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Iron deficiency is the most common nutritional deficiency worldwide, and is estimated to affect a quarter of the pregnant population.1 Low systemic iron levels have been associated with several maternal pregnancy complications, including infection and preeclampsia.2 However, these results from traditional observational studies may be affected by confounding. A systematic review of trials found that daily iron supplementation in pregnancy reduced iron deficiency anemia, but there was no clear effect on other maternal complications, which in large part may be due to low statistical power.3 To robustly evaluate whether systemic iron status affects risk of maternal pregnancy complications, we conducted a Mendelian randomization (MR) study. By leveraging genetic instruments for systemic iron status, the approach greatly reduced the risk of confounding and allowed for a large study sample.

Carotid intima-media thickness (cIMT) is a measure of atherosclerosis used to predict cardiovascular disease.1 Lipid traits and type 2 diabetes (T2D) have been implicated in the pathogenesis of cIMT. Statins, which reduce low-density lipoprotein cholesterol (LDL-C), have been shown in a meta-analysis of randomized trials to slow cIMT growth or even reduce cIMT. However, most research into cIMT has been conducted in European ancestry individuals, and little is known about whether the same causal risk factors apply for African ancestry populations.

Background: Observational studies identified elevated blood pressure (BP) as a strong risk factor for thoracic aortic dilation, and BP reduction is the primary medical intervention recommended to prevent progression of aortic aneurysms. However, although BP may impact aortic dilation, aortic size may also impact BP. The causal relationship between BP and thoracic aortic size has not been reliably established. Methods: Genome-wide association studies summary statistics were obtained for BP and ascending thoracic aortic diameter (AscAoD). Causal effects of BP on AscAoD were estimated using 2-sample Mendelian randomization using a range of pleiotropy-robust methods. Results: Genetically predicted increased systolic BP, diastolic BP, and mean arterial pressure all significantly associate with higher AscAoD (systolic BP: β estimate, 0.0041 mm/mm Hg [95% CI, 0.0008–0.0074]; P =0.02, diastolic BP: β estimate, 0.0272 mm/mm Hg [95% CI, 0.0224–0.0320]; P P P P P =0.02), while directly associated with diastolic BP (0.8203 mm Hg/mm [95% CI, 0.2735–1.3672]; P =0.004). Conclusions: BP likely contributes causally to ascending thoracic aortic dilation. Increased AscAoD likely contributes to lower systolic BP and pulse pressure, but not diastolic BP, consistent with the hemodynamic consequences of a reduced aortic diameter.

Background Fatty acids are important dietary factors that have been extensively studied for their implication in health and disease. Evidence from epidemiological studies and randomised controlled trials on their role in cardiovascular, inflammatory, and other diseases remains inconsistent. The objective of this study was to assess whether genetically predicted fatty acid concentrations affect the risk of disease across a wide variety of clinical health outcomes. Methods and findings The UK Biobank (UKB) is a large study involving over 500,000 participants aged 40 to 69 years at recruitment from 2006 to 2010. We used summary-level data for 117,143 UKB samples (base dataset), to extract genetic associations of fatty acids, and individual-level data for 322,232 UKB participants (target dataset) to conduct our discovery analysis. We studied potentially causal relationships of circulating fatty acids with 845 clinical diagnoses, using mendelian randomisation (MR) approach, within a phenome-wide association study (PheWAS) framework. Regression models in PheWAS were adjusted for sex, age, and the first 10 genetic principal components. External summary statistics were used for replication. When several fatty acids were associated with a health outcome, multivariable MR and MR-Bayesian method averaging (MR-BMA) was applied to disentangle their causal role. Genetic predisposition to higher docosahexaenoic acid (DHA) was associated with cholelithiasis and cholecystitis (odds ratio per mmol/L: 0.76, 95% confidence interval: 0.66 to 0.87). This was supported in replication analysis (FinnGen study) and by the genetically predicted omega-3 fatty acids analyses. Genetically predicted linoleic acid (LA), omega-6, polyunsaturated fatty acids (PUFAs), and total fatty acids (total FAs) showed positive associations with cardiovascular outcomes with support from replication analysis. Finally, higher genetically predicted levels of DHA (0.83, 0.73 to 0.95) and omega-3 (0.83, 0.75 to 0.92) were found to have a protective effect on obesity, which was supported using body mass index (BMI) in the GIANT consortium as replication analysis. Multivariable MR analysis suggested a direct detrimental effect of LA (1.64, 1.07 to 2.50) and omega-6 fatty acids (1.81, 1.06 to 3.09) on coronary heart disease (CHD). MR-BMA prioritised LA and omega-6 fatty acids as the top risk factors for CHD. Although we present a range of sensitivity analyses to the address MR assumptions, horizontal pleiotropy may still bias the reported associations and further evaluation in clinical trials is needed. Conclusions Our study suggests potentially protective effects of circulating DHA and omega-3 concentrations on cholelithiasis and cholecystitis and on obesity, highlighting the need to further assess them as prevention treatments in clinical trials. Moreover, our findings do not support the supplementation of unsaturated fatty acids for cardiovascular disease prevention.

