No genetic causal association between circulating alpha-tocopherol levels and osteoarthritis, a two-sample Mendelian randomization analysis

This study sought to investigate the causal effects of circulating C-reactive protein (CRP) level on risk of asthma and its subtypes by two-sample Mendelian randomization (MR) analysis. We utilized single nucleotide polymorphisms (SNPs) associated with both CRP and outcomes of asthma, allergic asthma, and obesity-related asthma as genetic variables via a genome-wide summary association study (GWAS). MR analysis mainly based on the inverse variance weighted (IVW) method was performed to infer the causal relationship between exposure and outcomes. Cochran's Q test and MR-Egger regression analysis were performed to determine respectively the heterogeneity and pleiotropy among instrumental variables (IVs), and leave-one-out analysis was conducted to determine the stability of the MR results. In our study, 42 SNPs were identified as IVs for MR analyses. According to the primary inference results by IVW methods, circulating CRP was demonstrated to be significantly associated with risk of asthma [odds ratio (OR):1.046; 95% confidence interval (95% CI): 1.004-1.090; P = .030] and obesity-related asthma (OR:1.072; 95% CI: 1.009-1.138; P = 0.025), whereas no distinct causality with allergic asthma was found (OR:1.051; 95% CI: 0.994-1.112; P = .081). Sensitivity analyses indicated that there was no horizontal pleiotropy among IVs, and the MR results were proved to be robust by leave-one-out sensitivity analysis, despite the presence of heterogeneity. The present study suggested that higher CRP might genetically predict an increased risk of developing asthma and obesity-related asthma, without causality with allergic asthma.

Although prior observational studies have suggested that patients with non-alcohol fatty liver disease (NAFLD) may have a higher risk of coronary artery calcification (CAC), these findings remain controversial. This study aimed to explore the causal association between NAFLD and CAC at genetic level by two-sample Mendelian randomization (MR) analysis. Utilizing summary-level data from multiple large-scale genome-wide association studies (GWAS) in European populations, a two-sample MR analysis was initially conducted to explore the potential causal association between NAFLD and CAC. The results of the MR analysis were pooled through random-effect meta-analysis. The inverse variance weighting (IVW) method served as the primary approach for MR analysis. Additionally, the weighted median, MR-Egger and MR-pleiotropy residual sum and outlier (MR-PRESSO) methods was applied for sensitivity analysis. Summary-level data on liver fatty content was utilized for validation analysis, while summary-level data on cirrhosis served as positive control, further ensuring the validity and robustness of our findings. Reverse MR analysis was performed to assess the association between CAC and NAFLD, employing instrument variables derived from CAC. The MR analysis indicated that genetically predicted NAFLD had no effects on the risk of CAC (Beta: 0.01, 95% CI: -0.02 to 0.03, P = 0.74). Likewise, the reverse MR analysis found no significant genetic association between CAC and NAFLD (OR: 1.00, 95% CI: 0.96 to 1.06, P = 0.88). Validation analysis yielded consistent results, showing no significant association between fatty liver content and CAC. Our two-sample MR analysis did not support that there is a causal association between NAFLD and CAC at genetic level. The association between NAFLD and CAC reported in some previous observational studies may rely on NAFLD complicated with metabolic disorders, rather than being directly linked to the hepatic steatosis.

BackgroundHepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide. Multiple observational studies demonstrated a negative association between the use of antithrombotic agents and the risk of HCC. However, the precise causal relationship between these factors remains uncertain. Therefore, our study used a two-sample Mendelian randomization (MR) analysis to assess the causal link between these two factors.MethodThe summary statistics of single nucleotide polymorphisms (SNPs) associated with the use of antithrombotic agents were acquired from a genome-wide association study (GWAS) performed on individuals of European descent. A two-sample MR analysis was performed using the inverse variance weighting (IVW), the weighted median estimate, the MR-Egger regression, and the weighted-mode estimate. Sensitivity analysis of the primary findings was performed using MR-PRESSO, MR-Egger regression, Cochran’s Q test, and Leave-one-out analysis.ResultsTen SNPs associated with the use of antithrombotic agents were selected as instrumental variables. The MR analysis performed using the four methods mentioned above revealed a negative causal association between the use of antithrombotic agents and HCC. Univariate MR estimates based on the inverse variance weighting (IVW) method suggested a negative causal association between the use of antithrombotic agents and HCC [odds ratio (OR) 0.444, 95% confidence interval (CI) 0.279 to 0.707, P = 0.001]. The other methods also produced similar results. No heterogeneity and horizontal pleiotropy were found.ConclusionOur findings suggested an inverse causal association of antithrombotic agents with the risk of HCC.

