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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.

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.

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.

Background The Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial is aimed at addressing the urgent need to find effective treatments for patients hospitalised with suspected or confirmed COVID-19. The trial has had many successes, including discovering that dexamethasone is effective at reducing COVID-19 mortality, the first treatment to reach this milestone in a randomised controlled trial. Despite this, it continues to use standard or `fixed’ randomization (FR) to allocate patients to treatments. We assessed the impact of implementing response adaptive randomization (RAR) within RECOVERY using an array of performance measures, to learn if it could be beneficial going forward. This design feature has recently been implemented within the REMAP-CAP trial.Methods Trial data was simulated to closely match the data for patients allocated to either standard care or dexamethasone in the RECOVERY trial from March-June 2020, representing two out of five arms tested throughout this period. Two forms of FR and two forms of RAR were tested. Randomization strategies were performed at the whole trial level as well as within three pre-specified patient subgroups defined by patients’ respiratory support level.ResultsRAR strategies led to more patients being given dexamethasone and a lower mortality rate in the trial. Subgroup specific RAR reduced mortality rates even further. RAR did not induce any meaningful bias in treatment effect estimates, but reduced statistical power compared to FR, with subgroup level adaptive randomizations exhibiting the largest power reduction.ConclusionsUsing RAR within RECOVERY could have resulted in fewer deaths in the trial. However, a larger trial would have been needed to attain the same study power. This could potentially have prolonged the time to full approval of the drug, unless RAR itself led to an increased recruitment rate. Deciding how to balance the needs of patients within a trial and future patients who have yet to fall ill is an important ethical question of our time. RAR deserves to be considered as a design feature in future trials of COVID-19 and other diseases.

ObjectiveTo estimate the effect of the SLCO1B1*5 genotype (decreases statin transport) on cholesterol control and treatment duration in male and female primary care patients prescribed common statin medications.Methods and Analysis69,185 European-ancestry UK Biobank cohort participants prescribed simvastatin or atorvastatin (aged 40 to 79 years at first prescription; treatment duration 1 month to 29 years, mean 5.7 years). Principal outcomes were clinically high total cholesterol (>5mmol/L) at baseline, plus treatment discontinuation.Results48.4% 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 Intervals 1.1 to 1.55, p=0.001). 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 1.19: 95%CI 1.03 to 1.37, p=0.01), 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 discontinuing >1 year after first prescription 1.3: 95%CI 1.08 to 1.56; p=0.006). In men SLCO1B1*5 was only associated with treatment discontinuation in the first year.ConclusionsIn 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 effectiveness of statin therapy in women may need to include SLCO1B1*5 genotype-guided statin selection.What is already known about this subjectGenetic variants affecting SLCO1B1 (statin transporter) gene function increase concentrations of unmetabolized statin molecules (mostly simvastatin and atorvastatin). Previous studies of statin-treated patients have reported reduced likelihood of achieving target cholesterol levels plus increased adverse effects and medication non-adherence mainly in the first year of treatment.However, little data have been available on key outcomes over longer follow-ups or on outcomes by sex, despite large differences in statin treatment patterns between men and women.What this study addsIn 69,185 UK Biobank participants reporting simvastatin or atorvastatin use at baseline assessment, substantially more women had clinically high total cholesterol (>5 mmol/L) compared to men (42% vs. 25%). Female carriers of the SLCO1B1*5 (decreased SLCO1B1 function) genetic variant were especially likely to have high cholesterol, despite being on statin treatment.In primary care records of atorvastatin and simvastatin prescribing (>10 years follow-up), female carriers of SLCO1B1*5 were more likely to stop statins. In men, SLCO1B1*5 was only associated with discontinuing statin treatment in the first year after starting treatment.

Over the last decade the availability of SNP-trait associations from genome-wide association studies has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification. In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. These estimates are then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method’s performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes. Our approach can be viewed as a generalization of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our work serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias., Author summary Uncovering causal mechanisms between risk factors and disease is challenging with observational data because of unobserved confounding. Mendelian randomization offers a potential solution by replacing an individual’s observed risk factor data with an unconfounded genetic proxy measure. Over the last decade an array of methods for performing Mendelian randomization studies (MR) using publicly available summary statistics gleaned from two separate genome-wide association studies. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well-developed summary data methods to individual level data. In this paper we describe a general procedure for optimally applying any summary data MR method using individual level data from one cohort study. Our approach may at first seem nonsensical: we create summary statistics that are intentionally biased by confounding. This bias can, however, be very accurately estimated, and the estimate then used to correct the results of a standard observational analysis. We apply our new way of performing an MR analysis to data from UK Biobank to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes.

People with Type 2 diabetes (T2D) are at high risk of many other long term conditions (LTC). These LTCs include Osteoarthritis, Alzheimer’s disease and Depression as well as cardiovascular conditions but the causes of these higher risks are uncertain and hard to study in trials or observational studies. We aimed to use a genetic approach to test the hypothesis that type 2 diabetes shares disease pathways with other LTCs and that BMI would account for some of the sharing. We used data from large (14,000 - 120,000 cases) genome wide association studies to calculate genetic correlations (Rg) between T2D and 16 LTCs. We then used Genomic structural equation models to test the extent to which the genetic component to BMI accounted for the correlations. This genetic approach has the advantage that type 2 diabetes can be compared to co-morbidities across databases. Eight of 16 LTCs were genetically correlated (Rg) with T2D. These were Heart failure (HF; Rg 0.49, p=1x10-50), coronary artery disease (CAD; 0.40, p=6x10-75), gastroesophageal reflux (GORD, 0.36, p=3x10-58), stroke (0.32, p=4x10-20), osteoarthritis (OA; 0.32, p=4x10-28), depression (0.14, p=4x10-11), hearing aid use (0.13, p=2x10-3) and asthma (0.13, p=9x10-9). The genetic component to BMI accounted for a large proportion of the genetic correlations between T2D and osteoarthritis (87% of Rg), hearing aid use (68%) and asthma (64%), but a smaller proportion of T2D’s genetic correlations with stroke (32%), CAD (35%), HF (47%), depression (39%) and GORD (44%). T2D shares a genetic component with multiple long term conditions and excess body weight is only partly responsible. We hypothesize that where the genetic correlation between T2D and another LTC dramatically diminishes upon adjustment for BMI, lifestyle interventions on BMI may successfully reduce the burden of both LTCs. Where there is less such attenuation, alternative risk factors, such as body fat distribution, need to be considered. Disclosure L. C. Pilling: None. F. Dudbridge: None. D. Melzer: None. J. Bowden: None. T. M. Frayling: None. Funding Medical Research Council (MR/V005359/1)

BackgroundObservational data have reported that being overweight or obese, compared to being normal weight, is associated with a lower risk for death - the “obesity paradox”. We used Mendelian randomization (MR) to estimate causal effects of body mass index (BMI) on mortality risks in people with coronary heart disease (CHD), type 2 diabetes mellitus (T2DM) or malignancy in whom this paradox has been often reported.MethodsWe studied 457,746 White British UK Biobank participants including three subgroups with T2DM (n=19,737), CHD (n=21,925) or cancer (n=42,612) at baseline and used multivariable-adjusted Cox models and MR approaches to describe relationships between BMI and mortality risk. ResultsObservational Cox models showed J-shaped relationships between BMI and mortality risk including within disease subgroups in which the BMI values associated with minimum mortality risk were within overweight/obese ranges (26.5-32.5 kg/m2). In all participants, MR analyses showed a positive linear causal effect of BMI on mortality risk (HR for mortality per unit higher BMI: 1.05; 95% CI: 1.03-1.08), also evident in people with CHD (HR: 1.08; 95% CI: 1.01-1.14). Point estimates for hazard ratios across all BMI values in participants with T2DM and cancer were consistent with overall positive linear effects but confidence intervals included the null. ConclusionThese data support the idea that population efforts to promote intentional weight loss towards the normal BMI range would reduce, not enhance, mortality risk in the general population including, importantly, individuals with CHD.

