
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-24-01652
00063
10.1097/MD.0000000000039514
3
3400
Research Article
Observational Study
Exploring the causal relationship between plasma lipids and varicose veins of lower extremity: A comprehensive two-sample Mendelian randomization study
https://orcid.org/0009-0001-2435-3527
Shen Kailin BM shentong119966@outlook.com
a
Zhu Fangtao MM 2928989136@qq.com
a
Cheng Cunwei MM 824258953@qq.com
a
https://orcid.org/0009-0001-0076-3310
Yu Haibin PhD a*
a Department of Intervention, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, P. R. China.
* Correspondence: Haibin Yu, Department of Intervention, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450014, Henan, China (e-mail: shentong119966@outlook.com).
06 9 2024
06 9 2024
103 36 e3951414 2 2024
08 8 2024
09 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Varicose veins of the lower extremities (VVs) is a common chronic vascular disease, with high prevalence rates in some countries; however, their pathogenesis remains unclear. Some studies have identified associations between changes in specific plasma lipid molecules, such as phosphatidylethanolamine (PE), phosphatidylcholine (PC), and sphingomyelin (SM), and the onset of VVs, but due to confounders and reverse causality, the causal relationship remains unclear. Meanwhile, studies on the potential link between other plasma lipids beyond PE, PC, and SM and the risk of VVs in the lower extremities are lacking. This study aimed to explore the potential causal relationship between VVs and plasma lipid levels to provide theoretical insights into the interrelation of plasma lipids and VVs in their occurrence and progression. We conducted a two-sample Mendelian randomization (MR) analysis to assess the potential connection between genetically predicted levels of individual plasma lipids and the risk of developing VVs. We utilized data from a large-scale genome-wide association study involving 7174 Finnish individuals for 179 plasma lipidomes along with VVs genome-wide association study data from 408,455 UK individuals. MR analysis employed methods, such as inverse-variance weighting, weighted median, Bayesian Weighted Mendelian Randomization, and MR-Egger regression. The inverse-variance weighting method was primarily used to assess causality. The validity of the results was demonstrated through sensitivity analysis. In total, 12 lipids were found to have their plasma levels associated with an increased risk of VVs. This includes 3 types of PE, 7 types of PC, and 2 types of phosphatidylinositol. However, no significant causal relationship was found between the plasma levels of 11 types of SM and VVs. These results support the existence of a potential causal relationship between specific types of lipid levels and the risk of VVs, which can provide clues for further studies on biological mechanisms and the exploration of potential therapeutic targets.

lipidomes
Mendelian randomization
varicose veins of the lower extremities
Science and Technology Department of Henan Province 10.13039/501100011447 Haibin YuOPEN-ACCESSTRUE
SDCT
==== Body
pmc1. Introduction

Varicose veins are a common vascular disease, with an incidence rate of 50% to 60% in some Western countries, making it one of the most common chronic diseases in Western medicine, with varicose veins of the lower extremities (VVs) being the most prevalent type.[1,2] The exact pathogenesis of VVs is complex, involving various risk factors, such as genetics, age, sex, metabolism, and lifestyle.[3] To date, the morphological characteristics of the disease have been well-documented, with known histological changes in dilated veins, including intimal hyperplasia and the destruction of smooth muscle cells and extracellular matrix.[4] Other contributing factors in its development include changes in hemodynamics, such as reduced laminar shear stress and increased venous filling pressure, endothelial activation, inflammation, hypoxia, and the dysregulation of matrix metalloproteinases and their tissue inhibitors.[5–8] Additionally, in literature, the familial aggregation and heritability of the condition have been widely recognized.[9–12] However, how disease variants at risk loci disrupt cellular biochemistry and how these events translate into the formation of varicose veins remain unclear, thus necessitating research to determine how biochemical abnormalities in VVs regulate metabolic pathways. The treatment of VVs primarily focuses on conservative and surgical methods, while the effectiveness of pharmacological treatment and early prevention is limited.[13–15] Therefore, exploring the pathogenesis of VVs can provide more insights into the prevention and treatment of this disease. Abnormal levels of certain lipid molecules, such as phosphatidylcholine (PC), phosphatidylethanolamine (PE), and sphingomyelin (SM), in varicose veins, indicate that these lipids might play a significant role in the progression of the condition, particularly in aspects related to inflammation and cellular pathways.[6,16] However, the causal impact of lipids on VVs remains unclear, and the relationship between other plasma lipid components and VVs has not been studied.