Causal association between 25-Hydroxyvitamin D and risk of abnormal spermatozoa: A bidirectional Mendelian randomization study

BackgroundVenous thromboembolism (VTE) is still one of the most severe health issues, increasing mortality and lengthening hospital stays. Different abundances of gut microbiota have been clinically linked to VTE and coagulopathy. However, whether gut microbiota affected VTE formation remained uncertain.MethodsThe causative links between VTE and 211 gut microbiota at phylum, class, order, family and genus level were separately investigated using two-sample Mendelian Randomization (MR) analysis. Firstly, single nucleotide polymorphisms (SNPs) locus-wide significantly (P < 1.0 × 10− 5) related with gut microbiome abundance were extracted from large genome-wide analysis (GWAS) meta-analysis summary data. Instrumental variables (IVs) without pleiotropy were selected using the PhenoScanner and MR PRESSO test. Then, the MR analysis was implemented using the inverse variance weighted (IVW) method. Moreover, weighted median method, MR Egger method, simple median method and MR PRESSO were conducted to validate the causal associations. The reliability of the results was also assessed utilizing various sensitivity analyses, reverse MR analysis and multivariate Mendelian Randomization analysis (MVMR).ResultsWe found the phylum Firmicutes was robustly protective against VTE with MR analysis. Moreover, five taxa of Actinobacteria phylum (Bifidobacteriales order, Actinomycetales order, Bifidobacteriaceae family, Actinomycetaceae family, Slackia genus) and two taxa of Firmicutes phylum (Bacillales order, Lachnospiraceae UCG-010 genus) were suggestively protective for VTE. While three taxa of Firmicutes phylum (Bacilli class, Lactobacillales order and Lactococcus genus) might suggestively increase the risk of VTE. Sensitivity analyses indicated no significant horizontal pleiotropy, heterogeneity, or reverse causal associations. Furthermore, MVMR analysis unveiled independently positive causal association of Firmicutes phylum and Lachnospiraceae UCG-010 genus with risk of VTE.ConclusionTwo taxa of gut microbes (Firmicutes phylum and Lachnospiraceae UCG-010 genus) were independently protective against VTE, which suggests a potential avenue for developing new cost-effective strategies with minor side effects for VTE prevention and treatment.

Purpose Diabetic retinopathy (DR) is a common microvascular complication of diabetes. Previous observation studies identified the association between Galectins (Gal) and the risk of DR, but the causal relationship between Gal and the risk of DR is still unclear. This study aimed to explore the causal associations between circulating galectins (Gal) family levels and the risk of diabetic retinopathy (DR) and its phenotypes including proliferative DR (PDR), background DR (BDR), and severe non-proliferative background DR (SNPDR) using two-sample Mendelian randomization (MR) analysis. Methods The single nucleotide polymorphism (SNP) as instrumental variables (IVs) were identified from previous genome-wide association studies (GWAS). Qualified SNPs associated with Gals were selected via strict screening process. The causal association between Gal family levels and the risk of DR was analyzed using MR analysis. The main MR method was Inverse variance weighted (IVW). Causal estimates before and after outlier removal were presented as odds ratio (OR) with 95% confidence interval (CI). Results The results of IVW indicated that Gal-4 levels were causally related to elevated risk of DR (OR = 1.068, 95%CI: 1.006–1.133), and PDR (OR = 1.084, 95%CI: 1.004–1.169). Gal-3 had causal effect on the risk of SNPDR (OR = 1.455, 95%CI: 1.103–1.919). Gal-3 binding protein was causally associated with higher risk of SNPDR (OR = 1.295, 95%CI: 1.014–1.654). In addition, Gal-9 had causal association with decreased risk of SNPDR (OR = 0.669, 95%CI: 0.491–0.911). Leave-one-out analysis depicted that no individual SNP could affect the causal associations of Gal-4 with DR and PDR, Gal-3, Gal-3 binding protein and Gal-9 with SNPDR. Conclusion Gal-4 levels were causally related to elevated risk of DR, and PDR. Gal-3 and Gal-3 binding protein had causal effect on increased risk of SNPDR while Gal-9 had causal effect on decreased risk of SNPDR.