ObjectiveTo examine the effects of sleep traits on glycated haemoglobin (HbA1c).DesignObservational multivariable regression (MVR), one-sample Mendelian randomization (1SMR), and two-sample summary data Mendelian randomization (2SMR).SettingUK Biobank (UKB) prospective cohort study and genome-wide association studies from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC).ParticipantsIn MVR and 1SMR, participants were adults (mean (SD) age 57 (8) years; 54% female) from the UKB (n=336,999); in 2SMR, participants were adults (53 (11) years; 52% female) from MAGIC (n=46,368). All participants were adults of European ancestry.ExposuresSelf-reported insomnia frequency (usually vs sometimes or rarely/never); sleep duration: 24-hour sleep duration (hours/day); short sleep (≤6 hours vs 7-8 hours) and long sleep (≥9 hours vs 7-8 hours); daytime sleepiness and daytime napping (each consisting of 3 categories: never/rarely, sometimes, usually); chronotype (5 categories from definite morning to definite evening preference).Main outcome measureHbA1c in standard deviation (SD) units.ResultsAcross MV, 1SMR, 2SMR, and their sensitivity analyses we found a higher frequency of insomnia (usually vs sometimes or rarely/never) was associated with higher HbA1c (MVR: 0.053 SD units, 95% confidence interval (0.046 to 0.061), 1SMR: 0.52, (0.42 to 0.63), 2SMR: 0.22, (0.10 to 0.35)). Results remained significant but point estimates were somewhat attenuated after excluding people with diagnosed diabetes. For other sleep traits, there was less consistency with significant associations when using some, but not all methods.ConclusionsThis study suggests that insomnia increases HbA1c levels. These findings could have important implications for developing and evaluating strategies that improve sleep habits to reduce hyperglycaemia and prevent diabetes.SUMMARY BOXWhat is already known on this topicIn observational data, insomnia, short sleep duration, and evening preference are associated with higher risk for type 2 diabetes.Mendelian randomization (MR) studies have not found evidence of a causal effect of short sleep on type 2 diabetes or glycaemic traits but have indicated an effect of insomnia on type 2 diabetes. It is unclear whether insomnia influences HbA1c levels, a marker of long-term hyperglycaemia, in the general population.Recently identified genetic variants robustly associated with insomnia, sleep duration, daytime sleepiness, napping, and chronotype can be used in MR studies to explore causal effects of these sleep traits on HbA1c levels.What this study addsThis study suggests that a higher frequency of insomnia increases HbA1c levels in the general population and after excluding people with diabetes.We found no robust evidence for causal effects of other sleep traits on HbA1c levels.These findings improve our understanding of the impact of sleep traits on HbA1c levels and have important implications for developing and evaluating strategies that improve sleep habits to reduce hyperglycaemia and prevent diabetes.

Background and Aims Coffee consumption and cigarette smoking are strongly associated, but whether this association is causal remains unclear. We sought to: 1) determine whether coffee consumption causally influences cigarette smoking, 2) estimate the magnitude of any association, and 3) explore potential mechanisms. Design We used Mendelian randomization (MR) analyses of observational data, using publicly available summarised data from the Tobacco and Genetics (TAG) consortium, individual level data from the UK Biobank, and in vitro experiments of candidate compounds. Setting The TAG consortium includes data from studies in several countries. The UK Biobank includes data from men and women recruited across England, Wales and Scotland. Participants. The TAG consortium provided data on N ≤ 38,181 participants. The UK Biobank provided data on N = 8,072 participants. Measurements In MR analyses, the exposure was coffee consumption (cups/day) and the outcome was heaviness of smoking (cigarettes/day). In our in vitro experiments we assessed the effect of caffeic acid, quercetin, and p-coumaric acid on the rate of nicotine metabolism in human liver microsomes and cDNA-expressed human CYP2A6. Findings Two-sample MR analyses of TAG consortium data indicated that heavier coffee consumption might lead to reduced heaviness of smoking (beta -1.49, 95% CI -2.88 to -0.09). However, in vitro experiments found the compounds investigated are unlikely to significantly inhibit the rate of nicotine metabolism following coffee consumption. Further MR analyses in UK Biobank found no evidence of a causal relationship between coffee consumption and heaviness of smoking (beta 0.20, 95% CI -1.72 to 2.12). Conclusions Amount of coffee consumption is unlikely to have a major causal impact on amount of cigarette smoking. If it does influence smoking, this is not likely to operate via effects of caffeic acid, quercetin, or p-coumaric acid on nicotine metabolism. The observational association between coffee consumption and cigarette smoking may be due to smoking impacting on coffee consumption, or confounding.

Background: Fibrinogen is an essential hemostatic factor and cardiovascular disease risk factor. Early attempts at evaluating the causal effect of fibrinogen on coronary heart disease (CHD) and myocardial infraction (MI) using Mendelian randomization (MR) used single variant approaches, and did not take advantage of recent genome-wide association studies (GWAS) or multi-variant, pleiotropy robust MR methodologies. Methods and Findings: We evaluated evidence for a causal effect of fibrinogen on both CHD and MI using MR. We used both an allele score approach and pleiotropy robust MR models. The allele score was composed of 38 fibrinogen-associated variants from recent GWAS. Initial analyses using the allele score used a meta-analysis of 11 European-ancestry prospective cohorts, free of CHD and MI at baseline, to examine incidence CHD and MI. We also applied 2 sample MR methods with data from a prevalent CHD and MI GWAS. Results are given in terms of the hazard ratio (HR) or odds ratio (OR), depending on the study design, and associated 95% confidence interval (CI).In single variant analyses no causal effect of fibrinogen on CHD or MI was observed. In multi-variant analyses using incidence CHD cases and the allele score approach, the estimated causal effect (HR) of a 1 g/L higher fibrinogen concentration was 1.62 (CI = 1.12, 2.36) when using incident cases and the allele score approach. In 2 sample MR analyses that accounted for pleiotropy, the causal estimate (OR) was reduced to 1.18 (CI = 0.98, 1.42) and 1.09 (CI = 0.89, 1.33) in the 2 most precise (smallest CI) models, out of 4 models evaluated. In the 2 sample MR analyses for MI, there was only very weak evidence of a causal effect in only 1 out of 4 models.Conclusions: A small causal effect of fibrinogen on CHD is observed using multi-variant MR approaches which account for pleiotropy, but not single variant MR approaches. Taken together, results indicate that even with large sample sizes and multi-variant approaches MR analyses still cannot exclude the null when estimating the causal effect of fibrinogen on CHD, but that any potential causal effect is likely to be much smaller than observed in epidemiological studies.

