
==== Front
Thromb J
Thromb J
Thrombosis Journal
1477-9560
BioMed Central London

647
10.1186/s12959-024-00647-y
Research
Causal relationship between varicose veins and mean corpuscular hemoglobin concentration based on Mendelian randomization study
Chen Shiwei 498487218@qq.com

Zhou Huandong
Liu Shicheng
Meng Luyang
https://ror.org/02sysn258 grid.440280.a The Third People’s Hospital of Hangzhou, Zhejiang Province, 310009 China
3 9 2024
3 9 2024
2024
22 798 7 2024
21 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Increased hemoglobin concentrations may increase the risk of varicose veins. However, the underlying relationship between them was not yet understood.

Methods

Mendelian randomization (MR) analysis was performed to investigate causal effect between mean corpuscular hemoglobin concentration (MCHC, exposure factor) and varicose veins (outcome). Afterward, sensitivity analysis was used to ensure the reliability of MR analysis results. Then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of SNPs were performed. A search tool for recurring instances of neighbouring genes (STRING) database was used to construct a protein-protein interaction (PPI) network.

Results

Therefore, the inverse-variance weighted (IVW) results showed there existed a causal relationship between MCHC and varicose veins (p = 0.0026), with MCHC serving as a significant risk factor. (odd ratio [OR] = 1.2321). In addition, the validity of the results of the forward MR analysis was verified by sensitivity analysis. Further, a PPI network of 92 single-nucleotide polymorphisms (SNPs) which used for forward MR analysis related genes was constructed. And they were found to be closely associated with the peroxisome proliferator-activated receptor (PPAR) signalling pathway and cellular response to external stimulus by enrichment analysis. In addition, we clarified that the effect of varicose veins on MCHC was minimal by reverse MR analysis, suggesting that the results of forward MR analysis were not disturbed by reverse results.

Conclusion

This study found a causal relationship between varicose veins and MCHC, which provided strong evidence for the effect of hemoglobin on varicose veins, and a new thought for the diagnosis and prevention of varicose veins in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12959-024-00647-y.

Keywords

Mean corpuscular hemoglobin concentration
Varicose veins
Mendelian randomization
Enrichment analysis
PPI network
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pmcIntroduction

Varicose veins are enlarged veins of the subcutaneous tissue, usually caused by faulty or damaged venous valves leading to impaired blood flow [1]. There are different prevalences of varicose vein disease described around the world, depending on factors such as personal lifestyle, obesity, and age [2]. A previous study found that telangiectasias occur in 43% of men and 55% of women, and varicose veins occur in 16% of men and 29% of women [3]. The exact pathophysiology is debated, however, genetic susceptibility, valvular insufficiency, weakened vascular wall and elevated venous pressure are associated with it. There are many risk factors for varicose veins, including a family history of venous disease, being female, advanced age, chronically elevated internal abdominal pressure due to obesity, pregnancy, chronic constipation, or tumors, and prolonged standing. Symptoms of varicose veins include heavy, painful sensations and itching or burning sensations, and these symptoms get worse with prolonged standing. Potential complications include infection, leg ulcers, stasis changes, and thrombosis [4]. Up till the present moment, conservative treatment options mainly include external compression, lifestyle changes such as avoiding prolonged standing and exertion, exercise, wearing non-restrictive clothing, modifying cardiovascular risk factors and interventions to reduce peripheral edema, elevation of the affected legs, weight loss, and medication [4]. However, the pathogenesis of varicose veins is not completely clear, it is important to further explore the exact etiology of varicose veins.

