Medication adherence is essential to ensure treatment effectiveness, but too often in routine care non-adherence compromises the desired outcome. We explore longitudinal causal modelling using observational data to estimate the time-varying effects of continuous drug adherence measures on health outcomes over a sustained period. The goal of such analyses is to quantify the potential impact of interventions to improve adherence on long-term health. We consider two established longitudinal causal approaches designed to handle time-varying confounding under the “no unmeasured confounding” (NUC) assumption: G-estimation and inverse probability of treatment weighting (IPTW). In randomized controlled trial, NUC-based methods have been applied to address non-adherence as an intercurrent event, and instrumental variable (IV) extensions of G-estimation have also been introduced for settings where the NUC assumption may fail. We adapt these methods to observational data settings and illustrate their use for assessing how adherence over time impacts health outcomes. We align the causal parameters across methods and show they can target the same causal estimand: the average effect among treated individuals of full adherence versus zero adherence. We set out the identification conditions for IPTW and G-estimation under NUC, and for an IV-based extension that has specific utility when the NUC assumption is implausible. We assess the statistical properties, strengths and weaknesses of each approach through Monte Carlo simulations designed to reflect longitudinal studies with a continuous exposure. We demonstrate these methods by quantifying the effect of full statin adherence on LDL cholesterol control in 13,000 UK Biobank participants with linked primary care data.

We previously identified genetic correlation between pairs of musculoskeletal (MSK) and respiratory conditions. Strategies to prevent or delay their onset remain underexplored in the context of multimorbidity. This study investigated whether MSK–respiratory disease pairs show evidence of potential causal relationships, identified modifiable risk factors, and quantified intervention windows to prevent progression to multimorbidity. We examined combinations of one respiratory condition (asthma, COPD) and one MSK condition [rheumatoid arthritis (RA), osteoarthritis (OA), polymyalgia rheumatica (PMR), psoriasis]. Two-sample Mendelian randomisation (MR) evaluated potential causal relationships in both directions. Linked electronic health records from CPRD (N = 11,042,985; age ≥ 40 years) were used to assess longitudinal disease trajectories, prognostic consequences, and mediation by potentially modifiable or treatable factors. We found evidence for bidirectional relationships between COPD and RA/OA (ORs 1.10–1.19) and between asthma and RA/OA (ORs 1.03–1.14). COPD genetic liability also increased PMR risk (OR 1.14, 95

BACKGROUND:Multimorbidity, the co-occurrence of multiple long-term conditions (LTCs), is an increasingly important clinical problem, but little is known about the underlying causes. We investigate the role of a critical multimorbidity risk factor, obesity, as measured by body mass index (BMI), in explaining shared genetics amongst 71 common LTCs. METHODS:In a population of northern Europeans, we estimated genetic correlation, between LTCs and partial genetic correlations after adjustment for the genetics of BMI. We used multiple causal inference methods to confirm that BMI causally affects individual LTCs, and their co-occurrence. Finally, we quantified the population-level impact of intervening and lowering BMI on the prevalence of 15 key common multimorbid LTC pairs. RESULTS:BMI partially explains some of the shared genetics for 740 LTC pairs (30% of all pairs considered). For a further 161 LTC pairs, the genetic similarity between the LTCs was entirely accounted for by BMI genetics. This list included diabetes and osteoarthritis and gout and osteoarthritis: Causal inference methods confirmed that higher BMI acts as a common risk factor for a subset of these pairs, and therefore BMI-lowering interventions would likely reduce their prevalence. For example, we estimated that a 1 standard deviation or 4.5 unit decrease in BMI would result in 17 fewer people with both chronic kidney disease and osteoarthritis per 1000 who currently have both LTCs. CONCLUSIONS:Our genetics-centred approach quantifies the contribution of obesity to multi-morbidity. Our method for calculating full and partial genetic correlations is published as an R package {partialLDSC}.

