Self-Reported Walking Pace and Risk of Cardiovascular Diseases: A Two-Sample Mendelian Randomization Study.

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, …

BackgroundAppendicular lean mass (ALM) is a good predictive biomarker for sarcopenia. And previous studies have reported the association between ALM and stroke or Alzheimer’s disease (AD), however, the causal relationship is still unclear, The purpose of this study was to evaluate whether genetically predicted ALM is causally associated with the risk of stroke and AD by performing Mendelian randomization (MR) analyses.MethodsA two-sample MR study was designed. Genetic variants associated with the ALM were obtained from a large genome-wide association study (GWAS) and utilized as instrumental variables (IVs). Summary-level data for stroke and AD were generated from the corresponding GWASs. We used random-effect inverse-variance weighted (IVW) as the main method for estimating causal effects, complemented by several sensitivity analyses, including the weighted median, MR-Egger, and MR-pleiotropy residual sum and outlier (MR-PRESSO) methods. Multivariable analysis was further conducted to adjust for confounding factors, including body mass index (BMI), type 2 diabetes mellitus (T2DM), low density lipoprotein-C (LDL-C), and atrial fibrillation (AF).ResultsThe present MR study indicated significant inverse associations of genetically predicted ALM with any ischemic stroke ([AIS], odds ratio [OR], 0.93; 95% confidence interval [CI], 0.89–0.97; P = 0.002) and AD (OR, 090; 95% CI 0.85–0.96; P = 0.001). Regarding the subtypes of AIS, genetically predicted ALM was related to the risk of large artery stroke ([LAS], OR, 0.86; 95% CI 0.77–0.95; P = 0.005) and small vessel stroke ([SVS], OR, 0.80; 95% CI 0.73–0.89; P < 0.001). Regarding multivariable MR analysis, ALM retained the stable effect on AIS when adjusting for BMI, LDL-C, and AF, while a suggestive association was observed after adjusting for T2DM. And the estimated effect of ALM on LAS was significant after adjustment for BMI and AF, while a suggestive association was found after adjusting for T2DM and LDL-C. Besides, the estimated effects of ALM were still significant on SVS and AD after adjustment for BMI, T2DM, LDL-C, and AF.ConclusionsThe two-sample MR analysis indicated that genetically predicted ALM was negatively related to AIS and AD. And the subgroup analysis of AIS revealed a negative causal effect of genetically predicted ALM on LAS or SVS. Future studies are required to further investigate the underlying mechanisms.

BackgroundChronic Obstructive Pulmonary Disease (COPD) currently ranks as the third leading cause of mortality worldwide, imposing substantial burdens on societal and individual health. Amongst health research tools, walking pace (WP) and hand grip strength (HGS) are cornerstones, extensively associated with diverse health conditions. However, the intricate interplay between these factors and COPD risk remains ambiguous. This study aims to elucidate the causal association of WP, HGS, with COPD risk through a bidirectional Mendelian randomization (MR) approach.MethodsBidirectional MR analysis was performed using Genome-wide association study (GWAS) data of European individuals for WP, HGS, and COPD. Inverse Variance Weighted (IVW) served as the primary MR analysis approach. To supplement the IVW findings, four additional MR methods [MR-Egger, weighted median, maximum likelihood, simple median] were used. To assess heterogeneity and pleiotropy, sensitivity analyses were performed. In addition, multivariate MR (MVMR) analysis was used to assess causality after adjustment for potential confounders.ResultsIVW method results show a significant negative association between WP and COPD risk in both initial (genome-wide threshold, odds ratio (OR) = 0.21, 95% confidence interval (CI) 0.09–0.51, P = 5.06 × 10− 4) and secondary (locus-wide threshold, OR = 0.27, 95%CI: 0.18–0.41, P = 4.88 × 10− 10) MR analysis. The reverse MR analysis suggested that COPD also diminishes WP. Additionally, a causal risk reduction for COPD with right HGS (OR = 0.74, 95% CI: 0.58–0.94, P = 1.44 × 10− 2) was only found in secondary MR analysis. The outcomes of the four additional MR methods also suggested similar causal relationships, and sensitivity analyses endorsed their robustness. Lastly, the MVMR analysis demonstrated that the WP’s effect on reducing COPD risk persisted independently of potential confounding variables.ConclusionA bidirectional causal relationship exists between typical WP and COPD risk. Conversely, a decrease in right HGS is unidirectionally associated with an increased risk of COPD. The study suggests that WP may serve as a predictive factor for COPD or as a simple evaluative indicator for prognosis.

