AbstractFrozen shoulder is a painful condition that often requires surgery and affects up to 5% of individuals aged 40-60 years. Little is known about the causes of the condition, but diabetes is a strong risk factor. To begin to understand the biological mechanisms involved, we aimed to identify genetic variants associated with frozen shoulder and to use Mendelian randomization to test the causal role of diabetes.We performed a genome wide association study (GWAS) of frozen shoulder in the UK Biobank using data from 2064 cases identified from ICD-10 codes. We used data from FinnGen for replication. We used one-sample and two-sample Mendelian randomization approaches to test for a causal association of diabetes with frozen shoulder.We identified a single genome-wide significant locus (lead SNP rs62228062; OR=1.34 [1.28-1.41], p=2×10−16) that contained WNT7B. A recent transcriptome study identified WNT7B as amongst the most enriched transcripts in anterior capsule tissue in patients undergoing arthroscopic capsulotomy surgery for frozen shoulder suggesting WNT7B as a potential causal gene at the locus. The lead SNP was also strongly associated with Dupuytren’s contracture (OR=2.61 [2.50, 2.72], p<1×10−100). The Mendelian randomization results provided evidence that type 1 diabetes is a causal risk factor for frozen shoulder (OR=1.04 [1.02-1.07], p=6×10−5). There was no evidence that obesity was causally associated with frozen shoulder, suggesting that diabetes influences risk of the condition through glycemic rather than mechanical effects.We have identified the first genetic variant associated with frozen shoulder. WNT7B is a potential causal gene at the locus. Diabetes is a likely causal risk factor. Our results provide evidence of biological mechanisms involved in this common painful condition.

Abstract
. 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.

AbstractBackgroundThis study evaluates longitudinal associations between glycaemic control (mean and within-patient variability of glycated haemaglobin (HbA1c) levels) in individuals with type 2 diabetes (T2D) and major depressive disorder (MDD).MethodsIn UK Biobank (UKB), T2D was defined using self-report, linked primary care records, prescription records and hospital episode statistics, then validated using polygenic scores. Repeated HbA1c measurements were extracted from primary care records and baseline UKB biomarker measures, and used as the outcome in mixed effects models to investigate the association between MDD and glycaemic control over a maximum 10-year T2D disease duration. The exposure investigated was MDD, with subgroups defined by the relative timings of MDD and T2D diagnoses (no MDD, MDD diagnosis pre-T2D or post-T2D).Multiple imputation (n=9264) and complete case (n=4233) analyses were performed.ResultsThe T2D diagnostic criteria were robustly associated with T2D polygenic scores. Using mixed effect models and multiple imputation (7.6 year median follow-up), temporal trends in mean HbA1c did not differ by MDD subgroup. Within-patient variability in HbA1c was 1.14 (95% CI: 1.12-1.16) times higher in UKB participants diagnosed with MDD after T2D compared those with no MDD diagnosis. Complete case analyses results differed, but the substantial evidence against missingness being completely at random, suggests that complete case analysis will be biased.ConclusionsThese findings suggest closer monitoring to improve T2D control is important in individuals who develop MDD after T2D diagnosis. Further, the study highlights the importance of correctly accounting for missing data.Key messagesMajor depressive disorder (MDD) is associated with poorer glycaemic control and an increased risk of complications and mortality in individuals with type 2 diabetes (T2D). Research suggests that individuals diagnosed with MDD after T2D (post-T2D MDD) may be driving these associations, but the effect of diagnostic order on glycaemic control has not been investigated longitudinally.Using UK Biobank primary care records, we investigated whether post-T2D MDD is associated with poorer glycaemic control over the 10 years following a T2D diagnosis, as measured by mean HbA1c trends and within-patient HbA1c variability.Individuals with post-T2D MDD had greater within-patient variability in HbA1c compared to those with no MDD diagnosis, indicating poorer glycaemic control, but no difference in mean HbA1c was found. Individuals with pre-existing MDD had similar glycaemic control to those with no MDD.Results suggest the importance of considering within-patient variability in HbA1c, in addition to the mean, when investigating glycaemic control over time, and highlight the role of the relative timing of diagnosis for MDD and T2D in glycaemic control.