Mendelian randomization (MR) is a method used in epidemiology to assess the potential causal relationships between specific risk factors and diseases. It mitigates issues of confounders and reverse causality by using genetic variations as tools for a natural experiment. This method is similar to traditional randomized controlled trials, in that it simulates experimental conditions through the random allocation of genes, thus reducing study biases. The MR method can mitigate issues of confounding, reverse causality, and the challenges of representativeness and feasibility inherent in observational studies and randomized controlled trials, respectively.[17] However, no study has explored the relationship between plasma lipid levels and VVs using the MR method.

Therefore, this study aimed to explore the potential causal relationship between VVs and plasma lipid levels to provide theoretical insights into the interrelation of plasma lipids and VVs in their occurrence and progression using the two-sample MR approach.

2. Methods

2.1. Research design

MR methods must satisfy 3 core assumptions: association, independence, and exclusion-restriction (Fig. 1). Specifically: ① the instrumental variable must be strongly associated with the exposure factor; ② the instrumental variable should not be associated with any confounders related to the “exposure–outcome” relationship; ③ The instrumental variable can only influence the outcome variable through the exposure factor.

Figure 1. The 3 key assumptions of MR research are: (1) SNP is closely related to various lipid levels; (2) SNP is independent of other known confounding factors; (3) SNP only affects the risk of VVs through various lipids. X indicates that the SNP chosen as an instrumental variable is not directly related to confounding factors and outcomes.

In this study, we conducted independent two-sample MR analyses on 179 types of plasma lipids, utilizing summary data from genome-wide association studies (GWAS) to evaluate their causal relationship with VVs and to screen for lipid types with positive results, as well as sensitivity analyses to test the reliability of the results. The study workflow is illustrated in Figure 2.

Figure 2. The flowchart of the study.

2.2. Data sources

The analysis utilized publicly available summary statistics from published GWAS, primarily focusing on European populations, including both males and females. For lipids GWAS data, we used a study whose univariate GWAS summary statistics are stored in the GWAS Catalog with accession numbers GCST90277238 to GCST90277416. This study includes GWAS data on 179 lipid species across 13 lipid categories (phosphatidylinositol [PI], phosphatidylethanolamine-ether [PEO], PE, phosphatidylcholine-ether [PCO], PC, Lyso-phosphatidylethanolamine [LPE], Lyso-phosphatidylcholine [LPC], Triacylglycerol [TAG], Diacylglycerol [DAG], Ceramide [Cer], SM, Cholesteryl ester [CE], Cholesterol [Chol]), covering 4 major lipid categories (glycerolipids, glycerophospholipids, sphingolipids, and sterols) from 7174 Finnish individuals. This study’s lipid subdivision is based on their chemical structural differences, including the length of fatty acid chains, their saturation level (i.e., the number of double bonds), and their position on the molecular backbone. These subtle chemical differences are crucial for the biological functions of lipids. For instance, PC (15:0_18:1) contains a 15-carbon saturated fatty acid chain at the sn-1 position and an 18-carbon fatty acid chain with 1 double bond at the sn-2 position. Again for instance, in PC (O-17:0_17:1), “O-17:0” indicates that the fatty acid at the sn-1 position is a 17-carbon saturated fatty acid linked via an ether bond, while “17:1” indicates that the fatty acid at the sn-2 position is a 17-carbon unsaturated fatty acid with 1 double bond, connected via a conventional ester linkage. Ether-linked phospholipids exhibit unique biological properties, such as increased resistance to oxidation and thermal stability, which may affect the fluidity of cell membranes and cellular functions. These structural differences affect the fluidity, curvature, and signaling functions of cell membranes, potentially influencing the pathophysiology of various diseases.[18] The GWAS summary statistics for lower extremity varicose veins were derived from a pan-ancestry genetic analysis by Gene ATLAS, including 10,861 cases and 397,594 controls of British ancestry, all cases and controls in this study were from the UK Biobank.[19] The UK Biobank is a prospective study that recruited over half a million participants aged 40 to 69 years between 2006 and 2010. It collected extensive phenotypic and genotypic details of the participants, including whole-genome genotyping and a wide range of health-related data.[20] Since the GWAS data used in this study are publicly accessible (https://www.ebi.ac.uk/gwas/home) and have received ethical clearance from relevant review boards, our analysis did not necessitate further ethical approval. The summary information for the significant exposure factors is displayed in Table 1.