Introduction Genetic associations for variants identified through genome-wide association studies (GWASs) tend to be overestimated in the original discovery data set as, if the association was underestimated, the variant may not have been detected. This bias, known as winner’s curse, can affect Mendelian randomization estimates, but its severity and potential impact are unclear. Methods We performed an empirical investigation to assess the potential bias from winner’s curse in practice. We considered Mendelian randomization estimates for the effect of body mass index (BMI) on coronary artery disease risk. We randomly divided a UK Biobank data set 100 times into three equally sized subsets. The first subset was treated as the ‘discovery GWAS’. We compared genetic associations estimated in the discovery GWAS to those estimated in the other subsets for each of the 100 iterations. Results For variants associated with BMI at P Conclusions Winner’s curse can bias Mendelian randomization estimates, although its practical impact may not be substantial. If avoiding sample overlap is infeasible, analysts should consider performing a sensitivity analysis based on variants strongly associated with the exposure.

Background and ObjectivesAngiotensin-converting enzyme (ACE) inhibitors are a commonly prescribed class of medication used to treat heart failure, hypertension, and chronic kidney disease. However, previous observational studies have shown conflicting directions of associations between ACE inhibitors and risk of Alzheimer disease. Genetic evidence has supported a protective effect of cerebral ACE against Alzheimer disease (AD). However, it is unclear whether this effect is mediated through blood pressure and extends to other neurodegenerative diseases.MethodsWe performed genetic colocalization investigating an effect of cortical ACE expression on AD risk in people of European ancestry. We further investigated whether any effect of ACE expression on AD risk is mediated through changes in blood pressure and whether effects extend to Parkinson disease, small-vessel disease, or cognitive function in a Mendelian randomization paradigm.ResultsThere was genetic evidence supporting a protective effect of cortical ACE expression on AD risk in people of European ancestry. Although higher cortical ACE expression was associated with higher blood pressure, there was no strong evidence to support that its association with AD was mediated through blood pressure nor that ACE expression affected risk of other neurodegenerative traits.DiscussionGenetic evidence supports protective effects of cerebral ACE expression on AD, but not other neurodegenerative outcomes in people of European ancestry. Further work is required to investigate whether therapeutic inhibition of ACE increases risk of Alzheimer disease.

