Causal relationship between serum metabolites and juvenile idiopathic arthritis: a mendelian randomization study.

BACKGROUND: Juvenile Idiopathic Arthritis (JIA) is a condition that occurs when individuals under the age of 16 develop arthritis that lasts for more than six weeks, and the cause is unknown. The development of JIA may be linked to serum metabolites. Nevertheless, the association between JIA pathogenesis and serum metabolites is unclear, and there are discrepancies in the findings across studies. METHODS: In this research, the association between JIA in humans and 486 serum metabolites was assessed using genetic variation data and genome-wide association study. The identification of causal relationships was accomplished through the application of univariate Mendelian randomization (MR) analysis. Various statistical methods, including inverse variance weighted and MR-Egger, were applied to achieve this objective. To ensure that the findings from the MR analysis were trustworthy, a number of assessments were carried out. To ensure the accuracy of the obtained results, a range of techniques were utilised including the Cochran Q test, examination of the MR-Egger intercept, implementation of the leave-one-out strategy, and regression analysis of linkage disequilibrium scores. In order to identify the specific metabolic pathways associated with JIA, our primary objective was to perform pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes. RESULTS: Two-sample summary data MR analyses and sensitivity analyses showed that five metabolites were significantly causally associated with JIA, including two risk factors-kynurenine (odds ratio [OR]: 16.39, 95% confidence interval [CI]: 2.07-129.63, p = 5.11 × 10- 6) and linolenate (OR: 16.48, 95% CI: 1.32-206.22, p = 0.030)-and three protective factors-3-dehydrocarnitine (OR: 0.32, 95% CI: 0.14-0.72, p = 0.007), levulinate (4-oxovalerate) (OR: 0.40, 95% CI: 0.20-0.80, p = 0.010), and X-14,208 (phenylalanylserine) (OR: 0.68, 95% CI: 0.51-0.92, p = 0.010). Furthermore, seven metabolic pathways, including α-linolenic acid metabolism and pantothenate and CoA biosynthesis, are potentially associated with the onset and progression of JIA. CONCLUSION: Five serum metabolites, including kynurenine and 3-dehydrocarnitine, may be causally associated with JIA. These results provide a theoretical framework for developing effective JIA prevention and screening strategies.

Association of dietary nutrient intake with type 2 diabetes: A Mendelian randomization study.

Observational research suggests that the evidence linking dietary nutrient intake (encompassing minerals, vitamins, amino acids, and unsaturated fatty acids) to type 2 diabetes (T2D) is both inconsistent and limited. This study aims to explore the potential causal relationship between dietary nutrients and T2D. Causal estimation utilized Mendelian randomization techniques. Single nucleotide polymorphisms linked to dietary nutrients were identified from existing genome-wide association studies and used as instrumental variables. Genome-wide association studies data pertinent to T2D were sourced from the DIMANTE consortium and the FinnGen database. Techniques including inverse variance weighting (IVW), weighted mode, weighted median, and Mendelian randomization-Egger were employed for causal inference, complemented by sensitivity analysis. Genetically predicted higher phenylalanine (IVW: odds ratio = 1.10 95% confidence interval 1.04-1.17, P = 1.5 × 10-3, q_pval = 3.4 × 10-2) and dihomo-gamma-linolenic acid (IVW: odds ratio = 1.001 95% confidence interval 1.0006-1.003, P = 3.7 × 10-3, q_pval = 4.1 × 10-2) levels were directly associated with T2D risk. Conversely, no causal relationships between other nutrients and T2D were established. We hypothesize that phenylalanine and dihomo-gamma-linolenic acid contribute to the pathogenesis of T2D. Clinically, the use of foods with high phenylalanine content may pose potential risks for patients with a heightened risk of T2D. Our study provides evidence supporting a causal link between dietary nutrient intake and the development of T2D.

Dietary factors and Alzheimer's disease risk: a Mendelian randomization study.

