E-Values for Mendelian Randomization.

In The Lancet Diabetes & Endocrinology, Jon White and colleagues1White J Sofat R Hemani G et al.Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.Lancet Diabetes Endocrinol. 2016; (published online Jan 15.)http://dx.doi.org/10.1016/S2213-8587(15)00386-1Google Scholar consider the causal role of plasma urate in coronary heart disease, using the technique known as mendelian randomisation. They assess the association between genetic predictors of the risk factor (plasma urate) and the disease outcome (coronary heart disease), noting a statistically robust association between a genetic score based on 31 variants associated with plasma urate and coronary heart disease risk, with a straightforward application of Mendelian randomisation estimating an 18% (95% CI 8-29) relative increase in coronary heart disease risk per 1 SD increase in plasma urate. This positive finding from the mendelian randomisation investigation was in line with the observational epidemiological analysis also reported by the investigators. However, the association of the genetic score with the outcome seems to be at least partly explained by pleiotropic (off-target) associations of the genetic score, and the pattern of associations of the individual genetic variants does not show the characteristic dose–response association that would be expected if the genetic associations were solely driven by the effect of the risk factor on the outcome. Mendelian randomisation is a relatively new, but well established epidemiological technique to assess whether a modifiable risk factor is a worthwhile target for clinical or pharmacological intervention.2Davey Smith G Ebrahim S ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?.Int J Epidemiol. 2003; 32: 1-22Crossref PubMed Scopus (2488) Google Scholar, 3Burgess S Thompson SG Mendelian randomization: methods for using genetic variants in causal estimation. Chapman & Hall, London2015Crossref Scopus (195) Google Scholar An association between a genetic predictor of the risk factor and the outcome is more likely to reflect that the risk factor has a causal relation with the outcome, compared with an association of the risk factor itself from a traditional epidemiological analysis, for several reasons. First, risk factors tend to be mutually correlated. It is difficult to distinguish whether the risk factor itself is the cause, or if a correlated risk factor is driving the identified association with an outcome. By contrast, most genetic variants tend to be uncorrelated with conventional epidemiological risk factors.4Davey Smith G Lawlor D Harbord R Timpson N Day I Ebrahim S Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology.PLoS Med. 2007; 4: e352Crossref PubMed Scopus (329) Google Scholar Second, genetic variants are fixed at conception. Hence, a genetic association cannot be affected by external factors that lead to confounded associations. Finally, the associations of genetic variants are not subject to reverse causation. The gene can only ever be the cause, and never the effect. In an ideal mendelian randomisation analysis, the genetic variants have a clear biological link with the risk factor (for example, genetic variants in the CRP gene region for the analysis of C-reactive protein5CRP CHD Genetics CollaborationAssociation between C reactive protein and coronary heart disease: Mendelian randomisation analysis based on individual participant data.BMJ. 2011; 342: d548Crossref PubMed Scopus (210) Google Scholar), and are associated with the risk factor, but not with alternative risk factors. The analysis proceeds analogously to a randomised trial,6Hingorani A Humphries S Nature's randomised trials.Lancet. 2005; 366: 1906-1908Summary Full Text Full Text PDF PubMed Scopus (141) Google Scholar and compares groups of individuals with (on average) genetically raised levels of the risk factor versus those with genetically lowered levels to assess whether the risk factor is a cause of the outcome, and therefore a promising target for prioritisation in drug development. White and colleagues' analysis1White J Sofat R Hemani G et al.Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.Lancet Diabetes Endocrinol. 2016; (published online Jan 15.)http://dx.doi.org/10.1016/S2213-8587(15)00386-1Google Scholar is far from an ideal mendelian randomisation investigation. This is not a criticism, but a consequence of their investigation of a risk factor that is not a protein biomarker, and so does not have a coding gene region, as in the case of C-reactive protein. However, the consideration of multiple gene regions gives some robustness in the analysis, in that the consistency of any causal finding can be assessed. If multiple independent genetic variants associated with the risk factor are all concordantly associated with the outcome (as in the case of LDL cholesterol with coronary heart disease7Ference BA Yoo W Alesh I et al.Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a Mendelian randomization analysis.J Am Coll Cardiol. 2012; 60: 2631-2639Summary Full Text Full Text PDF PubMed Scopus (544) Google Scholar), the separate genetic associations each provide independent evidence for a causal effect of the risk factor on the outcome, strengthening the evidence for a causal finding. Another deficiency is that the composite genetic instrument considered by White and colleagues1White J Sofat R Hemani G et al.Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.Lancet Diabetes Endocrinol. 2016; (published online Jan 15.)http://dx.doi.org/10.1016/S2213-8587(15)00386-1Google Scholar is not solely associated with urate concentrations, but also shows associations with other risk factors, including blood pressure and lipid fractions. This finding is not necessarily a problem provided that the genetic associations with these factors are mediated by urate concentrations, and so one single causal pathway is represented. However, these associations might reflect pleiotropy of the genetic variants, meaning that there are multiple causal pathways from gene to disease, thereby violating the assumption of mendelian randomisation that an association between gene and disease reflects a causal effect of the risk factor. White and colleagues1White J Sofat R Hemani G et al.Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.Lancet Diabetes Endocrinol. 2016; (published online Jan 15.)http://dx.doi.org/10.1016/S2213-8587(15)00386-1Google Scholar show a statistically robust association between their 31 variant composite instrument and disease risk. However, the authors are to be commended in not stopping here, as others have done previously, but instead seeking to assess whether potential violations of the instrumental variable assumptions invalidate a causal interpretation for their findings. They use two main statistical approaches: multivariable mendelian randomisation8Burgess S Thompson S Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects.Am J Epidemiol. 2015; 181: 251-260Crossref PubMed Scopus (434) Google Scholar and Egger regression.9Bowden J Davey Smith G Burgess S Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.Int J Epidemiol. 2015; 44: 512-525Crossref PubMed Scopus (1958) Google Scholar In multivariable mendelian randomisation, alternative risk factors are accounted for by adjusting for the genetic associations with these risk factors in the analysis model. In Egger regression, the assessment is not simply of whether genetic variants that are associated with the risk factor are also associated with the outcome, but whether there is a dose–response relation in these associations—that is, are genetic variants that are more strongly associated with the risk factor also more strongly associated with the outcome? Egger regression also encompasses a test for directional pleiotropy—whether this dose–response relation suggests that a variant having zero association with the risk factor also has zero association with the outcome (as expected if there is no pleiotropy, or balanced pleiotropy). Although multivariable mendelian randomisation does not suggest that measured pleiotropy completely accounts for the causal finding, Egger regression detects pleiotropy among the genetic variants, and provides a weaker causal estimate (a 5% [95% CI −8 to 20] relative increase coronary heart disease risk per 1 SD increase in plasma urate) that is compatible with the null once this pleiotropy is accounted for. Additionally, the genetic associations with the outcome are more variable than would be expected by chance alone; this heterogeneity suggests that the genetic associations with coronary heart disease risk are not solely mediated via plasma urate concentrations. The overall finding is not unequivocal. There is an undeniable association between genetic predictors of plasma urate concentrations and risk of coronary heart disease. However, there are suggestions that the association might represent pleiotropic effects of genetic variants included in the model, rather than a causal effect of urate. In summary, the evidence presented honestly by White and colleagues1White J Sofat R Hemani G et al.Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.Lancet Diabetes Endocrinol. 2016; (published online Jan 15.)http://dx.doi.org/10.1016/S2213-8587(15)00386-1Google Scholar suggests that urate lowering should be prioritised as a potential mechanism to improve cardiovascular outcomes, but without providing a smoking gun that puts the question of causality beyond doubt. I declare no competing interests. Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysisConventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. Full-Text PDF Open Access

