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Insights into the genetic basis of human disease are helping to address some of the key challenges in new drug development including the very high rates of failure. Here we review the recent history of an emerging, genomics-assisted approach to pharmaceutical research and development, and its relationship to Mendelian randomization (MR), a well-established analytical approach to causal inference. We demonstrate how human genomic data linked to pharmaceutically relevant phenotypes can be used for (1) drug target identification (mapping relevant drug targets to diseases), (2) drug target validation (inferring the likely effects of drug target perturbation), (3) evaluation of the effectiveness and specificity of compound-target engagement (inferring the extent to which the effects of a compound are exclusive to the target and distinguishing between on-target and off-target compound effects), and (4) the selection of end points in clinical trials (the diseases or conditions to be evaluated as trial outcomes). We show how genomics can help identify indication expansion opportunities for licensed drugs and repurposing of compounds developed to clinical phase that proved safe but ineffective for the original intended indication. We outline statistical and biological considerations in using MR for drug target validation (drug target MR) and discuss the obstacles and challenges for scaled applications of these genomics-based approaches.

IMPORTANCE: Cholesteryl ester transfer protein inhibition (CETP) has been shown to increase levels of high-density lipoprotein cholesterol (HDL-C) and reduce levels of low-density lipoprotein cholesterol (LDL-C). Current LDL-C target attainment is low, and novel phase 3 trials are underway to investigate whether CETP inhibitors result in reduction of cardiovascular disease risk in high-risk patients who may be treated with PCSK9-inhibiting agents. OBJECTIVE: To explore the associations of combined reduction of CETP and PCSK9 concentrations with risk of coronary artery disease (CAD) and other clinical and safety outcomes. DESIGN, SETTING, AND PARTICIPANTS: Two-sample 2 × 2 factorial Mendelian randomization study in a general population sample that includes data for UK Biobank participants of European ancestry. EXPOSURES: Separate genetic scores were constructed for CETP and PCSK9 plasma protein concentrations, which were combined to determine the associations of combined genetically reduced CETP and PCSK9 concentrations with disease. MAIN OUTCOMES AND MEASURES: Blood lipid and lipoprotein concentrations, blood pressure, CAD, age-related macular degeneration, type 2 diabetes, any stroke and ischemic stroke, Alzheimer disease, vascular dementia, heart failure, atrial fibrillation, chronic kidney disease, asthma, and multiple sclerosis. RESULTS: Data for 425 354 UKB participants were included; the median (IQR) age was 59 years (51-64), and 229 399 (53.9%) were female. The associations of lower CETP and lower PCSK9 concentrations with CAD are similar when scaled per 10-mg/dL reduction in LDL-C concentrations (CETP: odds ratio [OR], 0.74; 95% CI, 0.67 to 0.81; PCSK9: OR, 0.75; 95% CI, 0.71 to 0.79). Combined exposure to lower CETP and PCSK9 concentrations was associated with an additive magnitude with lipids and all outcomes, and we did not observe any nonadditive interactions, most notably for LDL-C (CETP: effect size, -1.11 mg/dL; 95% CI, -1.40 to -0.82; PCSK9: effect size, -2.13 mg/dL; 95% CI, -2.43 to -1.84; combined: effect size, -3.47 mg/dL; 95% CI, -3.76 to -3.18; P = .34 for interaction) and CAD (CETP: OR, 0.96; 95% CI, 0.94 to 1.00; PCSK9: OR, 0.94; 95% CI, 0.91 to 0.97; combined: OR, 0.90; 95% CI, 0.87 to 0.93; P = .83 for interaction). In addition, when corrected for multiple testing, lower CETP concentrations were associated with increased age-related macular degeneration (OR, 1.11; 95% CI, 1.04 to 1.19). CONCLUSIONS AND RELEVANCE: Our results suggest that joint inhibition of CETP and PCSK9 has additive effects on lipid traits and disease risk, including a lower risk of CAD. Further research may explore whether a combination of CETP- and PCSK9-related therapeutics can benefit high-risk patients who are unable to reach treatment targets with existing options.