Table 1 The summary information for the significant exposure factors in this study.

Trait	Year	Study accession	Sample size	n SNPs	
PC (15:0_18:1)	2023	GCST90277275	6468	11318730	
PC (17:0_20:4)	2023	GCST90277298	7106	11318730	
PC (18:2_20:4)	2023	GCST90277317	7049	11318730	
PC (O-16:0_16:0)	2023	GCST90277318	6605	11318730	
PC (O-17:0_17:1)	2023	GCST90277333	6880	11318730	
PC (O-18:0_20:4)	2023	GCST90277336	6956	11318730	
PC (O-18:2_20:4)	2023	GCST90277344	6127	11318730	
PE (O-16:1_20:4)	2023	GCST90277351	7169	11318730	
PE (O-16:1_22:5)	2023	GCST90277352	5609	11318730	
PE (O-18:1_20:4)	2023	GCST90277354	7172	11318730	
PI (18:0_18:2)	2023	GCST90277362	7170	11318730	
PI (18:1_18:2)	2023	GCST90277366	6489	11318730	
VVs	2019	GCST008057	408455	10829469	
PC = phosphatidylcholine, PE = phosphatidylethanolamine, PI = phosphatidylinositol, VVs = varicose veins of lower extremity.

2.3. Selection and validation of instrumental variable SNPs

We selected single nucleotide polymorphisms (SNPs) associated with 179 plasma lipid levels using 3 criteria. First, we chose SNPs that reached genome-wide significance levels (P < 5 × 10−5). Second, we assessed the independence of these selected SNPs through linkage disequilibrium analysis. SNPs in linkage disequilibrium and close to other SNPs with higher P-values were excluded; specifically, we removed SNPs within a 10,000 kb range of the most significant SNP with r2 > 0.001. Finally, to control for potential bias, our selected instrumental variables were evaluated for weak instrument bias through the calculation of F-statistics. An F-statistic >10 indicates the absence of weak instrument bias, thereby further validating the association assumption. The formula for calculating the F-statistic is F = [(N − K − 1)/ K] × [R²/ (1 − R²)], where N is the sample size for the exposure factor, K is the number of instrumental variables, and R² is the proportion of variance in the exposure factor explained by the instrumental variables. The information about the SNPs that are statistically significant with VVs can be found in Table 2 and Table S1 to S12, Supplemental Digital Content, http://links.lww.com/MD/N476. Table S1 to S12, Supplemental Digital Content, http://links.lww.com/MD/N476 which illustrate SNPs of each lipids with statistically significant threshold.

Table 2 Associations between the genetically predicted plasma levels of lipids and the risk of VVs.