Major depressive disorder (MDD) and type 2 diabetes (T2D) represent two global health challenges with a high degree of co-occurrence. Here, we aim to investigate the causal relationship between MDD and T2D in diverse ancestries using Mendelian randomization (MR) in GWAS summary statistic and individual level (UK Biobank (UKB)) data. We assessed the bi-directional causal relationship between: (a) MDD and T2D and (b) MDD and glycaemic biomarkers (e.g. TG:HDL-C ratio, a measure of insulin resistance, fasting glucose) in non-diabetic individuals. In UKB we also tested the role of T2D on treatment resistant depression (TRD). We used multivariable MR (MVMR) to assess the role of body mass index (BMI) in the MDD to T2D relationship. Our results demonstrated that a doubling in MDD genetic liability was associated with 1.14 higher odds of T2D (95% CI:1.09, 1.19), whilst a doubling in T2D genetic liability associated with 1.02 higher odds of MDD (95% CI:1.01, 1.03). Consistent effect estimates were observed in the UKB when stratifying by sex and suggested a role for T2D in TRD. T2D GWAS derived clusters of genetic variants highlighted the importance of specific pathways in the MDD relationship, including variants raising T2D risk via body fat (OR:1.04; 95% CI:1.02, 1.06), obesity mediated insulin resistance (OR:1.06; 95% CI:1.04, 1.09) and residual glycaemic (OR: 1.02; 95% CI:1.00, 1.04) pathways. MVMR with BMI attenuated the bidirectional relationship between MDD and T2D, particularly from MDD to T2D. Genetic liability to MDD was also associated with higher TG:HDL-C ratio in individuals without T2D (β:0.11; 95% CI:0.08, 0.14). We provide evidence of bidirectional causal association between MDD and T2D, with MDD strongly predicting insulin resistance and T2D. T2D predicted both MDD and TRD and highlighted the importance of obesity and body fat pathways in the T2D to MDD relationship.

Observational genome-wide association studies are now widely used for causal inference in genetic epidemiology. To maintain privacy, such data is often only publicly available as summary statistics, and often studies for the endogenous covariates and the outcome are available separately. This has necessitated methods tailored to two-sample summary statistics. Current state-of-the-art methods modify linear instrumental variable (IV) regression – with genetic variants as instruments – to account for unmeasured confounding. However, since the endogenous covariates can be high dimensional, standard IV assumptions are generally insufficient to identify all causal effects simultaneously. We ensure identifiability by assuming the causal effects are sparse and propose a sparse causal effect two-sample IV estimator, spaceTSIV, adapting the spaceIV estimator by Pfister and Peters (2022) for two-sample summary statistics. We provide two methods, based on L0- and L1-penalization, respectively. We prove identifiability of the sparse causal effects in the two-sample setting and consistency of spaceTSIV. The performance of spaceTSIV is compared with existing two-sample IV methods in simulations. Finally, we showcase our methods using real proteomic and gene-expression data for drug-target discovery.

Statins are prescribed to lower LDL cholesterol. Clinical guidelines recommend 30–50

Suboptimal sleep health is linked to higher risks for incident type 2 diabetes. We aimed to assess the clinical utility of adding self-reported sleep traits to a type 2 diabetes prediction model. In this cohort study, we used UK Biobank data and Cox proportional hazards models to examine how self-reported sleep duration and insomnia symptoms were associated with incident type 2 diabetes risk. Harrell’s C statistic and net reclassification improvement (NRI) were used to assess whether sleep traits improved the incident type 2 diabetes discrimination and predictive utility achieved using QDiabetes variables, with and without including a type 2 diabetes polygenic risk score (PGS). Independent replication was explored in the Nurses’ Health Study, the Nurses’ Health Study II and the Health Professionals Follow-up Study. Extremes of sleep duration and occasional or frequent insomnia symptoms were associated with higher risks for incident type 2 diabetes. In the UK Biobank and replication cohorts, adding sleep traits to the QDiabetes risk score did not improve type 2 diabetes prediction (C statistic: QDiabetes alone 0.8933; QDiabetes + sleep duration 0.8939; QDiabetes + insomnia 0.8931; QDiabetes + sleep traits 0.8935). The corresponding total NRI values were: 0.08 (95