Excessive daytime sleepiness (EDS) affects 10–20% of the population and is associated with substantial functional deficits. Here, we identify 42 loci for self-reported daytime sleepiness in GWAS of 452,071 individuals from the UK Biobank, with enrichment for genes expressed in brain tissues and in neuronal transmission pathways. We confirm the aggregate effect of a genetic risk score of 42 SNPs on daytime sleepiness in independent Scandinavian cohorts and on other sleep disorders (restless legs syndrome, insomnia) and sleep traits (duration, chronotype, accelerometer-derived sleep efficiency and daytime naps or inactivity). However, individual daytime sleepiness signals vary in their associations with objective short vs long sleep, and with markers of sleep continuity. The 42 sleepiness variants primarily cluster into two predominant composite biological subtypes - sleep propensity and sleep fragmentation. Shared genetic links are also seen with obesity, coronary heart disease, psychiatric diseases, cognitive traits and reproductive ageing., A main symptom of chronic insufficient sleep is excessive daytime sleepiness. Here, Wang et al. report 42 genome-wide significant loci for self-reported daytime sleepiness in 452,071 individuals from the UK Biobank that cluster into two biological subtypes of either sleep propensity or sleep fragmentation.

This study focused on resolving the relationship between body mass index (BMI) and type 2 diabetes. The availability of multiple variants associated with BMI offers a new chance to resolve the true causal effect of BMI on T2D, however the properties of these associations and their validity as genetic instruments need to be considered alongside established and new methods for undertaking Mendelian randomisation. We explore the potential for pleiotropic genetic variants to generate bias, revise existing estimates and illustrate value in new analysis methods. A two-sample Mendelian randomisation (MR) approach with 96 genetic variants was employed using three different analysis methods, two of which (MR-Egger and the weighted median) have been developed specifically to address problems of invalid instrumental variables. We estimate an odds ratio for type 2 diabetes per unit increase in BMI (kg/m2) of between 1.19 and 1.38, with the most stable estimate using all instruments and a weighted median approach (1.26 95%CI (1.17, 1.34)). TCF7L2(rs7903146) was identified as a complex effect or pleiotropic instrument and removal of this variant resulted in convergence of causal effect estimates from different causal analysis methods. This indicated the potential for pleiotropy to affect estimates and differences in performance of alternative analytical methods. In a real type 2 diabetes focused example, this study demonstrates the potential impact of invalid instruments on causal effect estimates and the potential for new approaches to mitigate the bias caused.

BackgroundFibrinogen is an essential hemostatic factor and cardiovascular disease risk factor. Early attempts at evaluating the causal effect of fibrinogen on coronary heart disease (CHD) and myocardial infraction (MI) using Mendelian randomization (MR) used single variant approaches, and did not take advantage of recent genome-wide association studies (GWAS) or multi-variant, pleiotropy robust MR methodologies.Methods and FindingsWe evaluated evidence for a causal effect of fibrinogen on both CHD and MI using MR. We used both an allele score approach and pleiotropy robust MR models. The allele score was composed of 38 fibrinogen-associated variants from recent GWAS. Initial analyses using the allele score incorporated data from 11 European-ancestry prospective cohorts to examine incidence CHD and MI. We also applied 2 sample MR methods with data from a prevalent CHD and MI GWAS. Results are given in terms of the hazard ratio (HR) or odds ratio (OR), depending on the study design, and associated 95% confidence interval (CI).In single variant analyses no causal effect of fibrinogen on CHD or MI was observed. In multi-variant analyses using incidence CHD cases and the allele score approach, the estimated causal effect (HR) of a 1 g/L higher fibrinogen concentration was 1.62 (CI = 1.12, 2.36) when using incident cases and the allele score approach. In 2 sample MR analyses that accounted for pleiotropy, the causal estimate (OR) was reduced to 1.18 (CI = 0.98, 1.42) and 1.09 (CI = 0.89, 1.33) in the 2 most precise (smallest CI) models, out of 4 models evaluated. In the 2 sample MR analyses for MI, there was only very weak evidence of a causal effect in only 1 out of 4 models.ConclusionsA small causal effect of fibrinogen on CHD is observed using multi-variant MR approaches which account for pleiotropy, but not single variant MR approaches. Taken together, results indicate that even with large sample sizes and multi-variant approaches MR analyses still cannot exclude the null when estimating the causal effect of fibrinogen on CHD, but that any potential causal effect is likely to be much smaller than observed in epidemiological studies.Author SummaryInitial Mendelian Randomization (MR) analyses of the causal effect of fibrinogen on coronary heart disease (CHD) utilized single variants and did not take advantage of modern, multivariant approaches. This manuscript provides an important update to these initial analyses by incorporating larger sample sizes and employing multiple, modern multi-variant MR approaches to account for pleiotropy. We used incident cases to perform a MR study of the causal effect of fibrinogen on incident CHD and the nested outcome of myocardial infarction (MI) using an allele score approach. Then using data from a case-control genome-wide association study for CHD and MI we performed two sample MR analyses with multiple, pleiotropy robust approaches. Overall, the results indicated that associations between fibrinogen and CHD in observational studies are likely upwardly biased from any underlying causal effect. Single variant MR approaches show little evidence of a causal effect of fibrinogen on CHD or MI. Multi-variant MR analyses of fibrinogen on CHD indicate there may be a small positive effect, however this result needs to be interpreted carefully as the 95% confidence intervals were still consistent with a null effect. Multi-variant MR approaches did not suggest evidence of even a small causal effect of fibrinogen on MI.

OBJECTIVE The obesity paradox in which overweight/obesity is associated with mortality benefits is believed to be explained by confounding and reverse causality rather than by a genuine clinical benefit of excess body weight. We aimed to gain deeper insights into the paradox through analyzing mortality relationships with several adiposity measures; assessing subgroups with type 2 diabetes, with coronary heart disease (CHD), with cancer, and by smoking status; and adjusting for several confounders. RESEARCH DESIGN AND METHODS We studied the general UK Biobank population (N = 502,631) along with three subgroups of people with type 2 diabetes (n = 23,842), CHD (n = 24,268), and cancer (n = 45,790) at baseline. A range of adiposity exposures were considered, including BMI (continuous and categorical), waist circumference, body fat percentage, and waist-to-hip ratio, and the outcome was all-cause mortality. We used Cox regression models adjusted for age, smoking status, deprivation index, education, and disease history. RESULTS For BMI, the obesity paradox was observed among people with type 2 diabetes (adjusted hazard ratio for obese vs. normal BMI 0.78 [95% CI 0.65, 0.95]) but not among those with CHD (1.00 [0.86, 1.17]). The obesity paradox was pronounced in current smokers, absent in never smokers, and more pronounced in men than in women. For other adiposity measures, there was less evidence for an obesity paradox, yet smoking status consistently modified the adiposity-mortality relationship. CONCLUSIONS The obesity paradox was observed in people with type 2 diabetes and is heavily modified by smoking status. The results of subgroup analyses and statistical adjustments are consistent with reverse causality and confounding.