	SNPs (n)		MR-Egger	Weighted median	Inverse-variance weighted	
PC (15:0_18:1)	17	OR (95% CI)	1.003 (0.998–1.007)	1.003 (1.000–1.006)	1.002 (1.001–1.005)	
		P-value	.230	.031	.003	
PC (17:0_20:4)	25	OR (95% CI)	0.999 (0.997–1.000)	0.998 (0.997–1.000)	0.998 (0.997–1.000)	
		P-value	.122	.018	.012	
PC (18:2_20:4)	27	OR (95% CI)	0.998 (0.994–1.001)	0.997 (0.995–1.000)	0.998 (0.997–1.000)	
		P-value	.201	.014	.022	
PC (O-16:0_16:0)	14	OR (95% CI)	1.000 (0.997–1.004)	1.003 (1.000–1.006)	1.002 (1.000–1.004)	
		P-value	.797	.057	.020	
PC (O-17:0_17:1)	25	OR (95% CI)	1.000 (0.996–1.003)	1.002 (0.999–1.004)	1.002 (1.000–1.003)	
		P-value	.813	.130	.034	
PC (O-18:0_20:4)	18	OR (95% CI)	0.998 (0.995–1.001)	0.997 (0.995–1.000)	0.998 (0.997–1.000)	
		P-value	.138	.015	.012	
PC (O-18:2_20:4)	18	OR (95% CI)	0.998 (0.995–1.002)	0.997 (0.995–0.999)	0.998 (0.996–0.999)	
		P-value	.423	.009	.011	
PE (O-16:1_20:4)	19	OR (95% CI)	0.997 (0.993–1.002)	0.997 (0.994–0.999)	0.997 (0.995–0.999)	
		P-value	.283	.006	.001	
PE (O-16:1_22:5)	13	OR (95% CI)	0.996 (0.993–1.000)	0.997 (0.995–1.000)	0.998 (0.996–1.000)	
		P-value	.046	.046	.041	
PE (O-18:1_20:4)	23	OR (95% CI)	0.999 (0.995–1.003)	0.998 (0.995–1.000)	0.998 (0.996–1.000)	
		P-value	.573	.048	.039	
PI (18:0_18:2)	31	OR (95% CI)	1.001 (0.999–1.002)	1.002 (1.000–1.003)	1.001 (1.000–1.002)	
		P-value	.463	.035	.028	
PI (18:1_18:2)	21	OR (95% CI)	1.005 (1.001–1.009)	1.002 (1.000–1.004)	1.002 (1.000–1.003)	
		P-value	.026	.104	.036	
PC = phosphatidylcholine, PE = phosphatidylethanolamine, PI = phosphatidylinositol.

2 .4. Two-sample MR analysis

Two-sample Mendelian randomization analysis was conducted using random-effects inverse-variance weighted (IVW), weighted median, and MR-Egger methods to assess the potential causal relationship between plasma lipids and VVs. Traditional IVW analysis may be susceptible to bias from invalid instruments or pleiotropy. Therefore, this study incorporates sensitivity analyses to test the validity and robustness of the IVW results. Additionally, we incorporated Bayesian Weighted Mendelian Randomization (BWMR) into our analysis. BWMR can account for uncertainties in the estimated weak effects and weak horizontal pleiotropy effects and adaptively detect outliers caused by some larger horizontal pleiotropy effects.[21] Further validation through BWMR indicates more robust and reliable results.

Sensitivity analysis is a crucial step in assessing the robustness of study results. It is designed to detect and quantify the impact of potential biases and heterogeneity, as well as the influence of individual genetic variations on the overall effect estimate. In this study, the average level of pleiotropy bias was assessed through MR-Egger regression, where P < .05 indicates the presence of pleiotropy between the exposure and outcome variables. MR-PRESSO is utilized to detect horizontal pleiotropy. For heterogeneity testing, we employ Cochran Q method, with P < .05 indicating the presence of heterogeneity among the included instrumental variable SNPs, suggesting that the study results are influenced by heterogeneity. The “leave-one-out” method involves sequentially removing each SNP and then conducting MR analysis with the remaining SNPs to observe changes in the overall effect estimate. If the removal of any single SNP results in a significant change in the effect estimate, this may indicate that the SNP has an undue influence on the overall estimate. The results of the sensitivity tests support the robustness of the research methodology and the reliability of the findings.

All statistical analyses were conducted using the “TwoSampleMR” package in R software, version 4.3.2.

