2.3 Statistical analyses
In the current research, we estimated the causal association between
sarcopenia (including ASM, LH, RH, WP and WP adjusted for BMI) and
COVID-19 (including COVID-19 infection, COVID-19 hospitalization and
severe COVID-19) using four complementary methods, which included
inverse variance weighting (IVW), MR-Egger, weighted and weighted
median. Meanwhile, we evaluated the potential level pleiotropy of IVs
using MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and MR-Egger
regression[32, 33]. Additionally, the MR-PRESSO
analysis was also used to find abnormal values in
IVs[33]. If MR-PRESSO detected a significant
horizontal pleiotropy, it should be removed and
then repeat MR-PRESSO and MR-Egger
tests to eliminate the horizontal pleiotropic SNP. After that we
detected and quantified the heterogeneity among IVs utilizing Cochran’s
Q statistic[34]. To ensure the accuracy of causal
association estimation, SNPs that significantly affect the outcomes were
identified and removed using leave-one-out sensitivity analysis. There
are five measures of sarcopenia (ASM, LH, RH, WP and WP adjusted for
BMI) and three measures of COVID-19 (COVID-19 infection, COVID-19
hospitalization and severe COVID-19) in this study, therefore the
Bonferroni correction was performed to adjust the results andP -value less than 0.0042 (0.05/4*3) was considered statistically
significantly[35, 36]. All statistical analyses
were performed using the package “TwoSampleMR” and “MRPRESSO” in R
version 4.1.1.