiv. Published online February 23, 2020. doi:10.1101/2020.02.20.20025536

66.

Liu

Zhang

Wang

, et al. Hyperglycemia is a strong predictor of poor prognosis in COVID-19. Diabetes Res Clin Pract. 2020;167:108338.

67.

Liu

Zhang

Yang

, et al. The role of interleukin-6 in monitoring severe case of coronavirus disease 2019. EMBO Mol Med. 2020;12:e12421.

68.

Liu

, et al. Potential therapeutic effects of dipyridamole in the severely ill patients with COVID-19. Acta Pharm Sin B. 2020;10:1205-1215.

69.

Shen

, et al. Analysis of inflammatory parameters and disease severity for 88 hospitalized COVID-19 patients in Wuhan, China. Int J Med Sci. 2020;17:2052-2062.

70.

Liu

Xue

, et al. Clinical characteristics and related risk factors of disease severity in 101 COVID-19 patients hospitalized in Wuhan, China. Acta Pharmacol Sin. 2022;43:64-75.

71.

Liu

Liao

Wan

Xiang

Zhang

Correlation between relative nasopharyngeal virus RNA load and lymphocyte count disease severity in patients with COVID-19. Viral Immunol. 2021;34:330-335.

72.

Liu

Tan

Chen

, et al. Dynamic changes in lymphocyte subsets and parallel cytokine levels in patients with severe and critical COVID-19. BMC Infect Dis. 2021;21:79.

73.

Luo

Liu

Jiang

Yue

Liu

Wei

IL-6 and CD8+ T cell counts combined are an early predictor of in-hospital mortality of patients with COVID-19. JCI Insight. 2020;5:e139024.

74.

Yang

, et al. Early immune responses and prognostic factors in children with COVID-19: a single-center retrospective analysis. BMC Pediatr. 2021;21:181.

75.

Cheng

, et al. Clinical characteristics and co-infections of 354 hospitalized patients with COVID-19 in Wuhan, China: a retrospective cohort study. Microbes Infect. 2020;22:195-199.

76.

Yin

Qian

Clinical characteristics and prognosis in cancer patients with COVID-19: a single center’s retrospective study. J Infect. 2020;81:318-356.

77.

Liu

Cao

, et al. COVID-19 myocarditis and severity factors: an adult cohort study. MedRxiv. Published online March 23, 2020. doi:10.1101/2020.03.19.20034124

78.

Maeda

Obata

Rizk Do

Kuno

The association of interleukin-6 value, interleukin inhibitors, and outcomes of patients with COVID-19 in New York City. J Med Virol. 2021;93:463-471.

79.

Mandel

Harari

Gurevich

Achiron

Cytokine prediction of mortality in COVID19 patients. Cytokine. 2020;134:155190. doi:10.1016/j.cyto.2020.155190

80.

McElvaney

McEvoy

McElvaney

, et al. Characterization of the inflammatory response to severe COVID-19 illness. Am J Respir Crit Care Med. 2020;202:812-821.

81.

Merza

Hwaiz

Hamad

Mohammad

Hama

Karim

AY.

Analysis of cytokines in SARS-CoV-2 or COVID-19 patients in Erbil city, Kurdistan Region of Iraq. PLoS One. 2021;16:e0250330.

82.

Mikami

Miyashita

Yamada

, et al. Risk factors for mortality in patients with COVID-19 in New York City. J Gen Intern Med. 2021;36:17-26.

83.

Xing

Xiao

, et al. Clinical characteristics of refractory coronavirus disease 2019 in Wuhan, China. Clin Infect Dis. 2021;73:e4208-e4213.

84.

Morrison

Johnson

Griebe

, et al. Clinical characteristics and predictors of survival in adults with coronavirus disease 2019 receiving tocilizumab. J Autoimmun. 2020;114:102512.

85.

Myhre

Prebensen

Strand

, et al. Growth differentiation factor 15 provides prognostic information superior to established cardiovascular and inflammatory biomarkers in unselected patients hospitalized with COVID-19. Circulation. 2020;142:2128-2137.

86.