3. Results

After conducting a two-sample MR analysis, we confirmed a potential causal relationship between 12 types of lipids and the risk of VVs. This includes 7 types of PC (phosphatidylcholine [15:0_18:1], phosphatidylcholine [17:0_20:4], phosphatidylcholine [18:2_20:4], phosphatidylcholine [O-16:0_16:0], phosphatidylcholine [O-17:0_17:1], phosphatidylcholine [O-18:0_20:4], phosphatidylcholine [O-18:2_20:4]), 3 types of PE (phosphatidylethanolamine [O-16:1_20:4], phosphatidylethanolamine [O-16:1_22:5], phosphatidylethanolamine [O-18:1_20:4]), and 2 types of PI (phosphatidylinositol [18:0_18:2], phosphatidylinositol [18:1_18:2]). Among them, PC (15:0_18:1) showed an odds ratio (OR) of 1.002, 95% confidence interval (CI) of 1.001 to 1.005, and a P-value of .003 in the IVW method, indicating a statistically significant positive correlation between this lipid level and the increased risk of VVs. PC (17:0_20:4) and PC (18:2_20:4) showed statistical significance in all MR methods, suggesting a potential association with a decreased risk of VVs. PC (O-16:0_16:0) and PC (O-17:0_17:1) showed a positive correlation with the risk of VVs in the IVW method, but with higher P-values in the MR-Egger method, indicating that potential pleiotropy might affect the interpretation of these relationships. PE (O-16:1_20:4) and PE (O-16:1_22:5) showed statistical significance in the IVW method on the impact of VVs risk, especially the impact of PE (O-16:1_20:4) was more significant, PI (18:0_18:2) and PI (18:1_18:2) both showed statistical significance related to the risk of VVs, particularly the OR value and P-value of PI (18:1_18:2) indicated a stronger positive correlation. The MR results of these 12 lipids, including their ORs, 95% CIs, and P-values, are presented in Table 2. Forest plots, funnel plots, and scatter plots are shown in Figure S1 to S3, Supplemental Digital Content, http://links.lww.com/MD/N476. The reliability of the above methods is also demonstrated by the BWMR results, which are shown in Table 3. Sensitivity testing further confirms the stability of the above results. The results of the sensitivity analysis are presented in Table 4 and in Figure S4, Supplemental Digital Content, http://links.lww.com/MD/N476.

Table 3 Association of the genetically predicted plasma levels of lipids with the risk of varicose veins in the lower extremities using BWMR.

Risk factors	OR	P-value	
PC (15:0_18:1)	1.003 (1.001–1.005)	.002834356	
PC (17:0_20:4)	0.999 (0.998–1.000)	.006932715	
PC (18:2_20:4)	0.998 (0.997–1.000)	.017201713	
PC (O-16:0_16:0)	1.002 (1.000–1.004)	.021596447	
PC (O-17:0_17:1)	1.002 (1.000–1.003)	.033008844	
PC (O-18:0_20:4)	0.998 (0.997–1.000)	.010930164	
PC (O-18:2_20:4)	0.997 (0.996–1.000)	.003875772	
PE (O-16:1_20:4)	0.997 (0.996–0.999)	.001672149	
PE (O-16:1_22:5)	0.998 (0.997–1.000)	.028813958	
PE (O-18:1_20:4)	0.998 (0.996–1.000)	.031385885	
PI (18:0_18:2)	1.001 (1.000–1.002)	.012187144	
PI (18:1_18:2)	1.002 (1.000–1.003)	.045972205	
BWMR = Bayesian Weighted Mendelian Randomization, PC = phosphatidylcholine, PE = phosphatidylethanolamine, PI = phosphatidylinositol.

Table 4 Pleiotropy and heterogeneity tests of selected SNPs.

Risk factors	Pleiotropy test
(MR-Egger regression)	Pleiotropy test (MR-PRESSO)	Heterogeneity test	
Beta (SE)	P-value	P-value	Cochran Q	P-value	
PC (15:0_18:1)	0.0002	.933	.508	15.583	.410	
PC (17:0_20:4)	0.0002	.961	.295	29.425	.167	
PC (18:2_20:4)	0.0002	.689	.669	22.004	.635	
PC (O-16:0_16:0)	0.0003	.207	.441	10.887	.539	
PC (O-17:0_17:1)	0.0002	.221	.904	14.083	.924	
PC (O-18:0_20:4)	0.0002	.808	.86	11.047	.807	
PC (O-18:2_20:4)	0.0003	.625	.144	23.028	.113	
PE (O-16:1_20:4)	0.0003	.928	.504	18.026	.387	
PE (O-16:1_22:5)	0.0003	.203	.599	9.073	.615	
PE (O-18:1_20:4)	0.0003	.683	.099	31.308	.069	
PI (18:0_18:2)	0.0001	.509	.458	30.179	.405	
PI (18:1_18:2)	0.0003	.096	.56	15.182	.711	
PC = phosphatidylcholine, PE = phosphatidylethanolamine, PI = phosphatidylinositol.