Background: Clinical guidelines recommend regular screening for arrhythmogenic right ventricular cardiomyopathy (ARVC) to monitor at-risk relatives, resulting in a significant burden on clinical resources. Prioritizing relatives on their probability of developing definite ARVC may provide more efficient patient care. Objectives: The aim of this study was to determine the predictors and probability of ARVC development over time among at-risk relatives. Methods: A total of 136 relatives (46% men, median age 25.5 years [IQR: 15.8-44.4 years]) from the Netherlands Arrhythmogenic Cardiomyopathy Registry without definite ARVC by 2010 task force criteria were included. Phenotype was ascertained using electrocardiography, Holter monitoring, and cardiac imaging. Subjects were divided into groups with “possible ARVC” (only genetic or familial predisposition) and “borderline ARVC” (1 minor task force criterion plus genetic or familial predisposition). Cox regression was performed to determine predictors and multistate modeling to assess the probability of ARVC development. Results were replicated in an unrelated Italian cohort (57% men, median age 37.0 years [IQR: 25.4-50.4 years]). Results: At baseline, 93 subjects (68%) had possible ARVC, and 43 (32%) had borderline ARVC. Follow-up was available for 123 relatives (90%). After 8.1 years (IQR: 4.2-11.4 years), 41 (33%) had developed definite ARVC. Independent of baseline phenotype, symptomatic subjects (P = 0.014) and those 20 to 30 years of age (P = 0.002) had a higher hazard of developing definite ARVC. Furthermore, patients with borderline ARVC had a higher probability of developing definite ARVC compared with those with possible ARVC (1-year probability 13% vs 0.6%, 3-year probability 35% vs 5%; P < 0.01). External replication showed comparable results (P > 0.05). Conclusions: Symptomatic relatives, those 20 to 30 years of age, and those with borderline ARVC have a higher probability of developing definite ARVC. These patients may benefit from more frequent follow-up, while others may be monitored less often.

Dysfunction of either the right or left ventricle can lead to heart failure (HF) and subsequent morbidity and mortality. We performed a genome-wide association study (GWAS) of 16 cardiac magnetic resonance (CMR) imaging measurements of biventricular function and structure. Cis- Mendelian randomization (MR) was used to identify plasma proteins associating with CMR traits as well as with any of the following cardiac outcomes: HF, non-ischemic cardiomyopathy, dilated cardiomyopathy (DCM), atrial fibrillation, or coronary heart disease. In total, 33 plasma proteins were prioritized, including repurposing candidates for DCM and/or HF: IL18R (providing indirect evidence for IL18), I17RA, GPC5, LAMC2, PA2GA, CD33, and SLAF7. In addition, 13 of the 25 druggable proteins (52%; 95% confidence interval, 0.31 to 0.72) could be mapped to compounds with known oncological indications or side effects. These findings provide leads to facilitate drug development for cardiac disease and suggest that cardiotoxicities of several cancer treatments might represent mechanism-based adverse effects.

Hypertrophic cardiomyopathy (HCM) is the most prevalent monogenic heart disease, commonly caused by pathogenic MYBPC3 variants, and a significant cause of sudden cardiac death. Severity is highly variable, with incomplete penetrance among genotype-positive family members. Previous studies demonstrated metabolic changes in HCM. We aimed to identify metabolite profiles associated with disease severity in carriers of MYBPC3 founder variants using direct-infusion high-resolution mass spectrometry in plasma of 30 carriers with a severe phenotype (maximum wall thickness ≥20 mm, septal reduction therapy, congestive heart failure, left ventricular ejection fraction