Background: Lower educational attainment is associated with increased rates of smoking, but ascertaining causality is challenging. We used two-sample Mendelian randomization (MR) analyses of summary statistics to examine whether educational attainment is causally related to smoking.Methods and Findings: We used summary statistics from genome-wide association studies (GWAS) of educational attainment and a range of smoking phenotypes (smoking initiation, cigarettes per day, cotinine levels and smoking cessation). Of 74 single nucleotide polymorphisms (SNPs) that predict educational attainment, 57 (or their highly correlated proxies) were present in the smoking initiation, cigarettes per day and smoking cessation GWAS, and 72 in the cotinine GWAS. Various complementary MR techniques (inverse variance weighted regression, MR Egger, weighted median regression) were used to test the robustness of our results. We found broadly consistent evidence across these techniques that higher educational attainment leads to reduced likelihood of smoking initiation, reduced heaviness of smoking among smokers (as measured via self-report [e.g. inverse variance weighted beta -2.25, 95% confidence interval (CI) -3.81, -0.70, P = 0.005] and cotinine levels [e.g. inverse variance weighted beta -0.34, 95% CI -0.67, -0.01, P = 0.057]), and greater likelihood of smoking cessation among smokers (inverse variance weighted beta 0.65, 95% CI 0.35, 0.95, P = 5.54 × 10-5). Less consistent across the different techniques were associations between educational attainment and smoking initiation.Conclusions: Our findings indicate a causal association between low educational attainment and increased risk of smoking, and may explain the observational associations between educational attainment and adverse health outcomes such as risk of coronary heart disease.

Insomnia is a common disorder linked with adverse long-term medical and psychiatric outcomes, but underlying pathophysiological processes and causal relationships with disease are poorly understood. Here we identify 57 loci for self-reported insomnia symptoms in the UK Biobank (n=453,379) and confirm their impact on self-reported insomnia symptoms in the HUNT study (n=14,923 cases, 47,610 controls), physician diagnosed insomnia in Partners Biobank (n=2,217 cases, 14,240 controls), and accelerometer-derived measures of sleep efficiency and sleep duration in the UK Biobank (n=83,726). Our results suggest enrichment of genes involved in ubiquitin-mediated proteolysis, phototransduction and muscle development pathways and of genes expressed in multiple brain regions, skeletal muscle and adrenal gland. Evidence of shared genetic factors is found between frequent insomnia symptoms and restless legs syndrome, aging, cardio-metabolic, behavioral, psychiatric and reproductive traits. Evidence is found for a possible causal link between insomnia symptoms and coronary heart disease, depressive symptoms and subjective well-being.One Sentence SummaryWe identify 57 genomic regions associated with insomnia pointing to the involvement of phototransduction and ubiquitination and potential causal links to CAD and depression.

Background: Pubertal timing has psychological and physical sequelae. While observational studies have demonstrated an association between age at menarche and adult body mass index (BMI), confounding makes it difficult to infer causality. Methods: The Mendelian randomization (MR) technique is not limited by traditional confounding and was used to investigate the presence of a causal effect of age at menarche on adult BMI. MR uses genetic variants as instruments under the assumption that they act on BMI only through age at menarche (no pleiotropy). Using a two-sample MR approach, heterogeneity between the MR estimates from individual instruments was used as a proxy for pleiotropy, with sensitivity analyses performed if detected. Genetic instruments and estimates of their association with age at menarche were obtained from a genome-wide association meta-analysis on 182,416 women. The genetic effects on adult BMI were estimated using data on 80,465 women from the UK Biobank. The presence of a causal effect of age at menarche on adult BMI was further investigated using data on 70,692 women from the GIANT Consortium. Results: There was evidence of pleiotropy among instruments. Using the UK Biobank data, after removing instruments associated with childhood BMI that were likely exerting pleiotropy, fixed-effect meta-analysis across instruments demonstrated that a 1 year increase in age at menarche reduces adult BMI by 0.38 kg/m2 (95% CI 0.25–0.51 kg/m2). However, evidence of pleiotropy remained. MR-Egger regression did not suggest directional bias, and similar estimates to the fixed-effect meta-analysis were obtained in sensitivity analyses when using a random-effect model, multivariable MR, MR-Egger regression, a weighted median estimator and a weighted mode-based estimator. The direction and significance of the causal effect were replicated using GIANT Consortium data. Conclusion: MR provides evidence to support the hypothesis that earlier age at menarche causes higher adult BMI. Complex hormonal and psychological factors may be responsible.

Background Observational studies on pubertal timing and asthma, mainly performed in females, have provided conflicting results about a possible association of early puberty with higher risk of adult asthma, possibly due to residual confounding. To overcome issues of confounding, we used Mendelian randomisation (MR), i.e., genetic variants were used as instrumental variables to estimate causal effects of early puberty on post-pubertal asthma in both females and males. Methods and findings MR analyses were performed in UK Biobank on 243,316 women using 254 genetic variants for age at menarche, and on 192,067 men using 46 variants for age at voice breaking. Age at menarche, recorded in years, was categorised as early (14); age at voice breaking was recorded and analysed as early (younger than average), normal (about average age), or late (older than average). In females, we found evidence for a causal effect of pubertal timing on asthma, with an 8% increase in asthma risk for early menarche (odds ratio [OR] 1.08; 95% CI 1.04 to 1.12; p = 8.7 × 10−5) and an 8% decrease for late menarche (OR 0.92; 95% CI 0.89 to 0.97; p = 3.4 × 10−4), suggesting a continuous protective effect of increasing age at puberty. In males, we found very similar estimates of causal effects, although with wider confidence intervals (early voice breaking: OR 1.07; 95% CI 1.00 to 1.16; p = 0.06; late voice breaking: OR 0.93; 95% CI 0.87 to 0.99; p = 0.03). We detected only modest pleiotropy, and our findings showed robustness when different methods to account for pleiotropy were applied. BMI may either introduce pleiotropy or lie on the causal pathway; secondary analyses excluding variants associated with BMI yielded similar results to those of the main analyses. Our study relies on self-reported exposures and outcomes, which may have particularly affected the power of the analyses on age at voice breaking. Conclusions This large MR study provides evidence for a causal detrimental effect of early puberty on asthma, and does not support previous observational findings of a U-shaped relationship between pubertal timing and asthma. Common biological or psychological mechanisms associated with early puberty might explain the similarity of our results in females and males, but further research is needed to investigate this. Taken together with evidence for other detrimental effects of early puberty on health, our study emphasises the need to further investigate and address the causes of the secular shift towards earlier puberty observed worldwide., Using genetic instruments, Cosetta Minelli and colleagues probe the causal nature of associations between early puberty, late puberty, and late-onset asthma., Author summary Why was this study done? Worldwide there has been a shift towards earlier puberty over time, possibly due to changes in childhood lifestyle and levels of adiposity. Early puberty has been linked to an increased risk of asthma in women, but findings are inconsistent across studies, and little research has been done in men. There is some suggestion that late puberty might also increase the risk of asthma. Studies performed so far have some methodological limitations that make it difficult to distinguish true effects from spurious findings. What did the researchers do and find? We studied the effect of age at puberty on asthma in women and men using Mendelian randomisation, an approach that exploits genetic data to overcome some limitations of classical observational studies. From the UK Biobank study, we analysed data on approximately 240,000 women and approximately 190,000 men with available genetic data and self-reported information on asthma and age at puberty (menarche for women and voice breaking for men). We found that women and men with early puberty had an increased risk of asthma of 8% and 7%, respectively. We found no evidence of a detrimental effect of late puberty on asthma. What do these findings mean? These findings, together with suggestions of other detrimental effects of early puberty on health, emphasise the importance of evaluating the overall evidence on health effects of early puberty. The similarity of findings in females and males might be explained by common biological or psychological factors related to early puberty, but further research is needed to investigate this.