BACKGROUND: Prior observational research has investigated the association between dietary patterns and Alzheimer's disease (AD) risk. Nevertheless, due to constraints in past observational studies, establishing a causal link between dietary habits and AD remains challenging. METHODS: Methodology involved the utilization of extensive cohorts sourced from publicly accessible genome-wide association study (GWAS) datasets of European descent for conducting Mendelian randomization (MR) analyses. The principal analytical technique utilized was the inverse-variance weighted (IVW) method. RESULTS: The MR analysis conducted in this study found no statistically significant causal association between 20 dietary habits and the risk of AD (All p > 0.05). These results were consistent across various MR methods employed, including MR-Egger, weighted median, simple mode, and weighted mode approaches. Moreover, there was no evidence of horizontal pleiotropy detected (All p > 0.05). CONCLUSION: In this MR analysis, our finding did not provide evidence to support the causal genetic relationships between dietary habits and AD risk.

MVMRmode: Introducing an R package for plurality valid estimators for multivariable Mendelian randomisation.

BACKGROUND: Mendelian randomisation (MR) is the use of genetic variants as instrumental variables. Mode-based estimators (MBE) are one of the most popular types of estimators used in univariable-MR studies and is often used as a sensitivity analysis for pleiotropy. However, because there are no plurality valid regression estimators, modal estimators for multivariable-MR have been under-explored. METHODS: We use the residual framework for multivariable-MR to introduce two multivariable modal estimators: multivariable-MBE, which uses IVW to create residuals fed into a traditional plurality valid estimator, and an estimator which instead has the residuals fed into the contamination mixture method (CM), multivariable-CM. We then use Monte-Carlo simulations to explore the performance of these estimators when compared to existing ones and re-analyse the data used by Grant and Burgess (2021) looking at the causal effect of intelligence, education, and household income on Alzheimer's disease as an applied example. RESULTS: In our simulation, we found that multivariable-MBE was generally too variable to be much use. Multivariable-CM produced more precise estimates on the other hand. Multivariable-CM performed better than MR-Egger in almost all settings, and Weighted Median under balanced pleiotropy. However, it underperformed Weighted Median when there was a moderate amount of directional pleiotropy. Our re-analysis supported the conclusion of Grant and Burgess (2021), that intelligence had a protective effect on Alzheimer's disease, while education, and household income do not have a causal effect. CONCLUSIONS: Here we introduced two, non-regression-based, plurality valid estimators for multivariable MR. Of these, "multivariable-CM" which uses IVW to create residuals fed into a contamination-mixture model, performed the best. This estimator uses a plurality of variants valid assumption, and appears to provide precise and unbiased estimates in the presence of balanced pleiotropy and small amounts of directional pleiotropy.

Association between air pollution and skin cutaneous melanoma: A Mendelian randomization study.

There has been a consistent and notable increase in the global prevalence of skin cutaneous melanoma (SKCM). Although genetic factors are closely associated with the occurrence and development of melanoma, the potential influence of environmental factors cannot be overlooked. The existing literature lacks a definitive consensus on the correlation between air pollution and the incidence rate of SKCM. This study seeks to investigate the causal relationship between air pollution, specifically focusing on particulate matter (PM) 2.5, PM2.5-10, PM10, and nitrogen oxides, and the risk of SKCM. A 2-sample Mendelian randomization (MR) method was applied, utilizing extensive publicly accessible genome-wide association studies summary datasets within European populations. The primary analytical method employed was the inverse variance weighted method. Supplementary methods, including the weighted median model, MR-Egger, simple model, and weighted model, were chosen to ensure robust analysis. Heterogeneity assessment was conducted using Cochran's Q test. To identify potential pleiotropy, both MR-Egger regression and the MR-PRESSO global test were employed. Additionally, a sensitivity analysis was performed using the leave-one-out method. The analysis revealed no statistically significant association between air pollution and SKCM risk, with specific findings as follows: PM2.5 (P = .485), PM2.5-10 (P = .535), PM10 (P = .136), and nitrogen oxides (P = .745). While some results exhibited heterogeneity, all findings demonstrated an absence of pleiotropy. This study did not find substantive evidence supporting a causal relationship between air pollution and the risk of SKCM within European populations. The comprehensive MR analysis, encompassing various pollutants, suggests that environmental factors such as air pollution may not be significant contributors to the development of SKCM.