Background: Microvascular damage from large artery stiffness (LAS) in pancreatic, hepatic, and skeletal muscles may affect glucose homeostasis. Our goal was to evaluate the association between LAS and the risk of type 2 diabetes using prospectively collected, carefully phenotyped measurements of LAS as well as Mendelian randomization analyses. Methods: Carotid-femoral pulse wave velocity (CF-PWV) and brachial and central pulse pressure were measured in 5676 participants of the FHS (Framingham Heart Study) without diabetes. We used Cox proportional hazards regression to evaluate the association of CF-PWV and pulse pressure with incident diabetes. We subsequently performed 2-sample Mendelian randomization analyses evaluating the associations of genetically predicted brachial pulse pressure with type 2 diabetes in the UKBB (United Kingdom Biobank). Results: In FHS, individuals with higher CF-PWV were older, more often male, and had higher body mass index and mean arterial pressure compared to those with lower CF-PWV. After a median follow-up of 7 years, CF-PWV and central pulse pressure were associated with an increased risk of new-onset diabetes (per SD increase, multivariable-adjusted CF-PWV hazard ratio, 1.36 [95% CI, 1.03–1.76]; P =0.030; central pulse pressure multivariable-adjusted CF-PWV hazard ratio, 1.26 [95% CI, 1.08–1.48]; P =0.004). In United Kingdom Biobank, genetically predicted brachial pulse pressure was associated with type 2 diabetes, independent of mean arterial pressure (adjusted odds ratio, 1.16 [95% CI, 1.00–1.35]; P =0.049). Conclusions: Using prospective cohort data coupled with Mendelian randomization analyses, we found evidence supporting that greater LAS is associated with increased risk of developing diabetes. LAS may play an important role in glucose homeostasis and may serve as a useful marker of future diabetes risk.

Mendelian randomization is an epidemiological technique that compares disease risk in groups of individuals defined based on their genetic variants to make causal inferences (1). Rather than comparing individuals with high levels of an exposure versus those with low levels of an exposure as in a conventional epidemiological analysis, the approach compares those with genetic variants predisposing them to increased versus decreased levels of the exposure. This is analogous to the analysis of a randomized controlled trial for a treatment that increases levels of the exposure, which does not compare those with high versus low levels of the exposure after treatment, but rather compares those who were randomly assigned to receive the treatment versus those who were randomly assigned to the control group. The logic is that randomization should be independent of all competing risk factors, meaning that the randomly-assigned groups only differ systematically with respect to their average levels of the treatment and any downstream consequences of the treatment. Hence, an association between randomization and the trial outcome is indicative of a causal effect of the treatment. In the same way, if genetic variants act analogously to randomization by dividing the population into groups that differ systematically only with respect to the exposure and its consequences, then an association between the genetic variants and the outcome is indicative of a causal effect of the exposure (2).

Funder: EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart grant 116074 The Royal Society (204623/Z/16/Z) United Kingdom Research and Innovation Medical Research Council (MC_UU_00002/7) NIHR Cambridge Biomedical Research Centre (BRC-1215-20014), Iron is essential for many biological processes, but iron levels must be tightly regulated to avoid harmful effects of both iron deficiency and overload. Here, we perform genome-wide association studies on four iron-related biomarkers (serum iron, serum ferritin, transferrin saturation, total iron-binding capacity) in the Trøndelag Health Study (HUNT), the Michigan Genomics Initiative (MGI), and the SardiNIA study, followed by their meta-analysis with publicly available summary statistics, analyzing up to 257,953 individuals. We identify 123 genetic loci associated with iron traits. Among 19 novel protein-altering variants, we observe a rare missense variant (rs367731784) in HUNT, which suggests a role for DNAJC13 in transferrin recycling. We further validate recently published results using genetic risk scores for each biomarker in HUNT (6% variance in serum iron explained) and present linear and non-linear Mendelian randomization analyses of the traits on all-cause mortality. We find evidence of a harmful effect of increased serum iron and transferrin saturation in linear analyses that estimate population-averaged effects. However, there was weak evidence of a protective effect of increasing serum iron at the very low end of its distribution. Our findings contribute to our understanding of the genes affecting iron status and its consequences on human health.