Epidemiological studies have reported inconsistent results of the association between celiac disease (CD) and cardiovascular diseases. Moreover, the causality remains largely unknown. Therefore, we aimed to investigate whether CD is causally associated cardiovascular diseases, including ischemic stroke, large artery stroke, cardioembolic stroke, small vessel stroke, coronary heart disease, myocardial infarction, angina, heart failure, atrial fibrillation, and venous thromboembolism using an mendelian randomization (MR) approach. Summary-level data for CD were derived from a large-sample genome-wide association study (GWAS) including 12,041 CD cases and 12,228 controls of European ancestry. The corresponding data for ischemic stroke (34,217 cases and 406,111 controls), large artery stroke (4,373 cases and 406,111 controls), cardioembolic stroke (7,193 cases and 406,111 controls), small vessel stroke (5,386 cases and 192,662 controls), coronary heart disease (22,233 cases and 64,762 controls), myocardial infarction (11,622 cases and 187,840 controls), angina (18,168 cases and 187,840 controls), heart failure (47,309 cases and 930,014 controls), atrial fibrillation (60,620 cases and 970,216 controls), and venous thromboembolism (9,176 cases and 209,616 controls) were obtained from the IEU GWAS database. We calculated the causal effect using the inverse variance weighted method. Sensitivity analyses and leave-one-out analyses were performed to ensure the consistency and robustness of causal estimates. The MR inverse variance weighted estimates indicated no causal effect of genetically predicted CD on ischemic stroke (OR = 1.001, 95% CI: 0.984-1.018), large artery stroke (OR = 1.003, 95% CI: 0.961-1.048), cardioembolic stroke (OR = 1.009, 95% CI: 0.977-1.042), small vessel stroke (OR = 1.023, 95% CI: 0.981-1.066), coronary heart disease (OR = 0.995, 95% CI: 0.977-1.013), myocardial infarction (OR = 0.994, 95% CI: 0.959-1.030), angina (OR = 1.006, 95% CI: 0.981-1.032), heart failure (OR = 0.999, 95% CI: 0.982-1.016), atrial fibrillation (OR = 1.000, 95% CI: 0.990-1.011), and venous thromboembolism (OR = 1.001, 95% CI: 0.971-1.032). Sensitivity analyses using the MR-Egger, weighted median, and simple mode methods yielded similar results. No evidence of horizontal pleiotropy was identified (MR Pleiotropy Residual Sum and Outlier global test and MR-Egger intercept with P > 0.05). Our findings do not support a causal contribution of CD itself to ischemic stroke, large artery stroke, cardioembolic stroke, small vessel stroke, coronary heart disease, myocardial infarction, angina, heart failure, atrial fibrillation, and venous thromboembolism risk.