The correlation between obesity and primary open-angle glaucoma (POAG) has not yet been fully established. The aim of this study was to investigate the causal relationship between obesity and POAG by a two-sample Mendelian randomization (MR) study. In this study, body mass index (BMI), an index to evaluate general obesity, and waist and hip circumference, indices to evaluate abdominal obesity, were selected as exposures in MR analysis. Single-nucleotide polymorphisms (SNPs) were chosen as instrumental variables (IVs). Summary data from genome-wide association studies (GWASs) based on a European ancestry by Locke et al., with regard to BMI, and Shungin et al., with regard to waist and hip circumference, were used. Genetic predictors of POAG were obtained from public GWAS summary data. To assess the causal effect of obesity on POAG, the inverse variance-weighted (IVW) method was used as the primary method, and other methods, such as MR–Egger, weighted median, simple mode, and weighted mode, were also used as complementary analyses. Finally, we performed Cochran’s Q statistic to assess heterogeneity, and sensitivity analysis was performed to evaluate the reliability and stability of the MR results. MR analysis showed that BMI has a positive effect on the risk of POAG, with 1 standard deviation (SD) increase in BMI; the risk of POAG increases by approximately 90.9% [OR = 1.909; 95% CI= (1.225, 2.975); p = 0.0042)] (analyzed by IVW); there were no heterogeneity and pleiotropy in the result; and waist circumference also had a positive effect on the risk of POAG [OR = 2.319; 95% CI= (1.071, 5.018); p = 0.033)] analyzed by weighted median. As hip circumference increases, with 1 SD increase in hip circumference, the risk of POAG increases by approximately 119% [OR = 2.199; 95% CI= (1.306, 3.703); p = 0.00305)] estimated by IVW, there were not heterogeneity and pleiotropy as for the result. Our study for the first time confirms that obesity might increase the risk of POAG using two-sample MR analysis. These results might provide guidance on the prevention and treatment of POAG.

ObjectiveThis study aimed to investigate the causal associations between several liver traits (liver iron content, percent liver fat, alanine transaminase levels, and liver volume) and colorectal cancer (CRC) risk using a Mendelian randomization (MR) approach to improve our understanding of the disease and its management.MethodsGenetic variants were used as instrumental variables, extracted from genome-wide association studies (GWAS) datasets of liver traits and CRC. The Two-Sample MR package in R was used to conduct inverse variance weighted (IVW), MR Egger, Maximum likelihood, Weighted median, and Inverse variance weighted (multiplicative random effects) MR approaches to generate overall estimates of the effect. MR analysis was conducted with Benjamini-Hochberg method-corrected P values to account for multiple testing (P < 0.013). MR-PRESSO was used to identify and remove outlier genetic variants in Mendelian randomization (MR) analysis. The MR Steiger test was used to assess the validity of the assumption that exposure causes outcomes. Leave-one-out validation, pleiotropy, and heterogeneity testing were also conducted to ensure the reliability of the results. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias.ResultsThe MR analysis suggested a causal effect between liver volume and a reduced risk of CRC (OR 0.60; 95% CI, 0.44–0.82; P = 0.0010) but did not provide evidence for causal effects of liver iron content, percent liver fat, or liver alanine transaminase levels. The MR-PRESSO method did not identify any outliers, and the MR Steiger test confirmed that the causal direction of the analysis results was correct in the Mendelian randomization analysis. MR results were consistent with heterogeneity and pleiotropy analyses, and leave-one-out analysis demonstrated the overall values obtained were consistent with estimates obtained when all available SNPs were included in the analysis. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias.ConclusionThe study provides tentative evidence for a causal role of liver volume in CRC, while genetically predicted levels of liver iron content, percent liver fat, and liver alanine transaminase levels were not associated with CRC risk. The findings may inform the development of targeted therapeutic interventions for colorectal liver metastasis (CRLM) patients, and the study highlights the importance of MR as a powerful epidemiological tool for investigating causal associations between exposures and outcomes.

