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BackgroundHigher urate levels associate with higher systolic blood pressure (SBP) in adults, and in pregnancy, with lower offspring birthweight. Mendelian randomization (MR) analyses suggest a causal effect of higher urate on higher SBP and of higher maternal SBP on lower offspring birthweight. If urate causally reduces birthweight, it might confound the effect of SBP on birth weight. We therefore tested for a causal effect of maternal urate on offspring birthweight.MethodsWe tested the association between maternal urate levels and offspring birthweight using multivariable linear regression in UK Biobank (UKB; n=133,187) and Exeter Family Study of Childhood Health (EFSOCH; n=872). We conducted two-sample MR to test for a causal effect of maternal urate (114 single nucleotide polymorphisms [SNPs]; n=288,649 European-ancestry) on offspring birthweight (n=406,063 European-ancestry; maternal SNP effect estimates adjusted for fetal effects). Using one-sample MR (n=199,768 UKB women), we also tested for a causal relationship between urate and SBP.ResultsHigher maternal urate was associated with lower offspring birthweight in UKB (28g lower birthweight per 1-SD higher urate [95% CI: -31, -25]; P=1.8×10−75), with a similar effect estimate in EFSOCH (22g [95%CI: -50, 6]; P=0.13). The MR causal effect estimate was directionally consistent, but smaller (−11g [95% CI: -25, 3]; PIVW=0.13). In women, higher urate was causally associated with higher SBP (1.7 mmHg higher SBP per 1-SD higher urate [95% CI: 1.4, 2.1]; P=7.8×10−22) consistent with that previously published in women and men.ConclusionsThe marked attenuation of the MR result of maternal urate on offspring birthweight, compared to the multivariable regression result suggests previous observational associations may be confounded. The 95% CIs of the MR result included the null but suggest a possible weak effect on birthweight. Maternal urate levels are unlikely to be an important contributor to offspring birthweight.Key MessagesPrevious research suggests higher maternal serum urate in pregnancy is associated with lower offspring birthweight, and Mendelian randomization studies suggest a causal relationship between urate and systolic blood pressure (SBP), and SBP and birthweight; a causal effect of urate on birthweight has not yet been estimated, and thus it is also unknown whether it confounds maternal SBP-birthweight effects.The causal effect estimate of urate on offspring birthweight was directionally consistent, but weaker than, observational estimates; the estimate had 95% confidence intervals which included the null.This study confirmed a causal association between serum urate and higher SBP in women consistent with that published from a sample of both women and men.Maternal urate is unlikely to be a major determinant of birthweight or an important confounder of the causal relationship between SBP and lower birthweight.

Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort studies. We show that existing sensitivity analyses, which are often conservative and difficult to implement, can be formulated in our framework and made significantly more informative via auxiliary information on the population. We conduct a simulation study to evaluate the finite sample performance of our inference procedure and conclude with a substantive motivating example on the causal effect of education on income in the highly-selected UK Biobank cohort. We demonstrate that our method can produce informative bounds using plausible population-level auxiliary constraints. We implement this method in the R package selectioninterval., 23 pages, 6 figures

BackgroundMultivariable Mendelian randomization (MVMR) is a statistical approach using genetic variants as instrumental variables to estimate direct causal effects of multiple exposures on an outcome simultaneously. In univariable MR findings are typically illustrated through plots created using summary data from genome-wide association studies (GWAS), yet analogous plots for MVMR have so far been unavailable due to the multidimensional nature of the analysis.MethodsWe propose a radial formulation of MVMR, and an adapted Galbraith radial plot, which allows for the direct effect of each exposure within an MVMR analysis to be visualised. Radial MVMR plots facilitate the detection of outlier variants, indicating violations of one or more assumptions of MVMR. In addition, the RMVMR R package is presented as accompanying software for implementing the methods described.ResultsWe demonstrate the effectiveness of the radial MVMR approach through simulations and applied analyses, estimating the effect of lipid fractions on coronary heart disease (CHD). We find evidence of a protective effect of high-density lipoprotein (HDL) and a positive effect of low-density lipoprotein (LDL) on CHD, however, the protective effect of HDL appeared to be smaller in magnitude when removing outlying variants. In combination with simulated examples, we highlight how important features of MVMR analyses can be explored using a range of tools incorporated within the RMVMR R package.ConclusionsRadial MVMR effectively visualises causal effect estimates, and provides valuable diagnostic information with respect to the underlying assumptions of MVMR.

Mendelian randomization (MR) is an epidemiological approach that uses genetic variants as instrumental variables for estimating the causal effect of a modifiable but likely confounded exposure on an outcome. Standard MR usually assumes that all included genetic variants are valid instruments and there is a single homogeneous causal effect of the exposure on the outcome. We allow violations of both assumptions such that the variants can be divided into clusters identifying distinct causal effects driven by different biological mechanisms and/or horizontal pleiotropy. We adapted the Agglomerative Hierarchical Clustering (AHC) method developed for individual-level data to the summary data MR setting, enabling the detection of such variant clusters using only genome-wide summary statistics. We also extend the method to handle two outcomes and a common exposure to aid investigation of the mechanisms of multimorbidity. We conduct Monte Carlo simulations to evaluate the performance of our ‘MR-AHC’ algorithm compared to the existing MR-Clust method, showing that it is both reliable and computationally efficient: it detects variant clusters with high accuracy and is much faster than MR-Clust. In an applied example, we use our method to analyze the causal effects of high body fat percentage on a pair of well-known multimorbid conditions, type 2 diabetes and osteoarthritis, discovering distinct variant clusters reflecting the heterogeneous shared pathways.