Background Higher concentrations of cholesterol-containing low-density lipoprotein (LDL-C) increase the risk of cardiovascular disease (CVD). The association of LDL-C with non-CVD traits remains unclear, as are the possible independent contributions of other cholesterol-containing lipoproteins and apolipoproteins. Methods Nuclear magnetic resonance spectroscopy was used to measure the cholesterol content of high density (HDL-C), very low-density (VLDL-C), intermediate-density (IDL-C), as well as low-density lipoprotein fractions, the apolipoproteins Apo-A1 and Apo-B, as well as total triglycerides (TG), remnant-cholesterol (Rem-Chol) and total cholesterol (TC). The causal effects of these exposures were assessed against 33 outcomes using univariable and multivariable Mendelian randomization (MR). Results The majority of cholesterol containing lipoproteins and apolipoproteins affect coronary heart disease (CHD), carotid intima-media thickness, carotid plaque, C-reactive protein (CRP) and blood pressure. Multivariable MR indicated that many of these effects act independently of HDL-C, LDL-C and TG, the most frequently measured lipid fractions. Higher concentrations of TG, VLDL-C, Rem-Chol and Apo-B increased heart failure (HF) risk; often independently of LDL-C, HDL-C or TG. Finally, a subset of these exposures associated with non-CVD traits such as Alzheimer’s disease (AD: HDL-C, LDL-C, IDL-C, Apo-B), type 2 diabetes (T2DM: VLDL-C, IDL-C, LDL-C), and inflammatory bowel disease (IBD: LDL-C, IDL-C). Conclusions The cholesterol content of a wide range of lipoprotein and apolipoproteins associate with measures of atherosclerosis, blood pressure, CRP, and CHD, with a subset affecting HF, T2DM, AD and IBD risk. Many of the observed effects appear to act independently of LDL-C, HDL-C, and TG, supporting the targeting of lipid fractions beyond LDL-C for disease prevention. UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. AFS is supported by British Heart Foundation (BHF) grant PG/18/5033837, PG/22/10989 and the UCL BHF Research Accelerator AA/18/6/34223. CF and AFS received additional support from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. MGM is supported by a BHF Fellowship FS/17/70/33482. ADH and DAL (NF-0616-10102) are an NIHR Senior Investigators. This work was funded by the Strategic Priority Fund “Tackling multimorbidity at scale” programme [MR/V033867/1] delivered by the Medical Research Council and the National Institute for Health and Care Research in partnership with the Economic and Social Research Council and in collaboration with the Engineering and Physical Sciences Research Council. The UCLEB Consortium is supported by a British Heart Foundation Programme Grant (RG/10/12/28456). DAL’s contribution to this research is supported by the Bristol BHF Accelerator Award (AA/18/1/34219), her BHF Chair (CH/F/20/90003) and the UK Medical Research Council (MC_UU_00011/1-6). MK is supported by the UK Medical Research Council (MRC MR/R024227/1), National Institute on Aging (NIA), US (R01AG056477), and the Wellcome Trust (221854/Z/20/Z). PC is supported by the Thailand Research Fund (MRG6280088). TRG receives funding from the UK Medical Research Council as part of the MRC Integrative Epidemiology Unit (MC_UU_00011/4). AH receives support from the British Heart Foundation (SP/F/21/150020) and UK Medical Research Council (MC_PC-20051). ADH receives support from the UK Medical Research Council (MC_UU_12019/1). NF received funding from the National Health Institutes (MD012765, DK117445). CG has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754490—MINDED project.

BACKGROUND: Globally, there is a paucity of multimorbidity and comorbidity data, especially for minority ethnic groups and younger people. We estimated the frequency of common disease combinations and identified non-random disease associations for all ages in a multiethnic population. METHODS: In this population-based study, we examined multimorbidity and comorbidity patterns stratified by ethnicity or race, sex, and age for 308 health conditions using electronic health records from individuals included on the Clinical Practice Research Datalink linked with the Hospital Episode Statistics admitted patient care dataset in England. We included individuals who were older than 1 year and who had been registered for at least 1 year in a participating general practice during the study period (between April 1, 2010, and March 31, 2015). We identified the most common combinations of conditions and comorbidities for index conditions. We defined comorbidity as the accumulation of additional conditions to an index condition over an individual's lifetime. We used network analysis to identify conditions that co-occurred more often than expected by chance. We developed online interactive tools to explore multimorbidity and comorbidity patterns overall and by subgroup based on ethnicity, sex, and age. FINDINGS: We collected data for 3 872 451 eligible patients, of whom 1 955 700 (50.5%) were women and girls, 1 916 751 (49.5%) were men and boys, 2 666 234 (68.9%) were White, 155 435 (4.0%) were south Asian, and 98 815 (2.6%) were Black. We found that a higher proportion of boys aged 1-9 years (132 506 [47.8%] of 277 158) had two or more diagnosed conditions than did girls in the same age group (106 982 [40.3%] of 265 179), but more women and girls were diagnosed with multimorbidity than were boys aged 10 years and older and men (1 361 232 [80.5%] of 1 690 521 vs 1 161 308 [70.8%] of 1 639 593). White individuals (2 097 536 [78.7%] of 2 666 234) were more likely to be diagnosed with two or more conditions than were Black (59 339 [60.1%] of 98 815) or south Asian individuals (93 617 [60.2%] of 155 435). Depression commonly co-occurred with anxiety, migraine, obesity, atopic conditions, deafness, soft-tissue disorders, and gastrointestinal disorders across all subgroups. Heart failure often co-occurred with hypertension, atrial fibrillation, osteoarthritis, stable angina, myocardial infarction, chronic kidney disease, type 2 diabetes, and chronic obstructive pulmonary disease. Spinal fractures were most strongly non-randomly associated with malignancy in Black individuals, but with osteoporosis in White individuals. Hypertension was most strongly associated with kidney disorders in those aged 20-29 years, but with dyslipidaemia, obesity, and type 2 diabetes in individuals aged 40 years and older. Breast cancer was associated with different comorbidities in individuals from different ethnic groups. Asthma was associated with different comorbidities between males and females. Bipolar disorder was associated with different comorbidities in younger age groups compared with older age groups. INTERPRETATION: Our findings and interactive online tools are a resource for: patients and their clinicians, to prevent and detect comorbid conditions; research funders and policy makers, to redesign service provision, training priorities, and guideline development; and biomedical researchers and manufacturers of medicines, to provide leads for research into common or sequential pathways of disease and inform the design of clinical trials. FUNDING: UK Research and Innovation, Medical Research Council, National Institute for Health and Care Research, Department of Health and Social Care, Wellcome Trust, British Heart Foundation, and The Alan Turing Institute.