Observational studies have suggested that cereal consumption is associated with a reduced risk of cardiovascular disease (CVD). However, the potential causal relationship is not clear. We aimed to investigate the association of cereal intake and cereal type with CVD risk. Two-step Mendelian randomization (MR) analysis was performed to confirm the causal association of cereal intake and cereal type with the risk of several common CVDs. Furthermore, two-step MR analysis was used to explore the mediating effect of cardiovascular metabolic factors, and multivariable MR analysis was used to assess the impact of socioeconomic status, such as education and income, on the causal association. The MR analysis indicated that genetically predicted cereal intake is associated with reduced risk of large artery stroke (LAS) (odd ratio (OR): 0.421; 95% confidence interval (CI) [0.193, 0.918]; p = 0.030), and muesli as the primary cereal intake is associated with reduced risk of coronary heart disease (CHD) (OR: 0.100; 95% CI [0.023, 0.437]; p = 0.002), myocardial infarction (MI) (OR: 0.101; 95% CI [0.020, 0.509]; p = 0.005), heart failure (OR: 0.210; 95% CI [0.064, 0.684]; p = 0.010), ischemic stroke (IS) (OR: 0.130; 95% CI [0.029, 0.591]; p = 0.008), LAS (OR: 0.017; 95% CI [0.0004, 0.737]; p = 0.034), and small-vessel stroke (OR: 0.021; 95% CI [0.001, 0.708]; p = 0.005). In contrast, genetically predicted biscuits as the primary cereal intake increased the risk of CHD (OR: 6.557; 95% CI [1.197, 36.031]; p = 0.031), and other cereals, such as cornflakes, as the primary cereal intake increased the risk of CHD (OR: 3.803; 95% CI [1.194, 12.111]; p = 0.024), MI (OR: 4.240; 95% CI [1.185, 15.174]; p = 0.026), stroke (OR: 3.154; 95% CI [1.070, 9.298]; p = 0.037), and IS (OR: 3.736; 95% CI [1.185, 11.782]; p = 0.024). Multivariable MR analysis underscored the significant role of education and income in the causal association, and two-step MR analysis indicated that body mass index, lipids, and blood glucose exerted important mediating effects in the causal association. The findings of our study underscore the causal beneficial influence of muesli as the primary cereal intake on CVDs. A reasonable consumption of muesli may provide primary prevention of CVDs.

PurposeCardiac troponin I (cTnI) is a well-established biomarker for stroke prediction, especially in patients with heart diseases. However, the causal effect of circulating cTnI on stroke remains unclear.MethodsWe employed Mendelian Randomization (MR) analysis to determine the associations between genetically predicted circulating cTnI levels and stroke and its subtypes. Summary-level data for exposure and outcomes were generated from different genome-wide association studies. Single-nucleotide polymorphisms (SNPs) associated with circulating cTnI at genome-wide significance level (P < 5 × 10−8) were employed as instrumental variables (IVs). We used fixed-effect inverse-variance weighted (IVW) as the main method for pooling MR estimates. Sensitivity analyses and multivariable MR analyses were carried out to assess the robustness of the results.ResultsUsing the fixed-effects IVW method, we found that genetically elevated plasma cTnI levels may have a causal effect on the risk of cardioembolic stroke (CES) (odds ratio (OR), 1.58; 95% confidence interval (CI), 1.17–2.13; P = 0.003). Additional analyses including multiplicative random-effects (mre) IVW, weighted median, MR-Egger and MR-PRESSO yielded similar results, but with broader CIs that span 1.0. The total effect of cTnI on CES was attenuated by adjusting for the effect of atrial fibrillation (OR,1.26; 95% CI, 0.76–2.11; P = 0.371) and smoking (OR,1.53; 95% CI, 0.87–2.66; P = 0.137). In addition, we found no causal effect of cTnI on the risk of any stroke and other stroke subtypes, including any ischemic stroke, large artery stroke, cardioembolic stroke, small vessel stroke, and intracerebral hemorrhage. These results were consistent across sensitivity analyses.ConclusionThis study provides little evidence that increased serum cTnI levels lead to a higher risk of stroke.