This study aimed to examine whether alcohol intake is causally associated with systemic lupus erythematosus (SLE). We performed a two-sample Mendelian randomization (MR) analysis using the inverse-variance weighted (IVW), weighted median, and MR-Egger regression methods. We used the publicly available summary statistics of alcohol intake frequency from the UK Biobank genome-wide association studies (GWASs; n = 336,965) as the exposure and an SLE GWAS consisting of 1311 SLE and 1783 control subjects of European descent as the outcome. We selected 20 single nucleotide polymorphisms (SNPs) associated with alcohol intake frequency at genome-wide significance as instrumental variables to improve inference. The IVW method found no evidence to support a causal association between alcohol intake and SLE (beta = -0.413, SE = 0.513, p = 0.421). The MR-Egger regression revealed that directional pleiotropy was unlikely to bias the result (intercept = 0.031, p = 0.582). The MR-Egger analysis found no causal association between alcohol intake and SLE (beta = -1.494, SE = 1.996, p = 0.464). Likewise, the weighted median approach also did not provide evidence of a causal association between alcohol intake and SLE (beta = -0.538, SE = 0.574, p = 0.349). The MR estimates determined using the IVW, weighted median, and MR-Egger regression methods were consistent and results from a "leave-one-out" analysis demonstrated that no single SNP was driving the IVW point estimate. The results of MR analysis do not support a causal inverse association between alcohol intake and SLE occurrence.

Objective This study aimed to examine whether rheumatoid arthritis (RA) is causally associated with type 2 diabetes (T2D). Methods We performed a two-sample Mendelian randomization (MR) analysis using the inverse-variance weighted (IVW), weighted median, and MR-Egger regression methods. We used the publicly available summary statistics datasets from a genomewide association studies (GWAS) meta-analysis of 5,539 autoantibody-positive individuals with RA and 20,169 controls of European descent, and a GWAS dataset of 10,247 individuals with T2D and 53,924 controls, overwhelmingly of European descent as outcomes. Results We selected 10 single-nucleotide polymorphisms from GWAS data on RA as instrumental variables to improve the inference. The IVW method supported a causal association between RA and T2D (β=0.044, standard error [SE]=0.022, p=0.047). The MR-Egger analysis showed a causal association between RA and T2D (β=0.093, SE=0.033, p=0.023). In addition, the weighted median approach supported a causal association between RA and T2D (β=0.056, SE=0.025, p=0.028). The association between RA and T2D was consistently observed using IVW, MR Egger, and weighted median methods. Cochran's Q test indicated no evidence of heterogeneity between instrumental variable estimates based on individual variants and MR-Egger regression revealed that directional pleiotropy was unlikely to have biased the results (in-tercept=−0.030; p=0.101). Conclusion MR analysis supports that RA may be causally associated with an increased risk of T2D.