Integrated analysis of single cell-RNA sequencing and Mendelian randomization identifies lactate dehydrogenase B as a target of melatonin in ischemic stroke.

AIMS: Despite the success of single-cell RNA sequencing in identifying cellular heterogeneity in ischemic stroke, clarifying the mechanisms underlying these associations of differently expressed genes remains challenging. Several studies that integrate gene expression and gene expression quantitative trait loci (eQTLs) with genome wide-association study (GWAS) data to determine their causal role have been proposed. METHODS: Here, we combined Mendelian randomization (MR) framework and single cell (sc) RNA sequencing to study how differently expressed genes (DEGs) mediating the effect of gene expression on ischemic stroke. The hub gene was further validated in the in vitro model. RESULTS: We identified 2339 DEGs in 10 cell clusters. Among these DEGs, 58 genes were associated with the risk of ischemic stroke. After external validation with eQTL dataset, lactate dehydrogenase B (LDHB) is identified to be positively associated with ischemic stroke. The expression of LDHB has also been validated in sc RNA-seq with dominant expression in microglia and astrocytes, and melatonin is able to reduce the LDHB expression and activity in vitro ischemic models. CONCLUSION: Our study identifies LDHB as a novel biomarker for ischemic stroke via combining the sc RNA-seq and MR analysis.

Multiple coronary heart diseases are risk factors for mental health disorders: A mendelian randomization study.

BACKGROUND: Previous observational studies have suggested associations between Coronary Heart Disease (CHD) and Mental Health Disorders (MHD). However, the causal nature of these relationships has remained elusive. OBJECTIVE: The purpose of this study is to elucidate the causal relationships between eight distinct types of CHD and six types of MHD using Mendelian randomization (MR) analysis. METHODS: The MR analysis employed a suite of methods including inverse variance-weighted (IVW), MR-Egger, weighted mode, weighted median, and simple mode techniques. To assess heterogeneity, IVW and MR-Egger tests were utilized. MR-Egger regression also served to investigate potential pleiotropy. The stability of IVW results was verified by leave-one-out sensitivity analysis. RESULTS: We analyzed data from over 2,473,005 CHD and 803,801 MHD patients, informed by instrumental variables from large-scale genomic studies on European populations. The analysis revealed a causal increase in the risk of Major Depressive Disorder and Mania associated with Coronary Artery Disease and Myocardial Infarction. Heart Failure was found to causally increase the risk for Bipolar Disorder and Schizophrenia. Atrial Fibrillation and Ischemic Heart Diseases were positively linked to Generalized Anxiety Disorder and Mania, respectively. There was no significant evidence of an association between Hypertensive Heart Disease, Hypertrophic Cardiomyopathy, Pulmonary Heart Disease, and MHD. Reverse MR analysis indicated that MHD do not serve as risk factors for CHD. CONCLUSIONS: The findings suggest that specific types of CHD may act as risk factors for certain MHDs. Consequently, incorporating psychological assessments into the management of patients with CHD could be advantageous.

Collider bias correction for multiple covariates in GWAS using robust multivariable Mendelian randomization.