BackgroundCalcium channel blockers (CCBs) are common antihypertensive medications. Pharmacogenetic variants affect CCB clinical outcomes, although effect sizes are modest in community samples. Variation in patient characteristics may also predict CCB outcomes, and variation attributable to relevant polygenic scores is less prone to confounding. We aimed to test associations between genetically predicted patient characteristics plus pharmacogenetic variants with CCB outcomes in a large community cohort.MethodsWe extended our analysis of 32,000 UK Biobank dihydropiridine CCBs treated participants (mean duration 5.9 years) testing 23 variants, whereNUMA1rs10898815 andRYR3rs877087 showed the most robust associations (for discontinuation and heart failure, respectively). We calculated polygenic scores for systolic and diastolic blood pressures (SBP and DBP), body fat mass, waist hip ratio, lean mass, serum calcium, eGFR, lipoprotein A, urinary sodium, and liver fibrosis. Outcomes were CCB discontinuation, heart failure, coronary heart disease and chronic kidney disease.ResultsFor heart failure, the highest risk 20% of polygenic scores for fat mass, lean mass and lipoprotein A were associated with increased risks (Hazard-Ratio (HR)Fat-mass 1.46, 95% CI 1.25-1.70, p=1*10-6; HRLean-mass 1.20, 95%CI 1.04-1.38, p=0.01; HRLipoproteinA 1.29, 95% CI 1.12 to 1.48, p= 4*10-4), versus the lowest risk 20% of each score respectively. Across the cohort,RYR3T-allele modestly increased heart failure risks (HR 1.13: 1.02-1.25) versus non-carriers, but in subsets with high fat mass, lean mass, and lipoprotein A scores, estimates were substantially larger, e.g., in females aged 65-70 the heart failure Relative Risk was 4.4 (95% CI 1.54-12.4) versus no T-alleles and low scores.For CCB discontinuation, high polygenic scores for fat mass and lean mass increased risks versus the lowest 20%, whereas high SBP and DBP scores decreased discontinuation risks. Hazard ratios for discontinuation with the pharmacogenetic NUMA1 rs10898815 A-allele (overall HR 1.07: 1.02-1.12) were higher (HR 1.17: 1.05-1.29) in those with high polygenic scores for fat mass and lean mass.ConclusionPolygenic scores affecting adiposity and lipoprotein A levels add to known pharmacogenetic variants in predicting key clinical outcomes in CCB treatment. Combining pharmacogenetic variants and relevant individual characteristic polygenic scores may help for personalizing prescribing.What is needed, what do we add?We previously showed that pharmacogenetic variants inRYR3andNUMA1were associated with key clinical outcomes in community CCB patients, although effect sizes were modest. Various patient characteristics reportedly affect CCB outcomes. We therefore tested effects of relevant patient characteristics using polygenic scores. They minimize the effect of unmeasured confounders as genotypes are invariant since conception and reflect lifetime exposure to the risk factor. We showed that combining associated scores with the pharmacogenetic variants improved outcome prediction.

Mendelian Randomization (MR) is a popular method for using genetics to estimate the causal effect of a modifiable exposure on a health outcome. Single Nucleotide Polymorphisms (SNPs) are typically selected for inclusion if they pass a genome-wide significance threshold in order to guarantee that they are strong genetic instruments, but this also induces Winner’s curse, as SNP-exposure associations tend to be overestimated. In this paper, we consider how to combine SNP-exposure data from discovery and replication samples using two-sample and three-sample approaches to best account for Winner’s curse, weak instrument bias, and pleiotropy within a summary data MR framework, using only GWAS summary statistics. After reviewing several existing methods, that often correct for Winner’s curse at the individual SNP level, we propose a simple alternative based on the technique of regression calibration that enacts a global correction to the causal effect estimate directly. This approach does not only correct for Winner’s curse, but also simultaneously accounts for weak instruments bias. Regression calibration can be used with a wide range of existing MR methods, including pleiotropy-robust methods such as median-based and mode-based estimators. Extensive simulations and real data examples are used to illustrate the utility of the new approach. Software is provided for users to implement the method in practice.Author SummaryMendelian randomization is a method to explore causation in health research which exploits the random inheritance of genes from parents to offspring as a ‘natural experiment’. It attempts to quantify the effect of intervening and modifying a health exposure, such as a person’s body mass, on a downstream outcome such as blood pressure. Causal estimates obtained using this method can be strongly influenced by the set of genes used, or more specifically, the rationale used to select them. For example, selecting only genes that are strongly associated with the health exposure can induce bias due to the ‘Winner’s curse’. Unfortunately, using genes with a small association can lead to so-called ‘weak instrument’ bias leading to a no-win paradox. In this paper, we present a novel approach based on the technique of regression calibration to de-bias causal estimates in an MR study. Our approach relies on the use of two independent samples for the exposure (discovery and replication) to estimate the amount of bias that is expected for a specific set of genes, so that causal estimates can be re-calibrated accordingly. We use extensive simulations and applied examples to compare our approach to current methods and provide software for researchers to implement our approach in future studies.

