COVID-19 and retinal layer thickness: A bidirectional Mendelian randomization study

被引:0
作者
Zhao, Kun [1 ]
Xiang, Xiqiao [1 ]
Zheng, Ziwei [2 ]
Zhang, Qingwei [3 ]
Gu, Bingxin [4 ]
Zhang, Yanyan [5 ]
Tang, Zhen [1 ]
Wei, Yuanhao [6 ]
Yuan, Lin [1 ]
Yang, Shaoling [2 ]
Lang, Lili [7 ]
机构
[1] Shanghai Jiaotong Univ Affiliated Sixth Peoples Ho, Dept PET CT Mol Imaging Ctr, South Campus, Shanghai, Peoples R China
[2] Shanghai Eighth Peoples Hosp, Dept Ultrasonog, 8 Caobao Rd, Shanghai 200235, Peoples R China
[3] Shanghai Jiao Tong Univ, Renji Hosp, Sch Med, NHC Key Lab Digest Dis,Dept Gastroenterol & Hepato, Shanghai, Peoples R China
[4] Fudan Univ, Shanghai Canc Ctr, Dept Nucl Med, Shanghai, Peoples R China
[5] Fudan Univ, Pudong Inst Prevent Med, Shanghai, Peoples R China
[6] Harbin Med Univ, Sch Publ Hlth, Harbin, Peoples R China
[7] Shanghai JiaoTong Univ Affiliated Sixth Peoples Ho, Dept Ophthalmol, South Campus, Shanghai 201499, Peoples R China
关键词
Mendelian randomization; COVID-19; GCIPL thickness; RNFL thickness;
D O I
10.1016/j.msard.2024.105700
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Background: Observational studies have reported that COVID-19 is associated with alterations in retinal layer thickness, including changes in the ganglion cell inner plexiform layer (GCIPL) and retinal nerve fiber layer (RNFL). However, the causal relationships remain unknown. Therefore, we assessed the direction and strength of the causal relationship between COVID-19 and GCIPL and RNFL thicknesses using a bidirectional two-sample Mendelian randomization (MR) design. Methods: Data were obtained from a large-scale COVID-19 Host Genetics Initiative (Nsample = 6,512,887), GCIPL dataset (Ncase = 31,434), and RNFL dataset (Ncase = 31,434). The inverse-variance weighted (IVW) method is the primary approach used to estimate causal effects. MR Egger, weighted median, weighted mode, MR Egger (bootstrap), and penalized weighted median methods were applied. Sensitivity analyses were implemented with RadialMR, MRPRESSO, MR-Egger regression, Cochran 's Q statistic, leave-one-out analysis, and the funnel plot. Results: Forward MR analysis revealed that genetically identified COVID-19 susceptibility significantly increased the risk of GCIPL thickness (OR = 2.428, 95 % confidence interval [CI]:1.493 -3.947, P IVW = 3.579 x 10 -4 ) and RNFL thickness (OR = 1.735, 95 % CI:1.198 -2.513, P IVW = 3.580 x 10 -3 ) after Bonferroni correction. Reverse MR analysis did not indicate a significant causal association between GCIPL and RNFL thicknesses and COVID-19 phenotypes. No significant horizontal pleiotropy was found in the sensitivity analysis. Conclusions: The host genetic liability to COVID-19 susceptibility was causally associated with increased GCIPL and RNFL thicknesses. Documenting this association increases our understanding of the pathophysiological mechanisms underlying COVID -19 susceptibility in retinopathy.
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