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Meta-Analysis
. 2023 Apr 24;12(1):43.
doi: 10.1186/s40249-023-01086-z.

Increased interleukin-6 is associated with long COVID-19: a systematic review and meta-analysis

Jing-Xian Yin  1 , Yannick Luther Agbana  2 , Zhi-Shan Sun  1 , Si-Wei Fei  1 , Han-Qing Zhao  1 , Xiao-Nong Zhou  1   3 , Jun-Hu Chen  4 , Kokouvi Kassegne  5
Affiliations
Meta-Analysis

Increased interleukin-6 is associated with long COVID-19: a systematic review and meta-analysis

Jing-Xian Yin et al. Infect Dis Poverty. .

Abstract

Background: Coronavirus disease 2019 (COVID-19) can involve persistence, sequelae, and other clinical complications that last weeks to months to evolve into long COVID-19. Exploratory studies have suggested that interleukin-6 (IL-6) is related to COVID-19; however, the correlation between IL-6 and long COVID-19 is unknown. We designed a systematic review and meta-analysis to assess the relationship between IL-6 levels and long COVID-19.

Methods: Databases were systematically searched for articles with data on long COVID-19 and IL-6 levels published before September 2022. A total of 22 published studies were eligible for inclusion following the PRISMA guidelines. Analysis of data was undertaken by using Cochran's Q test and the Higgins I-squared (I2) statistic for heterogeneity. Random-effect meta-analyses were conducted to pool the IL-6 levels of long COVID-19 patients and to compare the differences in IL-6 levels among the long COVID-19, healthy, non-postacute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (non-PASC), and acute COVID-19 populations. The funnel plot and Egger's test were used to assess potential publication bias. Sensitivity analysis was used to test the stability of the results.

Results: An increase in IL-6 levels was observed after SARS-CoV-2 infection. The pooled estimate of IL-6 revealed a mean value of 20.92 pg/ml (95% CI = 9.30-32.54 pg/ml, I2 = 100%, P < 0.01) for long COVID-19 patients. The forest plot showed high levels of IL-6 for long COVID-19 compared with healthy controls (mean difference = 9.75 pg/ml, 95% CI = 5.75-13.75 pg/ml, I2 = 100%, P < 0.00001) and PASC category (mean difference = 3.32 pg/ml, 95% CI = 0.22-6.42 pg/ml, I2 = 88%, P = 0.04). The symmetry of the funnel plots was not obvious, and Egger's test showed that there was no significant small study effect in all groups.

Conclusions: This study showed that increased IL-6 correlates with long COVID-19. Such an informative revelation suggests IL-6 as a basic determinant to predict long COVID-19 or at least inform on the "early stage" of long COVID-19.

Keywords: COVID-19; Immune mediator; Interleukin-6; Long COVID-19; Meta-analysis; SARS-CoV-2.

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Conflict of interest statement

Xiao-Nong Zhou is the Editor-in-Chief of Infectious Diseases of Poverty. He was not involved in the peer review or handling of the manuscript. The authors have no other competing interests to disclose.

Figures

Fig. 1
Fig. 1
Flowchart of the literature search strategy. The flow diagram was generated based on the PRISMA 2020 guidelines (https://estech.shinyapps.io/prisma_flowdiagram/). The checklist for the flow diagram is provided in Additional file 2
Fig. 2
Fig. 2
Pooled estimate of IL-6 for long COVID-19. Ref.: reference
Fig. 3
Fig. 3
Sensitivity analysis of the pooled estimate of IL-6 for long COVID-19. Ref.: reference
Fig. 4
Fig. 4
Comparison of the levels of IL-6 in the long COVID-19, non-PASC, and healthy individual groups. A Forest plot comparing long COVID-19 versus healthy individuals. B Forest plot comparing long COVID-19 versus non-PASC. Ref.: reference; *, standard deviation (pg/ml); #, total number of participants involved in the study or cohort
Fig. 5
Fig. 5
Comparison of levels of IL-6 in the long COVID-19, acute COVID-19, non-PASC, and healthy individual groups. A Forest plot comparing long COVID-19 versus acute COVID-19. B Forest plot comparing acute COVID-19 versus healthy individuals. C Forest plot comparing non-PASC versus healthy individuals. Ref.: reference; *, standard deviation (pg/ml); #, total number of participants involved in the study or cohort
Fig. 6
Fig. 6
Subgroup analysis based on the study design. Forest plot comparing five different study designs. Ref.: reference; *, standard deviation (pg/ml); #, total number of participants involved in the study or cohort
Fig. 7
Fig. 7
Subgroup analysis based on the data extraction method (direct mean or indirect mean). Forest plot comparing two different data extraction methods. Ref.: reference; *, standard deviation (pg/ml); #, total number of participants involved in the study or cohort
Fig. 8
Fig. 8
Publication bias analysis of the included studies. A Funnel plot of cohorts with long COVID-19 versus healthy individuals. B Funnel plot of cohorts with long COVID-19 versus acute COVID-19. C Funnel plot of cohorts with acute COVID-19 versus healthy individuals. D Funnel plot of cohorts with long COVID-19 versus non-PASC. E Funnel plot of cohorts with non-PASC versus healthy individuals
Fig. 9
Fig. 9
Sensitivity analysis of the association between pooled estimates of IL-6 levels and long COVID-19. A Sensitivity analysis for long COVID-19 vs healthy controls. B Sensitivity analysis for long COVID-19 vs non-PASC. Ref.: reference; MD: mean difference
See this image and copyright information in PMC

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