AbstractLinear mixed effects models are frequently used in biomedical statistics to model the trajectory of a repeatedly measured longitudinal variable, such as a biomarker, over time. However, population-level estimates may be biased by censoring bias resulting from exit criteria that depend on the variable in question. A joint longitudinal-survival model, in which the exit criteria and longitudinal variable are modelled simultaneously, may address this bias. Using blood glucose progression (change in HbA1c) in type 2 diabetes patients on metformin monotherapy as an example, we study the potential benefit of using joint models to model trajectory of a biomarker in observational data. 7,712 patients with type 2 diabetes initiating metformin monotherapy were identified in UK Biobank’s general practice (GP) linked records. Genetic information was extracted from UK Biobank, and prescription records, baseline clinical features and biomarkers, and longitudinal HbA1c measures were extracted from GP records. Exit criteria for follow-up for a patient was defined as progression to an additional glucose-lowering drug (which is more likely in patient with higher HbA1c). Estimates of HbA1c trajectory over time were compared using linear mixed effect model approaches (which do not account for censoring bias) and joint models. In the primary analysis, a 0.19 mmol/mol per year higher (p = 0.01) HbA1c gradient was estimated using the joint model compared to the linear mixed effects model. This difference between models was attenuated (0.13 mmol/mol per year higher, p=0.43) when baseline clinical features and biomarkers were included as additional covariates.Censoring bias should be carefully considered when modelling trajectories of repeatedly measured longitudinal variables in observational data. Joint longitudinal-survival models are a useful approach to identify and potentially correct for censoring bias when estimating population-level trajectories.Author SummaryModelling biomarkers that change over time in real world data is a challenging statistical problem due to many potential sources of bias. For example, when studying a chronic disease using a biomarker or other measurement that represents disease severity, medication intended to affect that measurement has a profound effect on how it will change over time. One common way to control for this is to study a cohort on the same treatment strategy. That way, results are not influenced by treatment change. If a patient progresses to stronger medication, then future data is no longer used. However, this approach introduces its own bias. Patients whose condition progress particularly quickly are more likely to change treatment more rapidly (and therefore be removed from further analysis, or ‘censored’), so the cohort is biased towards those whose condition progresses slower. In this paper we apply a technique called joint longitudinal-survival modelling which can adjust for this censoring bias and produce less biased estimates of progression rates. We use HbA1c (a widely used measure of glucose control) in type 2 diabetes as an example, however our methods are theoretically applicable to a range of problems across medicine in which a biomarker or feature is repeatedly measured in an individual.

Objectives to assess how accurately a genetic risk score (GRS) can identify incident prostate cancer in men seeing their general practitioner with lower urinary tract symptoms. Design Cohort study. Setting UK Biobank data linked to primary care records. Participants Men registered with the UK Biobank, eligible for the primary care data linkage, with a record showing that they consulted their general practitioner with lower urinary tract symptoms (LUTS) that could indicate possible undiagnosed prostate cancer. Main outcome measures a diagnosis of prostate cancer within two years of the patient’s first consultation with their general practitioner for LUTS. Results a GRS is associated with prostate cancer in men with symptoms (OR=2.54 [2.16 to 2.99] p=5e-29). An integrated risk model including age and GRS applied to symptomatic men predicted prostate cancer with an AUC of 0.768 (0.739 to 0.796). Men aged 40 years and under in the bottom four GRS quintiles, aged 50 years and under in the bottom two GRS quintiles, and aged 50 to 60 years in the bottom GRS quintile had a two-year prostate cancer incidence below 1%, despite the presence of symptoms. The negative predictive value of the combined model exceeded 99%. Conclusions This study is the first to apply a genetic risk score in a clinical setting to improve the triage of men with symptoms of prostate cancer. It demonstrates the added benefit of incorporating an estimate of genetic risk of prostate cancer into the clinical assessment of symptomatic men in primary care. Assessment of prostate cancer risk in men with LUTS is currently based on presenting clinical features alone. Men with the lowest genetic risk of developing prostate cancer could safely avoid invasive investigation, with adequate safety netting, whilst those identified with the greatest risk could be fast-tracked for further investigation.