Hemoglobin, a highly conserved protein, due to its ability to reversibly bind oxygen, is involved in the processes that underlie the aerobic life on planet Earth. The primary role of this protein is reflected in the maintenance of cellular homeostasis, by supporting its energy requirements [5]. Hemoglobin is a polyfunctional molecule that is involved in several functions, such as catalytic (nitrite reductase, NO dioxygenase, monooxygenase, myeloroperoxidase, esterase, lipoxygenase); nitric oxide metabolism; metabolic reprogramming; pH regulation and maintaining redox balance [6]. It has important functions in the body: oxygen carrying function, CO2 transport, acid-base balance regulation, immune response and so on. Besides its ability to transport oxygen, hemoglobin within erythrocytes plays an important role in cellular signaling and modulation of the inflammatory response either directly by binding gas molecules (NO, CO, and CO2) or indirectly by acting as their source. Once hemoglobin reaches the extracellular environment, it acquires several secondary functions affecting surrounding cells and tissues [5]. Research suggest that increased hemoglobin concentration may lead to certain changes, such as: (1) increased blood viscosity, slower blood flow, and increased blood vessel wall pressure; (2) increased number of red blood cells, increased blood volume, resulting in blood vessel deformation and dilation; (3) increased platelet aggregation promotes inflammation of the blood vessel wall and thrombosis. Therefore, it may increase the risk of varicose veins [7, 8]. In diseases characterized by hemoglobinemia, the incidence of thrombosis is increased [9]. Furthermore, hemoglobin, as a highly reactive molecule, can lead to local oxidative stress, inflammation, and tissue damage [5]. However, once hemoglobin reaches the extracellular environment, in conditions such as trauma, inflammation or infection, hemoglobin exerts other potentially harmful effects on cells and tissues and may be involved in the etiology and pathophysiology of various diseases [10–12]. One MR study demonstrated the causal effects of genetically proxied red blood cell distribution width (RDW), mean corpuscular volume of reticulocyte (MCVr), mean corpuscular volume (MCV), and monocyte count (MONO) on the risk of venous thromboembolism (VTE). Blood cells affect the occurrence of VTE to a certain extent [13]. Another MR study using MR based on bayesian model averaging (MR-BMA), to choose between 12 correlated RBC traits suggests hemoglobin is the Red blood cell (RBC) trait most relevant to VTE [14]. The relationship between hemoglobin and varicose veins, however, remains elusive, the underlying molecular regulatory mechanism between red blood cells and varicose veins remains unclear.

MR is a type of instrumental variable (IV) analysis that uses genetic variation as IV to detect and quantify causality [15]. In traditional observational studies, their ability to infer causality was affected due to potential confounding and reverse causality. By contrast, due to MR analysis can overcome these impacts, it has been used more and more widely in observational studies in recent years. However, earlier MR studies were often conducted in small sample populations and used only a small amount of genetic variation, making MR studies less powerful. With the discovery of a large number of genetic variants strongly associated with specific traits, and with many large GWAS that have publicly published pooled data on hundreds of thousands of exposures and disease-related genetic variants, there has been a revolution in this field. These aggregated data have facilitated the development of MR studies by enabling researchers to estimate genetic associations in large samples of data. In varicose histological changes are observed in veins such as intimal fibrosis, luminal dilation and progressive vascular wall thickening. In an early population-based case-control study, varicose veins were found to be an independent risk factor of VTE in an age-dependent manner, with people aged 45 suffering the highest risk (OR: 4.19) [16]. Recent studies show that patients with varicose veins are up to 5-fold increased risk for developing deep vein thrombosis (DVT) [8, 17]. Epidemiological studies have established multiple risk factors such as age, female sex, pregnancy, obesity and prior DVT [18–20]. There is also a clear familial component to varicose vein disease, but prior genetic studies have been small in size and have provided conflicting results [21–24].

In this study, we chose mean corpuscular hemoglobin concentration (MCHC) as a candidate exposure factor for MR analysis, aiming to investigate its causal relationship with varicose veins. If such a causal relationship is established, it suggests that regulating MCHC may be a strategy for the prevention or treatment of varicose veins, thus providing a more effective treatment for patients and offering new perspectives and approaches to the treatment and management of varicose veins.