No statistical relationship was found between SM and the risk of VVs. The MR results for the 11 types of SM with VVs are shown in Table 5.

Table 5 Associations between the genetically predicted plasma levels of SM and the risk of VVs.

Risk factors	P-value (IVW)	
SM (d32:1)	.555892570424861	
SM (d34:0)	.607688319668095	
SM (d34:1)	.644496431471553	
SM (d34:2)	.767938398521698	
SM (d36:1)	.608328749809907	
SM (d36:2)	.0568243756446325	
SM (d38:1)	.961835090085724	
SM (d38:2)	.944556249031821	
SM (d40:1)	.227288510703765	
SM (d40:2)	.879874536677981	
SM (d42:2)	.956958845599248	
IVW = inverse-variance weighted, SM = sphingomyelin.

Overall, these results support a potential causal relationship between specific types of lipid levels and the risk of VVs.

4. Discussion

This study’s findings reveal potential aspects of the pathogenesis of VVs, emphasizing the role of specific lipid molecules. The results suggest a potential causal relationship between certain types of lipid levels and the risk of VVs. Some lipids, such as PC (15:0_18:1) and PE (O-16:1_20:4), showed significant positive or negative correlations with the risk of VVs. However, for other lipids, this association might be weaker or not significant. These findings provide important clues for future studies on biological mechanisms and potential preventive or therapeutic strategies.

The current understanding of the relationship between plasma lipids and the risk of VVs requires further research. Our study extends existing knowledge about VVs, highlighting the potential causal relationship between specific plasma lipid components and the development of VVs. Previous research by H. Tanaka et al using imaging mass spectrometry revealed a unique lipid distribution in primary varicose veins. They found increased concentrations of PC, Lyso-phosphatidylcholine (LPC), and SM around the veins in patients with VVs[.[16] LPC is associated with inflammation, being a chemotactic factor for macrophages and lymphocytes, known to induce the expression of Vascular Cell Adhesion Molecule-1 (VCAM-1) and Intercellular Adhesion Molecule-1 (ICAM-1) in endothelial cells. Due to its detergent-like properties, high concentrations of LPC can lyse cells. The specific localization of LPC on the valves of VV tissues suggests that the valves could be damaged due to LPC-induced inflammation or its detergent-like properties.[22] SM is a precursor of sphingolipid mediators. Over 36 studies have shown that sphingolipid mediators, including ceramides and ceramide 1-phosphate, play an indispensable role in the inflammatory process. The accumulation of PC (1-acyl 36:4) and SM (d18:1/16:0) around the valvular areas may severely exacerbate tissue inflammation around varicose vein valves. Valve dysfunction leads to venous reflux, thereby leading to the development of varicose veins. These lipids are related to inflammation and cellular pathways. The aggregation of PC can lead to the aggregation of phospholipase and its decomposition into LPC and free fatty acids. Furthermore, a study by Anwar et al using analytical techniques provided a comprehensive metabolic profile of the disease. In their study, compared with the non-varicose vein group, the lipid extracts from varicose vein group had higher concentrations of PC, PE, and SM. Pathway analysis indicates that PC and SM are associated with inflammation, while inositol is related to cell proliferation.[6] SM, PC, phosphocholine, ceramide, and diacylglycerol influence cell survival, with the first 3 promoting growth and survival, and the latter 2 inducing cell death and quiescence.[23,24] This suggests that the local chemical environment and physical stress cells experience following chronic blood stasis could trigger changes in metabolic balance and disrupt lipid homeostasis, leading to the production of certain pro-survival molecules and their downstream effects.