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Importance Positive associations between inflammatory biomarkers and risk of psychiatric disorders, including schizophrenia, have been reported in observational studies. However, conventional observational studies are prone to bias, such as reverse causation and residual confounding, thus limiting our understanding of the effect (if any) of inflammatory biomarkers on schizophrenia risk. Objective To evaluate whether inflammatory biomarkers have an effect on the risk of developing schizophrenia. Design, Setting, and Participants Two-sample mendelian randomization study using genetic variants associated with inflammatory biomarkers as instrumental variables to improve inference. Summary association results from large consortia of candidate gene or genome-wide association studies, including several epidemiologic studies with different designs, were used. Gene-inflammatory biomarker associations were estimated in pooled samples ranging from 1645 to more than 80 000 individuals, while gene-schizophrenia associations were estimated in more than 30 000 cases and more than 45 000 ancestry-matched controls. In most studies included in the consortia, participants were of European ancestry, and the prevalence of men was approximately 50%. All studies were conducted in adults, with a wide age range (18 to 80 years). Exposures Genetically elevated circulating levels of C-reactive protein (CRP), interleukin-1 receptor antagonist (IL-1Ra), and soluble interleukin-6 receptor (sIL-6R). Main Outcomes and Measures Risk of developing schizophrenia. Individuals with schizophrenia or schizoaffective disorders were included as cases. Given that many studies contributed to the analyses, different diagnostic procedures were used. Results The pooled odds ratio estimate using 18 CRP genetic instruments was 0.90 (random effects 95% CI, 0.84-0.97; P = .005) per 2-fold increment in CRP levels; consistent results were obtained using different mendelian randomization methods and a more conservative set of instruments. The odds ratio for sIL-6R was 1.06 (95% CI, 1.01-1.12; P = .02) per 2-fold increment. Estimates for IL-1Ra were inconsistent among instruments, and pooled estimates were imprecise and centered on the null. Conclusions and Relevance Under mendelian randomization assumptions, our findings suggest a protective effect of CRP and a risk-increasing effect of sIL-6R (potentially mediated at least in part by CRP) on schizophrenia risk. It is possible that such effects are a result of increased susceptibility to early life infection.

BackgroundSummary data furnishing a two-sample Mendelian randomization study are often visualized with the aid of a scatter plot, in which single nucleotide polymorphism (SNP)-outcome associations are plotted against the SNP-exposure associations to provide an immediate picture of the causal effect estimate for each individual variant. It is also convenient to overlay the standard inverse variance weighted (IVW) estimate of causal effect as a fitted slope, to see whether an individual SNP provides evidence that supports, or conflicts with, the overall consensus. Unfortunately, the traditional scatter plot is not the most appropriate means to achieve this aim whenever SNP-outcome associations are estimated with varying degrees of precision and this is reflected in the analysis.MethodsWe propose instead to use a small modification of the scatter plot - the Galbraith radial plot - for the presentation of data and results from an MR study, which enjoys many advantages over the original method. On a practical level it removes the need to recode the genetic data and enables a more straightforward detection of outliers and influential data points. Its use extends beyond the purely aesthetic, however, to suggest a more general modelling framework to operate within when conducting an MR study, including a new form of MR-Egger regression.ResultsWe illustrate the methods using data from a two-sample Mendelian randomization study to probe the causal effect of systolic blood pressure on coronary heart disease risk, allowing for the possible effects of pleiotropy. The radial plot is shown to aid the detection of a single outlying variant which is responsible for large differences between IVW and MR-Egger regression estimates. Several additional plots are also proposed for informative data visualisation.ConclusionThe radial plot should be considered in place of the scatter plot for visualising, analysing and interpreting data from a two-sample summary data MR study. Software is provided to help facilitate its use.

A major application for genome-wide association studies (GWAS) has been the emerging field of causal inference using Mendelian randomization (MR), where the causal effect between a pair of traits can be estimated using only summary level data. MR depends on SNPs exhibiting vertical pleiotropy, where the SNP influences an outcome phenotype only through an exposure phenotype. Issues arise when this assumption is violated due to SNPs exhibiting horizontal pleiotropy. We demonstrate that across a range of pleiotropy models, instrument selection will be increasingly liable to selecting invalid instruments as GWAS sample sizes continue to grow. Methods have been developed in an attempt to protect MR from different patterns of horizontal pleiotropy, and here we have designed a mixture-of-experts machine learning framework (MR-MoE 1.0) that predicts the most appropriate model to use for any specific causal analysis, improving on both power and false discovery rates. Using the approach, we systematically estimated the causal effects amongst 2407 phenotypes. Almost 90% of causal estimates indicated some level of horizontal pleiotropy. The causal estimates are organised into a publicly available graph database (http://eve.mrbase.org), and we use it here to highlight the numerous challenges that remain in automated causal inference.

Insulin may serve as a key causal agent which regulates fat accumulation in the body. Here we assessed the causal relationship between fasting insulin and adiposity using publicly-available results from two large-scale genome-wide association studies for body mass index and fasting insulin levels in a two-sample, bidirectional Mendelian Randomized approach. This approach is only valid on the condition that the two instruments are independent of one another. In analysis excluding overlapping loci, there was an increase of 0.20 (0.17, 0.23) log pmol/L fasting insulin per SD increase in BMI (P= 2.80 x 10−36), while there was a null effect of fasting insulin on BMI, with a 0.01 (−0.39, 0.38) SD decrease in BMI per log pmol/L increase in fasting insulin (P= 0.98). Furthermore, a high degree of heterogeneity in the causal estimates was obtained from the insulin-related variants, which may be attributed to varying mechanisms of action of the insulin-associated variants. Results were largely consistent when an Egger regression technique and weighted median and mode estimators were applied. Findings suggest that the positive correlation between adiposity and fasting insulin levels are at least in part explained by the causal effect of adiposity on increasing insulin, rather than vice versa.