Materials and methods

Data source

The varicose veins dataset and MCHC dataset were obtained from Integrative Epidemiology Unit Open genome-wide association statistics (IEU OpenGWAS) database (https://gwas.mrcieu.ac.uk/) [25]. The GWAS dataset for varicose veins (finn-b-I9_VARICVE) contains 207,055 samples, all from European populations, and 16,380,438 single-nucleotide polymorphisms (SNPs), while the MCHC dataset (ukb-d-30060_irnt) contains 350,468 samples (European) and 13,586,285 SNPs (Table s1).

Selecting instrumental variables for MR analysis

To ensure valid IVs values, three basic assumptions of MR analysis should be satisfied: (1) IVs are closely related to exposure factors, (2) IVs cannot be associated with any confounding factors, (3) Exposure is the only way that genetic variation can affect the outcome [26]. In this study, extract_instruments in TwoSampleMR [27] was utilised for exposure factor reading and SNP screening. SNPs with significant correlation with exposure factors were searched based on p < 5 × 10− 8; clump = TRUE to remove the SNP for linkage disequilibrium (LD); r2 = 0.001; kb = 10,000. The “harmonise_data” function in the “TwoSampleMR” package was used to harmonize the effect size of the selected IVs. Finally, 92 SNPs associated with MCHC (Table s2) and 358 SNPs associated with varicose veins (Table s3) were obtained for forward and reverse MR analysis, respectively.

MR analysis

In this study, “TwoSampleMR” (v0.5.6) R package [27] was used for two-sample MR analysis between exposures and outcome, five common MR methods were used for features that contained more than one IV: MR-Egger regression [28], inverse-variance weighted (IVW) method [29], the weighted median test [30], the weighted mode test [25] and the simple mode test [31]. The IVW test was primary method for studying the causal relationship between varicose veins and MCHC (p < 0.05). If the assumption that all included SNPs can be used as valid IV was satisfied, the IVW method provided an accurate estimate [32]. The other methods were used as supplementary analysis methods. This result was presented through scatter plots, forest plots and funnel plots. Where if the odd ratio (OR) value is greater than 1, it indicates a risk factor and vice versa for a protective factor. For forward MR analysis, the MCHC was exposure and the varicose veins was outcome, for reverse MR analysis, the MCHC was outcome and the varicose veins was exposure.

Sensitivity analysis

Sensitivity analysis was performed in this study to verify the reliability of MR analysis results. First of all, IVW method and MR-Egger regression were used for heterogeneity test [33]. The heterogeneity were quantified by Cochran Q statistic, if the P-value of heterogeneity test was less than 0.05, it indicated the existence of heterogeneity, which will increase the uncertainty of the result. Then, horizontal pleiotropy was analyzed by MR-Egger regression. If P-value > 0.05, it indicated that there was no horizontal pleiotropy and the results of MR analysis were reliable [34]. Lastly, The leave-one-out (LOO) method was used to detect whether the remaining SNPs had an impact on the overall analysis, even if one SNP was removed [35].

Functional enrichment analysis and construction of protein-protein interaction (PPI) networks

To determine the potential impact of the association variants identified in the MR analysis at the functional level, we used the variant effect predictor (VEP) comments tool (https://asia.ensembl.org/Tools/VEP) to obtain the genes corresponding to the SNPs used for forward MR analysis. Further, “clusterProfiler” package in R language [36] was employed for enrichment analysis of these genes to understand their potential mechanisms and biological functions, including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) (p.value < 0.05). Among them, GO contains three parts, which are cellular components (CC), molecular functions (MF) and biological processes (BP). In addition, PPI networks of genes were constructed in STRING database (https://cn.string-db.org) and Cytoscape [37] to explore their interactions at the protein level.