Establishing new approaches for the prevention and treatment of stroke relies on identifying modifiable risk factors that contribute to the development of this complex disease. Mendelian randomization (MR) studies, analogous to naturally occurring randomized trials, can assess causality of potentially modifiable biomarkers and offer new insights into biological pathways. Stroke is the second leading cause of death worldwide and the chief determinant of long-term disability. Stroke is a heterogeneous disease arising from several distinct underlying pathologies and is typically classified as ischemic or hemorrhagic, and further subclassified using imaging data. Ischemic stroke (IS), including its 3 main subtypes: small vessel disease, large vessel disease, and cardioembolic stroke, accounts for ≈80% of stroke and is the result of an interrupted blood supply, leading to localized areas of ischemia in the brain. Small vessel disease may be a consequence of nonatherosclerotic, as well as atherosclerotic, mechanisms that result in an occlusion of the small perforating arteries, whereas large vessel disease results from occlusions or emboli from plaque rupture in larger vessels, such as a carotid artery. Cardioembolic stroke arises typically from emboli from the heart. By contrast, hemorrhagic stroke is a consequence of intracerebral hemorrhage (bleeding into the brain) or subarachnoid hemorrhage (bleeding into the subarachnoid space). These diverse stroke subtypes have distinct underlying pathologies reflecting different risk factor distributions. MR studies, using genetic variants as instrumental variables, afford a powerful approach to assessing causality of risk factors and avoid biases inherent in observational studies, including confounding and reverse causation. This review considers the contribution of MR studies to stroke epidemiology and their relevance to understanding risk factors and new therapeutic targets for stroke. Meta-analyses of large prospective studies have enhanced our knowledge of classical and emerging risk factors for stroke.1–4 Classical risk factors for stroke include nonmodifiable characteristics, …