To estimate the effect of rs4149056 (SLCO1B1*5) genotype (decreases statin transport) on cholesterol control and treatment duration in male and female primary care patients prescribed common statin medications.This study comprised 69 185 European-ancestry UK Biobank cohort participants prescribed simvastatin or atorvastatin (aged 40-79 years at first prescription, treatment duration 1 month to 29 years, mean 5.7 years). Principal outcomes were clinically high total cholesterol (5 mmol/L) at baseline, plus treatment discontinuation.A total of 48.4% of 591 females homozygous for SLCO1B1*5 decreased function genotype had raised cholesterol vs 41.7% of those with functioning SLCO1B1 (odds ratio 1.31, 95% confidence interval [CI] 1.1-1.55, P = .001). Fewer males had high cholesterol and the genotype effect was attenuated. In primary care prescribing, females homozygous for SLCO1B1*5 were more likely to stop receiving these statins (29.5%) than women with normal SLCO1B1 (25.7%) (hazard ratio [HR] 1.19, 95% CI 1.03-1.37, P = .01), amounting to five discontinuations per 100 statin-years in the SLCO1B1*5 group vs four in the normal SLCO1B1 function group. This remained significant after the first year of treatment (HR for discontinuing1 year after first prescription 1.3, 95% CI 1.08-1.56, P = .006). In men SLCO1B1*5 was only associated with treatment discontinuation in the first year.In this large community sample of patients on commonly prescribed statins, the SLCO1B1*5 decreased function variant had much larger effects on cholesterol control and treatment duration in women than in men. Efforts to improve the effectiveness of statin therapy in women may need to include SLCO1B1*5 genotype-guided statin selection.

Outcome values in randomized controlled trials (RCTs) may be missing not at random (MNAR), if patients with extreme outcome values are more likely to drop out (e.g., due to perceived ineffectiveness of treatment, or adverse effects). In such scenarios, estimates from complete case analysis (CCA) and multiple imputation (MI) will be biased. The trimmed means (TM) estimator operates by setting missing values to the most extreme value, and then “trimming” away equal fractions of both treatment groups, estimating the treatment effect using the remaining data. The TM estimator relies on two assumptions, which we term the “strong MNAR” and “location shift” assumptions. In this article, we derive formulae for the bias resulting from the violation of these assumptions for normally distributed outcomes. We propose an adjusted estimator, which relaxes the location shift assumption and detail how our bias formulae can be used to establish the direction of bias of CCA, MI and TM estimates under a range of plausible data scenarios, to inform sensitivity analyses. The TM approach is illustrated with simulations and in a sensitivity analysis of the CoBalT RCT of cognitive behavioural therapy (CBT) in 469 individuals with 46 months follow-up. Results were consistent with a beneficial CBT treatment effect. The MI estimates are closer to the null than the CCA estimate, whereas the TM estimate was further from the null. We propose using the TM estimator as a sensitivity analysis for data where it is suspected that extreme outcome values are missing.

ImportanceThe benefit of physical activity (PA) for increasing longevity is well-established, however, the impact of diurnal timing of PA on mortality remains poorly understood.ObjectiveTo derive PA patterns and investigate their associations with all-cause mortality.Design, Setting, and ParticipantsThis population-based prospective cohort study analyzed UK Biobank baseline data collected between 2006 and 2010 from adults aged 40 to 79 years in England, Scotland, and Wales. Participants were invited by email to participate in an additional accelerometer study from 2013 to 2015, 7 years (median) after baseline. Participants’ vital status was assessed via linkage with mortality registries through September 2021 (England/Wales) and October 2021 (Scotland). Data analyses were performed in July 2022.ExposureLoading scores of functional principal components (fPCs) obtained from wrist accelerometer-measured activity metrics. The ‘Euclidean norm minus one’ was used as a summary metric of bodily acceleration aggregated to 24 hourly averages across seven days. These timeseries were used for functional principal component analysis (fPCA).Main Outcomes and MeasuresExamination of time-dependent PA patterns obtained using functional principal component analysis in relation to all-cause mortality estimated by multivariable Cox proportional hazard models.ResultsAmong 96,361 participants (56% female), 2,849 deaths occurred during 6.9 (SD 0.9) years of follow-up. Four distinct functional principal components (fPCs) accounted for 96% of the variation of the accelerometry data. The association of fPC1 and mortality was non-linear (pConclusions and RelevanceHigher risks of death were found for patterns denoting lower overall PA and higher late day and nocturnal PA. Conversely, higher levels of PA, distributed continuously, in one, or in two activity peaks during daytime, were inversely associated with lower mortality. Daily timing of PA may have public health implications, as our results suggest that some level of elevated PA during the day and a nighttime rest is associated with longevity.