Results

The causal effect of MCHC on varicose veins

The results of IVW showed that there was statistical significance between MCHC and Varicose veins (P = 0.0026) and the OR was greater than 1 (OR = 1.2321, 95%CI = 1.0754–1.4115) (Table 1). More important, the direction of OR of the other four methods was the same as that of IVW, indicating that MCHC had a causal relationship with the occurrence of varicose veins. The scatter plot showed a positive line slope, indicating that MCHC was a risk factor for varicose veins (Fig. 1a). In the funnel plot, the SNP sites were almost symmetrical based on the IVW method, indicating that the analysis results were consistent with Mendel’s second Law (Fig. 1b). Based on the above results, this study further explored the causal effect of varicose veins on MCHC. The P-value of IVW method was 0.0033, but the p-value of the other four methods was greater than 0.05, however the OR value was smaller than the forward MR analysis result, indicating that the impact of varicose veins on MCHC variables was statistically significant, but the impact was minimal. Varicose veins had little effect on MCHC (Table 2).

Fig. 1 Relationship between MCHC and varicose veins. (a) Scatter plot of forward MR analysis.The points on the plot represent each SNP site, the abscissa was the SNP effect on exposure, the ordinate was the SNP effect on outcome, and the colored lines indicated the fit results of the different MR algorithms. A line with a regular slope indicated a risk factor, and a line with a negative slope indicated a safety factor. When an intercept was present, it implied the presence of confounding factors. (b). Funnel plot of forward MR analysis. Mendel’ s second law, also known as the law of independent segregation of genes, stipulates that the inheritance of different traits is independent and will not affect each other. In order to make a random judgment, if the samples were symmetrically distributed along the IVW line, MR was randomly assigned in accordance with Mendelian’s second law. It was found that the SNP sites were almost symmetrical based on the IVW method, indicating that the analysis results were in accordance with Mendelian’s second law

Table 1 Associations of MCHC with risk of varicose veins in forward MR analyses

Outcome	Exposure	Method	Nsnp	Beta	Se	B_lci95	B_uci95	Pvalue	OR (95% CI)	OR_lci95	OR_uci95	
Varicose veins	Mean corpusc-ular haemog-lobin concentr-ation	MR Egger	92	0.3295	0.1381	0.0589	0.6002	0.0191	1.3903	1.0607	1.8225	
Weighted median	92	0.2832	0.0919	0.1030	0.4634	0.0021	1.3274	1.1085	1.5894	
Inverse variance weighted	92	0.2087	0.0694	0.0727	0.3447	0.0026	1.2321	1.0754	1.4115	
Simple median	92	0.3089	0.1745	-0.0331	0.6508	0.0800	1.3619	0.9675	1.9171	
Weighted mode	92	0.3376	0.1060	0.1298	0.5453	0.0020	1.4015	1.1386	1.7251	
* B value represented the effect size of exposure factors on outcome variables, B > 0 indicated that exposure factors were risk factors, B < 0 indicated that exposure factors were protective factors. Se was the standard error of the B value (effect value). OR value represented the risk ratio, OR value greater than 1, was a risk factor, less than 1 was a protective factor

Table 2 Associations of varicose veins with risk of MCHC in reverse MR analyses

Outcome	Exposure	Method	Nsnp	Beta	Se	B_lci95	B_uci95	Pvalue	OR (95% CI)	OR_lci95	OR_uci95	
Mean corpusc-ular haemog-lobin concentr-ation	Varicose veins	MR Egger	358	0.0100	0.0053	-0.0004	0.0205	0.0603	1.0101	0.9996	1.0207	
Weighted median	358	0.0024	0.0031	-0.0037	0.0084	0.4431	1.0024	0.9963	1.0085	
Inverse variance weighted	358	0.0080	0.0027	0.0027	0.0133	0.0033	1.0080	1.0027	1.0134	
Simple median	358	0.0019	0.0092	-0.0161	0.0200	0.8354	1.0019	0.9840	1.0202	
Weighted mode	358	-0.0008	0.0059	-0.0124	0.0108	0.8939	0.9992	0.9877	1.0108	
* B value represented the effect size of exposure factors on outcome variables, B > 0 indicated that exposure factors are risk factors, B < 0 indicated that exposure factors are protective factors. Se was the standard error of the B value (effect value). OR value represented the risk ratio, OR value greater than 1, was a risk factor, less than 1 was a protective factor

