Discussion
MR, VTE, venous thrombosis, DVT
VENOUS THROMBOSIS, DVT
In this study, we used bidirectional MR studies to analyse the causal relationship between VTE (DVT and PE) and classical lipids (LDL, HDL and TGs). Through our MR analysis, we did not find a causal relationship between VTE and classical lipids. Even though the IVW model of some analyses (such as LDL-DVT and TG-DVT) had a P value less than 0.05, after MR-PRESSO removed the outliers, there was no causal relationship between them.We found that there is no causal relationship between VTE and blood lipids from the point of view of genetics, which provides evidence for clinical research. Of course, the results may vary dependent on population, sample size, genotyping, alternative genetic methods etc. Additionally, there are further issues such as confounding, instrument strength, population stratification that may bias results. At present, there is still some controversy in clinical research on the causal relationship between LDL and VTE. For example, Petter and others have found that there is no correlation between LDL and VTEA previous meta-analysis showed that patients with venous thrombosis had higher average levels of TGs and lower average levels of HDLThe current bidirectional MR analysis has several advantages. First, this study is the first to infer a causal relationship between classical lipids (LDL, HDL and TGs) and VTE (DVT and PE) from a genetic perspective. Moreover, bidirectional analysis ensured the inference of bidirectional causality between VTE and lipids. However, this study has some limitations. First, the people in the MR analysis were of European descent, so this study may not be applicable to other races. Second, there may be overlapping participants in the exposure and outcome studies, but it is difficult to estimate the extent of sample overlap. In addition, although many cases of VTE were found in the current GWAS analysis, they could not be stratified or adjusted for in the analysis. Of course, larger sample sizes of clinical studies and experiments are needed to confirm our conclusions. We could confirm that an exposure-associated SNP is thus far only reported to be associated with that particular exposure, but we cannot guarantee that the same SNP is not associated with other traits (confounders); the association might remain to be identified, or the SNP might be associated with an underlying risk factor that is unrecognized. Although researchers have proposed many solutions that meet the assumptions of the MR model, these strategies can only detect a violation of the hypothesis but can never confirm whether it is true. Therefore, these shortcomings may lead to biased estimates.