Genome-wide association studies (GWAS) have identified many genetic loci associated with complex traits and diseases in the past 20 years. Multiple heritable covariates may be added into GWAS regression models to estimate direct effects of genetic variants on a focal trait, or to improve the power by accounting for environmental effects and other sources of trait variations. When one or more covariates are causally affected by both genetic variants and hidden confounders, adjusting for them in GWAS will produce biased estimation of SNP effects, known as collider bias. Several approaches have been developed to correct collider bias through estimating the bias by Mendelian randomization (MR). However, these methods work for only one covariate, some of which utilize MR methods with relatively strong assumptions, both of which may not hold in practice. In this paper, we extend the bias-correction approaches in two aspects: first we derive an analytical expression for the collider bias in the presence of multiple covariates, then we propose estimating the bias using a robust multivariable MR (MVMR) method based on constrained maximum likelihood (called MVMR-cML), allowing the presence of invalid instrumental variables (IVs) and correlated pleiotropy. We also established the estimation consistency and asymptotic normality of the new bias-corrected estimator. We conducted simulations to show that all methods mitigated collider bias under various scenarios. In real data analyses, we applied the methods to two GWAS examples, the first a GWAS of waist-hip ratio with adjustment for only one covariate, body-mass index (BMI), and the second a GWAS of BMI adjusting metabolomic principle components as multiple covariates, illustrating the effectiveness of bias correction.

Genome- and epigenome-wide association studies identify susceptibility of CpG sites and regions for metabolic syndrome in a Korean population.

BACKGROUND: While multiple studies have investigated the relationship between metabolic syndrome (MetS) and its related traits (fasting glucose, triglyceride, HDL cholesterol, blood pressure, waist circumference) and DNA methylation, our understanding of the epigenetic mechanisms in MetS remains limited. Therefore, we performed an epigenome-wide meta-analysis of blood DNA methylation to identify differentially methylated probes (DMPs) and differentially methylated regions (DMRs) associated with MetS and its components using two independent cohorts comprising a total of 2,334 participants. We also investigated the specific genetic effects on DNA methylation, identified methylation quantitative trait loci (meQTLs) through genome-wide association studies and further utilized Mendelian randomization (MR) to assess how these meQTLs subsequently influence MetS status. RESULTS: We identified 40 DMPs and 27 DMRs that are significantly associated with MetS. In addition, we identified many novel DMPs and DMRs underlying inflammatory and steroid hormonal processes. The most significant associations were observed in 3 DMPs (cg19693031, cg26974062, cg02988288) and a DMR (chr1:145440444-145441553) at the TXNIP, which are involved in lipid metabolism. These CpG sites were identified as coregulators of DNA methylation in MetS, TG and FAG levels. We identified a total of 144 cis-meQTLs, out of which only 13 were found to be associated with DMPs for MetS. Among these, we confirmed the identified causal mediators of genetic effects at CpG sites cg01881899 at ABCG1 and cg00021659 at the TANK genes for MetS. CONCLUSIONS: This study observed whether specific CpGs and methylated regions act independently or are influenced by genetic effects for MetS and its components in the Korean population. These associations between the identified DNA methylation and MetS, along with its individual components, may serve as promising targets for the development of preventive interventions for MetS.

Causal influence of gut microbiota on small cell lung cancer: a Mendelian randomization study.

BACKGROUND: Previous studies have hinted at a significant link between lung cancer and the gut microbiome, yet their causal relationship remains to be elucidated. METHODS: GWAS data for small cell lung cancer (SCLC) was extracted from the FinnGen consortium, comprising 179 cases and 218 613 controls. Genetic variation data for 211 gut microbiota were obtained as instrumental variables from MiBioGen. Mendelian randomization (MR) was employed to determine the causal relationship between the two, with inverse variance weighting (IVW) being the primary method for causal analysis. The MR results were validated through several sensitivity analyses. RESULTS: The study identified a protective effect against SCLC for the genus Eubacterium ruminantium group (OR = 0.413, 95% CI: 0.223-0.767, p = 0.00513), genus Barnesiella (OR = 0.208, 95% CI: 0.0640-0.678, p = 0.00919), family Lachnospiraceae (OR = 0.319, 95% CI: 0.107-0.948, p = 0.03979), and genus Butyricimonas (OR = 0.376, 95% CI: 0.144-0.984, p = 0.04634). Conversely, genus Intestinibacter (OR = 3.214, 95% CI: 1.303-7.926, p = 0.01125), genus Eubacterium oxidoreducens group (OR = 3.391, 95% CI: 1.215-9.467, p = 0.01973), genus Bilophila (OR = 3.547, 95% CI: 1.106-11.371, p = 0.03315), and order Bacillales (OR = 1.860, 95% CI: 1.034-3.347, p = 0.03842) were found to potentially promote the onset of SCLC. CONCLUSION: We identified potential causal relationships between certain gut microbiota and SCLC, offering new insights into microbiome-mediated mechanisms of SCLC pathogenesis, resistance, mutations, and more.