BackgroundThere has been considerable interest in statins due to their pleiotropic effects beyond their lipid-lowering properties. Many of these pleiotropic effects are predominantly ascribed to their capacity to inhibit the isoprenylation of Rho small guanosine triphosphatases (Rho GTPases). We aimed to genetically investigate the role of lipids and statin interventions on multiple sclerosis (MS) risk and severity.MethodWe employed two-sample Mendelian randomization (MR) to: (1) investigate the causal role of lipids (high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG)) levels in MS risk and severity, (2) genetically mimic both cholesterol-dependent (via low-density lipoprotein cholesterol (LDL-C) and cholesterol biosynthesis pathway) and cholesterol-independent (via Rho GTPases) effects of statins on MS risk and MS severity.ResultsThe results of MR using the inverse variance weighted method show that lifelong higher HDL-C (OR 1.14 (95% CI 1.04 to1.26), p-value 7.94E-03) increase MS risk, but LDL-C and TG were not. MR results also show that genetically predicted RAC2 (OR 0.86 (95% CI 0.78 to 0.95), p-value 3.80E-03) is implicated causally in reducing MS risk. Furthermore, we found no evidence for the causal role of lipids and genetically mimicked statins on MS severity. There is no evidence of reverse causation between MS risk and lipids.ConclusionsEvidence from this study suggests that HDL-C is a risk factor for MS development. The MR findings suggest that RAC2 (a member of Rho GTPases) is a potent genetic modifier of MS risk. Since RAC2 has been reported to mediate some of the pleiotropic effects of statins, we suggest that statins reduce MS risk via a RAC2-related mechanism(s) (i.e., cholesterol-independent pathway).

Background Nuclear magnetic resonance (NMR) spectroscopy allows triglycerides to be subclassified into 14 different classes based on particle size and lipid content. We recently showed that these subfractions have differential associations with cardiovascular disease events. Here we report the distributions and define reference interval ranges for 14 triglyceride-containing lipoprotein subfraction metabolites. Methods Lipoprotein subfractions using the Nightingale NMR platform were measured in 9073 participants from four cohort studies contributing to the UCL-Edinburgh-Bristol consortium. The distribution of each metabolite was assessed, and reference interval ranges were calculated for a disease-free population, by sex and age group (65 years), and in a subgroup population of participants with cardiovascular disease or type 2 diabetes. We also determined the distribution across body mass index and smoking status. Results The largest reference interval range was observed in the medium very-low density lipoprotein subclass (2.5th 97.5th percentile; 0.08 to 0.68 mmol/L). The reference intervals were comparable among male and female participants, with the exception of triglyceride in high-density lipoprotein. Triglyceride subfraction concentrations in very-low density lipoprotein, intermediate-density lipoprotein, low-density lipoprotein and high-density lipoprotein subclasses increased with increasing age and increasing body mass index. Triglyceride subfraction concentrations were significantly higher in ever smokers compared to never smokers, among those with clinical chemistry measured total triglyceride greater than 1.7 mmol/L, and in those with cardiovascular disease, and type 2 diabetes as compared to disease-free subjects. Conclusion This is the first study to establish reference interval ranges for 14 triglyceride-containing lipoprotein subfractions in samples from the general population measured using the nuclear magnetic resonance platform. The utility of nuclear magnetic resonance lipid measures may lead to greater insights for the role of triglyceride in cardiovascular disease, emphasizing the importance of appropriate reference interval ranges for future clinical decision making.