Background Observational evidence indicates that patients with atrial fibrillation (AF) are at a four- to fivefold increased risk of stroke. By two-sample mendelian randomization (MR), we have shown in our previous work that specifically the cardioembolic stroke subtype is caused by AF. This risk elevation is however variable and appears to be modulated by other risk factors such as hypertension traits and coronary artery disease (CAD). Purpose This study seeks to determine (1) whether genetic predisposition to cardioembolic stroke by AF is independent from modulators, and (2) whether the other stroke subtypes are causally linked with different exposures. Methods The associations between AF, hypertension traits, and CAD as exposures and stroke subtypes as outcomes were individually tested in a two-sample mendelian randomisation (MR) analysis using genetic instruments from publicly available genome-wide association studies (GWAS) in large cohorts. For atrial fibrillation, genetic instruments were obtained from the dataset by Nielsen et al. with 60,620 atrial fibrillation cases and 970,216 controls, collected from six contributing studies including the AFGen Consortium. Associations with stroke subtypes were evaluated in the MEGASTROKE genome-wide association study data set (67, 162 cases; 454, 450 controls). The genetic markers for hypertension traits were taken from a GWAS by Evangelou et al. which combines data from the UK Biobank (UKB) and the International Consortium of Blood Pressure (ICBP) with a total sample size of 757,601 individuals. Genetic instruments for CAD were obtained from the CARDIoGRAMplusC4D consortium (60,801 cases and 123,504 controls from 48 studies). Several MR regression methods such as MR-Egger regression and inverse-variance weighted regression as well as sensitivity analyses were applied. A multivariable MR model served to determine which of the exposures had an independent causal effect on each of the stroke subtypes. Results Atrial fibrillation turned out to be the sole independent causal risk factor for cardioembolic stroke with a beta coefficient of 0.75 (SE = ± 0.04; P = 8.59 x 10e-100) and an OR of 2.16 (1.97-2.27) in a multivariable model adjusted for AF, CAD, systolic blood pressure (SBP) and diastolic blood pressure. In this model, SBP and CAD were non-causative for cardioembolic stroke, but exhibited significant, albeit weak causal associations with small vessel stroke (SBP: OR 1.04 (1.02-1.06); CAD: OR 1.14 (1.02-1.28)) and large artery stroke (SBP: OR 1.09 (1.06-1.11); CAD: OR 1.59 (1.40-1.79). Conclusions Causality of the association between the cardioembolic stroke subtype and AF is independent from modulators such as SBP and CAD. However, SBP and CAD play a complementary role as causal exposures for small vessel and large artery stroke which in turn are not causally linked with AF.

In recent years, the incidence rates of rheumatoid arthritis (RA) and heart disease (HD) have noticeably increased worldwide. Previous studies have found that patients with RA are more likely to develop HD, while the cause and effect have still remained elusive. In this study, Mendelian randomization (MR) analysis was used to indicate whether there was a potential association between RA and HD. Data of RA, ischemic heart disease (IHD), myocardial infarction (MI), atrial fibrillation (AF), and arrhythmia were based on the genome-wide association study (GWAS) dataset. No disease group was intersected. Inverse-variance weighted (IVW) method was used to calculate MR estimates, and sensitivity analysis was performed. The primary MR analysis showed that genetic susceptibility to RA was significantly associated with the risk of IHD and MI, rather than with AF and arrhythmia. Besides, there was no heterogeneity and horizontal pleiotropy between the primary and replicated analyses. There was a significant correlation between RA and the risk of IHD (odds ratio (OR), 1.0006; 95% confidence interval (CI), 1.000244-1.00104; P = 0.001552), meanwhile, there was a significant correlation between RA and the risk of MI (OR, 1.0458; 95% CI, 1.07061-1.05379; P = 0.001636). The results were similar to those of sensitivity analysis, and the sensitivity analysis also verified the conclusion. Furthermore, sensitivity and reverse MR analyses suggested that no heterogeneity, horizontal pleiotropy or reverse causality was found between RA and cardiovascular comorbidity. RA was noted to be causally associated with IHD and MI, rather than with AF and arrhythmia. This MR study might provide a new genetic basis for the causal relationship between RA and the risk of CVD. The findings suggested that the control of RA activity might reduce the risk of cardiovascular disease.