Self-reported (SR) shorter/longer sleep duration, insomnia, and evening preference are associated with hyperglycaemia in observational analyses, with similar results observed in small studies using accelerometer-derived (AcD) sleep traits. Mendelian randomization (MR) studies support an effect of SR insomnia, but not other sleep traits, on glycated haemoglobin (HbA1c). We used MR to explore effects of AcD sleep traits (duration, mid-point least active 5-hours, mid-point most active 10-hours, sleep fragmentation, and efficiency) on HbA1c in European adults from the UK Biobank (UKB) (n = 73,797) and the MAGIC consortium (n = 149,054). Main and sensitivity MR analyses showed no causal effect of any AcD sleep trait on HbA1c. Similar MR results for SR sleep traits in the UKB sub-sample with AcD measures suggested our results were not explained by selection bias. Genetic correlation analyses suggested complex relationships between SR and AcD traits indicating that they may reflect different types of exposure.

Introduction: Major depressive disorder (MDD) has a significant impact on global burden of disease. Complications in clinical management can occur when response to pharmacological modalities is considered inadequate and symptoms persist (treatment-resistant depression (TRD)). We aim to investigate inflammation, proxied by C-reactive protein (CRP) levels, and body mass index (BMI) as putative causal risk factors for depression and subsequent treatment resistance, leveraging genetic information to avoid confounding via Mendelian Randomization (MR). Methods: We used the European UK Biobank subcohort (n = 451, 025), the mental health questionnaire (MHQ) and clinical records. For treatment resistance, a previously curated phenotype based on GP records and prescription data was employed. We applied univariable and multivariable MR models to genetically predict the exposures and assess their causal contribution to a range of depression outcomes . We used weak and pleiotropy, robust estimation techniques within univariable, multivariable and mediation MR models in order to address our research question with maximum rigour. In addition, we developed a novel statistical procedure to apply pleiotropy robust multivariable MR to one sample data and employed an unfamiliar bootstrap procedure to accurately quantify estimate uncertainty in mediation analysis which outperforms standard approaches. Results: In univariable MR models, genetically predicted BMI was positively associated with depression outcomes, including MDD and TRD, with a larger magnitude in women and with age acting as a moderator of the effect of BMI on PHQ9 (p = 0.011). Multivariable MR analyses suggested an independent causal effect of BMI on TRD not through CRP. Our mediation analyses suggested that the effect of CRP on PHQ9 was partly mediated by BMI. Individuals with TRD (n = 2, 199) observationally had higher CRP and BMI compared with individuals with MDD alone and healthy controls. A positive causal association was noted for both the exposures, but in multivariable analyses only BMI retained statistical significance at the 5% level. Discussion: Our work supportst the assertion BMI exerts a causal effect on a range of clinical and questionnaire-based depression phenotypes, with the effect being stronger in women and in younger individuals. We show that this effect is independent of inflammation proxied by CRP levels as the effects of CRP do not persist when jointly estimated with BMI. This is consistent with previous evidence suggesting that overweight contributed to depression even in the absence of any metabolic consequences. It appears that BMI exerts an effect on TRD that persists when we account for BMI influencing MDD.

Positivity for islet autoantibodies is used for diagnosis of type 1 diabetes. However, the importance of the autoantibody level at diagnosis of type 1 diabetes is not clear. Here, we assessed the association of glutamate decarboxylase (GADA), islet antigen-2 (IA-2A) and zinc transporter 8 (ZnT8A) autoantibody levels, measured using radiobinding assays, on genetic and clinical characteristics at diagnosis of 1536 participants with diabetes who were positive for these autoantibodies. We show that GADA and IA-2A levels had bimodal distributions, but ZnT8A level did not. The comparison of genetic and clinical characteristics between high and low level categories showed high GADA level was associated with older age at diagnosis, female sex and HLA-DR3-DQ2, whereas high IA-2A level was associated with younger age of diagnosis, ZnT8A positivity and HLA-DR4-DQ8. We replicated our findings in an independent cohort of 427 people with type 1 diabetes where autoantibodies were measured using enzyme-linked immunosorbent assays. In conclusion, Islet autoantibody levels provide additional information over positivity in type 1 diabetes at diagnosis. The bimodality of islet autoantibody levels highlights the novel aspect of heterogeneity of type 1 diabetes which may have implications on prediction, treatment and prognosis.Islet autoantibodies are commonly used in the diagnosis and prediction of type 1 diabetes. They are well established as the biomarkers of the underlying autoimmune pathogenesis (1). Autoantibodies to islet cell antigen (ICA), glutamate decarboxylase (GADA), islet antigen-2 (IA-2A), insulin (IAA) and zinc transporter 8 (ZnT8A) are the most commonly used islet autoantibodies at diagnosis (2). As detectable islet autoantibodies overlap between health and disease, a test is usually considered positive for a given islet autoantibody when the antibody level is higher than a 97.5–99th centile of a control population (3; 4). In routine clinical practice, quantitative islet autoantibody results are usually interpreted as positive or negative, and the level of the islet autoantibody, is not thought to be clinically meaningful.Islet autoantibody levels may provide additional information over positivity in type 1 diabetes at diagnosis. Similar to type 1 diabetes, autoantibodies to a specific antigen are commonly used for diagnosis in many other autoimmune diseases (such as TSH receptor antibodies in Graves’ disease and Rheumatoid Factor and Citrullinated Protein in rheumatoid arthritis). For Graves’ disease and rheumatoid arthritis, along with autoantibody positivity for these antigens, autoantibody level at diagnosis is associated with disease severity, prognosis and treatment success (5; 6). Multiple studies have shown a role for islet autoantibody level in the prediction of onset of type 1 diabetes, those with a higher levels of IA-2A, IAA and ICA have an increased risk of developing type 1 diabetes in at-risk populations (1; 7-9). However, it is not clear if the islet autoantibody level at diagnosis of type 1 diabetes, in addition to its interpretation as ‘positive’, is associated with the clinical phenotype similar to other autoimmune diseases.In this study, we undertook an analysis of GADA, IA-2A and ZnT8A levels at diagnosis in a large cohort of participants with type 1 diabetes, assessing the association of islet autoantibody levels on genetic and clinical characteristics at diagnosis in people with type 1 diabetes.