BackgroundObservational evidence suggests that higher educational attainment is protective for Alzheimer’s disease (AD). It is unclear whether this association is causal or confounded by demographic and socioeconomic characteristics. We examined the causal effect of educational attainment on AD in a two-sample MR framework.MethodsWe extracted all available effect estimates of the 74 single nucleotide polymorphisms (SNPs) associated with years of schooling from the largest genome-wide association study (GWAS) of educational attainment (N=293,723) and the GWAS of AD conducted by the International Genomics of Alzheimer’s Project (n=17,008 AD cases and 37,154 controls). SNP-exposure and SNP-outcome coefficients were combined using an inverse variance weighted approach, providing an estimate of the causal effect of each SD increase in years of schooling on AD. We also performed appropriate sensitivity analyses examining the robustness of causal effect estimates to the various assumptions and conducted simulation analyses to examine potential survival bias of MR analyses.FindingsWith each SD increase in years of schooling (3.51 years), the odds of AD were, on average, reduced by approximately one third (odds ratio= 0.63, 95% confidence interval [CI]: 0.48 to 0.83, pInterpretationOur findings support a causal role of educational attainment on AD, whereby an additional ∼3.5 years of schooling reduces the odds of AD by approximately one third.

BackgroundPositive associations between inflammatory biomarkers and risk of psychiatric disorders, including schizophrenia, have been reported in observational studies. However, conventional observational studies are prone to bias such as reverse causation and residual confounding.MethodsIn this study, we used summary data to evaluate the association of genetically elevated C reactive protein (CRP), interleukin-1 receptor antagonist (IL-1Ra) and soluble interleukin-6 receptor (IL-6R) levels with schizophrenia in a two-sample Mendelian randomisation design.ResultsThe pooled odds ratio estimate using 18 CRP genetic instruments was 0.90 (95% CI: 0.84; 0.97) per two-fold increment in CRP levels; consistent results were obtained using different Mendelian randomisation methods and a more conservative set of instruments. The odds ratio for soluble IL-6R was 1.06 (95% CI: 1.01; 1.12) per two-fold increment. Estimates for IL-1Ra were inconsistent among instruments and pooled estimates were imprecise and centred on the null.ConclusionUnder Mendelian randomisation assumptions, our findings suggest a protective causal effect of CRP and a risk-increasing causal effect of soluble IL-6R (potentially mediated at least in part by CRP) on schizophrenia risk.

ObjectivesTo determine whether educational attainment is a causal risk factor in the development of coronary heart disease.DesignMendelian randomization study, where genetic data are used as proxies for education, in order to minimize confounding. A two-sample design was applied, where summary level genetic data was analysed from two publically available consortia.SettingIn the main analysis, we analysed genetic data from two large consortia (CARDIoGRAM and SSGAC), comprising of 112 cohorts from predominantly high-income countries. In addition, we also analysed genetic data from 7 additional large consortia, in order to identify putative causal mediators.ParticipantsThe main analysis was of 589 377 men and women, predominantly of European origin.ExposureA one standard deviation increase in the genetic predisposition towards higher education (i.e. 3.6 years of additional schooling). This was measured by 162 genetic variants that have been previously associated with education.Main outcomeCombined fatal and nonfatal coronary heart disease (63 746 events).Results3.6 years of additional education lowered the risk of coronary heart disease by a third (odds ratio = 0.67, 95% confidence interval [CI], 0.59 to 0.77, p=0.01). Equivalent increases in education were also causally associated with reductions in smoking, BMI and improvements in blood lipid profiles.ConclusionsMore time spent in education is causally associated with a large reduction in the risk of coronary heart disease. This may be partly explained by changes to smoking, BMI and a blood lipids. These findings offer support for policy interventions that increase education, in order to also reduce the burden of cardiovascular disease.

Consensus has yet to emerge regarding the optimal choice of therapy in the management of malignant pericardial effusion. We review the literature to evaluate the existing evidence on the clinical effectiveness of surgical and interventional cardiological approaches. A formal lit- erature search for all studies addressing the treatment of pericardial effusion in cancer patients was undertaken using predefined keywords. Abstracts were screened and reviewed, and data extracted. Data on intervention type, number of patients treated, number of patients sur- viving the procedure, effusion recurrences, need for further interventions and procedure-related complications were obtained from each study and collated in a quantitative synthesis. Of 1181 articles identified, 59 contained sufficient quantitative information to be included in the synthesis. A total of 2322 patients with symptomatic pericardial effusion were identified, of which 1399 patients were reported to have underlying malignancy. Three surgical approaches were described in a total of 19 studies, with overall success rates ranging from 93.3 to 100% and associated complication rates ranging from 4.5 to 10.3%. The remaining 40 studies reported four non-surgical treatment modal- ities, with success rates of 55.1-90.4% and complication rates of 5.9-32%. Data from the literature suggest that surgical drainage of the peri- cardium is superior to non-surgical approaches for symptom relief, effusion recurrence and morbidity; however, the lack of randomized controlled trials means that selection bias remains an important limitation to the field and definitive adequately controlled trials should be a priority.

Chronic sleep disturbances, associated with cardio-metabolic diseases, psychiatric disorders and all-cause mortality1,2, affect 25–30% of adults worldwide3. While environmental factors contribute importantly to self-reported habitual sleep duration and disruption, these traits are heritable4–9, and gene identification should improve our understanding of sleep function, mechanisms linking sleep to disease, and development of novel therapies. We report single and multi-trait genome-wide association analyses (GWAS) of self-reported sleep duration, insomnia symptoms including difficulty initiating and/or maintaining sleep, and excessive daytime sleepiness in the UK Biobank (n=112,586), with discovery of loci for insomnia symptoms (nearMEIS1, TMEM132E, CYCL1, TGFBIin females andWDR27in males), excessive daytime sleepiness (nearAR/OPHN1) and a composite sleep trait (nearINADLandHCRTR2), as well as replication of a locus for sleep duration (atPAX-8). Genetic correlation was observed between longer sleep duration and schizophrenia (rG=0.29,p=1.90x10−13) and between increased excessive daytime sleepiness and increased adiposity traits (BMI rG=0.20,p=3.12x10−09; waist circumference rG=0.20,p=2.12x10−07).