The poor transferability of genetic risk scores (GRSs) derived from European ancestry data in diverse populations is a cause of concern. We set out to evaluate whether GRSs derived from data of African American individuals and multiancestry data perform better in sub-Saharan Africa (SSA) compared to European ancestry-derived scores. Using summary statistics from the Million Veteran Program (MVP), we showed that GRSs derived from data of African American individuals enhance polygenic prediction of lipid traits in SSA compared to European and multiancestry scores. However, our GRS prediction varied greatly within SSA between the South African Zulu (low-density lipoprotein cholesterol (LDL-C), R2 = 8.14%) and Ugandan cohorts (LDL-C, R2 = 0.026%). We postulate that differences in the genetic and environmental factors between these population groups might lead to the poor transferability of GRSs within SSA. More effort is required to optimize polygenic prediction in Africa.

Pulse pressure (PP), the difference between systolic blood pressure (SBP) and diastolic blood pressure (DBP), arises due to pulsatile ejection of blood from the left ventricle. Previous observational studies have identified an inverse association of PP with aortic diameter1, and positive associations of PP with aortic wall stiffness and thickness2. However, it is not known whether these associations reflect a causal effect of PP on the risk of AAA, an effect of the aorta on PP, a shared etiology, or confounding from environmental factors.

Inflammation contributes to atherosclerosis and coronary artery disease (CAD). In order to help identify therapeutic targets, it is important to ascertain whether biomarkers associated with CAD risk are causal. In a recent meta-analysis of clinical trials, neutrophil-to lymphocyte ratio (NLR) was associated with increased cardiovascular risk 1 . We investigate a potential causal nature of this relationship by performing Mendelian randomization (MR) analyses

Funder: National Institute for Health Research (NIHR), It remains unclear whether the association between obstructive sleep apnoea (OSA), a form of sleep-disordered breathing (SDB), and atrial fibrillation (AF) is causal or mediated by shared co-morbidities such as obesity. Existing observational studies are conflicting and limited by confounding and reverse causality. We performed Mendelian randomisation (MR) to investigate the causal relationships between SDB, body mass index (BMI) and AF. Single-nucleotide polymorphisms associated with SDB (n = 29) and BMI (n = 453) were selected as instrumental variables to investigate the effects of SDB and BMI on AF, using genetic association data on 55,114 AF cases and 482,295 controls. Primary analysis was conducted using inverse-variance weighted MR. Higher genetically predicted SDB and BMI were associated with increased risk of AF (OR per log OR increase in snoring liability 2.09 (95% CI 1.10-3.98), p = 0.03; OR per 1-SD increase in BMI 1.33 (95% CI 1.24-1.42), p < 0.001). The association between SDB and AF was not observed in sensitivity analyses, whilst associations between BMI and AF remained consistent. Similarly, in multivariable MR, SDB was not associated with AF after adjusting for BMI (OR 0.68 (95% CI 0.42-1.10), p = 0.12). Higher BMI remained associated with increased risk of AF after adjusting for OSA (OR 1.40 (95% CI 1.30-1.51), p < 0.001). Elevated BMI appears causal for AF, independent of SDB. Our data suggest that the association between SDB, in general, and AF is attributable to mediation or confounding from obesity, though we cannot exclude that more severe SDB phenotypes (i.e., OSA) are causal for AF.

Funder: NIHR Cambridge Biomedical Research Centre, Clustering genetic variants based on their associations with different traits can provide insight into their underlying biological mechanisms. Existing clustering approaches typically group variants based on the similarity of their association estimates for various traits. We present a new procedure for clustering variants based on their proportional associations with different traits, which is more reflective of the underlying mechanisms to which they relate. The method is based on a mixture model approach for directional clustering and includes a noise cluster that provides robustness to outliers. The procedure performs well across a range of simulation scenarios. In an applied setting, clustering genetic variants associated with body mass index generates groups reflective of distinct biological pathways. Mendelian randomization analyses support that the clusters vary in their effect on coronary heart disease, including one cluster that represents elevated body mass index with a favourable metabolic profile and reduced coronary heart disease risk. Analysis of the biological pathways underlying this cluster identifies inflammation as potentially explaining differences in the effects of increased body mass index on coronary heart disease.