Reliability of MR analysis results

Based on IVW method and MR-Egger regression, heterogeneity test results showed heterogeneity among datasets (IVW method: Q_pval = 0.0014, MR-Egger regression: Q_pval = 0.0015). However, the result of IVW method (P < 0.05) did not affect the final result (Table 3). In this study, the horizontal pleiotropy test results showed that P-value > 0.05 (P = 0.3141), indicating that there was no horizontal pleiotropy in the MR analysis results of this study (Table 4). LOO analysis showed that there were no points with serious bias, and no single SNP had a large effect on the outcome (Fig. 2). In summary, these results were reliable for MR analysis.

Fig. 2 Forest plot from Leave-one-out analysis of 92 SNPs

Table 3 Heterogeneity test

Outcome	Exposure	Method	Q	Q_df	Q_Pvalue	
Varicose veins	Mean corpuscular hemoglobin concentration	Inverse variance weighted	136.65	91	0.0014	
MR Egger	135.12	90	0.0015	

Table 4 Horizontal pleiotropy test

Outcome	Exposure	Egger_intercept	Se	Pvaluel	
Varicose veins	Mean corpuscular hemoglobin concentration	-0.0043	0.0042	0.3141	

Functional analysis and PPI network of SNPs-related genes

For forward MR analysis, the VEP annotation tool was used to obtain the gene symbol corresponding to SNP, and a total of 96 genes were obtained. A total of 348 items were enriched by GO analysis, including 46 CC, 40 MF and 262 BP (Fig. 3a), for instance Cell leading edge, response to starvation, Cargo receptor activity, and Basal part of cell and so on. A total of 12 functional pathways were enriched in KEGG (Fig. 3b), including peroxisome proliferator-activated receptor (PPAR) signalling pathway, ATP-binding cassette (ABC) transporters, Malaria and Collecting duct acid secretion and the others. Further, we constructed a PPI network of SNP-related genes, which contained 94 points and 88 edges (Fig. 3c), such as SLC4A1-SPTB, SLC4A1-ANK1, etc., which had strong interactions with each other, and might become potential biomarkers or therapeutic targets for diseases.

Fig. 3 Enrichment analysis and PPI. (a) GO analysis. Top10 entries (significance order) for each section are shown, box size indicates the number of genes included, and color indicates significance.(b) KEGG analysis. Box size indicates the number of genes included, and color indicates significance. (c) PPI network of targets generated using STRING. Nodes represent proteins, edges represent PPIs

Discussion

Varicose veins are a common manifestation of chronic venous disease. It is estimated that more than 30 million adults in the United States have varicose veins, with interventions consuming more than $1 billion in direct health care resources per year [38]. Varicose veins are an important manifestation of chronic venous insufficiency (CVI) or chronic venous disease (CVD) and is usually related to incompetent valves which lead to the reflux of blood and the resulting venous hypertension. However, the mechanism of etiology and pathogenesis of this disease remain unclear. Therefore, this study explored the causal relationship between MCHC and varicose veins for the first time through MR analysis, observed and confirmed the causal mechanism between them from the perspective of genetics, and provided an important reference and demonstration for the study of varicose veins.