Rationale:Asthma is more common in females and more common in night shift workers. Since increasing numbers of females are becoming shift workers, it is important to determine if the risk of shift work-associated asthma is higher in females. The objective of the present study was to determine if increasing frequency of shift work is more strongly related to prevalent asthma in females than in males. Method:We used cross-sectional data from >280 000 UK Biobank participants and logistic regression models adjusted for demographic and lifestyle factors to describe sex differences in prevalent asthma phenotypes related to shift work frequency. To obtain mechanistic insights, we explored associations with chronotype, sex hormones and menopause. Results:Female permanent night shift workers had higher covariate-adjusted odds of moderate-severe asthma (odds ratio (OR) 1.50, 95% confidence interval (CI) 1.18-1.91) than female dayworkers, but there was no corresponding relationship among males (OR 0.95, 95% CI 0.72-1.26; sex interaction p-value=0.01). Similar relationships were observed for "all asthma" and for "wheeze or whistling in the chest". Female shift work-related asthma was driven by relationships in postmenopausal women not using hormone replacement therapy (HRT) (adjusted OR 1.89 (95% CI 1.24-2.87) for moderate-severe asthma; sex interaction p-value=0.02 in permanent night shift workers, compared with dayworkers), but these relationships attenuated to the null in postmenopausal women using HRT. Conclusion:Our finding that increasing shift work frequency is more strongly related to asthma in females than in males could have public health implications. Intervention studies should determine if modifying shift work schedules or using HRT can reduce asthma risk in females.

Background: Hypertension and type 2 diabetes (T2D) are two of the most frequently co-occurring long-term conditions, but their shared mechanisms are not fully understood, often being attributed to adiposity pathways. Here, we aimed to identify shared genetic mechanisms independent of adiposity. Methods: We performed genome-wide association study meta-analyses of T2D and, separately, hypertension. We investigated the bidirectional causal relationship using Mendelian randomisation and quantified genetic correlation before and after accounting for common modifiable risk factors. We then applied a Bayesian GWAS approach to re-estimate SNP-disease effects after accounting for the causal genetic effects of adiposity-related traits. Colocalisation analysis identified shared causal genetic variants, and we investigated the biological pathways involved. Results: We observed a bidirectional causal relationship, and substantial genetic correlation between the two traits (rg = 0.48, 95%CI 0.45-0.52), which persisted after accounting for the genetic contributions of BMI, waist-hip ratio (WHR), and triglycerides (rg = 0.29, 95%CI 0.24-0.34). This indicated shared mechanisms beyond those captured by standard measures of adiposity. We found 98 genetic loci containing variants significantly associated with both hypertension and T2D; colocalisation analysis identified 37 that contained specific shared causal variants. Of these, eight remained statistically significant after adjusting for genetic measures of adiposity, and four were identified only after removing the causal effect of adiposity measures. Shared variants include an allele within PCSK7 associated with risk of both T2D and hypertension and with circulating PCSK7 protein levels, as well as a variant in the 3′ untranslated region of ZNF101 , within the TM6SF2 locus, likely reflecting regulatory variation affecting hepatic lipid metabolism and cardiometabolic traits.