Meta-analyses based on systematic literature reviews are commonly used to obtain a quantitative summary of the available evidence on a given topic. However, the reliability of any meta-analysis is constrained by that of its constituent studies. One major limitation is the possibility of small-study effects, when estimates from smaller and larger studies differ systematically. Small-study effects may result from reporting biases (ie, publication bias), from inadequacies of the included studies that are related to study size, or from reasons unrelated to bias. We propose two estimators based on the median and mode to increase the reliability of findings in a meta-analysis by mitigating the influence of small-study effects. By re-examining data from published meta-analyses and by conducting a simulation study, we show that these estimators offer robustness to a range of plausible bias mechanisms, without making explicit modelling assumptions. They are also robust to outlying studies without explicitly removing such studies from the analysis. When meta-analyses are suspected to be at risk of bias because of small-study effects, we recommend reporting the mean, median and modal pooled estimates.

Background There is growing evidence that polygenic risk scores (PRSs) can identify individuals with elevated lifetime risk of coronary artery disease (CAD). Whether they can also be used to stratify the risk of subsequent events among those surviving a first CAD event remain uncertain, with possible biological differences between CAD onset and progression, and the potential for index event bias. Methods Using two baseline subsamples of UK Biobank: prevalent CAD cases (N = 10 287) and individuals without CAD (N = 393 108), we evaluated associations between a CAD PRS and incident cardiovascular and fatal outcomes. Results A 1 SD higher PRS was associated with an increased risk of incident myocardial infarction (MI) in participants without CAD (OR 1.33; 95% CI 1.29, 1.38), but the effect estimate was markedly attenuated in those with prevalent CAD (OR 1.15; 95% CI 1.06, 1.25) and heterogeneity P = 0.0012. Additionally, among prevalent CAD cases, we found an evidence of an inverse association between the CAD PRS and risk of all-cause death (OR 0.91; 95% CI 0.85, 0.98) compared with those without CAD (OR 1.01; 95% CI 0.99, 1.03) and heterogeneity P = 0.0041. A similar inverse association was found for ischaemic stroke [prevalent CAD (OR 0.78; 95% CI 0.67, 0.90); without CAD (OR 1.09; 95% CI 1.04, 1.15), heterogeneity P Conclusions Bias induced by case stratification and survival into UK Biobank may distort the associations of PRS derived from case-control studies or populations initially free of disease. Differentiating between effects of possible biases and genuine biological heterogeneity is a major challenge in disease progression research.

ObjectivesWomen have a worse prognosis after coronary artery bypass grafting (CABG) surgery compared to men. We sought to quantify to what extent this difference in post-CABG survival could be attributed to sex itself, or whether this was mediated by difference between men and women at the time of intervention. Additionally, we explored to what extent these effects were homogenous across patient subgroups.MethodsTime to all-cause mortality was available for 102,263 CABG patients, including 20,988 (21%) women, sourced through an individual participant data meta-analysis of five cohort studies. Difference between men and women in survival duration was assessed using Kaplan–Meier estimates, and Cox’s proportional hazards model.ResultsDuring a median follow-up of 5 years, 13,598 (13%) patients died, with women more likely to die than men: female HR 1.20 (95%CI 1.16; 1.25). We found that differences in patient characteristics at the time of CABG procedure mediated this sex effect, and accounting for these resulted in a neutral female HR 0.98 (95%CI 0.94; 1.02). Next we performed a priori defined subgroup analyses of the five most prominent mediators: age, creatinine, peripheral vascular disease, type 2 diabetes, and heart failure. We found that women without peripheral vascular disease (PVD) or women aged 70+, survived longer than men (interaction p-values 0.04 and 6 × 10–5, respectively), with an effect reversal in younger women.ConclusionSex differences in post-CABG survival were readily explained by difference in patient characteristics and comorbidities. Pre-planned analyses revealed patient subgroups (aged 70+, or without PVD) of women that survived longer than men, and a subgroup of younger women with comparatively poorer survival.