The Framingham Heart Study has contributed importantly to understanding of the causes of coronary heart disease (CHD), stroke, and other cardiovascular diseases. Framingham research has helped define the quantitative and additive nature of these causes or, as they are now called, “cardiovascular risk factors.”1 The National Cholesterol Education Program (NCEP)2 3 has made extensive use of Framingham data in developing its strategy for preventing CHD by controlling high cholesterol levels. The NCEP guidelines2 3 adjust the intensity of cholesterol-lowering therapy with absolute risk as determined by summation of risk factors. The National High Blood Pressure Education Program (NHBPEP) has set forth a parallel approach for blood pressure control. In contrast to the NCEP,2 however, earlier NHBPEP reports issued through the Joint National Committee4 did not match the intensity of therapy to absolute risk for CHD. “Normalization” of blood pressure is the essential goal of therapy regardless of risk status. Blood pressure–lowering therapy is carried out as much for prevention of stroke and other cardiovascular complications as for reduction of CHD risk. Nonetheless, risk assessment could be important for making decisions about type and intensity of therapy for hypertension. Thus, the most recent Joint National Committee report5 gives more attention to risk stratification for adjustment of therapy for hypertension. Although Framingham data have already been influential in the development of national guidelines for risk factor management, the opportunity may exist for both cholesterol and blood pressure programs to draw more extensively from Framingham results when formulating improved risk assessment guidelines and recommending more specific strategies for risk factor modification. The American Heart Association has previously used Framingham risk factor data to prepare charts for estimating CHD risk. Framingham investigators of the National Heart, Lung, and Blood Institute prepared the original charts and have now revised …