The causal role of circulating immunity-inflammation in preeclampsia: A Mendelian randomization.

Patients with systemic autoimmune diseases, such as systemic lupus erythematosus, were at a higher risk for preeclampsia. The causal relationship between immunological inflammation and preeclampsia (PE) remains uncertain. We aimed to investigate the causal relationship between circulating immune inflammation and PE. Genetically predicted blood immune cells and circulating inflammatory proteins were identified using two genome-wide association studies (GWAS). We used a two-sample Mendelian randomization (MR) method to determine whether circulating immunological inflammation causes PE. Our findings indicated that ten immunophenotypes were identified to be significantly associated with PE risk: CD62L- Dendritic Cell Absolute Count, CD86+ myeloid Dendritic Cell %Dendritic Cell, CD62L- myeloid Dendritic Cell Absolute Count, CD86+ myeloid Dendritic Cell Absolute Count, CD62L- myeloid Dendritic Cell %Dendritic Cell, CD62L- CD86+ myeloid Dendritic Cell %Dendritic Cell, CD62L- CD86+ myeloid Dendritic Cell Absolute Count, CD16 on CD14+ CD16+ monocyte, HLA DR+ Natural Killer Absolute Count, and T cell Absolute Count. Ninety-one inflammation-related proteins had no statistically significant effect on PE following false discovery rate (FDR) correction. Certain proteins exhibited unadjusted low p-values that merited mention. These proteins include interleukin-10 (OR = 0.76, 95%CI = 0.63-0.93, p = .006), fibroblast growth factor 21 (OR = 1.23, 95%CI = 1.01-1.47, p = .035), and Caspase 8 (OR = 0.65, 95%CI = 0.50-0.85, p = .001). The ELISA analysis demonstrated elevated levels of FGF-21 and decreased levels of IL-10 and Caspase-8 in the plasma of patients with PE. These findings reveal that immunophenotypes and circulating inflammatory proteins may induce PE, confirming the importance of peripheral Immunity-Inflammation in PE. The discovery has the potential to lead to earlier detection and more effective treatment techniques.

Lipid levels and risk of acute pancreatitis using bidirectional Mendelian randomization.

Previous studies found lipid levels, especially triglycerides (TG), are associated with acute pancreatitis, but their causalities and bi-directions were not fully examined. We determined whether abnormal levels of TG, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) are precursors and/or consequences of acute pancreatitis using bidirectional two-sample Mendelian randomization (MR) with two non-overlapping genome-wide association study (GWAS) summary statistics for lipid levels and acute pancreatitis. We found phenotypic associations that both higher TG levels and lower HDL-C levels contributed to increased risk of acute pancreatitis. Our GWAS meta-analysis of acute pancreatitis identified seven independent signals. Genetically predicted TG was positively associated with acute pancreatitis when using the variants specifically associated with TG using univariable MR [Odds ratio (OR), 95% CI 2.02, 1.22-3.31], but the reversed direction from acute pancreatitis to TG was not observed (mean difference = 0.003, SE = 0.002, P-value = 0.138). However, a bidirectional relationship of HDL-C and acute pancreatitis was observed: A 1-SD increment of genetically predicted HDL-C was associated with lower risk of acute pancreatitis (OR, 95% CI 0.84, 0.76-0.92) and genetically predisposed individuals with acute pancreatitis have, on average, 0.005 SD lower HDL-C (mean difference = - 0.005, SE = 0.002, P-value = 0.004). Our MR analysis confirms the evidence of TG as a risk factor of acute pancreatitis but not a consequence. A potential bidirectional relationship of HDL-C and acute pancreatitis occurs and raises the prospect of HDL-C modulation in the acute pancreatitis prevention and treatment.

