
==== Front
J Intensive Med
J Intensive Med
Journal of Intensive Medicine
2097-0250
2667-100X
Elsevier

S2667-100X(24)00014-8
10.1016/j.jointm.2024.02.001
Letter
The causal role of immune cells in susceptibility and severity of COVID-19: A bidirectional Mendelian randomization study
Shang Weifeng #
Qian Hang #
Zhang Sheng #
Pan Xiaojun
Huang Sisi
Wen Zhenliang
Liu Jiao catherine015@163.com
⁎
Chen Dechang 18918520002@189.cn
⁎
Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
⁎ Corresponding authors: Dechang Chen and Jiao Liu, Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, Shanghai 200025, China. catherine015@163.com18918520002@189.cn
# Weifeng Shang, Hang Qian and Sheng Zhang contributed equally to this article.

21 3 2024
10 2024
21 3 2024
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7 2 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Managing Editor: Jingling Bao/ Zhiyu Wang
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pmcTo the Editor,

Previous studies have demonstrated close associations of specific immune cell profiles in the occurrence and development of COVID-19.[1,2] However, due to confounding factors and reverse causality in traditional observational studies, it remains unclear whether these associations reflect a causal link. Mendelian randomization (MR) is a reliable method of causal inferences that uses genetic variants (usually single-nucleotide polymorphisms [SNPs]) as instrumental variables (IVs) to estimate the causal effect of exposure on outcome. By leveraging random genetic transmission through gametes and occurring before disease onset, MR is less susceptible to confounding and reverse causality. We applied a bidirectional MR to assess the causal effects of 731 immune cell traits on COVID-19 susceptibility and severity (Supplementary Figure S1).

The genome-wide association study (GWAS) statistics for 731 immune traits were selected from the GWAS Catalog (accession numbers from GCST90001391 to GCST90002121) (Supplementary Table S1).[3] While the COVID-19-related data were extracted from the COVID-19 Host Genetics Initiative. COVID-19 phenotypes included COVID-19 susceptibility and COVID-19 severity. The exposed gene IVs used were significantly associated with features (P <5.0 × 10−8). To ensure independence, we set the linkage disequilibrium threshold at 0.001 and a 10 MB clumping window. We calculated the F-statistic to assess genetic variation, excluding SNPs with an F-statistic <10. Harmonization eliminated ambiguous/palindromic SNPs. Outcomes associated with P <5.0 × 10−8 and less than 3 SNP exposures were excluded. The remaining SNPs were then used as IVs. Inverse variance weighting (IVW) was our primary method to estimate the causal effects. Weighted median and MR-Egger regression served as complements. Cochran's Q statistic and MR-Egger intercept tests detected heterogeneity and pleiotropy, while MR‐Pleiotropy Residual Sum and Outlier (MR‐PRESSO) evaluated and corrected horizontal pleiotropy. We also searched the PhenoScanner database for previously published confounders related to included SNPs with genome-wide significance. Furthermore, reverse MR analysis investigated the possible reverse causality between COVID-19 and immune cells. Additionally, we used the Benjamini–Hochberg method and controlled for the false discovery rate (FDR). A statistically significant association was considered with a Benjamini–Hochberg-adjusted P <0.05. Furthermore, we defined a suggestive association as having a P <0.05 but a P-FDR ≥0.05 using the IVW approach. All analyses were run using the R package TwoSampleMR (version 0.5.6) in statistical software R (version 4.1.1; R Foundation for Statistical Computing).

Our results showed that 10 immune cell traits were associated with COVID-19 susceptibility in the forward MR (Supplementary Figure S2). Through sensitivity analysis, 7 of them fulfilled the criteria (Supplementary Table S2). Also, 7 immune cell signatures were associated with COVID-19 severity (Supplementary Figure S2). Sensitivity analysis suggested the absence of heterogeneity and horizontal pleiotropy (Supplementary Tables S2–S3). To be clear, MR-PRESSO values and the global test could not be measured because there were insufficient instrumental variables for several immune cell traits. However, after FDR correction, the associations mentioned above lost their statistical significance (P-FDR >0.05) (Supplementary Tables S2–S3). Moreover, three SNPs (rs2286975, rs3869145, rs9270599) for HLA DR on B cells were associated with asthma-related phenotypes through searching the Phenoscanner V2 website, and the results became non-significant after the removal of these SNPs (Supplementary Tables S4–S5). In the reverse MR, IVW analysis indicated COVID-19 susceptibility was associated with 27 immune cell traits (Supplementary Figure S3), and COVID-19 severity was associated with 30 immune cell traits (Supplementary Figure S4). The three MR analyses showed inconsistent direction of effect values for COVID-19 susceptibility in 6 immune cells and COVID-19 severity in 2 immune cells (Supplementary Tables S6–S7). A series of sensitivity analyses confirmed the rigidity of these results (Supplementary Tables S6–S7). After FDR correction, the above associations lost statistical significance for COVID-19 susceptibility (P-FDR >0.05) but remained statistically significant for COVID-19 severity with 5 immune cell signatures (P-FDR <0.05) (Supplementary Figure S4).

This study is the initial MR analysis investigating the causal links between 731 immune cell markers and COVID-19. Our results showed several immune cells may potentially be associated with the occurrence and development of COVID-19, which is partially consistent with previous studies (Supplementary Table S8). From the perspective of host genetic variation, it may be the host factors rather than viral properties that determine the onset and poor prognosis of COVID-19. In addition, we have also identified some significant or suggestive causal relationships between COVID-19 and immune cell signatures, suggesting COVID-19 infection increases the risk of immune imbalance, which in turn exacerbates disease. Notably, Naive CD4+ T cells are the progenitors of all effector T cell subsets. Evidence supports a significant decrease in lymphocyte subsets, including naive CD4+ T cells in severe COVID-19, compared to healthy individuals and those with mild to moderate disease, which is consistent with our findings.[4,5]. We speculated the reduction of T cell subsets, including Naive CD4+ T cells caused by severe COVID-19, might correlate with an increased susceptibility to other infections in severe patients or COVID-19 survivors.

In conclusion, this bidirectional MR study suggested several immune cell signatures and COVID-19 may have important causal effects on each other, such as Naive CD4+ T cells, offering new insights into the immunological mechanisms of COVID-19.

Appendix Supplementary materials

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Author Contributions

Weifeng Shang: Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation. Hang Qian: Writing – original draft, Visualization, Investigation, Data curation. Sheng Zhang: Writing – original draft, Validation, Supervision, Formal analysis, Data curation. Xiaojun Pan: Investigation, Data curation. Sisi Huang: Investigation, Data curation. Zhenliang Wen: Investigation, Data curation. Jiao Liu: Writing – review & editing, Conceptualization. Dechang Chen: Writing – review & editing, Conceptualization.

Acknowledgments

We thank all the authors and participants of the GWASs.

Funding

This work was supported by the 10.13039/501100001809 National Natural Science Foundation of China (grant numbers: 82241044 and 82172152 ) and the 10.13039/501100003399 Science and Technology Commission of Shanghai (grant numbers: 22692192200 ).

Ethics Statement

This study has been conducted using published studies and online datasets. All original studies have been approved by the corresponding ethical statement. In addition, no individual data were used. Therefore, no new ethic approval was required.

Conflict of Interest

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

Data Availability

The data sets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jointm.2024.02.001.
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