Background and purposeResearch shows that people with multiple sclerosis (MS) are more likely to experience cardiovascular complications. However, the precise mechanisms underlying this association remain unclear. This study investigated the causal relationship between MS and coronary heart disease (CHD) using Mendelian randomization (MR) techniques to clarify direct effects and identify relevant target genes.MethodsWe conducted various methods, including two-sample MR. method, reverse, and multivariable MR analyses, to examine the causal relationship between MS and CHD. These. methodologies effectively mitigate confounding variables and neutralize adverse causal effects. Additionally, the study explored the involvement of social factors through a two-step MR analysis. The research team performed a thorough screening of differentially expressed genes in MS based on GEO database, identifying potential target genes that may be associated with genetic risk of CHD. Enrichment analyses and protein-protein interaction studies were used to elucidate biological functions associated with these genes. We included colocalization analysis and summary data-based Mendelian randomization (SMR) method for further screening of core genes to obtain target genes.Finally, we investigated how these genes might affect health by conducting a phenome-wide MR analysis.ResultsOur findings revealed that genetic predisposition to MS significantly increases the risk of CHD, with an IVW-MR analysis yielding an odds ratio of 1.091 (95% CI: 1.030, 1.155, P = 0.0029). Mediation analysis revealed that frailty mediated 20.2% of the effect of MS on CHD (P = 0.026), suggesting that frailty is a critical pathway in this relationship. Additionally, low-density lipoprotein (LDL) is associated with an increased risk of developing both MS and CHD. We identified 3025 differentially expressed genes and 130 genes causally linked to CHD. Protein-protein interaction network analysis identified 77 interacting genes, with core genes such as SREBF1 involved in organelle regulation and nucleic acid metabolism. Colocalization analysis further supported the presence of shared genetic variants between IL6R and SREBF1 associated with CHD, with posterior probabilities (PPH4) of 90.2% and 92.3%, respectively. Interestingly, summary mendelian randomization (SMR) analysis revealed that SREBF1 may be a target gene for MS(bSMR=-0.174,PSMR = 0.0218, PHEIDI = 0.2806, topSNP: rs12951376). Further analysis of the phenome-wide MR did not find significant evidence of side effect associated with targeted therapy against SREBF1.ConclusionThis study provided genetic evidence indicating that indivduals with MS face higher risk of coronary heart disease. Furthermore, SREBF1 maybe a critical target gene which would significantly contribute to drug development.

Observational studies indicate that serum sex hormone-binding globulin (SHBG) levels are inversely correlated with blood lipid levels and coronary heart disease (CHD) risk. Given that dyslipidemia is an established risk factor for CHD, we aim to employ Mendelian randomization (MR) in conjunction with mediation analysis to confirm the mediating role of blood lipid levels in the association between SHBG and CHD. First, we assessed the causality between serum SHBG levels and five cardiovascular diseases using univariable MR. The results revealed causality between SHBG levels and reduced risk of CHD, myocardial infarction, as well as hypertension. Specifically, the most significant reduction was observed in CHD risk, with an odds ratio of 0.73 (95% CI 0.63–0.86) for each one-standard-deviation increase in SHBG. The summary-level data of serum SHBG levels and CHD are derived from a sex-specific genome-wide association study (GWAS) conducted by UK Biobank (sample size = 368,929) and a large-scale GWAS meta-analysis (60,801 cases and 123,504 controls), respectively. Subsequently, we further investigated the mediating role of blood lipid level in the association between SHBG and CHD. Mediation analysis clarified the mediation proportions for four mediators: high cholesterol (48%), very low-density lipoprotein cholesterol (25.1%), low-density lipoprotein cholesterol (18.5%), and triglycerides (44.3%). Summary-level data for each mediator were sourced from the UK Biobank and publicly available GWAS. The above results confirm negative causality between serum SHBG levels and the risk of CHD, myocardial infarction, and hypertension, with the causal effect on reducing CHD risk largely mediated by the improvement of blood lipid profiles.

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.

The causal relationship between work-related factors and amyotrophic lateral sclerosis (ALS) is unclear. We used a Mendelian randomization (MR) analysis to investigate the unconfounded association between work-related factors and ALS. Univariable MR analyses were conducted to evaluate the causal effects of work-related factors on ALS. Instrumental variables from the UK Biobank on work-related factors (n=263,615) were used as proxies. The outcome dataset used ALS (n case=20,806, n control=59,804) summary-level data from a large-scale genome-wide association study based on European ancestry. MR analysis used inverse variance weighted (IVW), MR-Egger, and weighted median (WM) to assess causal effects and other methods of MR for sensitivity analysis. Further multivariable MR analyses were performed to explore potential mediating effects. In univariable MR, IVW methods support evidence that genetically determined job involves heavy manual or physical work (OR=2.04, 95% CI: 1.26-3.31; p=.004) was associated with an increased risk of ALS, and the WM methods also confirm this result (OR=2.36, 95% CI: 1.30-4.28; p=.005). No evidence of heterogeneity or horizontal pleiotropy was found in the results. In multivariable MR, the association was absent after adjusting for smoking and blood pressure. Our MR analysis results demonstrate the potential causal relationship between jobs that involve heavy manual or physical work and ALS, which might be mediated by smoking and high systolic blood pressure.