Background: Scientific studies on cardiovascular disease (CVD) burden and risk factors are predominantly based on short-term risk in Westerner populations, and such information may not be applicable to Asian populations, especially over the longer term. This review aims to estimate the long-term (>10 years) CVD burden, including coronary heart disease (CHD) and stroke, as well as associated risk factors in Asian populations. Methods: PubMed, Embase and Web of Science were systematically searched, and hits screened on: Asian adults, free of CVD at baseline; cohort study design (follow-up >10 years). Primary outcomes were fatal and non-fatal CVD events. Pooled estimates and between-study heterogeneity were calculated using random effects models, Q and I2 statistics. Results: Overall, 32 studies were eligible for inclusion (follow-up: 11–29 years). The average long-term rate of fatal CVD is 3.68 per 1000 person-years (95% CI 2.84–4.53), the long-term cumulative risk 6.35% (95% CI 4.69%–8.01%, mean 20.13 years) and the cumulative fatal stroke/CHD risk ratio 1.5:1. Important risk factors for long-term fatal CVD (RR, 95% CI) were male gender (1.49, 1.36–1.64), age over 60/65 years (7.55, 5.59–10.19) and current smoking (1.68, 1.26–2.24). High non-HDL-c, and β- and γ-tocopherol serum were associated only with CHD (HR 2.46 [95% CI 1.29–4.71] and 2.47 [1.10–5.61] respectively), while stage 1 and 2 hypertensions were associated only with fatal stroke (2.02 [1.19–3.44] and 2.89 [1.68–4.96] respectively). Conclusions: Over a 10 year + follow-up period Asian subjects had a higher risk of stroke than CHD. Contrary to CVD prevention in Western countries, strategies should also consider stroke instead of CHD only.

Despite many reported associations, the direct cause of neurodegeneration responsible for cognitive loss in Alzheimer’s disease and some other common dementias is not known. The normal human plasma protein, serum amyloid P component, a constituent of all human fibrillar amyloid deposits and present on most neurofibrillary tangles, is cytotoxic for cerebral neurones in vitro and in experimental animals in vivo. The neocortical content of serum amyloid P component was immunoassayed in 157 subjects aged 65 or more with known dementia status at death, in the large scale, population-representative, brain donor cohort of the Cognitive Function and Ageing Study, which avoids the biases inherent in studies of predefined clinico-pathological groups. The serum amyloid P component values were significantly higher in individuals with dementia, independent of serum albumin content measured as a control for plasma in the cortex samples. The odds ratio for dementia at death in the high serum amyloid P component tertile was 5.24 (95% confidence interval 1.79–15.29) and was independent of Braak tangle stages and Thal amyloid-β phases of neuropathological severity. The strong and specific association of higher brain content of serum amyloid P component with dementia, independent of neuropathology, is consistent with a pathogenetic role in dementia., The normal blood protein, serum amyloid P component (SAP), is toxic for cerebral neurones but normally is largely excluded from the brain. Ellmerich et al. report that higher neocortical content of SAP is strongly associated with dementia at death, consistent with a pathogenetic role of SAP in neurodegeneration., Graphical Abstract Graphical Abstract

