tetano
Editor, Senior Moderator
Front Immunol
. 2023 Aug 18;14:1217444.
doi: 10.3389/fimmu.2023.1217444. eCollection 2023. The impact of immunoglobulin G N-glycosylation level on COVID-19 outcome: evidence from a Mendelian randomization study
Feiwu Long[SUP] 1 [/SUP], Chenghan Xiao[SUP] 2 [/SUP], Huijie Cui[SUP] 3 [/SUP], Wei Wang[SUP] 1 [/SUP], Zongze Jiang[SUP] 1 [/SUP], Mingshuang Tang[SUP] 3 [/SUP], Wenqiang Zhang[SUP] 3 [/SUP], Yunjie Liu[SUP] 3 [/SUP], Rong Xiang[SUP] 3 [/SUP], Li Zhang[SUP] 3 [/SUP], Xunying Zhao[SUP] 3 [/SUP], Chao Yang[SUP] 3 [/SUP], Peijing Yan[SUP] 3 [/SUP], Xueyao Wu[SUP] 3 [/SUP], Yutong Wang[SUP] 3 [/SUP], Yanqiu Zhou[SUP] 3 [/SUP], Ran Lu[SUP] 4 5 6 [/SUP], Yulin Chen[SUP] 5 [/SUP], Jiayuan Li[SUP] 3 [/SUP], Xia Jiang[SUP] 7 [/SUP], Chuanwen Fan[SUP] 1 3 5 8 9 [/SUP], Ben Zhang[SUP] 3 6 [/SUP]
Affiliations
Background: The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID-19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.
Methods: We conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochran's Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.
Results: We found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92-0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.
Conclusions: Our study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential implications for developing targeted interventions to improve COVID-19 outcomes by modulating IgG N-glycosylation levels.
Keywords: COVID‐19; IgG N-glycosylation; Mendelian randomization; causality; inflammation.
. 2023 Aug 18;14:1217444.
doi: 10.3389/fimmu.2023.1217444. eCollection 2023. The impact of immunoglobulin G N-glycosylation level on COVID-19 outcome: evidence from a Mendelian randomization study
Feiwu Long[SUP] 1 [/SUP], Chenghan Xiao[SUP] 2 [/SUP], Huijie Cui[SUP] 3 [/SUP], Wei Wang[SUP] 1 [/SUP], Zongze Jiang[SUP] 1 [/SUP], Mingshuang Tang[SUP] 3 [/SUP], Wenqiang Zhang[SUP] 3 [/SUP], Yunjie Liu[SUP] 3 [/SUP], Rong Xiang[SUP] 3 [/SUP], Li Zhang[SUP] 3 [/SUP], Xunying Zhao[SUP] 3 [/SUP], Chao Yang[SUP] 3 [/SUP], Peijing Yan[SUP] 3 [/SUP], Xueyao Wu[SUP] 3 [/SUP], Yutong Wang[SUP] 3 [/SUP], Yanqiu Zhou[SUP] 3 [/SUP], Ran Lu[SUP] 4 5 6 [/SUP], Yulin Chen[SUP] 5 [/SUP], Jiayuan Li[SUP] 3 [/SUP], Xia Jiang[SUP] 7 [/SUP], Chuanwen Fan[SUP] 1 3 5 8 9 [/SUP], Ben Zhang[SUP] 3 6 [/SUP]
Affiliations
- PMID: 37662938
- PMCID: PMC10472139
- DOI: 10.3389/fimmu.2023.1217444
Background: The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID-19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.
Methods: We conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochran's Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.
Results: We found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92-0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.
Conclusions: Our study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential implications for developing targeted interventions to improve COVID-19 outcomes by modulating IgG N-glycosylation levels.
Keywords: COVID‐19; IgG N-glycosylation; Mendelian randomization; causality; inflammation.