This study uses bidirectional, two-sample Mendelian randomization (MR) analysis through genome-wide association studies (GWAS) statistics to assess the causal association between periodontitis and breast cancer. Periodontitis data from the FinnGen project and breast cancer data from OpenGWAS was used, with all data subjects being of European ancestry. Periodontitis cases were categorized by probing depths or self-reporting according to the Centers for Disease Control and Prevention (CDC)/American Academy of Periodontology definition. 3046 periodontitis cases and 195,395 control cases along with 76,192 cases of breast cancer and 63,082 control cases were obtained from GWAS data. R (version4.2.1) through TwoSampleMR and MRPRESSO were used for data analysis. Primary analysis was conducted using the inverse-variance weighted method. Horizontal pleiotrophy was corrected and causal effects were examined through detection methods of the weighted median method, weighted mode method, simple mode, MR-Egger regression method, and MR-pleiosis residual and outlier method (MR-PRESSO). A heterogeneity test was adopted for the inverse-variance weighted (IVW) analysis method and MR-Egger regression (P > 0.05). Pleiotropy was evaluated using the MR-Egger intercept value. The P-value of the pleiotropy test was then used to analyze the existence of pleiotropy. Where P > 0.05, the possibility of pleiotropy in the causal analysis was considered to be less or non-existent. Leave-one-out analysis was used to tested the consistency of results. 171 single nucleotide polymorphisms were extracted for MR analysis where breast cancer was the exposure and periodontitis the outcome. The total sample size of periodontitis was 198,441, and 139,274 for breast cancer. Overall results showed breast cancer had no effect on periodontitis (IVW P = 0.1408, MR-egger P = 0.1785, weighted median P = 0.1885), with Cochran's Q analysis showing no heterogenicity amongst these instrumental variables (P > 0.05). 7 single nucleotide polymorphisms were extracted for MR analysis where periodontitis was the exposure and breast cancer the outcome. No significant correlation was found between periodontitis and breast cancer (IVW P = 0.8251, MR-egger P = 0.6072, weighted median P = 0.6848). Based on the use of different MR analysis methods, there is no evidence to support a causal relationship between periodontitis and breast cancer.

BackgroundThe causal effects of plasma lipid concentrations and the risk of primary open angle glaucoma (POAG) are still unclear. Thus, the purpose of this study was to identify, applying a two-sample Mendelian randomization (MR) analysis, whether plasma lipid concentrations are causally associated with the risk of POAG.MethodsTwo-sample MR analysis of data from a genome-wide association study (GWAS) was performed to investigate the causal role of plasma lipid levels and POAG. A total of 185 independent single-nucleotide polymorphisms (SNPs) associated with plasma lipid levels were selected as instrumental variables (IVs). The SNPs were obtained from a meta-analysis of GWAS based on 188,577 European-ancestry individuals for MR analyses. Association with POAG for the SNPs was obtained from a GWAS conducted among the United Kingdom (UK) Biobank study participants with a total of 463,010 European-ancestry individuals. Four MR methods (inverse variance weighted [IVW], weighted mode, weighted median, and MR-Egger regression) were applied to obtain the overall causal estimate for multiple, instrumental SNPs.ResultsUsing the IVW analysis method, no evidence was found to support a causal association between plasma LDL-C level and POAG risk (β = − 0.00026; 95% CI = -0.00062, 0.00011; P = 0.165) with no significant heterogeneity among SNPs. The overall causal estimate between plasma LDL-C level and POAG was consistent using the other three MR methods. Using the four MR methods, no evidence of an association between plasma HDL-C (β = 0.00023; 95% CI = -0.00015, 0.00061; P = 0.238; IVW method) or TG levels (β = − 0.00028; 95% CI = -0.00071, 0.00015; P = 0.206; IVW method) and POAG risk was found. Sensitivity analyses did not reveal any sign of directional pleiotropy.ConclusionsThe present study did not find any evidence for a causal association between plasma lipid levels and POAG risk. Further research is needed to elucidate the potential biological mechanisms to provide a reasonable interpretation for these results.