Systemic Inflammatory Regulators Associated with Osteoporosis: A Bidirectional Mendelian Randomization Study.

To elucidate the precise upstream and downstream regulatory mechanisms of inflammatory factors in osteoporosis (OP) progression and to establish a causal relationship between inflammatory factors and OP. We conducted bidirectional Mendelian randomization (MR) analyses using data for 41 cytokines obtained from three independent cohorts comprising 8293 Finnish individuals. Estimated bone mineral density (eBMD) data were derived from 426,824 UK Biobank White British individuals (55% female) and fracture data from 416,795 UK Biobank participants of European ancestry. The inverse variance-weighted method was the primary MR analysis approach. We employed other methods as complementary approaches for mutual corroboration. To test for pleiotropy and heterogeneity, we used the MR-Egger regression, MR-pleiotropy residual sum and outlier global test, and the Cochrane Q test. Macrophage inflammatory protein (MIP)-1α and interleukin (IL)-12p70 expression associated negatively and causally with eBMD (ß = -0.017 [MIP-1α], ß = -0.011 [IL-12p70]). Conversely, tumor necrosis factor-related apoptosis-inducing ligand was associated with a decreased risk of fractures (Odds Ratio: 0.980). Additionally, OP influenced the expression of multiple inflammatory factors, including growth-regulated oncogene-α, interferon-gamma, IL-6, beta nerve growth factor, and IL-2. Finally, we discovered complex bidirectional causal relationships between IL-8, IL-10, and OP. Specific inflammatory factors may contribute to OP development or may be causally affected by OP. We identified a bidirectional causal relationship between certain inflammatory factors and OP. These findings provide new perspectives for early prediction and targeted treatment of OP. Larger cohort studies are necessary in the future to further validate these findings.

Characterizing the Causal Pathway From Childhood Adiposity to Right Heart Physiology and Pulmonary Circulation Using Lifecourse Mendelian Randomization.

BACKGROUND: Observational epidemiological studies have reported an association between childhood adiposity and altered cardiac morphology and function in later life. However, whether this is due to a direct consequence of being overweight during childhood has been difficult to establish, particularly as accounting for other measures of body composition throughout the lifecourse can be exceptionally challenging. METHODS AND RESULTS: In this study, we used human genetics to investigate this using a causal inference technique known as lifecourse Mendelian randomization. This approach allowed us to evaluate the effect of childhood body size on 11 measures of right heart and pulmonary circulation independent of other anthropometric traits at various stages in the lifecourse. We found strong evidence that childhood body size has a direct effect on an enlarged right heart structure in later life (eg, right ventricular end-diastolic volume: ß=0.24 [95% CI, 0.15-0.33]; P=3×10-7) independent of adulthood body size. In contrast, childhood body size effects on maximum ascending aorta diameter attenuated upon accounting for body size in adulthood, suggesting that this effect is likely attributed to individuals remaining overweight into later life. Effects of childhood body size on pulmonary artery traits and measures of right atrial function became weaker upon accounting for adulthood fat-free mass and childhood height, respectively. CONCLUSIONS: Our findings suggest that, although childhood body size has a long-term influence on an enlarged heart structure in adulthood, associations with the other structural components of the cardiovascular system and their function may be largely attributed to body composition at other stages in the lifecourse.

Educational attainment, income, and attention deficit hyperactivity disorder: A mediation analysis based on two-step Mendelian randomization.