BACKGROUND:Multimorbidity, the presence of two or more conditions in one person, is common but studies are often limited to observational data and single datasets. We address this gap by integrating large-scale primary-care and genetic data from multiple studies to interrogate multimorbidity patterns and producing digital resources to support future research. METHODS:We defined chronic, common, and heritable conditions in individuals aged ≥65 years, using two large primary-care databases [CPRD (UK) N = 2,425,014 and SIDIAP (Spain) N = 1,053,640], and estimated heritability using the same definitions in UK Biobank (N = 451,197). We used logistic regression to estimate the co-occurrence of pairs of conditions in the primary care data. Linkage disequilibrium score regression was used to estimate genetic similarity between pairs of conditions. Meta-analyses were conducted across databases, and up to three sources of genetic data, for each pair of conditions. We classified pairs of conditions as across or within-domain based on the international classification of disease. FINDINGS:We identified 72 chronic conditions, with 43.6% of 2546 pairs showing higher co-occurrence than chance in primary care and evidence of shared genetics. Many across-domain pairs exhibited substantial shared genetics (e.g., iron deficiency anaemia and peripheral arterial disease: genetic correlation Rg = 0.45 [95% Confidence Intervals 0.27:0.64]). 33 pairs displayed negative genetic correlations, such as skin cancer and rheumatoid arthritis (Rg = -0.14 [-0.21:-0.06]), due to potential adverse drug effects. Discordance between genetic and primary care data was also observed, e.g., abdominal aortic aneurysm and bladder cancer co-occurred in primary care but were not genetically correlated (Odds-Ratio = 2.23 [2.09:2.37], Rg = 0.04 [-0.20:0.28]) and schizophrenia and fibromyalgia were less likely to co-occur together in primary care but were positively genetically correlated (OR = 0.84 [0.75:0.94], Rg = 0.20 [0.11:0.29]). INTERPRETATION:Most pairs of chronic conditions show evidence of shared genetics, and co-occurrence in primary care, suggesting shared mechanisms. The identified patterns of shared genetics, negative correlations and discordance between genetic and observational data provide a foundation for future multimorbidity research. FUNDING:UK Medical Research Council [MR/W014548/1].

Mendelian Randomisation Egger regression (MR-Egger) is a popular method for causal inference using single-nucleotide polymorphisms (SNPs) as instrumental variables. It allows all SNPs to have direct pleiotropic effects on the outcome, provided that those effects are independent of the effects on the exposure, known as the InSIDE assumption. However, the results of MR-Egger, and the InSIDE assumption itself, are sensitive to which allele is coded as the effect allele for each SNP. A pragmatic convention is to code the alleles with positive effects on the exposure, which has some advantages in interpretation but some statistical limitations. Here we show that if the InSIDE assumption holds under all-positive coding of the exposure effects, it cannot hold under all-positive coding of the pleiotropic effects, and argue that this undermines the soundness of MR-Egger. We propose a modification that has the Genotype Recoding Invariance Property (GRIP), achieving the main aim of MR-Egger without the difficulties of allele coding. Our approach, MR-GRIP, is valid under a "Variance Independent of Covariance Explained" assumption (VICE), which amounts to an inverse relationship between exposure effects and pleiotropic effects. Examples and simulations suggest that MR-GRIP can reconcile differences between MR-Egger and alternative methods.

Preeclampsia is a serious condition affecting 2-4% of pregnancies globally. However, it is more common in pregnancies with diabetes (e.g. 10-20% in Type 1 diabetes) than in pregnancies without diabetes. In the general population, higher maternal fasting glucose levels are observationally associated with higher risk of preeclampsia, but whether the relationship is causal is unknown. Based on previous research, we hypothesise that higher fasting glucose increases placental weight, likely through fetal insulin mediated growth, and higher placental weight increases the risk of preeclampsia. We used data from published genome-wide association studies on fasting glucose (N=281,416), placental weight (Nfetal=65,405, Nmaternal=61,228) and preeclampsia (Nfetal=377,975, Nmaternal=723,181) and individual level data from the Exeter Family Study of Childhood Health (N=948) and the Avon Longitudinal Study of Parents and Children (N=5,214). We compared observational multivariable regression analysis with Mendelian Randomization (MR), an analytical approach that uses genetic variants to estimate causal effects of exposures (e.g. fasting glucose) on outcomes (e.g. risk of preeclampsia). We found that genetically instrumenting a 1mmol/l higher fasting glucose caused a higher placental weight of 44.16g (95%CI: [29.82,58.49]). Furthermore, we showed that fetal genetic predisposition to higher placental weight increased the risk of preeclampsia (OR=1.99, 95% CI: [1.25, 3.19]). Using the two-step MR method, we estimated that a maternal genetic predisposition to higher fasting glucose increases the risk of preeclampsia through increasing placental weight with an OR of 1.26 (95% CI: [1.05,1.49]). We found evidence for a causal effect of higher maternal fasting glucose levels on risk of preeclampsia, and our results are consistent with this effect being mediated via placental weight. Further well-powered studies are needed to confirm the causal relationship between higher maternal fasting glucose and preeclampsia. If confirmed, our findings suggest research focusing on fetal insulin may help elucidate preeclampsia disease mechanisms.