Numerous observational studies have found a relationship between systemic lupus erythematosus (SLE) and epilepsy; however, their causal relationship remains unclear. This study aimed to investigate the causal role of SLE in epilepsy or any of its subtypes using a two-sample Mendelian randomization (MR) analysis. Single nucleotide polymorphisms (SNPs) linked to SLE were utilized as instrumental variables in MR analysis to assess their causal impact on epilepsy. The primary MR findings were derived using the inverse variance weighted (IVW) method, which was further supported by the weighted median and MR-Egger regression techniques. Additionally, sensitivity analyses, including Cochran's Q test and pleiotropy tests, were conducted to evaluate the influence of these SNPs on epilepsy, particularly looking for signs of horizontal pleiotropy and heterogeneity. We selected 43 SNPs that reached genome-wide significance from genome-wide association studies (GWASs) on SLE to serve as instrumental variables in this study. The IVW method showed no evidence to support a causal association between SLE and epilepsy (all epilepsy: odds ratio (OR) = 1.006, 95% confidence interval (CI) = 0.994-1.018; focal epilepsy: OR = 1.006, 95% CI = 0.994-1.019; generalized epilepsy: OR = 1.015, 95% CI = 0.996-1.035). Other MR complementary methods revealed consistent results. Furthermore, there was no evidence indicating heterogeneity or horizontal pleiotropy. The findings of MR analysis did not support a genetically predicted causal relationship between SLE and epilepsy, but emphasized the need for further research on shared pathophysiological mechanisms, particularly the role of immune system abnormalities and potential influences such as chronic inflammation and therapeutic interventions. The etiology of epilepsy is complex and diverse, including immune factors. Through a Mendelian randomization analysis, we did not find evidence of a genetic causal relationship between systemic lupus erythematosus and epilepsy. However, this does not invalidate epidemiological evidence, and further exploration is needed to investigate factors influencing the relationship between the two.

BackgroundRecent observational research has documented that dermatomyositis (DM) has lower gut microbial diversity and a distinct taxonomic composition[1]. However, the underlying causality remains unclear.ObjectivesTo explore the potential causal associations between...

BackgroundAlthough anxiety disorders are one of the most prevalent mental disorders, their underlying biological mechanisms have not yet been fully elucidated. In recent years, genetically determined metabolites (GDMs) have been used to reveal the biological mechanisms of mental disorders. However, this strategy has not been applied to anxiety disorders. Herein, we explored the causality of GDMs on anxiety disorders through Mendelian randomization study, with the overarching goal of unraveling the biological mechanisms.MethodsA two-sample Mendelian randomization (MR) analysis was implemented to assess the causality of GDMs on anxiety disorders. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas four different GWAS datasets of anxiety disorders were the outcomes. Notably, all datasets were acquired from publicly available databases. A genetic instrumental variable (IV) was used to explore the causality between the metabolite and anxiety disorders for each metabolite. The MR Steiger filtering method was implemented to examine the causality between metabolites and anxiety disorders. The standard inverse variance weighted (IVW) method was first used for the causality analysis, followed by three additional MR methods (the MR-Egger, weighted median, and MR-PRESSO (pleiotropy residual sum and outlier) methods) for sensitivity analyses in MR analysis. MR-Egger intercept, and Cochran’s Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. Bonferroni correction was used to determine the causative association features (P < 1.03 × 10–4). Furthermore, metabolic pathways analysis was performed using the web-based MetaboAnalyst 5.0 software. All statistical analysis were performed in R software. The STROBE-MR checklist for the reporting of MR studies was used in this study.ResultsIn MR analysis, 85 significant causative relationship GDMs were identified. Among them, 11 metabolites were overlapped in the four different datasets of anxiety disorders. Bonferroni correction showing1-linoleoylglycerophosphoethanolamine (ORfixed-effect IVW = 1.04; 95% CI 1.021–1.06; Pfixed-effect IVW = 4.3 × 10–5) was the most reliable causal metabolite. Our results were robust even without a single SNP because of a “leave-one-out” analysis. The MR-Egger intercept test indicated that genetic pleiotropy had no effect on the results (intercept = − 0.0013, SE = 0.0006, P = 0.06). No heterogeneity was detected by Cochran’s Q test (MR-Egger. Q = 7.68, P = 0.742; IVW. Q = 12.12, P = 0.436). A directionality test conducted by MR Steiger confirmed our estimation of potential causal direction (P < 0.001). In addition, two significant pathways, the “primary bile acid biosynthesis” pathway (P = 0.008) and the “valine, leucine, and isoleucine biosynthesis” pathway (P = 0.03), were identified through metabolic pathway analysis.ConclusionThis study provides new insights into the causal effects of GDMs on anxiety disorders by integrating genomics and metabolomics. The metabolites that drive anxiety disorders may be suited to serve as biomarkers and also will help to unravel the biological mechanisms of anxiety disorders.