BackgroundEvidence suggests that coronavirus disease 2019 (COVID-19) is associated with the risk of cardiovascular diseases (CVDs). However, the results are inconsistent, and the causality remains to be established. We aimed to investigate the potential causal relationship between COVID-19 and CVDs by using two-sample Mendelian randomization (MR) analysis.MethodsSummary‐level data for COVID-19 and CVDs including myocarditis, heart failure (HF), acute myocardial infarction (AMI), arrhythmia and venous thromboembolism (VTE) were obtained from the IEU OpenGWAS project, a public genome-wide association study (GWAS). Single nucleotide polymorphisms (SNPs) were used as instrumental variables. Five complementary MR methods were performed, including inverse variance weighted (IVW), MR-Egger, weighted median, weighted mode and simple mode methods. IVW method was considered as the primary approach. Besides, sensitivity analyses, including Cochran’s Q test, MR-Egger intercept test, and leave-one-out analysis, were performed to evaluate the robustness of the results.ResultsAccording to the IVW results, our MR study indicated that genetically predicted COVID-19 was not causally connected with the risk of CVDs [myocarditis: odds ratio (OR) = 1.407, 95% confidence interval (CI) = 0.761-2.602, p-value = 0.277; HF: OR = 1.180, 95% CI = 0.980-1.420, p-value = 0.080; AMI: OR = 1.002, 95% CI = 0.998-1.005, p-value = 0.241; arrhythmia: OR = 0.865, 95% CI = 0.717-1.044, p-value = 0.132; VTE: OR = 1.013, 95% CI = 0.997-1.028, p-value = 0.115]. The supplementary MR methods showed similar results. Sensitivity analyses suggested that the causal estimates were robust.ConclusionThis two-sample MR analysis did not provide sufficient evidence for a causal relationship between COVID-19 and the risk of acute CVDs, which may provide new insights into the prevention of acute CVDs in COVID-19 patients.

BackgroundSex differences and causality of the association between heart rate variability (HRV) and atrial fibrillation (AF) in the general population remain unclear.Methods12,334 participants free of AF from the population-based Rotterdam Study were included. Measures of HRV including the standard deviation of normal RR intervals (SDNN), SDNN corrected for heart rate (SDNNc), RR interval differences (RMSSD), RMSSD corrected for heart rate (RMSSDc), and heart rate were assessed at baseline and follow-up examinations. Joint models, adjusted for cardiovascular risk factors, were used to determine the association between longitudinal measures of HRV with new-onset AF. Genetic variants for HRV were used as instrumental variables in a Mendelian randomization (MR) analysis using genome-wide association studies (GWAS) summary-level data.ResultsDuring a median follow-up of 9.4 years, 1302 incident AF cases occurred among 12,334 participants (mean age 64.8 years, 58.3% women). In joint models, higher SDNN (fully-adjusted hazard ratio (HR), 95% confidence interval (CI) 1.24, 1.04–1.47, p = 0.0213), and higher RMSSD (fully-adjusted HR, 95% CI 1.33, 1.13–1.54, p = 0.0010) were significantly associated with new-onset AF. Sex-stratified analyses showed that the associations were mostly prominent among women. In MR analyses, a genetically determined increase in SDNN (odds ratio (OR), 95% CI 1.60, 1.27–2.02, p = 8.36 × 10–05), and RMSSD (OR, 95% CI 1.56, 1.31–1.86, p = 6.32 × 10–07) were significantly associated with an increased odds of AF.ConclusionLongitudinal measures of uncorrected HRV were significantly associated with new-onset AF, especially among women. MR analyses supported the causal relationship between uncorrected measures of HRV with AF. Our findings indicate that measures to modulate HRV might prevent AF in the general population, in particular in women.Graphical abstractAF; atrial fibrillation, GWAS; genome-wide association study, IVW; inverse variance weighted, MR; Mendelian randomization, MR-PRESSO; MR-egger and mendelian randomization pleiotropy residual sum and outlier, RMSSD; root mean square of successive RR interval differences, RMSSDc; root mean square of successive RR interval differences corrected for heart rate, SDNN; standard deviation of normal to normal RR intervals, SDNNc; standard deviation of normal to normal RR intervals corrected for heart rate, WME; weighted median estimator.aRotterdam Study n=12,334bHRV GWAS n=53,174cAF GWAS n=1,030,836