Pharmacogenetic variants impact dihydropyridine calcium-channel blockers (dCCBs; e.g., amlodipine) treatment efficacy, yet evidence on clinical outcomes in routine primary care is limited. Reported associations in pharmacogenomics knowledge base PharmGKB have weak supporting evidence. We aimed to estimate associations between reported pharmacogenetic variants and incident adverse events in a community-based cohort prescribed dCCB.We analysed up to 32 360 UK Biobank participants prescribed dCCB in primary care (from UK general practices, 1990-2017). We investigated 23 genetic variants. Outcomes were incident diagnosis of coronary heart disease, heart failure (HF), chronic kidney disease, oedema and switching antihypertensive medication.Participants were aged 40-79 years at first dCCB prescription. Carriers of rs877087 T allele in RYR3 had increased risk of hazard ratio (HF 1.13: 95% confidence interval 1.02 to 1.25, P = .02). Although nonsignificant after multiple testing correction, the association is consistent with prior evidence. We estimated that if rs877087 T allele could experience the same treatment effect as noncarriers, the incidence of HF in patients prescribed dCCB would reduce by 9.2% (95% confidence interval 3.1 to 15.4). In patients with a history of heart disease prior to dCCB (n = 2296), rs877087 homozygotes had increased risk of new coronary heart disease or HF compared to CC variant. rs10898815 in NUMA1 and rs776746 in CYP3A5 increased likelihood of switching to an alternative antihypertensive. The remaining variants were not strongly or consistently associated with studied outcomes.Patients with common genetic variants in NUMA1, CYP3A5 and RYR3 had increased adverse clinical outcomes. Work is needed to establish whether outcomes of dCCB prescribing could be improved by prior knowledge of pharmacogenetics variants supported by clinical evidence of association with adverse events.

BackgroundMissing data is a common problem in regression modelling. Much of the literature focuses on handling missing outcome variables, but there are also challenges when dealing with missing predictor information, particularly when trying to build prediction models for use in practice.MethodsWe develop a flexible Bayesian approach for handling missing predictor information in regression models. For prediction this provides practitioners with full posterior predictive distributions for both the missing predictor information and the outcome variable, conditional on the observed predictors. We apply our approach to a previously proposed treatment selection model for type 2 diabetes second-line therapies. Our approach combines a regression model and a Dirichlet process mixture model (DPMM), where the former defines the treatment selection model and the latter provides a flexible way to model the joint distribution of the predictors.ResultsWe show that under missing-completely-at-random (MCAR) and missing-at-random (MAR) assumptions (with respect to the missing predictors), the DPMM can model complex relationships between predictor variables, and predict missing values conditionally on existing information. We also demonstrate that in the presence of multiple missing predictors, the DPMM model can be used to explore which variable(s), if collected, could provide the most additional information about the likely outcome.ConclusionsOur approach can provide practitioners with supplementary information to aid treatment selection decisions in the presence of missing data, and can be readily extended to other types of response model.Key MessagesMissing predictor variables present a significant challenge when building and implementing prediction models in clinical practice.Removing individuals with missing information and performing a complete case analysis can lead to imprecision and bias. Multiple imputation approaches typically translate uncertainty through prediction model parameter standard errors, as opposed to a consistent joint probability model.Alternatively, a Bayesian approach using Dirichlet process mixture models (DPMMs) offers a flexible way to model complex joint distributions of predictor variables, which can be used to estimate posterior (predictive) distributions for the missing predictors, conditional on the observed predictors.Using a DPMM, in this way allows uncertainties around missing predictor data to be propagated through to a prediction model of interest using a Bayesian hierarchical framework. This allows prediction models to be developed using datasets with incomplete predictor information (assuming missing-completely-at-random/missing-at-random). Furthermore, predictions can be made on new individuals even if they have incomplete predictor information (under the same assumptions).This approach provides full posterior predictive probability distributions for both missing predictor variables and the outcome variable, allowing a wide range of probabilistic models outputs to be derived to support clinical decision making.