Despite the advantages of well-designed, large-scale prospective studies, they are still susceptible to the effects of residual confounding and reverse causation. Therefore, our study employed MR analysis to explore the relationship between plasma lipid levels and the risk of VVs. In MR analysis, plasma lipid levels are determined genetically; thus, they are not influenced by confounding factors. Utilizing large-scale GWAS data for a two-sample MR analysis provides a more robust method of establishing causality compared to traditional observational studies. The association between 12 types of lipids and the risk of VVs reached statistical significance across various MR analysis methods, enhancing the robustness of these findings. Particularly, when both the IVW and the weighted median methods showed similar significant results, it provided reliable evidence to support the study. For some lipid types, a higher P-value in MR-Egger might indicate potential pleiotropy bias. Careful consideration should be given to potential biases and other confounding factors when interpreting these relationships. Overall, these results support a potential causal relationship between specific types of lipid levels and the risk of VVs. Moreover, the introduction of BWMR addressed potential biases more effectively than traditional MR methods, enhancing the reliability of our findings. The detection of pleiotropy and heterogeneity also supports the robustness and reliability of our results.

Previous research has highlighted the potential role of lipid molecules in the development of VVs, particularly in relation to inflammation and cellular pathways. We have further expanded the research in this area, proposing not only the potential causal relationship between PC and PE types of lipids and VVs but also identifying an association with specific types of PI. Our study provides causal evidence for the role of these lipids in the development of VVs through genetic methods. Although our research did not find a potential causal relationship between SM and VVs, this does not negate the role of SM in the inflammatory process, as was also shown in the studies by Tanaka et al. The differences may stem from the design and analytical methods of our study, highlighting the need for further research to fully understand the complex roles of different lipid molecules in the development of VVs.

This study has some limitations. First, the accuracy of MR analysis highly depends on the validity of the chosen instrumental variables (SNPs). These SNPs must be strongly associated with the exposure and affect the outcome variable only through the exposure. If there are other pathways affecting the outcome, such as horizontal pleiotropy, it might lead to bias. Second, genetic heterogeneity between populations could affect the generalizability of the relationship between SNPs and the exposure, and the GWAS data used in this study primarily come from a specific population (European descent), so the results may not be directly generalizable to people of other backgrounds. Third, MR analysis often assumes a linear relationship between exposure and outcome. If the actual relationship is nonlinear, this simplification could lead to incorrect conclusions. Finally, the quality and accuracy of MR analysis also depend on the quality and availability of the underlying GWAS studies. Any errors or deficiencies in the data could affect the outcomes of the MR analysis. Even if MR analysis indicates a causal relationship, identifying the specific biological mechanisms remains a challenge. Additionally, MR analysis cannot provide information on the size of intervention effects.[17] Overall, while this study provides valuable insights, these limitations should be considered when interpreting the results and generalizing the findings. Future research may need to be conducted across multiple populations and combined with experimental studies to further validate causal relationships and clarify biological mechanisms.

In conclusion, this study not only advances our understanding of the pathophysiology of VVs but also highlights the potential of lipid molecules as biomarkers and therapeutic targets in vascular diseases. The potential causal links pave the way for further investigation into lipid metabolism’s role in VVs, offering insights for more effective management of this condition.

Author contributions

Conceptualization: Haibin Yu.

Data curation: Kailin Shen, Haibin Yu.

Methodology: Kailin Shen, Haibin Yu.

Resources: Kailin Shen.

Software: Kailin Shen.

Supervision: Fangtao Zhu, Cunwei Cheng, Haibin Yu.

Validation: Kailin Shen.

Visualization: Kailin Shen.

Writing – original draft: Kailin Shen, Fangtao Zhu, Cunwei Cheng.

Writing – review & editing: Kailin Shen, Fangtao Zhu, Cunwei Cheng, Haibin Yu.

Supplementary Material

Abbreviations:

BWMR Bayesian Weighted Mendelian Randomization

GWAS genome-wide association study

IVW inverse-variance weighting

MR Mendelian randomization

PC phosphatidylcholine

PE phosphatidylethanolamine

PI phosphatidylinositol

SM sphingomyelin

SNPs single nucleotide polymorphisms

VVs varicose veins of the lower extremities

Scientific and Technological Funding sources: Project of the Education Department of Henan Province.

The authors have no conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Supplemental Digital Content is available for this article.