Previous studies of obstructive sleep apnea (OSA) in relation to stroke have been noted. However, the exact causality remains to be clearly defined. We aimed to adopt a two-sample Mendelian randomization study to investigate the causal effects of OSA on stroke and its subtypes. A two-sample Mendelian randomization (MR) analysis was conducted to evaluate the causal effect of OSA on stroke and its subtypes, including, based on publicly genome-wide association studies (GWAS) databases. The inverse variance weighted (IVW) method was used as the main analysis. MR-Egger regression, weighted mode, weighted median, and MR pleiotropy residual sum and outlier (MR-PRESSO) were performed methods and were adopted as supplementary analysis to ensure the robustness of the results. Genetically predicted OSA was not related to the risk of stroke (odds ratio (OR), 0.99, 95% CI, 0.81-1.21, p = 0.909), and its subtypes, ischemic stroke (IS) (OR, 1.01, 95% CI, 0.82-1.23, p = 0.927), large vessel stroke (LVS) (OR, 1.05, 95% CI, 0.73-1.51, p = 0.795), cardioembolic stroke (CES) (OR, 1.03, 95% CI, 0.74-1.43, p = 0.855), small vessel stroke (SVS) (OR, 1.13, 95% CI, 0.88-1.46, p = 0.329), lacunar stroke (LS) (OR, 1.07, 95% CI, 0.74-1.56, p = 0.721) as well as intracerebral hemorrhage (ICH) (OR, 0.37, 95% CI = 0.09, 1.48, p = 0.160) (Wald ratio method). Other supplementary MR methods also confirmed similar results. There may be no direct causal relationship between OSA and stroke or its subtypes.

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 aim of this study was to assess the causal relationship between habitual walking pace and cardiovascular disease risk using a Mendelian randomisation approach. We performed both one- and two-sample Mendelian randomisation analyses in a sample of 340,000 European ancestry participants from UK Biobank, applying a range of sensitivity analyses to assess pleiotropy and reverse causality. We used a latent variable framework throughout to model walking pace as a continuous exposure, despite being measured in discrete categories, which provided more robust and interpretable causal effect estimates. Using one-sample Mendelian randomisation, we estimated that a 1mph (i.e., 1.6kph) increase in self-reported habitual walking pace corresponds to a 63% (hazard ratio (HR) = 0.37, 95% confidence interval (CI), 0.25-0.55, P = 2.0 × 10-6) reduction in coronary artery disease risk. Using conditional analyses, we also estimated that the proportion of the total effect on coronary artery disease mediated through BMI was 45% (95% CI 16-70%). We further validated findings from UK Biobank using two-sample Mendelian randomisation with outcome data from the CARDIoGRAMplusC4D consortium. Our findings suggest that interventions that seek to encourage individuals to walk more briskly should lead to protective effects on cardiovascular disease risk.

Electrolyte balance is an important factor to sustain the homeostasis of human body environment and in sepsis pathogenesis. Many current cohort-based studies have already revealed that electrolyte disorder may intensify sepsis and induce stroke. However, the corresponding randomized controlled trials did not show that electrolyte disorder in sepsis has a harmful effect on stroke. The aim of this study was to examine the association of genetically sepsis-derived electrolyte disorder with stroke risk using meta-analysis and Mendelian randomization. In four studies (182,980 patients), electrolyte disorders were compared with stroke incidence in patients with sepsis. The pooled odds ratio (OR) of stroke is 1.79 [95% confidence interval (CI): 1.23-3.06; p < 0.05], which shows a significant association between electrolyte disorder and stroke in sepsis patients. Furthermore, in order to evaluate the causal association between stroke risk and sepsis-derived electrolyte disorder, a two-sample Mendelian randomization (MR) study was conducted. The genetic variants extracted from a genome-wide association study (GWAS) of exposure data that are strongly associated with frequently used sepsis were used as instrumental variables (IVs). Based on the IVs' corresponding effect estimates, we estimated overall stroke risk, cardioembolic stroke risk, and stroke induced by large/small vessels from a GWAS meta-analysis with 10,307 cases and 19,326 controls. As a final step to verify the preliminary MR results, we performed sensitivity analysis using multiple types of Mendelian randomization analysis. Our study revealed the association between electrolyte disorder and stroke in sepsis patients, and the correlation between genetic susceptibility to sepsis and increased risk of cardioembolic stroke, hinting that cardiogenic diseases and accompanying electrolyte disorder interference in due course could help sepsis patients get more benefits in stroke prevention.

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.