(1) Aim: To investigate the causal effects of T2DM liability and glycated haemoglobin (HbA1c) levels on various cardiovascular disease outcomes, both in the general population and in non-diabetic individuals specifically. (2) Methods: We selected 243 variants as genetic instruments for T2DM liability and 536 variants for HbA1c. Linear Mendelian randomization analyses were performed to estimate the associations of genetically-predicted T2DM liability and HbA1c with 12 cardiovascular disease outcomes in 367,703 unrelated UK Biobank participants of European ancestries. We performed secondary analyses in participants without diabetes (HbA1c < 6.5% with no diagnosed diabetes), and in participants without diabetes or pre-diabetes (HbA1c < 5.7% with no diagnosed diabetes). (3) Results: Genetically-predicted T2DM liability was positively associated (p < 0.004, 0.05/12) with peripheral vascular disease, aortic valve stenosis, coronary artery disease, heart failure, ischaemic stroke, and any stroke. Genetically-predicted HbA1c was positively associated with coronary artery disease and any stroke. Mendelian randomization estimates generally shifted towards the null when excluding diabetic and pre-diabetic participants from analyses. (4) Conclusions: This genetic evidence supports causal effects of T2DM liability and HbA1c on a range of cardiovascular diseases, suggesting that improving glycaemic control could reduce cardiovascular risk in a general population, with greatest benefit in individuals with diabetes.

Aims/hypothesis Our aim was to investigate the relationship between average blood glucose levels and incident CHD in individuals without diabetes mellitus. Methods To investigate average blood glucose levels, we studied HbA1c as predicted by 40 variants previously shown to be associated with both type 2 diabetes and HbA1c. Linear and non-linear Mendelian randomisation analyses were performed to investigate associations with incident CHD risk in 324,830 European ancestry individuals from the UK Biobank without diabetes mellitus. Results Every one mmol/mol increase in genetically proxied HbA1c was associated with an 11% higher CHD risk (HR 1.11, 95% CI 1.05, 1.18). The dose–response curve increased at all levels of HbA1c, and there was no evidence favouring a non-linear relationship over a linear one. Conclusions/interpretations In individuals without diabetes mellitus, lowering average blood glucose levels may reduce CHD risk in a dose-dependent way. Graphical abstract

BackgroundThe relationship between coronavirus disease 2019 (Covid-19) and ischemic stroke is poorly defined. We aimed to leverage genetic data to investigate reported associations.MethodsGenetic association estimates for liability to Covid-19 and cardiovascular traits were obtained from large-scale consortia. Analyses primarily focused on critical Covid-19, defined as hospitalization with Covid-19 requiring respiratory support or resulting in death. Cross-trait linkage disequilibrium score regression was used to estimate genetic correlations of critical Covid-19 with ischemic stroke, other related cardiovascular outcomes, and risk factors common to both Covid-19 and cardiovascular disease (body mass index, smoking and chronic inflammation, estimated using C-reactive protein). Mendelian randomization analysis was performed to investigate whether liability to critical Covid-19 was associated with increased risk of any of the cardiovascular outcomes for which genetic correlation was identified.ResultsThere was evidence of genetic correlation between critical Covid-19 and ischemic stroke (rg=0.29, FDRp-value=4.65×10−3), body mass index (rg=0.21, FDR-p-value=6.26×10−6) and C-reactive protein (rg=0.20, FDR-p-value=1.35×10−4), but none of the other considered traits. In Mendelian randomization analysis, liability to critical Covid-19 was associated with increased risk of ischemic stroke (odds ratio [OR] per logOR increase in genetically predicted critical Covid-19 liability 1.03, 95% confidence interval 1.00-1.06,p-value=0.03). Similar estimates were obtained when considering ischemic stroke subtypes. Consistent estimates were also obtained when performing statistical sensitivity analyses more robust to the inclusion of pleiotropic variants, including multivariable Mendelian randomization analyses adjusting for potential genetic confounding through body mass index, smoking and chronic inflammation. There was no evidence to suggest that genetic liability to ischemic stroke increased the risk of critical Covid-19.ConclusionsThese data support that liability to critical Covid-19 is associated with an increased risk of ischemic stroke. The host response predisposing to severe Covid-19 is likely to increase the risk of ischemic stroke, independent of other potentially mitigating risk factors.