Previous studies have reported the relationship between educational attainment and attention deficit hyperactivity disorder (ADHD). However, the mechanism of this relationship remains unknown. It is well known that educational attainment correlates with income. Therefore, based on summary data from a genome-wide association study we used two-step Mendelian randomization (MR) to explore the role of income between education and ADHD. The inverse variance weighted (IVW) method was used in our analysis. The IVW results suggested that educational attainment and income were protective factors against ADHD. Educational attainment affects ADHD through income [ADHD: Beta = -0.68, 95% confidence interval (CI) = -0.87, -0.49; female: Beta = -0.87, 95% CI = -1.28, -0.47; male: Beta = -1.01, 95% CI = -1.34, -0.68; childhood: Beta = -0.52, 95% CI = -0.74, -0.30; late-diagnosed: Beta = -0.78, 95% CI = -1.11, -0.47; persistent: Beta = -0.82, 95% CI = -1.33, -0.31]. Income also affected ADHD through educational attainment [female: Beta = -1.08, 95% CI = -1.35, -0.83; male: Beta = -1.16, 95% CI = -1.57, -0.77; persistent: Beta = -1.48, 95% CI = -2.09, -0.94]. In the final analysis, data with heterogeneity were analyzed using IVW random effects results. The mechanism is that income will mediate the relationship between educational attainment and ADHD.

Investigating the association between genetically proxied circulating levels of immune checkpoint proteins and cancer survival: protocol for a Mendelian randomisation analysis.

INTRODUCTION: Compared with the traditional drug development pathway, investigating alternative uses for existing drugs (ie, drug repurposing) requires substantially less time, cost and resources. Immune checkpoint inhibitors are licensed for the treatment of certain breast, colorectal, head and neck, lung and melanoma cancers. These drugs target immune checkpoint proteins to reduce the suppression of T cell activation by cancer cells. As T cell suppression is a hallmark of cancer common across anatomical sites, we hypothesise that immune checkpoint inhibitors could be repurposed for the treatment of additional cancers beyond the ones already indicated. METHODS AND ANALYSIS: We will use two-sample Mendelian randomisation to investigate the effect of genetically proxied levels of protein targets of two immune checkpoint inhibitors-programmed cell death protein 1 and programmed death ligand 1-on survival of seven cancer types (breast, colorectal, head and neck, lung, melanoma, ovarian and prostate). Summary genetic association data will be obtained from prior genome-wide association studies of circulating protein levels and cancer survival in populations of European ancestry. Various sensitivity analyses will be performed to examine the robustness of findings to potential violations of Mendelian randomisation assumptions, collider bias and the impact of alternative genetic instrument construction strategies. The impact of treatment history and tumour stage on the findings will also be investigated using summary-level and individual-level genetic data where available. ETHICS AND DISSEMINATION: No separate ethics approval will be required for these analyses as we will be using data from previously published genome-wide association studies which individually gained ethical approval and participant consent. Results from analyses will be submitted as an open-access peer-reviewed publication and statistical code will be made freely available on the completion of the analysis.

A data-adaptive method for investigating effect heterogeneity with high-dimensional covariates in Mendelian randomization.

BACKGROUND: Mendelian randomization is a popular method for causal inference with observational data that uses genetic variants as instrumental variables. Similarly to a randomized trial, a standard Mendelian randomization analysis estimates the population-averaged effect of an exposure on an outcome. Dividing the population into subgroups can reveal effect heterogeneity to inform who would most benefit from intervention on the exposure. However, as covariates are measured post-"randomization", naive stratification typically induces collider bias in stratum-specific estimates. METHOD: We extend a previously proposed stratification method (the "doubly-ranked method") to form strata based on a single covariate, and introduce a data-adaptive random forest method to calculate stratum-specific estimates that are robust to collider bias based on a high-dimensional covariate set. We also propose measures based on the Q statistic to assess heterogeneity between stratum-specific estimates (to understand whether estimates are more variable than expected due to chance alone) and variable importance (to identify the key drivers of effect heterogeneity). RESULT: We show that the effect of body mass index (BMI) on lung function is heterogeneous, depending most strongly on hip circumference and weight. While for most individuals, the predicted effect of increasing BMI on lung function is negative, it is positive for some individuals and strongly negative for others. CONCLUSION: Our data-adaptive approach allows for the exploration of effect heterogeneity in the relationship between an exposure and an outcome within a Mendelian randomization framework. This can yield valuable insights into disease aetiology and help identify specific groups of individuals who would derive the greatest benefit from targeted interventions on the exposure.