ImportanceTelomere attribution is one of biological aging hallmarks and may be intervened to target multiple aging-related diseases, including Alzheimer’s disease and Alzheimer’s disease related dementias (AD/ADRD).ObjectiveTo assess associations of leukocyte telomere length (TL) with AD/ADRD and early markers of AD/ADRD, including cognitive performance and brain magnetic resonance imaging (MRI) phenotypes.Design, Setting, and ParticipantsData from European-ancestry participants in the UK Biobank (n=435,046) were used to evaluate whether mid-life leukocyte TL is associated with incident AD/ADRD over a mean follow-up of 12.2 years. In a subsample without AD/ADRD and with brain imaging data (n=43,390), we associated TL with brain MRI phenotypes related to AD or vascular dementia pathology.ExposuresRelative mean leukocyte TL at baseline was measured in T/S ratio, using a multiplex quantitative polymerase chain reaction technique by comparing the amount of the telomere amplification product (T) to that of a single-copy gene (S).Main Outcomes and MeasuresAD/ADRD was confirmed by mapping multi-source data to 3-charcter ICD codes (G30; F00; F01; F03; G31), excluding other-cause dementias (F02).ResultsLonger TL was associated with a lower risk of incident AD/ADRD (adjusted HR [aHR] per SD=0.93, 95% CI 0.90 to 0.96, P=3.37×10−7). Longer TL also was associated with better cognitive performance in specific cognitive domains, larger hippocampus volume (adjusted standardized β [aβ] per SD=0.012, 95% CI 0.003 to 0.021, P=0.012), lower total volume of white matter hyperintensities (aβ per SD= -0.015, 95% CI -0.024 to -0.006, P=0.002), and higher fractional anisotropy (FA; aβ per SD=0.018, 95% CI 0.009 to 0.027, P=9.44×10−5) and lower mean diffusivity (MD; aβ per SD=0.045, 95% CI 0.035 to 0.056, P=1.07×10−17) in the fornix.Conclusions and RelevanceLonger TL is a protective factor against AD/ADRD, cognitive impairment, and brain structural lesions toward the development of AD/ADRD. A better understanding of underlying mechanisms may help improve diagnosis and management of AD/ADRD in older adults.Key PointsQuestionIs leukocyte telomere length (TL) associated with Alzheimer’s disease (AD) and AD related dementias (AD/ADRD) and early markers of AD/ADRD, including cognitive performance and brain magnetic resonance imaging phenotypes?FindingsLonger TL was associated with a lower risk of incident AD/ADRD, larger hippocampus volume, lower total volume of white matter hyperintensities, higher fractional anisotropy and lower mean diffusivity in the fornix.MeaningLonger TL in midlife may play a protective role against AD/ADRD, and a better understanding of underlying mechanisms may help improve diagnosis and management of AD/ADRD in older adults.

BackgroundWith genome-wide association data for many exposures and outcomes now available from large biobanks, one-sample Mendelian randomization (MR) is increasingly used to investigate causal relationships. Many robust MR methods are available to address pleiotropy, but these assume independence between the gene-exposure and gene-outcome association estimates. Unlike in two-sample MR, in one-sample MR the two estimates are obtained from the same individuals, and the assumption of independence does not hold in the presence of confounding.MethodsWith simulations mimicking a typical study in UK Biobank we assessed the performance, in terms of bias and precision of the MR estimate, of the fixed-effect and (multiplicative) random-effects meta-analysis method, weighted median estimator, weighted mode estimator and MR-Egger regression, used in both one-sample and two-sample data. We considered scenarios differing for: presence/absence of a true causal effect; amount of confounding; presence and type of pleiotropy (none, balanced or directional).ResultsEven in the presence of substantial correlation due to confounding, all methods performed well when used in one-sample MR except for MR-Egger, which resulted in bias reflecting direction and magnitude of the confounding. Such bias was much reduced in the presence of very high variability in instrumental strength across variants (I2GX of 97%).ConclusionsTwo-sample MR methods can be safely used for one-sample MR performed within large biobanks, expect for MR-Egger. MR-Egger is not recommended for one-sample MR unless the correlation between the gene-exposure and gene-outcome estimates due to confounding can be kept low, or the variability in instrumental strength is very high.Key MessagesCurrent availability of phenotypic and genetic data from large biobanks, such as UK Biobank, has led to increasing use of one-sample Mendelian randomization (MR) to investigate causal relationships in epidemiological researchRobust MR methods have been developed to address pleiotropy, but they assume independence between the gene-exposure and gene-outcome association estimates; this holds in two-sample MR but not in one-sample MRWe illustrate the practical implications, in terms of bias and precision of the MR causal effect estimate, of using robust two-sample methods in one-sample MR studies performed within large biobanksTwo-sample MR methods can be safely used for one-sample MR performed within large biobanks, expect for MR-Egger regressionMR-Egger is not recommended for one-sample MR unless the correlation between the gene-exposure and gene-outcome estimates due to confounding can be kept low, or the variability in instrumental strength is very high