The STEP 1 randomized trial evaluated the effect of taking semaglutide versus placebo on body weight over a 68-week duration. As with any study evaluating an intervention delivered over a sustained period, nonadherence was observed. This was addressed in the original trial analysis within the Estimand Framework by viewing nonadherence as an intercurrent event. The primary analysis applied a treatment policy strategy which viewed it as an aspect of the treatment regimen, and thus made no adjustment for its presence. A supplementary analysis used a hypothetical strategy, targeting an estimand that would have been realized had all participants adhered, under the assumption that no post-baseline variables confounded adherence and change in body weight. In this article, we propose an alternative instrumental variable (IV) method to adjust for nonadherence which does not rely on the same "unconfoundedness" assumption and is less vulnerable to positivity violations (e.g., it can give valid results even under conditions where nonadherence is guaranteed). Unlike many previous IV approaches, it makes full use of the repeatedly measured outcome data, and allows for a time-varying effect of treatment adherence on a participant's weight. We show that it provides a natural vehicle for defining two distinct hypothetical estimands: the treatment effect if all participants would have adhered to semaglutide, and the treatment effect if all participants would have adhered to both semaglutide and placebo. When applied to the STEP 1 study, they suggest a sustained, slowly decaying weight loss effect of semaglutide treatment.

Background:Observational studies play an important role in assessing the comparative effectiveness of competing treatments. In clinical trials the randomization of participants to treatment and control groups generally results in balanced groups with respect to possible confounders, which makes the analysis straightforward. However, when analysing observational data, the potential for unmeasured confounding makes comparing treatment effects more challenging. Methods:Causal inference methods such as Instrumental Variable and Prior Event Rate Ratio approaches enable the estimation of causal effects even in the presence of unmeasured or imperfectly measured confounding factors. Direct confounder adjustment via multivariable regression and propensity score matching also have considerable utility. Each method relies on a different set of assumptions and leverages different aspects of the data.The assumptions of each method are described, and the impact of their violation is assessed in a simulation study. We propose the prior outcome augmented Instrumental Variable method that leverages data from before and after treatment initiation and is robust to key assumption violations. Finally, we propose a heterogeneity statistic to decide if two or more estimates are statistically dissimilar, considering their correlation. We illustrate our framework in an application study assessing the risk of genital infection in type 2 diabetes patients prescribed SGLT2-inhibitors versus DPP4-inhibitors using UK primary care data. Results:Our proposed approach can estimate treatment effects without bias in scenarios where assumptions of other methods are violated. Furthermore, the application study exemplified the usefulness of discussing the consistency of estimation results from different estimation methods using triangulation. Conclusion:Triangulating results of different estimation methods is important in observational data to derive high quality evidence. The proposed triangulation framework and heterogeneity statistic are valuable tools to discuss the consistency of estimation results from different methods to shed light on possible sources of bias.