BackgroundObservational studies link moderate alcohol consumption to reduced risk of cardiometabolic diseases, including coronary heart disease (CHD) and type 2 diabetes mellitus (T2D). Mendelian randomization (MR) studies suggest that these associations are due to confounding. We present observed and genetically proxied associations between alcohol consumption and the incidence of CHD and T2D among African Americans (AA), European Americans (EA), and Hispanic Americans (HA) from the Million Veteran Program.MethodsWe conducted two retrospective, nested case–control studies of 33,053 CHD and 28,278 T2D cases matched to five controls each at the time of the event (index date). We used the Alcohol Use Disorders Identification Test–Consumption (AUDIT‐C) score closest in time prior to the index date to estimate alcohol exposure. Models were adjusted for smoking, body mass index (BMI), chronic kidney disease, rheumatoid arthritis, and the use of statins or antihypertensive medications. MR analyses used either a single variant in ADH1B or a genetic score (GS) as instrumental variables.ResultsObservational analysis showed a U‐shaped association of alcohol consumption with CHD and T2D risk. However, in MR analyses, neither ADH1B genotype‐predicted (in 36,465 AAs, 146,464 EAs, and 11,342 HAs) nor GS‐predicted (in EAs) alcohol consumption was associated with CHD risk. Similarly, T2D was not associated with alcohol consumption predicted either by ADH1B genotype (in 42,008 AAs, 109,351 EAs, and 13,538 HAs) or GS (in EAs). Multivariable MR analyses that adjusted for the effects of blood pressure and smoking also showed no association between alcohol consumption and cardiometabolic diseases.ConclusionsWe replicate prior observational studies that show a U‐shaped association between alcohol consumption and cardiometabolic diseases, but MR findings show no causal association between these traits. This is largely consistent with previous MR analyses in EAs and expands the literature by providing similar findings in AA and HA populations.

Observational studies suggest a potential link between obesity and constipation, but existing results are conflicting. Therefore, we conducted a Mendelian randomization (MR) study and meta-analysis to assess the causal relationship between obesity and the risk of constipation. In this study, independent genetic variants closely related to constipation were acquired from a genome-wide association study (GWAS) to analyze the relationship between genetically predisposed obesity and the risk of constipation. Waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), and body mass index (BMI) were collected from the GWAS. Then, the causal relationship between constipation and obesity was explored using a two-sample MR study in both directions. The robustness of the results was evaluated using sensitivity analysis. Furthermore, a systemic review and meta-analysis were performed to calculate relative risks (RRs) with corresponding 95% confidence intervals (95% CIs). Subgroup analyses stratified by age and obesity degree were completed. To evaluate whether the current studies were affected by unmeasured confounders, E-values of each study were determined. In MR analysis, the incidence of constipation increased with the increase in BMI [inverse variance-weighted (IVW) odds ratio (OR) = 1.138 (1.029, 1.260), p = 0.012]. In addition, constipation was impacted by WC [IVW OR = 1.220 (1.061, 1.402), p = 0.005]. However, there was no evidence that WHR [IVW OR = 1.833 (0.826, 4.065), p = 0.136] or HC [IVW OR = 0.949, (0.836, 1.077), p = 0.415] has a causal effect on constipation. In reverse MR analysis, there was no evidence supporting the causality between constipation and obesity [BMI IVW OR = 1.010 (0.998, 1.022), p = 0.089; WHR IVW OR = 1.000 (0.946, 1.057), p = 0.994; WC IVW OR = 1.008 (0.995, 1.022), p = 0.217; HC IVW OR = 0.996 (0.982, 1.011), p = 0.626]. In the meta-analysis, 14 eligible articles were included, involving 43,488 subjects. According to the results of the meta-analysis, the risk of obesity and overweight significantly increased the risk of constipation [RR = 1.145 (0.952, 1.376)]. This was consistent with the MR analysis results. Moreover, overweight and obesity were significantly related to a higher constipation risk among children [overweight RR = 1.112 (0.943, 1.312); obesity RR = 1.407 (1.282, 1.544)]. Additionally, overweight in adults could decrease the risk of constipation [RR = 0.940 (0.827, 1.068)]. Nevertheless, no significant association was observed between obesity in adults and the risk of constipation [RR = 1.000 (0.768, 1.303)]. Sensitivity analysis revealed the robustness of our findings. In this combined MR study and meta-analysis, obesity is associated with an increased risk of constipation. The MR analysis demonstrates the causal relationship between genetically predisposed obesity and the risk of constipation. More research is required to investigate the potential correlation between obesity and the risk of constipation and associated mechanisms.