Causal role of immune cell traits in stroke: A Mendelian randomization study.

OBJECTIVES: Immune mechanisms play a crucial role in the development of stroke. However, immune-related phenotypes are diverse and their associations with stroke are largely unknown. Here, we aimed to systematically explore the causal role of immune cell traits in stroke and its subtypes by leveraging data from genome-wide association studies (GWASs). MATERIALS AND METHODS: Exposure data were obtained from a recent GWAS on 731 immune cell traits profiled by flow cytometry involving 3757 individuals. By conducting two-sample univariable Mendelian randomization (MR) analyses, each immune cell trait was assessed for causal relationships with stroke outcomes from the MEGASTROKE Consortium (40,585 cases and 406,111 controls). The robustness of the MR results was verified by a series of sensitivity analyses. RESULTS: We identified three significant associations after Bonferroni correction (P < 1.37E-05). Increased CD27 expression on memory B cell (OR = 1.23, 95% CI = 1.14-1.33, P = 2.78E-08), IgD-CD38dim B cell (OR = 1.16, 95% CI = 1.09-1.23, P = 5.98E-06) and unswitched memory B cell (OR = 1.18, 95% CI = 1.10-1.27, P = 1.09E-05) were associated with a higher risk of large-artery atherosclerotic stroke (LAS). Furthermore, expression quantitative trait loci data also indicated elevated blood CD27 mRNA level was a risk factor for LAS (OR = 1.37, 95% CI = 1.02-1.84, P = 0.037). CONCLUSIONS: This study provided genetic evidence of the causal relationship between immune cell traits and stroke, highlighting the role of CD27 on memory B cell as a novel factor for LAS risk.

Causal relationship between lactate dehydrogenase and risk of developing ischemic stroke: A Mendelian randomized study.

BACKGROUND AND OBJECTIVE: Ischemic stroke (IS) is one of the major global health problems. It is not clear whether there is a causal relationship between lactate dehydrogenase (LDH) and the risk of IS attacks. The purpose of this study was to investigate whether LDH has a causal relationship with the development of IS. METHODS: The genome-wide association data of LDH and IS were obtained through a Mendelian randomization-based platform. Single nucleotide polymorphisms (SNP) that were significantly associated with LDH were identified and used as instrumental variables, and a two-sample Mendelian randomization study was used to examine the causal relationship between LDH and IS. The statistical methods included Inverse-variance weighted approach, MR-Egger regression, and weighted median estimator. RESULTS: We selected 15 SNPs of genome-wide significance from Genome-wide association study database with LDH as instrumental variables. A consistent causal association between LDH and IS was observed by different assessment methods. The results of the inverse-variance weighted method suggested an inverse association between LDH and higher genetic predictability of IS risk (OR, 0.997; 95%CI 0.995-0.999). The weighted median estimate showed consistent results with the MR-Egger method (weighted median estimate: OR, 0.995; 95%CI 0.992-0.999; MR-Egger method: OR, 0.996; 95%CI 0.992-0.999). The inverse-variance weighted method indicates a causal association between LDH and IS (ß = -0.002563, SE = 0.00128, p = .0453). MR-Egger analysis (ß = -0.004498, SE = 0.001877, p = .03) and the weighted median method suggested that LDH and IS also existed causal relationship (ß = -0.004861, SE = 0.001801, p = .00695). CONCLUSIONS: Our Mendelian randomization results suggest that LDH is inversely associated with the risk of developing IS, and are contrary to the results of previous observational studies.