Background Observational studies play an important role in assessing the comparative effectiveness of competing treatments. In clinical trials the randomization of participants to treatment and control groups generally results in balanced groups with respect to possible confounders, which makes the analysis straightforward. However, when analysing observational data, the potential for unmeasured confounding makes comparing treatment effects more challenging. Methods Causal inference methods such as Instrumental Variable and Prior Event Rate Ratio approaches enable the estimation of causal effects even in the presence of unmeasured or imperfectly measured confounding factors. Direct confounder adjustment via multivariable regression and propensity score matching also have considerable utility. Each method relies on a different set of assumptions and leverages different aspects of the data. The assumptions of each method are described, and the impact of their violation is assessed in a simulation study. We propose the prior outcome augmented Instrumental Variable method that leverages data from before and after treatment initiation and is robust to key assumption violations. Finally, we propose a heterogeneity statistic to decide if two or more estimates are statistically dissimilar, considering their correlation. We illustrate our framework in an application study assessing the risk of genital infection in type 2 diabetes patients prescribed SGLT2-inhibitors versus DPP4-inhibitors using UK primary care data. Results Our proposed approach can estimate treatment effects without bias in scenarios where assumptions of other methods are violated. Furthermore, the application study exemplified the usefulness of discussing the consistency of estimation results from different estimation methods using triangulation. Conclusion Triangulating results of different estimation methods is important in observational data to derive high quality evidence. The proposed triangulation framework and heterogeneity statistic are valuable tools to discuss the consistency of estimation results from different methods to shed light on possible sources of bias.

Phenotypic heterogeneity at genomic loci encoding drug targets can be exploited by multivariable Mendelian randomization to provide insight into the pathways by which pharmacological interventions may affect disease risk. However, statistical inference in such investigations may be poor if overdispersion heterogeneity in measured genetic associations is unaccounted for. In this work, we first develop conditional F statistics for dimension-reduced genetic associations that enable more accurate measurement of phenotypic heterogeneity. We then develop a novel extension for two-sample multivariable Mendelian randomization that accounts for overdispersion heterogeneity in dimension-reduced genetic associations. Our empirical focus is to use genetic variants in the GLP1R gene region to understand the mechanism by which GLP1R agonism affects coronary artery disease (CAD) risk. Colocalization analyses indicate that distinct variants in the GLP1R gene region are associated with body mass index and type 2 diabetes (T2D). Multivariable Mendelian randomization analyses that were corrected for overdispersion heterogeneity suggest that bodyweight lowering rather than T2D liability lowering effects of GLP1R agonism are more likely contributing to reduced CAD risk. Tissue-specific analyses prioritized brain tissue as the most likely to be relevant for CAD risk, of the tissues considered. We hope the multivariable Mendelian randomization approach illustrated here is widely applicable to better understand mechanisms linking drug targets to diseases outcomes, and hence to guide drug development efforts.

Background Calcium 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.Methods We extended our analysis of 32,000 UK Biobank dihydropiridine CCBs treated participants (mean duration 5.9 years) testing 23 variants, where NUMA1 rs10898815 and RYR3 rs877087 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.Results For 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, RYR3 T-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.Conclusion Polygenic 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 in RYR3 and NUMA1 were 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.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementDT is funded by the Ministry of National Education, Republic of Turkey. CP and DM are supported by the University of Exeter Medical School. JB is funded by an Expanding Excellence in England (E3) research grant awarded to the University of Exeter. JM is funded by a National Institute for Health Research Fellowship (NIHR301445). JD is also supported by the Alzheimers Society [grant, 338 (AS-JF-16b-007)]. This publication presents independent research funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The funders had no input in the study design; in the collection, analysis, and interpretation of data, in the writing of the report; or in the decision to submit the article for publication. The researchers acted independently from the study sponsors in all aspects of this study.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Northwest Multi-Centre Research Ethics Committee approved the collection and use of UK Biobank data (Research Ethics Committee reference 11/NW/0382).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe genetic and phenotypic UK Biobank data are available upon application to the UK Biobank (www.ukbiobank.ac.uk/register-apply).