Nie

Zhao

, et al. Metabolic disturbances and inflammatory dysfunction predict severity of coronavirus disease 2019 (COVID-19): a retrospective study. MedRxiv. Published online March 26, 2020. doi:10.1101/2020.03.24.20042283

87.

Pandolfi

Fossali

Frangipane

, et al. Broncho-alveolar inflammation in COVID-19 patients: a correlation with clinical outcome. BMC Pulm Med. 2020;20:301-310.

88.

Qin

Zhou

, et al. Dysregulation of immune response in patients with coronavirus 2019 (COVID-19) in Wuhan, China. Clin Infect Dis. 2020;71:762-768.

89.

Quartuccio

Sonaglia

Pecori

, et al. 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. 2020;92:2852-2856.

90.

Quiroga

Muñoz Ramos

Giorgi

, et al. Dynamic assessment of interleukin-6 during hemodialysis and mortality in coronavirus disease-19. Ther Apher Dial. 2021;25:908-916.

91.

Rastrelli

Di Stasi

Inglese

, et al. Low testosterone levels predict clinical adverse outcomes in SARS-CoV-2 pneumonia patients. Andrology. 2021;9:88-98.

92.

Ruan

Yang

Wang

Jiang

Song

Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020;46:846-848.

93.

Sabaka

Koščálová

Straka

, et al. Role of interleukin 6 as a predictive factor for a severe course of covid-19: retrospective data analysis of patients from a long-term care facility during covid-19 outbreak. BMC Infect Dis. 2021;21:308.

94.

Sarfaraz

Shaikh

Saleem

, et al. Determinants of in-hospital mortality in COVID-19; a prospective cohort study from Pakistan. PLoS One. 2021;16:e0251754.

95.

Sarhan

Shalaby

Zidan

, et al. Cytokine storm in COVID-19 patients: association between cytokines and disease severity. Zagazig Univ Med J. 2021. doi:10.21608/ZUMJ.2021.76794.2237.

96.

Shi

Sun

, et al. Clinical characteristics and prognostic factors of 148 COVID-19 cases in a secondary epidemic area. SSRN Electron J. Published online April 23, 2020. doi:10.2139/ssrn.3572911

97.

Shi

Liu

Xiao

, et al. Prediction of adverse clinical outcomes in patients with coronavirus disease 2019. J Clin Lab Anal. 2021;35:e23598.

98.

Shi

Qin

Yang

Bai

Jing

Mei

Value of interleukin-6 and CD4. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2020;32:1165-1170.

99.

Simioli

Annunziata

Langella

Martino

Musella

Fiorentino

Early prone positioning and non-invasive ventilation in a critical COVID-19 subset. A single centre experience in Southern Italy. Turk Thorac J. 2021;22:57-61.

100.

Song

YQ.

COVID-19 early warning score: a multi-parameter screening tool to identify highly suspected patients. MedRxiv. Published online March 8, 2020. doi:10.1101/2020.03.05.20031906

101.

Song

Deng

Wang

, et al. Self-reported taste and smell disorders in patients with COVID-19: distinct features in China. Curr Med Sci. 2021;41:14-23.

102.

Song

Zhang

Fan

, et al. Immunological and inflammatory profiles in mild and severe cases of COVID-19. Nat Commun. 2020;11:3410.

103.

Sun

Ning

Tao

, et al. Risk factors for mortality in 244 older adults with COVID-19 in Wuhan, China: a retrospective study. J Am Geriatr Soc. 2020;68:E19-E23.

104.

Sun

Chen

Nie

, et al. Lipid profile features and their associations with disease severity and mortality in patients with COVID-19. Front Cardiovasc Med. 2020;7:584987.

105.

Sun

Dong

Wang

, et al. Characteristics and prognostic factors of disease severity in patients with COVID-19: the Beijing experience. J Autoimmun. 2020;112:102473.

106.

Taha

Shata

El-Sehsah

, et al. Evaluation of the predictive value of C-reactive protein, interleukin-6 and their derived immune-inflammatory indices in COVID-19 Egyptian patients. Microbes Infect Dis. 2022; 3(1):13-23. doi:10.21608/mid.2021.103331.1207.