BACKGROUND: When used proactively, drug-tolerant anti-tumour necrosis factor (TNF) antibody assays provide early opportunity to suppress immunogenicity. AIM: to validate positivity thresholds of IDKmonitor drug-tolerant anti-infliximab and -adalimumab antibody assays. METHODS: We applied positivity thresholds, defined by testing sera from 498 anti-TNF naive healthy adults, from the Exeter Ten Thousand study to data from our therapeutic drug monitoring (TDM) service and Personalised Anti-TNF Therapy in Crohn's disease (PANTS) cohort to explore associations with drug level and treatment outcomes. RESULTS: the 80% one-sided lower confidence interval of the 99th centile concentration for anti-infliximab and -adalimumab antibodies were lower than the manufacturers threshold of 10 arbitrary units (AU)/mL; 9 and 6 AU/mL, respectively. Using these new thresholds in the TDM cohort, more adalimumab- than infliximab- (11.2% [814/7272] vs 3.1% [390/12 683] P 

BACKGROUND: Anti-TNF exposure has been linked to demyelination events. We sought to describe the clinical features of demyelination events following anti-TNF treatment and to test whether affected patients were genetically predisposed to multiple sclerosis [MS]. METHODS: We conducted a case-control study to describe the clinical features of demyelination events following anti-TNF exposure. We compared genetic risk scores [GRS], calculated using carriage of 43 susceptibility loci for MS, in 48 cases with 1219 patients exposed to anti-TNF who did not develop demyelination. RESULTS: Overall, 39 [74%] cases were female. The median age [range] of patients at time of demyelination was 41.5 years [20.7-63.2]. The median duration of anti-TNF treatment was 21.3 months [0.5-99.4] and 19 [36%] patients were receiving concomitant immunomodulators. Most patients had central demyelination affecting the brain, spinal cord, or both. Complete recovery was reported in 12 [23%] patients after a median time of 6.8 months [0.1-28.7]. After 33.0 months of follow-up, partial recovery was observed in 29 [55%] patients, relapsing and remitting episodes in nine [17%], progressive symptoms in three [6%]: two [4%] patients were diagnosed with MS. There was no significant difference between MS GRS scores in cases (mean -3.5 × 10-4, standard deviation [SD] 0.0039) and controls [mean -1.1 × 10-3, SD 0.0042] [p = 0.23]. CONCLUSIONS: Patients who experienced demyelination events following anti-TNF exposure were more likely female, less frequently treated with an immunomodulator, and had a similar genetic risk to anti-TNF exposed controls who did not experience demyelination events. Large prospective studies with pre-treatment neuroimaging are required to identify genetic susceptibility loci.

Abstract
.
. Background
. Gastro-oesophageal reflux disease (GORD) is associated with multiple risk factors but determining causality is difficult. We used a genetic approach [Mendelian randomization (MR)] to identify potential causal modifiable risk factors for GORD.
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. Methods
. We used data from 451 097 European participants in the UK Biobank and defined GORD using hospital-defined ICD10 and OPCS4 codes and self-report data (N = 41 024 GORD cases). We tested observational and MR-based associations between GORD and four adiposity measures [body mass index (BMI), waist–hip ratio (WHR), a metabolically favourable higher body-fat percentage and waist circumference], smoking status, smoking frequency and caffeine consumption.
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.
. Results
. Observationally, all adiposity measures were associated with higher odds of GORD. Ever and current smoking were associated with higher odds of GORD. Coffee consumption was associated with lower odds of GORD but, among coffee drinkers, more caffeinated-coffee consumption was associated with higher odds of GORD. Using MR, we provide strong evidence that higher WHR and higher WHR adjusted for BMI lead to GORD. There was weak evidence that higher BMI, body-fat percentage, coffee drinking or smoking caused GORD, but only the observational effects for BMI and body-fat percentage could be excluded. This MR estimated effect for WHR equates to a 1.23-fold higher odds of GORD per 5-cm increase in waist circumference.
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.
. Conclusions
. These results provide strong evidence that a higher waist–hip ratio leads to GORD. Our study suggests that central fat distribution is crucial in causing GORD rather than overall weight.
.