Background The causal relevance of elevated blood pressure for several cardiovascular diseases (CVDs) is uncertain, as is the population impact of blood pressure lowering. This study systematically assesses evidence of causality for various CVDs in a 2‐sample Mendelian randomization framework, and estimates the potential reduction in the prevalence of these diseases attributable to long‐term population shifts in the distribution of systolic blood pressure (SBP). Methods and Results We investigated associations of genetically predicted SBP as predicted by 256 genetic variants with 21 CVDs in UK Biobank, a population‐based cohort of UK residents. The sample consisted of 376 703 participants of European ancestry, aged 40 to 69 years at recruitment. Genetically predicted SBP was positively associated with 14 of the outcomes ( P Conclusions Risk of many CVDs is influenced by long‐term differences in SBP. The burden of a broad range of CVDs could be substantially reduced by long‐term population‐wide reductions in the distribution of blood pressure.

Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.

BackgroundGenetic variants can be used to prioritize risk factors as potential therapeutic targets via Mendelian randomization (MR). An agnostic statistical framework using Bayesian model averaging (MR-BMA) can disentangle the causal role of correlated risk factors with shared genetic predictors. Here, our objective is to identify lipoprotein measures as mediators between lipid-associated genetic variants and coronary artery disease (CAD) for the purpose of detecting therapeutic targets for CAD.MethodsAs risk factors we consider 30 lipoprotein measures and metabolites derived from a high-throughput metabolomics study including 24,925 participants. We fit multivariable MR models of genetic associations with CAD estimated in 453,595 participants (including 113,937 cases) regressed on genetic associations with the risk factors. MR-BMA assigns to each combination of risk factors a model score quantifying how well the genetic associations with CAD are explained. Risk factors are ranked by their marginal score and selected using false discovery rate (FDR) criteria. We perform sensitivity and replication analyses varying the dataset for genetic associations with CAD.ResultsIn the main analysis, the top combination of risk factors ranked by the model score contains apolipoprotein B (ApoB) only. ApoB is also the highest ranked risk factor with respect to the marginal score (FDR< 0.005). Additionally, ApoB is selected in all replication analyses. No other measure of cholesterol or triglyceride is consistently selected otherwise.ConclusionsOur agnostic genetic investigation prioritizes ApoB across all datasets considered, suggesting that ApoB, representing the total number of hepatic-derived lipoprotein particles, is the primary lipid determinant of CAD.Key messagesIt is a common consensus that lipoproteins increase cardiovascular disease risk, yet little is known about the exact mechanisms.We use genetic associations with high-throughput metabolomics features to draw a detailed picture of lipid traits and characteristics allowing for an unprecedented resolution when considering lipids as risk factors for cardiovascular disease.This study integrates genetic data from a large scale metabolomics study including 25,000 samples and the largest study on cardiovascular disease risk including 113,937 cases and 339,658 controls.MR-BMA, a novel algorithm for multivariable MR (Zuber and Burgess, Nature Communications 2020; 11(1):29) is used to identify the most likely causal lipid determinants of cardiovascular disease from a large set of candidate risk factors with shared genetic predictors.Our agnostic genetic investigation prioritizes apolipoprotein B across all datasets considered, suggesting that apolipoprotein B, representing the total number of hepatic-derived lipoprotein particles, is the primary lipid determinant of cardiovascular disease risk.

Dipender Gill and Stephen Burgess discuss the accompanying study by James Yarmolinsky and colleagues investigating the associations between genetically-proxied inhibition of antihypertensive drug targets and risk of common cancer subtypes using Mendelian randomization.