107.

Tang

Sun

Pan

Yao

Jiao

Selection of an optimal combination panel to better triage COVID-19 hospitalized patients. J Inflamm Res. 2020;13:773-787.

108.

Tian

Yuan

Xiao

, et al. Clinical characteristics and risk factors associated with COVID-19 disease severity in patients with cancer in Wuhan, China: a multicentre, retrospective, cohort study. Lancet Oncol. 2020;21:893-903.

109.

Toniati

Piva

Cattalini

, et al. 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. 2020;19:102568.

110.

Cao

Liu

Clinicolaboratory study of 25 fatal cases of COVID-19 in Wuhan. Intensive Care Med. 2020;46:1117-1120.

111.

Wan

Fan

, et al. Relationships among lymphocyte subsets, cytokines, and the pulmonary inflammation index in coronavirus (COVID-19) infected patients. Br J Haematol. 2020;189:428-437.

112.

Wang

Yang

Dong

, et al. Clinical characteristics of 28 patients with diabetes and covid-19 in Wuhan, China. Endocr Pract. 2020;26:668-674.

113.

Wang

Luo

Shen

, et al. Multiple enzyme release, inflammation storm and hypercoagulability are prominent indicators for disease progression in COVID-19: a multi-centered, correlation study with CT imaging score. Lancet. 2020. Accessed December 20, 2021. https://ssrn.com/abstract=3544837

114.

Wang

, et al. Coronavirus disease 2019 in elderly patients: characteristics and prognostic factors based on 4-week follow-up. Int J Infect. 2020;80:639-645.

115.

Wang

Pan

Zhang

, et al. Epidemiological and clinical features of 125 hospitalized patients with COVID-19 in Fuyang, Anhui, China. Internet J Infect Dis. 2020;95:421-428.

116.

Wang

Zhu

Wang

, et al. Children hospitalized with severe COVID-19 in Wuhan. Pediatr Infect Dis J. 2020;39:e91-e94.

117.

Wang

Yang

Wen

Zhang

Clinical features of 69 cases with coronavirus disease 2019 in Wuhan, China. Clin Infect Dis. 2020;71:769-777.

118.

Wang

Shu

Ran

Xie

Zhang

Critically ill patients with coronavirus disease 2019 in a designated ICU: clinical features and predictors for mortality. Risk Manag Healthc Policy. 2020;13:833-845.

119.

Webb

Peltan

Jensen

, et al. Clinical criteria for COVID-19-associated hyperinflammatory syndrome: a cohort study. Lancet Rheumatol. 2020;2:e754-e763.

120.

Wei

Yang

, et al. Elevations of serum cancer biomarkers correlate with severity of COVID-19. J Med Virol. 2020;92:2036-2041.

121.

Chen

Cai

, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med. 2020;180:934-943.

122.

Zhu

Yuan

, et al. Clinical and immune features of hospitalized pediatric patients with coronavirus disease 2019 (COVID-19) in Wuhan, China. JAMA Netw Open. 2020;3:e2010895.

123.

Huang

Sun

, et al. Clinical characteristics and immune injury mechanisms in 71 patients with COVID-19. mSphere. 2020;5:e00362.

124.

Xiao

Lili

Xiaohua

, et al. The clinical features of the 143 patients with COVID-19 in north-east of Chongqing. J Third Mil Med Univ. 2020;6:549-554.

125.

Xie

You

, et al. Impact of cardiovascular disease on clinical characteristics and outcomes of coronavirus disease 2019 (COVID-19). Circ J. 2020;84:1277-1283.

126.

Fan

Wang

, et al. Suppressed T cell-mediated immunity in patients with COVID-19: a clinical retrospective study in Wuhan, China. J Infect. 2020;81:e51-e60.

127.

Yang

Huang

, et al. A novel risk-stratification models of the high-flow nasal cannula therapy in COVID-19 patients with hypoxemic respiratory failure. Front Med. 2020;7:1-9.

128.

Zhao

Han

Zhang

Analysis of the clinical characteristics and early warning model construction of severe/critical coronavirus disease 2019 patients. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2020;32:401-406.