How to cite this article: Shen K, Zhu F, Cheng C, Yu H. Exploring the causal relationship between plasma lipids and varicose veins of lower extremity: A comprehensive two-sample Mendelian randomization study. Medicine 2024;103:36(e39514).
==== Refs
References

[1] Beebe-Dimmer JL Pfeifer JR Engle JS Schottenfeld D . The epidemiology of chronic venous insufficiency and varicose veins. Ann Epidemiol. 2005;15 :175–84.15723761
[2] Zolotukhin IA Seliverstov EI Shevtsov YN . Prevalence and risk factors for chronic venous disease in the general russian population. Eur J Vasc Endovasc Surg. 2017;54 :752–8.29031868
[3] Oklu R Habito R Mayr M . Pathogenesis of varicose veins. J Vasc Interv Radiol. 2012;23 :33–9; quiz 40.22030459
[4] Lim CS Davies AH . Pathogenesis of primary varicose veins. Br J Surg. 2009;96 :1231–42.19847861
[5] Raffetto JD . Pathophysiology of chronic venous disease and venous ulcers. Surg Clin North Am. 2018;98 :337–47.29502775
[6] Anwar MA Adesina-Georgiadis KN Spagou K . A comprehensive characterisation of the metabolic profile of varicose veins; implications in elaborating plausible cellular pathways for disease pathogenesis. Sci Rep. 2017;7 :2989–13.28592827
[7] Pfisterer L König G Hecker M Korff T . Pathogenesis of varicose veins - lessons from biomechanics. Vasa. 2014;43 :88–99.24627315
[8] Lim CS Kiriakidis S Sandison A Paleolog EM Davies AH . Hypoxia-inducible factor pathway and diseases of the vascular wall. J Vasc Surg. 2013;58 :219–30.23643279
[9] Grant Y Onida S Davies A . Genetics in chronic venous disease. Phlebology. 2017;32 :3–5.
[10] Krysa J Jones GT van Rij AM . Evidence for a genetic role in varicose veins and chronic venous insufficiency. Phlebology. 2012;27 :329–35.22308533
[11] Ellinghaus E Ellinghaus D Krusche P . Genome-wide association analysis for chronic venous disease identifies EFEMP1 and KCNH8 as susceptibility loci. Sci Rep. 2017;7 :45652.28374850
[12] . Robert Bell EYDC . A large scale genome wide association study of varicose veins in the 23andMe Cohort.
[13] Kaspar S Kaspar D . Complications and pitfalls of endovenous laser therapy for varicose veins of lower extremities. Rozhl Chir. 2022;101 :369–74.36208931
[14] Kremastiotis J Jfri A Litvinov IV Barolet D Netchiporouk E . Treatment modalities for varicose veins of lower extremities. J Cutan Med Surg. 2020;24 :203–4.32208017
[15] Carroll BJ Piazza G Goldhaber SZ . Sulodexide in venous disease. J Thromb Haemost. 2019;17 :31–8.30394690
[16] Tanaka H Zaima N Yamamoto N . Imaging mass spectrometry reveals unique lipid distribution in primary varicose veins. Eur J Vasc Endovasc Surg. 2010;40 :657–63.20817502
[17] Richmond RC Davey SG . Mendelian Randomization: concepts and scope. Cold Spring Harb Perspect Med. 2022;12 :1.
[18] Ottensmann L Tabassum R Ruotsalainen SE . Genome-wide association analysis of plasma lipidome identifies 495 genetic associations. Nat Commun. 2023;14 :6934.37907536
[19] Shadrina AS Sharapov SZ Shashkova TI Tsepilov YA . Varicose veins of lower extremities: Insights from the first large-scale genetic study. PLoS Genet. 2019;15 :e1008110.30998689
[20] Sudlow C Gallacher J Allen N . UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12 :e1001779.25826379
[21] Zhao J Ming J Hu X Chen G Liu J Yang C . Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinformatics. 2020;36 :1501–8.31593215
[22] Kume N Gimbrone MJ . Lysophosphatidylcholine transcriptionally induces growth factor gene expression in cultured human endothelial cells. J Clin Invest. 1994;93 :907–11.7509351
[23] Glunde K Shah T Winnard PT . Hypoxia regulates choline kinase expression through hypoxia-inducible factor-1 alpha signaling in a human prostate cancer model. Cancer Res. 2008;68 :172–80.18172309
[24] Chao R Khan W Hannun YA . Retinoblastoma protein dephosphorylation induced by D-erythro-sphingosine. J Biol Chem. 1992;267 :23459–62.1385423