BackgroundReduced heart rate (HR) increase (HRI), recovery (HRR), and higher resting HR are associated with cardiovascular (CV) disease, but causal inferences have not been deduced. We investigated causal effects of HRI, HRR, and resting HR on CV risk, all-cause mortality (ACM), atrial fibrillation (AF), coronary artery disease (CAD), and ischemic stroke (IS) using Mendelian Randomization.Methods11 variants for HRI, 11 for HRR, and two sets of 46 and 414 variants for resting HR were obtained from four genome-wide association studies (GWASs) on UK Biobank. We performed a lookup on GWASs for CV risk and ACM in UK Biobank (N = 375,367, 5.4% cases and N = 393,165, 4.4% cases, respectively). For CAD, AF, and IS, we used publicly available summary statistics. We used a random-effects inverse-variance weighted (IVW) method and sensitivity analyses to estimate causality.ResultsIVW showed a nominally significant effect of HRI on CV events (odds ratio [OR] = 1.0012, P = 4.11 × 10–2) and on CAD and AF. Regarding HRR, IVW was not significant for any outcome. The IVW method indicated statistically significant associations of resting HR with AF (OR = 0.9825, P = 9.8 × 10–6), supported by all sensitivity analyses, and a nominally significant association with IS (OR = 0.9926, P = 9.82 × 10–3).ConclusionOur findings suggest no strong evidence of an association between HRI and HRR and any outcome and confirm prior work reporting a highly significant effect of resting HR on AF. Future research is required to explore HRI and HRR associations further using more powerful predictors, when available.

Background Cardiomyopathies, defined as diseases involving mainly the heart muscles, are linked to an estimated 5.9 of 100,000 deaths globally. In sub-Saharan Africa, cardiomyopathies constitute 21.4% of heart failure cases, with dilated cardiomyopathy (DCM) being the most common form. The etiology of DCM is heterogeneous and is broadly categorized as genetic or nongenetic, as well as a mixed disease in which genetics interact with intrinsic and environmental factors. Factors such as age, gender, family history, and ethnicity are nonmodifiable, whereas modifiable risk factors include poor nutrition, physical inactivity, and excessive alcohol consumption, among others. However, the relative contribution of the different risk factors to the etiology of DCM is not known in sub-Saharan Africa, and the prevalence of DCM among heart failure patients has not been systematically studied in the region. Objective The aim of this review is to synthesize available literature from sub-Saharan Africa on the prevalence of DCM among patients with heart failure, as well as the literature on factors associated with DCM. This paper outlines the protocol that will be followed to conduct the systematic review. Methods A limited search of the PubMed database will be performed to identify relevant keywords contained in the title, abstract, and subject descriptors using initial search terms “heart failure,” “cardiomyopathy,” and “sub-Saharan Africa.” These search terms and their synonyms will then be used in an extensive search in PubMed, and will address the first research question on prevalence. To address the second research question on risk factors, the terms “heart failure,” “cardiomyopathy,” and “cardiovascular risk factors” in “Sub-Saharan Africa” will be used, listing them one by one. Articles published from 2000 and in the English language will be included. Indexed articles in PubMed and Embase will be included, as well as the first 300 articles retrieved from a Google Scholar search. Collected data will be organized in Endnote and then uploaded to the Rayyan web app for systematic reviews. Two reviewers will independently select articles against the inclusion criteria. Discrepancies in reviewer selections will be resolved by an arbitrator. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting systematic reviews will be applied. A map of sub-Saharan Africa with colors to show disease prevalence in each country will be included. For quantitative data, where possible, odds ratios (for categorical outcome data) or standardized mean differences (for continuous data) and their 95% CIs will be calculated. Results The primary outcomes will be the prevalence of DCM among patients with heart failure and cardiovascular risk factors associated with DCM in sub-Saharan Africa. The literature search will begin on January 1, 2021, and data analysis is expected to be completed by April 30, 2021. Conclusions This review will provide information on the current status of the prevalence and associated factors of DCM, and possibly identify gaps, including paucity of data or conflicting results that need to be addressed to improve our understanding of DCM in sub-Saharan Africa. International Registered Report Identifier (IRRID) PRR1-10.2196/18229

Drug target Mendelian randomization (MR) studies use DNA sequence variants in or near a gene encoding a drug target, that alter the target’s expression or function, as a tool to anticipate the effect of drug action on the same target. Here we apply MR to prioritize drug targets for their causal relevance for coronary heart disease (CHD). The targets are further prioritized using independent replication, co-localization, protein expression profiles and data from the British National Formulary and clinicaltrials.gov. Out of the 341 drug targets identified through their association with blood lipids (HDL-C, LDL-C and triglycerides), we robustly prioritize 30 targets that might elicit beneficial effects in the prevention or treatment of CHD, including NPC1L1 and PCSK9, the targets of drugs used in CHD prevention. We discuss how this approach can be generalized to other targets, disease biomarkers and endpoints to help prioritize and validate targets during the drug development process., Drug target Mendelian randomization (MR) uses genetic variation in or near a gene encoding a drug target to anticipate the effect of drug action on the same target. Using drug target MR, the authors prioritized 30 targets that might elicit beneficial effects in the prevention or treatment of coronary heart disease.