Multivariable Mendelian randomization (MVMR) is an instrumental variable technique that generalizes the MR framework for multiple exposures. Framed as a linear regression problem, it is subject to the pitfall of multi-collinearity. The bias and efficiency of MVMR estimates thus depends on the correlation of exposures. Dimensionality reduction techniques such as principal component analysis (PCA) provide transformations of all the included variables that are effectively uncorrelated. We propose the use of sparse PCA (sPCA) algorithms that create principal components of subsets of the exposures and may provide more interpretable and reliable MR estimates. The approach consists of three steps. We first apply a sparse dimension reduction method and transform the variant-exposure summary statistics to principal components. We then choose a subset of the principal components based on data-driven cutoffs, and estimate their strength as instruments with an adjusted F-statistic. Finally, we perform MR with these transformed exposures. This pipeline is demonstrated in a simulation study of highly correlated exposures and an applied example using summary data from a genome-wide association study of 118 highly correlated lipid metabolites. As a positive control, we tested the causal associations of the transformed exposures on CHD. Compared to the conventional inverse-variance weighted MVMR method and a weak-instrument robust MVMR method (MR GRAPPLE), sparse component analysis achieved a superior balance of sparsity and biologically insightful grouping of the lipid traits.Key MessagesIn multivariable MR, investigation of multiple highly correlated exposures can hinder the efficiency of the estimators and mask true associations.Dimensionality reduction approaches such as principal component analysis (PCA) appear to be effective in summarising the variant-exposure summary statistics data in an example of correlated metabolite data.Sparse PCA approaches have the additional benefit of providing interpretable PCs as only a few exposures contribute to each. This benefit is shown in simulation studies where there is a gain in accuracy over PCA.In a positive control analysis, the sparse PCs were representing biologically meaningful groups of metabolites (VLDL, LDL, HDL) and in general were associated with coronary heart disease in an anticipated mannert.

Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner. Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.

OBJECTIVE To examine the effects of sleep traits on glycated hemoglobin (HbA1c). RESEARCH DESIGN AND METHODS This study triangulated evidence across multivariable regression (MVR) and one- (1SMR) and two-sample Mendelian randomization (2SMR) including sensitivity analyses on the effects of five self-reported sleep traits (i.e., insomnia symptoms [difficulty initiating or maintaining sleep], sleep duration, daytime sleepiness, napping, and chronotype) on HbA1c (in SD units) in adults of European ancestry from the UK Biobank (for MVR and 1SMR analyses) (n = 336,999; mean [SD] age 57 [8] years; 54% female) and in the genome-wide association studies from the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC) (for 2SMR analysis) (n = 46,368; 53 [11] years; 52% female). RESULTS Across MVR, 1SMR, 2SMR, and their sensitivity analyses, we found a higher frequency of insomnia symptoms (usually vs. sometimes or rarely/never) was associated with higher HbA1c (MVR 0.05 SD units [95% CI 0.04–0.06]; 1SMR 0.52 [0.42–0.63]; 2SMR 0.24 [0.11–0.36]). Associations remained, but point estimates were somewhat attenuated after excluding participants with diabetes. For other sleep traits, there was less consistency across methods, with some but not all providing evidence of an effect. CONCLUSIONS Our results suggest that frequent insomnia symptoms cause higher HbA1c levels and, by implication, that insomnia has a causal role in type 2 diabetes. These findings could have important implications for developing and evaluating strategies that improve sleep habits to reduce hyperglycemia and prevent diabetes.

OBJECTIVES: Delirium is common in older adults, especially following hospitalization. As low vitamin D levels may be associated with increased delirium risk, we aimed to determine the prognostic value of blood vitamin D levels, extending our previous genetic analyses of this relationship. DESIGN: Prospective cohort analysis. SETTING: Community-based cohort study of adults from 22 cities across the United Kingdom (the UK Biobank). PARTICIPANTS: Adults aged 60 years or greater by the end of follow-up in the linked hospital inpatient admissions data, up to 14 years after baseline (n=351,320). MEASUREMENTS: At baseline, serum vitamin D (25-OH-D) levels were measured. We used time-to-event models to estimate Hazard Ratios (HR) and 95% Confidence Intervals (CIs) for the association between vitamin D deficiency and incident hospital-diagnosed delirium, adjusted for age, sex, assessment month, assessment center, and ethnicity. We performed Mendelian randomization genetic analysis in European participants to further investigate vitamin D and delirium risk. RESULTS: 3,634 (1.03%) participants had at least one incident hospital-diagnosed delirium episode. Vitamin D deficiency (50 nmol/L). Increased risk was not limited to the deficient group: insufficient levels (25–50 nmol/L) were also at increased risk (HR 1.38: 1.28 to 1.49, p=4*10(−18)). The association was independent of calcium levels, hospital-diagnosed fractures, dementia, and other relevant co-factors. In genetic analysis, participants carrying more vitamin D-increasing variants had reduced likelihood of incident delirium diagnosis (HR 0.80 per standard deviation increase in genetically instrumented vitamin D: 0.73 to 0.87; p=2*10(−7)). CONCLUSION: Progressively lower vitamin D levels predicted increased risks of incident hospital-diagnosed delirium, and genetic evidence supports a shared causal pathway. As low vitamin D levels are simple to detect and inexpensive and safe to correct, an intervention trial to confirm these results is urgently needed.