129.

Zeng

, et al. Clinical characteristics of SARS-CoV-2 pneumonia compared to controls in Chinese Han population. MedRxiv. Published online March 10, 2020. doi:10.1101/2020.03.08.20031658

130.

Yan

Yang

Wang

, et al. Clinical characteristics and outcomes of patients with severe covid-19 with diabetes. BMJ Open Diabetes Res Care. 2020;8:e001343.

131.

Yang

Tao

, et al. Infection with SARS-CoV-2 causes abnormal laboratory results of multiple organs in patients. Aging. 2020;12:10059-10069.

132.

Yang

Wang

Tao

. Analysis of clinical features of 76 cured patients of COVID-19. Acta Med Univ Sci Technol Huazhong. 2020;49:609-613.

133.

Yang

Ding

, et al. Epidemiological and clinical features of COVID-19 patients with and without pneumonia in Beijing, China. MedRxiv. Published online March 3, 2020. doi:10.1101/2020.02.28.20028068

134.

Yang

Wen

, et al. Suppressive monocytes impair MAIT cells response via IL-10 in patients with severe COVID-19. J Immunol. 2021;207:1848-1856.

135.

Yuan

Tong

Wang

Coagulopathy in elderly patients with coronavirus disease 2019. Aging Med (Milton). 2020;3:260-265.

136.

Yuan

Huang

, et al. Changes of hematological and immunological parameters in COVID-19 patients. Int J Hematol. 2020;112:553-559.

137.

Zeng

Zhang

Gao

, et al. Analysis of clinical characteristics of 49 cases of COVID-19. Zhonghua Jie He He Hu Xi Za Zhi. 2020;43:654-658.

138.

Zeng

Chen

, et al. Longitudinal changes of inflammatory parameters and their correlation with disease severity and outcomes in patients with COVID-19 from Wuhan, China. Crit Care. 2020;24:525.

139.

Zhang

Zhou

Zhu

, et al. Immune phenotyping based on the neutrophil-to-lymphocyte ratio and IgG level predicts disease severity and outcome for patients with COVID-19. Front Mol Biosci. 2020;7:157.

140.

Zhang

Zhou

Qiu

, et al. Clinical characteristics of 82 cases of death from COVID-19. PLoS One. 2020;15:e0235458.

141.

Zhang

Dong

Song

Wei

Liu

Triglyceride to high-density lipoprotein cholesterol ratio is an important determinant of cardiovascular risk and poor prognosis in coronavirus disease-19: a retrospective case series study. Diabetes Metab Syndr Obes. 2020;13:3925-3936.

142.

Zhang

Wang

, et al. Potential factors for prediction of disease severity of COVID-19 patients. MedRxiv. Published online March 23, 2020. doi:10.1101/2020.03.20.20039818

143.

Zhang

Tong

Liu

Tang

LV.

Predictive factors for disease progression in hospitalized patients with coronavirus disease 2019 in Wuhan, China. J Clin Virol. 2020;127:104392.

144.

Zhang

Huang

Xia

, et al. Retrospective analysis of clinical features in 134 coronavirus disease 2019 cases. Epidemiol Infect. 2020;148:e199.

145.

Zhang

Wei

Chen

Wan

Chen

Clinical analysis of risk factors for severe COVID-19 patients with type 2 diabetes. J Diabetes Complications. 2020;34:107666.

146.

Zhang

Xiong

Zhu

Very fast-progressive pulmonary opacities and high inflammatory factors levels are associated with decease of young coronavirus disease 2019 patients. Medicine. 2021;100:e24668.

147.

Zhang

Tan

Ling

, et al. Viral and host factors related to the clinical outcome of COVID-19. Nature. 2020;583:437-440.

148.

Zhao

Bai

Wang

, et al. Risk factors related to the severity of COVID-19 in Wuhan. Int J Med Sci. 2021;18:120-127.

149.

Zhao

Qin

Zhang

, et al. Longitudinal COVID-19 profiling associates IL-1RA and IL-10 with disease severity and RANTES with mild disease. JCI Insight. 2020;5:e139834.