Obesity is a highly prevalent risk factor for cardiometabolic diseases. Observationalstudies suggest that obesity is associated with psychiatric traits, but causalinference from such studies has several limitations. We used two-sample Mendelianrandomization methods (inverse variance weighting, weighted median and MR-Eggerregression) to evaluate the association of body mass index (BMI) with threepsychiatric traits using data from the Genetic Investigation of AnthropometricTraits and Psychiatric Genomics consortia. Causal odds ratio estimates per1-standard deviation increment in BMI ranged from 0.88 (95% CI: 0.62; 1.25) to 1.23(95% CI: 0.65; 2.31) for bipolar disorder; 0.93 (0.78; 1.11) to 1.41 (0.87; 2.27)for schizophrenia; and 1.15 (95% CI: 0.92; 1.44) to 1.40 (95% CI: 1.03; 1.90) formajor depressive disorder. Analyses removing potentially influential SNPs suggestedthat the effect estimates for depression might be underestimated. Our findings donot support the notion that higher BMI increases risk of bipolar disorder andschizophrenia. Although the point estimates for depression were consistent in allsensitivity analyses, the overall statistical evidence was weak. However, the factthat SNP-depression associations were estimated in relatively small samples reducedpower to detect causal effects. This should be re-addressed when SNP-depressionassociations from larger studies become available.

In Phase II oncology trials, therapies are increasingly being evaluated for their effectiveness in specific populations of interest. Such targeted trials require designs that allow for stratification based on the participants' molecular characterisation. A targeted design proposed by Jones and Holmgren (JH) Jones CL, Holmgren E: 'An adaptive Simon two-stage design for phase 2 studies of targeted therapies', Contemporary Clinical Trials 28 (2007) 654-661.determines whether a drug only has activity in a disease sub-population or in the wider disease population. Their adaptive design uses results from a single interim analysis to decide whether to enrich the study population with a subgroup or not; it is based on two parallel Simon two-stage designs. We study the JH design in detail and extend it by providing a few alternative ways to control the familywise error rate, in the weak sense as well as the strong sense. We also introduce a novel optimal design by minimising the expected sample size. Our extended design contributes to the much needed framework for conducting Phase II trials in stratified medicine. © 2016 The Authors Pharmaceutical Statistics Published by John Wiley & Sons Ltd.

The process of undertaking a meta-analysis involves a sequence of decisions, one of which is deciding which measure of treatment effect to use. In particular, for comparative binary data from randomised controlled trials, a wide variety of measures are available such as the odds ratio and the risk difference. It is often of interest to know whether important conclusions would have been substantively different if an alternative measure had been used. Here we develop a new type of sensitivity analysis that incorporates standard measures of treatment effect. Thus, rather than examining the implications of a variety of measures in an ad hoc manner, we can simultaneously examine an entire family of possibilities, including the odds ratio, the arcsine difference and the risk difference. Copyright © 2012 John Wiley & Sons, Ltd.

Two-stage trial designs provide the flexibility to stop early for efficacy or futility and are popular because they have a smaller sample size on average than a traditional trial has with the same type I and II error rates. This makes them financially attractive but also has the ethical benefit of reducing, in the long run, the number of patients who are given ineffective treatments. Designs that minimise the expected sample size are often referred to as ‘optimal’. However, two-stage designs can impart a substantial bias into the parameter estimate at the end of the trial. In this paper, we argue that the expected performance of one's chosen estimation method should also be considered when deciding on a two-stage trial design. We review the properties of standard and bias-adjusted maximum likelihood estimators as well as mean and median unbiased estimators. We then identify optimal two-stage design and analysis procedures that balance projected sample size considerations with those of estimator performance. We make available software to implement this new methodology. Copyright © 2012 John Wiley & Sons, Ltd.

Asymmetry in the funnel plot for a meta-analysis suggests the presence of dissemination bias. This may be caused by publication bias through the decisions of journal editors, by selective reporting of research results by authors or by a combination of both. Typically, study results that are statistically significant or have larger estimated effect sizes are more likely to appear in the published literature, hence giving a biased picture of the evidence-base. Previous statistical approaches for addressing dissemination bias have assumed only a single selection mechanism. Here we consider a more realistic scenario in which multiple dissemination processes, involving both the publishing authors and journals, are operating. In practical applications, the methods can be used to provide sensitivity analyses for the potential effects of multiple dissemination biases operating in meta-analysis.

The problem of selection bias has long been recognised in the analysis of two-stage trials, where promising candidates are selected in stage 1 for confirmatory analysis in stage 2. To efficiently correct for bias, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been proposed for a wide variety of trial settings, but where the populations are assumed to be independent. We relax this assumption and derive the UMCVUE in the multivariate normal setting with an arbitrary known covariance structure. We explore the application of our estimator to two-stage adaptive seamless phase II/III clinical trials. In stage 1, multiple experimental treatments are simultaneously compared with a control. The one that shows the highest standardised treatment difference is then selected for confirmatory analysis in stage. Our UMVCUE allows for unequal number of subjects allocated to each treatment in stage 1, and different (known) treatment variances. We demonstrate the use of our estimator in simulation studies, and show the gain in efficiency over just using the stage 2 data.

Mendelian randomization (MR), the use of genetic variants as instrumental variables (IVs) to test causal effects, is increasingly used in aetiological epidemiology. Few of the methodological developments in MR have considered the specific situation of using genetic IVs to test the causal effect of exposures in pregnant women on postnatal offspring outcomes. In this paper we describe specific ways in which the IV assumptions might be violated when MR is used to test such intrauterine effects. We highlight the importance of considering the extent to which there is overlap between genetic variants in offspring that influence the offspring outcome of interest overlap with genetic variants used as IVs in their mothers. Where there is such overlap, and particularly if it generates a strong association of maternal genetic IVs with offspring outcome via the offspring genotype, the exclusion restriction assumption of instrumental variable analyses will be violated and the causal effect estimate biased. Simple adjustment for offspring genotype might suffice in some circumstances but might also introduce further bias via the father’s genotype. We recommend a set of analyses that ought to be considered when MR is used to address research questions concerned with intrauterine effects on post-natal offspring outcomes, and provide details of how these can be undertaken and interpreted. These additional analyses include the use of genetic data from offspring and fathers, examining associations using maternal non-transmitted alleles, and using simulated data in sensitivity analyses (for which we provide code). We explore the extent to which new methods that have been developed for exploring violation of the exclusion restriction assumption in the two-sample setting (MR-Egger and median based methods) might be used when exploring intrauterine effects in one-sample MR. We provide a list of recommendations that researchers should use when applying MR to test the effects of intrauterine exposures on postnatal offspring outcomes and use an illustrative example with real data to demonstrate how our recommended analysis steps and approaches can be applied and appropriately interpreted.

In a traditional randomised clinical trial, patients are allocated to an experimental treatment or the standard therapy arm - with equal probability - for its entire duration. However, using data from past patients to change allocation probabilities for future patients can increase the average number of patients who receive the best treatment. A drawback of doing this is that the power of the trial can be considerably reduced. The Bernoulli Multiarmed bandit problem (MABP) is an idealised model that illustrates such a conflict. For such model, Gittins & Jones (1974) provided a rule that maximises the expected patient benefit in a large trial. Bandit models for clinical trials have been extensively studied in theory, yet the resulting schemes have been rarely used in practice. There are many reasons for this, however the power limitations to detect a significant treatment effect is a major drawback. In this presentation, we discuss allocating patients between several experimental treatments and a shared control group in a multi-arm trial, using the Gittins index. We modify the MABP design so that the shared control group is allocated separately to protect the power of the trial. The design provides considerable gains not only in efficiency over separate randomised trials but also in the average proportion of patients allocated to the best experimental treatment. This design can be advantageously extended for trials in rare diseases, where it becomes particularly relevant to optimize the choice of treatment both for patients in the trial and for those to treat after it.