The forward MR analysis was performed (outcome: Varicose veins; exposure factor: MCHC). This study suggested a significant causal relationship between them, and the MCHC as a risk factor affected the occurrence of varicose veins. The reliability of the MR results was also verified by sensitivity analysis.Heterogeneity testing is employed to evaluate the presence of a significant disparity in the effect estimates stemming from various sources of genetic variation, such as SNPs, when utilized as instrumental variables. The results of the heterogeneity test in this study indicate Q_pval < 0.05, signifying the existence of heterogeneity, potentially attributable to inherent gene complexity and interactions, including linkage disequilibrium. Discrepancies in genetic background, health status, and environmental factors among selected samples may also contribute to observed heterogeneity effects on outcomes. Consequently, a random effects IVW model was applied for analysis. In addition to assessing heterogeneity, level-specific multiple-mediator testing and Leave-one-out analysis were conducted to validate the robustness of the findings. Level-specific multiple-mediator testing revealed no discernible impact on MR results in this study. Meanwhile, Leave-one-out analysis identified no outliers, affirming result reliability despite existing heterogeneity. Subsequently, reverse MR analysis was performed (outcome: MCHC; exposure factors: Varicose veins), the P values of the four algorithms were all greater than 0.05 (IVW method, 0.0033), this conclusion further proved that the effect of MCHC on varicose veins was rarely interfered by reverse causality. Above all, this was the first time to find a direct causal relationship between them, which provided a theoretical reference for the research and treatment of varicose veins in the future.

Interestingly, categorization of GO functions revealed that SNPs-related genes were mainly enriched in “cellular response to external stimulus”, “receptor-mediated endocytosis”, and “cellular response to nutrient levels”. It is known that the body produces an inflammatory response when cells are stimulated, suggesting that inflammation played an important role in MCHC. Many studies concerning varicose vein disease focus on the endothelial cell integrity and function. An increase in vein wall tension leads to the expression of matrix metalloproteinases, triggers leukocyte infiltration and activation, which causes an inflammation around the vein wall and endothelial cell injury [39]. Additionally, hypoxia and inflammation play an important role in the pathology of varicose vein disease [40]. Chronic venous insufficiency and changes in the vessel wall caused by turbulent blood flow lead to increased release of different inflammatory and endothelial markers, promoting the coagulation process. Pathogenesis of varicose vein conducts to deterioration of venous endothelial function and in consequence decreased bioavailability of nitric oxide (NO) [1]. NO is known as a potent vasodilator and cellular signaling molecule, inhibitor of platelet adherence and aggregation, which also reduces leukocytes adhesion to the endothelium [41]. The lower NO levels in varicose vein wall were reported [42]. Leucocyte activation releases cytokines, leucocyte-derived oxygen free radicals, proteolytic enzymes and platelet activating factor. In varicose veins blood, significantly increased concentrations of IL-6, IL-8 appear, as compared to the same patient’s antecubital vein blood, which confirms the inflammation process [43]. It was postulated that cytokines IL-6 and IL-8 and IL-1β are involved in hypercoagulability of whole blood, abnormal clot formation, erythrocytes damage and platelets hyperactivation. The morphological changes of the erythrocytes, due to the presence of IL-8 resemble those typically seen in eryptosis [44].

In addition, we found that the PPAR signaling pathway is involved in the occurrence and development of many diseases, and has certain regulatory effects on diseases. Wang R [45]found that PPAR signaling was universally and abnormally activated in CRC tumors. Blockade of the PPAR pathway both inhibited the growth of CRC organelles and promoted apoptosis of CRC organelles in vitro, suggesting that aberrant activation of PPAR signaling plays a key role in CRC tumorigenesis. Li X [46] found that bezafibrate, a panactivator of the PPAR signaling pathway, significantly enhanced the activity of H9C2 cardiomyocytes by enhancing Cpt1α expression. Activation of the PPAR signaling pathway may become a therapeutic strategy for the treatment of cardiovascular diseases. Therefore, we speculate that the PPAR signaling pathway plays a certain role in varicose veins and hemoglobin, but the specific regulatory mechanisms remain to be investigated.