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

Background: In observational studies, the self-reported walking pace has been associated with the risk of cardiovascular diseases (CVD). However, whether those associations indicate causal links remains unclear. We performed two-sample Mendelian randomization (MR) analyses to evaluate the causal effect of walking pace on several CVD outcomes, including atrial fibrillation (AF), heart failure (HF), any stroke, ischemic stroke (IS), and IS subtypes. Methods: Genetic variants associated with self-reported walking pace were selected as instrumental variables (IVs) from the latest genome-wide association studies (GWAS). Summary-level data for outcomes were obtained from the corresponding GWAS and the FinnGen consortium. The random-effects inverse variance weighted (IVW) method was used as the main MR analysis, supplemented by replication analyses using data from the FinnGen. To explore the effect of pleiotropy due to adiposity-related traits, we further conducted MR analyses by excluding the adiposity-related IVs and regression-based multivariable MR adjusting for body mass index (BMI). Results: The MR results indicated significant inverse associations of self-reported walking pace with risks of AF [odds ratio (OR), 0.577; 95% confidence interval (CI), 0.442, 0.755; p = 5.87 × 10−5], HF (OR, 0.307; 95% CI, 0.229, 0.413; p = 5.31 × 10−15), any stroke (OR, 0.540; 95% CI, 0.388, 0.752; p = 2.63 × 10−4) and IS (OR, 0.604; 95% CI, 0.427, 0.853; p = 0.004) and suggestive inverse association of self-reported walking pace with cardioembolic stroke (CES) (OR, 0.492; 95% CI, 0.259, 0.934; p = 0.030). Similar results were replicated in the FinnGen consortium and persisted in the meta-analysis. However, there was no causality between walking pace and large artery stroke (OR, 0.676; 95% CI, 0.319, 1.434; p = 0.308) or small vessel stroke (OR, 0.603; 95% CI, 0.270, 1.349; p = 0.218). When excluding adiposity-related IVs and adjusting for BMI, the associations for HF and any stroke did not change substantially, whereas the associations for AF, IS, and CES were weakened. Conclusion: Our findings suggested that genetically predicted increasing walking pace exerted beneficial effects on AF, HF, any stroke, IS, and CES. Adiposity might partially mediate the effect of walking pace on AF, IS, and CES.

We evaluated the causal effects of blood lipid levels on systemic lupus erythematosus with a two-sample Mendelian randomization analysis. Independent single-nucleotide polymorphisms related to blood lipids levels (p < 5 × 10−8) were selected as instrumental variables (IVs) from a published genome-wide association study (GWAS). SLE GWAS analysis that included 4036 cases and 6959 controls of European ancestry provided the related roles between instrumental variables and result (SLE). The causal effects were evaluated with two-sample Mendelian randomization (MR) analyses. According to the inverse-variance weighted approaches, genes predictive of increased LDL cholesterol (OR: 1.131; 95% CI: 0.838, 1.528; p = 0.420), HDL cholesterol (OR: 1.093; 95% CI: 0.884, 1.352; p = 0.412), triglycerides (OR: 0.903; 95% CI: 0.716, 1.137; p = 0.384), Apolipoprotein A-I (OR: 0.854; 95% CI: 0.680, 1.074; p = 0.177), and Apolipoprotein B (OR: 0.933; 95% CI: 0.719, 1.211; p = 0.605) were not causally related to the risk of SLE, consistent with multivariate Mendelian randomization analysis. The reverse-MR analyses showed no massive causal roles between SLE and LDL cholesterol (OR: 0.998; 95% CI: 0.994, 1.001; p = 0.166) as well as Apolipoprotein B (OR: 0.998; 95% CI: 0.994, 1.001; p = 0.229). Nevertheless, a causal role of SLE in decreasing HDL cholesterol (OR: 0.993; 95% CI: 0.988, 0.997; p = 0.002), triglycerides (OR: 0.996; 95% CI: 0.993, 0.999; p = 0.010), and Apolipoprotein A-I (OR: 0.995; 95% CI: 0.990, 0.999; p = 0.026) was validated to some extent. Our study found no causal association between abnormal blood lipids and SLE nor a causal effect between SLE and LDL cholesterol as well as Apolipoprotein B. Nevertheless, some evidence showed that SLE exerted a causal effect on lowering HDL cholesterol, Apolipoprotein A-I, and triglyceride levels.