BackgroundDihydropiridine calcium channel blockers (dCCB) (e.g. amlodipine) are widely used for treating hypertension. Pharmacogenetic variants impact treatment efficacy, yet evidence on clinical outcomes in routine primary care is limited. We aimed to estimate associations between reported pharmacogenetic variants and incident adverse events in a community-based cohort prescribed dCCB, including in high-risk subgroups.MethodsWe analysed up to 32,360 UK Biobank European-ancestry participants prescribed dCCB in primary care electronic health records (from UK General Practices, 1990 to 2017). We investigated 23 genetic variants in 16 genes reported in PharmGKB, including CYP3A5 and RYR3. Outcomes were incident diagnosis of coronary heart disease (CHD), heart failure (HF), chronic kidney disease (CKD), edema, and switching antihypertensive medication. Secondary analysis in patients with history of heart disease was also performed.ResultsParticipants were aged 40 to 79 years at first dihydropyridine prescription (treatment duration 1 month to 40 years, mean 5.9 years). Carriers of rs877087 T allele in the ryanodine receptor 3 (RYR3) had increased risk of HF (Hazard Ratio 1.13: 95% Confidence Intervals 1.02 to 1.25, p=0.02). We estimated that if rs877087 T allele carriers were prescribed an alternative treatment the incidence of HF in patients prescribed dCCB would reduce by 9.2% (95%CI 3.1 to 15.4). In patients with a history of heart disease when first prescribed dCCB (N=2,296), RYR3 rs877087 homozygotes had increased risk of new CHD or HF compared to CC variant (HR 1.25, 95%CI 1.09 to 1.44, p=0.002). Two variants increased likelihood of switching to an alternate antihypertensive medication (rs10898815 in gene NUMA1 HR 1.16: 95%CI 1.07 to 1,27, p=0.0009; rs776746 in CYP3A5 HR 1.59: 95%CI 1.09 to 2.32, p=0.02). rs776746 in CYP3A5 also increased CKD risk (HR 2.12, p=0.002). The remaining previously reported variants were not strongly or consistently associated with the studied clinical outcomes.ConclusionsIn this large primary care cohort, patients with common genetic variants in NUMA1, CYP3A5 and RYR3 had increased adverse clinical outcomes. Work is needed to establish whether outcomes of dCCB prescribing could be improved by prior knowledge of such pharmacogenetics variants supported by clinical evidence of association with adverse events.

Funder: Expanding Excellence in England (E3); Id: http://dx.doi.org/10.13039/100006428, Funder: Issac Newton Trust, Two-sample summary data Mendelian randomization is a popular method for assessing causality in epidemiology, by using genetic variants as instrumental variables. If genes exert pleiotropic effects on the outcome not entirely through the exposure of interest, this can lead to heterogeneous and (potentially) biased estimates of causal effect. We investigate the use of Bayesian model averaging to preferentially search the space of models with the highest posterior likelihood. We develop a Metropolis-Hasting algorithm to perform the search using the recently developed MR-RAPS as the basis for defining a posterior distribution that efficiently accounts for pleiotropic and weak instrument bias. We demonstrate how our general modeling approach can be extended from a standard one-component causal model to a two-component model, which allows a large proportion of SNPs to violate the InSIDE assumption. We use Monte Carlo simulations to illustrate our methods and compare it to several related approaches. We finish by applying our approach to investigate the causal role of cholesterol on the development age-related macular degeneration.

Background Anti-TNF drugs increase the risk of serious respiratory infections and impair protective immunity following pneumococcal, influenza, and viral hepatitis vaccinations. Therefore, we sought to determine whether patients with inflammatory bowel disease treated with infliximab have attenuated serological responses following SARS-CoV-2 infection. Methods CLARITY IBD is a multicentre, prospective observational cohort study. Antibody responses in participants treated with infliximab were compared to a reference cohort treated with vedolizumab, a gut-selective anti-integrin α4β7 monoclonal antibody that is not associated with impaired vaccine responses or increased susceptibility to systemic infections. 6935 patients were recruited from 92 UK hospitals between 22nd September and 23rd December 2020. Nucleocapsid anti-SARS-CoV2 antibodies were measured using the Roche Elecsys assay. Clinical data and serum were collected every 8 weeks. Durability was defined as nonreduction in antibody level by at least 50% from baseline. Results At baseline, rates of symptomatic and proven SARS-CoV-2 infection were similar between groups. Seroprevalence was lower in infliximab- than vedolizumab-treated patients (3.4% [161/4685], vs 6.0% [134/2250], p Conclusion Seroprevalence, seroconversion in PCR-confirmed cases, and the magnitude and durability of anti-SARS-CoV2 antibodies were reduced in infliximab- compared with vedolizumab-treated patients. Serological testing and virus surveillance should be considered in patients treated with anti-TNF drugs to detect suboptimal vaccine responses, persistent infection, and viral evolution to inform public health policy.