Protein-protein interactions are integral to all physiological processes, and a comprehensive understanding of these interactions is essential for unraveling the cellular dynamics of a specific organism. PPI networks derived from experimental data using state-of-the-art methods have found widespread application in the realm of biomedical research [47].This study revealed a robust interaction between SLC4A1-SPTB and SLC4A1-ANK1, with both SLC4A1 and Anion Exchanger 1 (AE1) being expressed. AE1 is responsible for secreting acidic substances in red blood cells and renal α-intercalated cells [48]; SPTB is a constituent of the cytoskeleton located beneath the erythrocyte membrane, and mutations in the SPTB gene represent a prevalent etiology for conditions such as hereditary spherocytosis [49], ANK1 plays a pivotal role in maintaining the stability of red blood cell membranes, and its mutations are also implicated in diseases such as hereditary spherocytosis. Furthermore, it has potential as a biomarker for Parkinson’s disease [50].They are linked to a range of red blood cell disorders, typically characterized by compromised stability and function of the red blood cell membrane, closely associated with the roles of these proteins as identified in our PPI network analysis. The findings from the PPI network analysis offer potential biomarkers and therapeutic targets, providing valuable insights and directions for subsequent experimental validation and drug development.

Although the observed data are consistent, determining whether MCHC is a factor that increases the risk of varicose veins remains challenging. Despite adjusting for confounders in observational studies, it may still be vulnerable to biases (e.g., residual confounding) that may affect its validity. Thus, determining the ability to answer causal questions remains a challenge. Compared with traditional observational studies, MR analyses are less affected by confounders and reverse causation.

The participants in the GWAS database used in this study were of predominantly European descent, and due to genetic differences between races, it is uncertain whether the results of this study can be extended to individuals of non-European descent. Furthermore, the absence of essential replication analyses or external validation may constrain the reliability and generalizability of these findings. Moreover, the relationship between annotated pathways and causal effects cannot be fully determined in enrichment analyses, and the results need to be subsequently tested in combination with other methods, such as genome wide association studies or functional annotation. In addition, PPI network analyses may be affected by the quality of the data and the set of genes selected, making understanding of the results somewhat difficult. Therefore, there is still a need to follow up and improve this study through a variety of methods.We will endeavor to identify opportunities for collaboration in acquiring additional datasets for replication analysis, while also keeping abreast of advancements in new statistical methods and tools to enhance their application in future research. Through these endeavors, we aim to bolster the reliability and generalizability of our research findings and contribute more valuable outcomes to the scientific community. Through the above analysis, the causal relationship between MCHC and varicose veins was obtained, which provided theoretical basis and reference value in the study of MR.

Conclusions

This study found a causal relationship between varicose veins and MCHC. This study provided strong evidence for the effect of hemoglobin on varicose veins, which provided a new thought for the diagnosis and prevention of varicose veins in the future. For instance, it could potentially offer a novel therapeutic approach for the diagnosis of varicose veins. Enhancing hemoglobin levels or addressing abnormalities in hemoglobin may have the potential to prevent and manage varicose veins. Moreover, establishing a correlation between hemoglobin abnormalities and varicose veins might provide new diagnostic indicators and screening methods, facilitating early identification of high-risk individuals prone to developing varicose veins for prompt intervention and treatment. Furthermore, comprehending the causal relationship between hemoglobin abnormalities and varicose veins can aid physicians in assessing overall patient risk more effectively and devising personalized management strategies.In this study, the causal relationship between varicose veins and MCHC was demonstrated for the first time by MR analysis based on large-scale aggregated GWAS data and more genetic information. Then, we will continue to pay attention to the analysis of varicose veins and MCHC, and find other exposure factors for varicose veins.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

Not applicable.

Author contributions

Shiwei Chen, Huandong Zhou and ShichengLiu, Luyang Meng wrote the main manuscript text and prepared figures and tables. All authors reviewed the manuscript.

Funding

None.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

MR Mendelian randomization

MCHC mean corpuscular hemoglobin concentration

STRING search tool for recurring instances of neighbouring genes

PPI protein-protein interaction

IVW inverse-variance weighted

IV instrumental variable

LD linkage disequilibrium

OR odd ratio

SNPs single-nucleotide polymorphisms

PPAR peroxisome proliferator-activated receptor

LOO leave-one-out

VEP variant effect predictor

GO Gene Ontology

KEGG Kyoto Encyclopedia of Genes and Genomes

CC cellular components

MF molecular functions

BP biological processes

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