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.

Preterm birth has been associated with an increased risk of myopia, but the causal relationship between these two factors remains unclear. Traditional epidemiological studies are limited by confounding factors and reverse causality. Mendelian randomization (MR) analysis, utilizing genetic variants as instrumental variables, provides a robust approach to investigate causal relationships. In this study, we aimed to explore the potential causal link between preterm birth and myopia risk using a two-sample MR analysis strategy. We conducted a Mendelian randomization study to investigate the causal relationship between preterm birth and myopia risk. Genetic variants (single nucleotide polymorphisms, SNPs) were used as instrumental variables, and summary data from genome-wide association studies (GWAS) were utilized. Four regression models, including MR-Egger regression, weighted median regression, inverse variance weighted regression, and Weighted mode regression, were employed to validate the causal relationship. Sensitivity analysis was performed using the leave-one-out method. At the same time, the funnel diagram and MR-Egger test were used to judge the stability of the research results. The MR analysis revealed a significant causal effect of preterm birth on myopia risk. Both the inverse variance weighted regression and weighted median regression models showed a p-value less than 0.05, indicating a robust association. The risk of myopia increased by approximately 30% for everyone standard deviation increase in preterm birth. Sensitivity analysis, funnel plot and MR-Egger test all confirm the stability of the research results. Our findings provide evidence supporting a causal relationship between preterm birth and myopia risk. Preterm infants are at a higher risk of developing myopia, and this association is not likely to be influenced by confounding factors or reverse causality. The SNP loci rs6699397, rs10871582, and rs2570497 should be closely monitored as they may lead to abnormal concentrations of intraocular cytokines, particularly vascular endothelial growth factor, potentially elucidating one of the pathogenic mechanisms contributing to the higher incidence of myopia in preterm infants. However the complex interconnections involved extend beyond these factors alone.

We aimed to elucidate the associations between aspirin use with risk of lung cancer, by conducting a meta-analysis and Mendelian randomization (MR) analyses from published Genome-Wide Association Studies (GWAS). Cohort studies, nested case-control studies, and randomized controlled trials (RCTs) investigating the impact of aspirin exposure and lung cancer incidence were included. Relative risk (RR) and its 95% confidence interval (CI) were evaluated in eligible studies. Subgroup analyses regarding gender, pathologic subtypes and smoking status were also executed. MR analyses were conducted using summary statistics obtained from two large consortia [Neale Lab and International Lung Cancer Consortium (ILCCO)] to assess the possible causal relationship of aspirin on lung cancer incidence. Sixteen eligible studies involving 1,522,687 patients were included. The combined RR of aspirin use for the incidence of lung cancer was 0.95 (95% confidence interval (CI) 0.91-0.98). In subgroup meta-analyses, a significant protective effect was observed in squamous cell lung cancer (RR = 0.80; 95% CI 0.65-0.98). In terms of gender, the chemopreventive value was only observed among men (RR = 0.87; 95% CI 0.77-0.97). The MR risk analysis suggested a causal effect of aspirin on lung cancer incidence, with evidence of a decreased risk for overall lung cancer (OR = 0.042; 95% CI 0.003-0.564) and squamous cell lung cancer (OR = 0.002; 95% CI 1.21 × 10-5-0.301). Our study provided evidence for a causal protective effect of aspirin on the risk of lung cancer incidence among men, particularly on the squamous cell lung cancer risk.