150.

Zheng

Fan

, et al. Viral load dynamics and disease severity in patients infected with SARS-CoV-2 in Zhejiang province, China, January-March 2020: retrospective cohort study. BMJ. 2020;369:m1443.

151.

Zheng

Sun

, et al. Clinical characteristics of 34 COVID-19 patients admitted to intensive care unit in Hangzhou, China. J Zhejiang Univ Sci B. 2020;21:378-387.

152.

Zhou

Han

Chen

, et al. Clinical and autoimmune characteristics of severe and critical cases of COVID-19. Clin Transl Sci. 2020;13:1077-1086.

153.

Zhu

Cai

Fan

, et al. Clinical value of immune-inflammatory parameters to assess the severity of coronavirus disease 2019. Internet J Infect Dis. 2020;95:332-339.

154.

Zou

Dai

Zhang

, et al. Clinical characteristics and risk factors for disease severity and death in patients with coronavirus disease 2019 in Wuhan, China. Front Med. 2020;7:532.

155.

Udomsinprasert

Jittikoon

Sangroongruangsri

Chaikledkaew

Circulating levels of interleukin-6 and interleukin-10, but not tumor necrosis factor-alpha, as potential biomarkers of severity and mortality for COVID-19: systematic review with meta-analysis. J Clin Immunol. 2021;41:11-22.

156.

Brábek

Jakubek

Vellieux

, et al. Interleukin-6: molecule in the intersection of cancer, ageing and COVID-19. Int J Mol Sci. 2020;21:7937.

157.

Huang

Wang

, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497-506.

158.

Hui

DSC

Zumla

Severe acute respiratory syndrome: historical, epidemiologic, and clinical features. Infect Dis Clin North Am. 2019;33:869-889.

159.

Azhar

Hui

DSC

Memish

Drosten

Zumla

The Middle East respiratory syndrome (MERS). Infect Dis Clin North Am. 2019;33:891-905.

160.

Fattori

Cappelletti

Costa

, et al. Defective inflammatory response in interleukin 6-deficient mice. J Exp Med. 1994;180:1243-1250.

161.

Stüber

Wrigge

Schroeder

, et al. Kinetic and reversibility of mechanical ventilation-associated pulmonary and systemic inflammatory response in patients with acute lung injury. Intensive Care Med. 2002;28:834-841.

162.

Parsons

Eisner

Thompson

, et al. Lower tidal volume ventilation and plasma cytokine markers of inflammation in patients with acute lung injury. Crit Care Med. 2005;33:1-6.

163.

Yonas

Alwi

Pranata

, et al. Elevated interleukin levels are associated with higher severity and mortality in COVID 19 - a systematic review, meta-analysis, and meta-regression. Diabetes Metab Syndr. 2020;14:2219-2230.

164.

Villaescusa

Zaragozá

Gayo-Abeleira

Zaragozá

A new approach to the management of COVID-19. Antagonists of IL-6: Siltuximab. Adv Ther. 2022;39:1126-1148.

165.

Calabrese

Rajendram

Sacha

Calabrese

Practical aspects of targeting IL-6 in COVID-19 disease. Cleve Clin J Med. Published online March 14, 2020. doi:10.3949/ccjm.87a.ccc018

166.

Panda

Nanda

Tripathy

Mangaraj

Immuno-inflammatory predictors of disease severity in COVID-19: a systematic review and meta-analysis. J Fam Med Prim Care. 2021;10:1102-1116.

167.

Bao

Zhang

Kang

Chen

Comparative analysis of laboratory indexes of severe and non-severe patients infected with COVID-19. Clin Chim Acta. 2020;509:180-194.

168.

Zawawi

Naser

Alwafi

Minshawi

Profile of circulatory cytokines and chemokines in human coronaviruses: a systematic review and meta-analysis. Front Immunol. 2021;12:666223.

169.

Yan

Chen

Bigambo

Wei

Wang

Xia

Differences of blood cells, lymphocyte subsets and cytokines in COVID-19 patients with different clinical stages: a network meta-analysis. BMC Infect Dis. 2021;21:156.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB