| Literature DB >> 34952598 |
Yi Wang1, Hui Deng1, Yihuai Pan1, Lijian Jin2, Rongdang Hu1, Yongyong Lu3, Wenhai Deng4, Weijian Sun5, Chengshui Chen6,7, Xian Shen8, Xiu-Feng Huang9,10.
Abstract
BACKGROUND: Emerging evidence shows that periodontal disease (PD) may increase the risk of Coronavirus disease 2019 (COVID-19) complications. Here, we undertook a two-sample Mendelian randomization (MR) study, and investigated for the first time the possible causal impact of PD on host susceptibility to COVID-19 and its severity.Entities:
Keywords: COVID-19; Mendelian randomization; Periodontal disease; Risk factor
Mesh:
Year: 2021 PMID: 34952598 PMCID: PMC8708510 DOI: 10.1186/s12967-021-03198-2
Source DB: PubMed Journal: J Transl Med ISSN: 1479-5876 Impact factor: 5.531
Fig. 1Principles of MR and the assumptions required to obtain an unbiased causal effect estimate
Description of the GWAS summary statistics
| Trait | GWAS Catalog | Sample size | Number of SNPs | Population | References |
|---|---|---|---|---|---|
| Periodontal disease (Socransky phenotype) | ebi-a-GCST003484 | 975 | 2,077,804 | European | [ |
| COVID-19 susceptibility (COVID-19 vs. population) | ebi-a-GCST010776 | 1,299,010 | 11,435,708 | European | [ |
| COVID-19 severity (hospitalized vs. population) | ebi-a-GCST010777 | 908,494 | 12,832,272 | European | [ |
| COVID-19 severity (very severe respiratory confirmed vs. not hospitalized) | ebi-a-GCST010775 | 957 | 9,201,012 | European | [ |
MR results of periodontal disease on risk of COVID-19 susceptibility and severity
| Outcomes | Methods | OR | (95% CI) | |
|---|---|---|---|---|
COVID-19 susceptibility (COVID-19 vs. population) | Inverse variance weighted | |||
| MR Egger | 1.064 | 0.933–1.214 | 0.418 | |
| Weighted median | ||||
| Weighted mode | 1.031 | 0.998–1.066 | 0.135 | |
COVID-19 severity (Hospitalized vs. population) | Inverse variance weighted | |||
| MR Egger | 1.053 | 0.893–1.240 | 0.580 | |
| Weighted median | ||||
| Weighted mode | 1.046 | 0.999–1.094 | 0.139 | |
COVID-19 severity (Very severe respiratory confirmed vs. not hospitalized) | Inverse variance weighted | 0.964 | 0.797–1.166 | 0.711 |
| MR Egger | 1.305 | 0.356–4.784 | 0.713 | |
| Weighted median | 1.004 | 0.784–1.287 | 0.967 | |
| Weighted mode | 1.087 | 0.789–1.499 | 0.652 |
OR odds ratio, 95% CI 95% confidence interval
Significant associations (P value < 0.05) are highlighted in bold format
Fig. 2MR analysis and leave-one-out analysis of the causal effect of PD on COVID-19 susceptibility. A Scatter plots for MR analyses of the causal effect of PD on COVID-19 susceptibility. The slope of each line corresponding to the estimated MR effect per method. B Leave-one-out analysis of the causal effect of PD on COVID-19 susceptibility. Each black point represents the IVW MR method applied to estimate the causal effect of PD on COVID-19 susceptibility excluding that particular variant from the analysis. The red point represents the IVW estimate using all SNPs
Fig. 3MR analysis and leave-one-out analysis of the causal effect of PD on COVID-19 severity (Hospitalized vs. population). A Scatter plots for MR analyses of the causal effect of PD on COVID-19 severity (Hospitalized vs. population). The slope of each line corresponding to the estimated MR effect per method. B Leave-one-out analysis of the causal effect of PD on COVID-19 severity (Hospitalized vs. population). Each black point represents the IVW MR method applied to estimate the causal effect of PD on COVID-19 susceptibility excluding that particular variant from the analysis. The red point represents the IVW estimate using all SNPs