Background and aimsWe sought to define temporal changes in prevalence of inflammatory bowel disease (IBD) in East Devon, UK, in order to facilitate service planning over the next 5 years.MethodsMultiple primary and secondary care databases were used to identify and verify cases. Point prevalence and incidence of IBD were reported in April 2017 and from 2008 to 2016, respectively. Future prevalence and healthcare activity requirements were estimated by linear regression.ResultsPrevalence of ulcerative colitis (UC), Crohn’s disease (CD) and inflammatory bowel disease unclassified (IBDU) were 479.72, 265.94 and 35.34 per 100 000 persons, respectively. In 2016, the incidence rates of UC, CD and IBDU were 15.4, 10.7 and 1.4 per 100 000 persons per year, respectively. There were no significant changes in the incidence of CD (p=0.49, R=0.26) or UC (p=0.80, R=0.10). IBD prevalence has increased by 39.9% (95% CI 28.2 to 53.7) in the last 10 years without differences in the rate of change between UC and CD. Overall, 27% of patients were managed in primary care, a quarter of whom were eligible but not receiving endoscopic surveillance. Outpatient clinics, MRI and biologic use, but not helpline calls, admissions, or surgeries increased over and above the change in IBD prevalence.ConclusionsWe report one of the highest prevalence and incidence rates of IBD from Northern Europe. Overall, IBD incidence is static, but prevalence is increasing. We estimate that 1% of our population will live with IBD between 2025 and 2030.

BACKGROUND AND AIMS: the causes of microscopic colitis are currently poorly understood. Previous reports have found clinical associations with coeliac disease and genetic associations at the human leukocyte antigen [HLA] locus on the ancestral 8.1 haplotype. We investigated pharmacological and genetic factors associated with microscopic colitis in the UK Biobank. METHODS: in total, 483 European UK Biobank participants were identified by ICD10 coding, and a genome-wide association study was performed using BOLT-LMM, with a sensitivity analysis performed excluding potential confounders. The HLA*IMP:02 algorithm was used to estimate allele frequency at 11 classical HLA genes, and downstream analysis was performed using FUMA. Genetic overlap with inflammatory bowel disease [Crohn's disease and ulcerative colitis] was investigated using genetic risk scores. RESULTS: We found significant phenotypic associations with smoking status, coeliac disease and the use of proton-pump inhibitors but not with other commonly reported pharmacological risk factors. Using the largest sample size to date, we confirmed a recently reported association with the MHC Ancestral 8.1 Haplotype. Downstream analysis suggests association with digestive tract morphogenesis. By calculating genetic risk scores, we also report suggestive evidence of shared genetic risk with Crohn's disease, but not with ulcerative colitis. CONCLUSIONS: This report confirms the role of genetic determinants in the HLA in the pathogenesis of microscopic colitis. The genetic overlap with Crohn's disease suggests a common underlying mechanism of disease.

Atrial fibrillation (AF), the most prevalent cardiac arrhythmia, is commonly initiated by ectopic beats originating from a small myocardial sleeve extending over the pulmonary veins. Pulmonary vein isolation therapy attempts to isolate the pulmonary veins from the left atrium by ablating tissue, commonly by using radiofrequency ablation. During this procedure, the cardiologist records electrical activity using a lasso catheter, and the activation pattern recorded is used as a guide toward which regions to ablate. However, poor contact between electrode and tissue can lead to important regions of electrical activity not being recorded in clinic. We reproduce these signals through the use of a phenomenological model of the cardiac action potential on a cylinder, which we fit to post-AF atrial cells, and model the bipolar electrodes of the lasso catheter by an approximation of the surface potential. The resulting activation pattern is validated by direct comparison with those of clinical recordings. A potential application of the model is to reconstruct the missing electrical activity, minimizing the impact of the information loss on the clinical procedure, and we present results to demonstrate this.