Background: MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error' (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. Methods: An adaptation of the I2 statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it IGX2. The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. Results: In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of IGX2), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We demonstrate our proposed approach for a two-sample summary data MR analysis to estimate the causal effect of low-density lipoprotein on heart disease risk. A high value of IGX2 close to 1 indicates that dilution does not materially affect the standard MR-Egger analyses for these data. Conclusions: Care must be taken to assess the NOME assumption via the IGX2 statistic before implementing standard MR-Egger regression in the two-sample summary data context. If IGX2 is sufficiently low (less than 90%), inferences from the method should be interpreted with caution and adjustment methods considered.

Introduction: Recently introduced treatments have improved survival outcomes in relapsed or refractory multiple myeloma (rrMM), with positive outlooks from ongoing trials. However, evidence on relative effectiveness to inform best practice is lacking due to the paucity of head-to-head clinical trials. This systematic review aimed to compare all treatments in rrMM via a mixed treatment comparison (MTC), taking into account prior lines of therapy. Novel statistical techniques using a network meta-analysis (NMA) were developed to estimate relative effectiveness. Methods: A literature search was conducted August 2014 and repeated December 2014. Randomised control trials (RCTs) were included if they reported median duration of progression-free survival (PFS), overall survival (OS) or time to progression (TTP) as a primary or secondary rrMM treatment outcomes. A Bayesian NMA using non-informative prior distributions was fitted in the software application R using JAGS. Such models allow for the estimation of all pairwise comparisons within a connected network of evidence. Fixed effects were assumed, as each direct comparison in the network is informed by a maximum of two trials, which does not allow for the estimation of a heterogeneity parameter. Considerable heterogeneity was observed across studies. In particular the number of prior treatment lines among patients recruited into trials in rrMM varied markedly and was often not reported in enough detail to include this potentially important variable in the analysis. As a result, trials conducted in heavily pre-treated patient populations (3 or more prior lines of therapy) were excluded from the primary analysis. Results: A total of 24 RCTs reporting relevant outcomes for 20 different treatment regimens were identified for data extraction. It was not possible to link all 20 regimens within a single evidence network, but the majority (16) were incorporated within two networks (see figure 1). As a result, the analysis estimated all pairwise comparisons within each of the networks; it is not possible to draw conclusions for comparisons across networks. Results are presented in figure 2 for the yellow (larger) and blue networks (smaller). Three studies were excluded from the presented analysis as median PFS had not yet been reached at follow up. Median follow up across all identified studies ranged from 5.59 to 36 months. Within the yellow network, carfilzomib in combination with lenalidomide and dexamethasone was the most effective treatment, followed by lenalidomide and dexamethasone and then bortezomib. In the smaller blue evidence network, bortezomib in combination with dexamethasone and panobinostat was the most effective treatment. Discussion: Decision-making to optimise patient care, supported by clinical guidelines, requires evidence-based assessment of available treatments. In practise this is often restricted to a series of pair-wise comparisons of treatments such that drawing appropriate inferences across all available options is not possible. To our knowledge, the application of NMA and subsequent results presented here are the first of their kind in rrMM. Previous meta-analyses have been reported for individual treatments, but not for all available options. Our analysis appears broadly consistent with evidence from clinical trials for licensed treatments which are now the established standard of care in rrMM. One limitation of our analysis relates to the published evidence on prior treatment lines in rrMM patients. Fitting a meta-regression to explain heterogeneity may lead to confounding of the treatment effect given the available evidence. Patient level data would allow for a more reliable analysis of the effect of prior treatment lines on PFS. A further consideration is the level of evidence included within our analysis. NMA typically focuses on data drawn from RCTs though methods are available to allow for the inclusion of non-RCT evidence. A great deal of published evidence of this sort is available in the rrMM setting and future analytical approaches should explore how the inclusion of this evidence affects our findings. Figure 1. Reduced RCT evidence network (red links for trials which not reporting OS outcomes) Figure 1. Reduced RCT evidence network (red links for trials which not reporting OS outcomes) Figure 2. Odds ratio and 95% credible intervals for pairwise comparisons A versus B. Significant differences shaded in green. Estimates below 1 favour drug A, estimates above 1 favour drug B. Figure 2. Odds ratio and 95% credible intervals for pairwise comparisons A versus B. Significant differences shaded in green. Estimates below 1 favour drug A, estimates above 1 favour drug B. Figure 3. Figure 3. Disclosures Ruggeri: Cogentia UK: Research Funding. Maguire:Cogentia Healthcare Consulting: Research Funding. Cook:Celgene: Consultancy, Research Funding, Speakers Bureau; BMS: Consultancy; Sanofi: Consultancy, Speakers Bureau; Amgen: Consultancy, Speakers Bureau; Takeda Oncology: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Research Funding, Speakers Bureau. O'Dwyer:Celgene: Honoraria, Research Funding.

Background Biomarkers play different roles in trials, being accordingly classified into ‘prognostic’, ‘predictive’, ‘surrogate’, or combinations thereof. Knowledge of a biomarker’s role enables focused testing in late Phase trials through a better choice from available designs (e.g. ‘stratified’, ‘strategy’, and ‘enrichment’). Preliminary studies can inform a biomarker’s role and the relative value of multiple biomarkers.

There is a considerable interest in using response-adaptive randomisation in clinical trials. Several reasons motivate this. First, they improve individual patient response in clinical trials by increasing the allocation to a best performing treatment (if it exists). Skewing its allocation probability as data accumulates. Second, in multi-armed trials, by protecting the allocation to the control treatment, one can identify trial designs that score highly in both statistical power and patient benefit. Moreover, in this context, by testing many promising treatments simultaneously, multi-arm trials increase the probability of finding a successful new treatment, speeding up the process of doing so. Despite this, response-adaptive designs have seldom been used in practice. One of the major criticisms of response-adaptive randomisation is the potential for type-one error inflation due to “patient drift”. Patient drift can include changes in patients characteristics or the effectiveness of treatments over time. There have been few papers quantifying the extent to which this phenomena actually affects the operating characteristics and how to best correct for it. In this talk we present work addressing both questions for novel adaptive randomisation methods. We consider several scenarios and allocation rules to assess how the type and magnitude of the drift affects the type-one error rate for these designs. We further suggest and assess statitiscal procedures that preserve type one error in the presence of unknown patient drift in both a two-arm and a multi-arm trial setting.

Biomarker-stratified clinical trials assess the biomarker signature of subjects and split them into subgroups so that treatment is of benefit to those who are likely to respond. Since multiple hypotheses are tested, it becomes important to control the type I error. Current methods control the false positive rate where one rejects the null hypothesis while in reality that was true. For two subgroups, the false positive rate is controlled across the two hypotheses as a Family Wise Error Rate (FWER) to an overall predetermined significance level.