Introduction: The impact of the sympathetic nervous system (SNS) modulation on the risk of heart failure (HF) outside of ß1 receptor blockade remains controversial. Methods: We performed a two-sample Mendelian randomization (MR) study using common independent genetic variants located in the cis region of genes encoding the 9 SNS receptors (α1 A, B, D, α2 A, B, C and ß 1, 2 and 3) that were associated at genome-wide significance (P-value ≤ 5х10 –8 ) with blood pressure (BP) and/or heart rate (HR) in published genome-wide association studies (GWAS) available for BP and HR. Variants were filtered out by Linkage Disequilibrium clumping (LD R 2 > 0.1) and based on their minor allele frequency (MAF < 0.01). The effects of selected variants on the genetic risk of HF were extracted from a GWAS of HF from the HERMES consortium, based on a non-overlapping sample population. MR estimates were obtained using the Wald estimator for a single variant or the inverse variance weighted method for multiple variants. Results: 542,362 controls and 40,805 HF cases were evaluated. Independent variants in genes encoding 4 SNS receptors associated with BP or HR were identified as follows: α1A (diastolic BP), α2B (diastolic BP and HR), ß1 and ß2 (diastolic and systolic BP). MR analysis of α1A and ß1, weighted by their effects on diastolic BP, estimated an association with a higher risk of HF, while α2B variants were associated with a lower risk. We found no evidence for an effect of ß2. A similar relationship with systolic BP was found for ß1 and ß2. HR increasing effect of α2B variants was associated with a decreased odd of HF. Conclusions: Mindful of pleiotropic effects, these findings are consistent with the known benefits of ß1 blockade in HF and support a similar role for α1A blockade; conversely, they suggest a detrimental lowering effect of BP and HR through α2B modulation that deserves further studies. No evidence for a role of ß2 in HF was found.

ObjectiveTo compare performance of general and diabetes specific cardiovascular risk prediction scores in type 2 diabetes patients (T2DM).DesignCohort study.SettingScores were identified through a systematic review and included irrespective of predicted outcome, or inclusion of T2DM patients. Performance was assessed using data from routine practice.ParticipantsA contemporary representative sample of 203,172 UK T2DM patients (age ≥ 18 years).Main outcome measuresCardiovascular disease (CVD i.e., coronary heart disease and stroke) and CVD+ (including atrial fibrillation and heart failure).ResultsWe identified 22 scores: 11 derived in the general population, 9 in only T2DM patients, and 2 that excluded T2DM patients. Over 10 years follow-up, 63,000 events occurred. The RECODE score, derived in people with T2DM, performed best for both CVD (c-statistic 0.731 (0.728,0.734), and CVD+ (0.732 (0.729,0.735)). Overall, neither derivation population, nor original predicted outcome influenced performance. Calibration slopes (1 indicates perfect calibration) ranged from 0.38 (95%CI 0.37;0.39) to 1.05 (95%CI 1.03;1.07). A simple, population specific recalibration process considerably improved performance, ranging between 0.98 and 1.03. Risk scores performed badly in people with pre-existing CVD (c-statistic ∼0.55). Scores with more predictors did not perform scores better: for CVD+ QRISK3 (19 variables) c-statistic 0.69 (95%CI 0.68;0.69), compared to CHD Basic (8 variables) 0.71 (95%CI 0.70; 0.71).ConclusionsCVD risk prediction scores performed well in T2DM, irrespective of derivation population and of original predicted outcome. Scores performed poorly in patients with established CVD. Complex scores with multiple variables did not outperform simple scores. A simple population specific recalibration markedly improved score performance and is recommended for future use.

Drug development of cholesteryl ester transfer protein (CETP) inhibition to prevent coronary heart disease (CHD) has yet to deliver licensed medicines. To distinguish compound from drug target failure, we compared evidence from clinical trials and Mendelian randomization (MR) results. Findings from meta-analyses of CETP inhibitor trials (≥ 24 weeks follow-up) were used to judge between-compound heterogeneity in treatment effects. Genetic data were extracted on 190+ pharmacologically relevant outcomes; spanning 480,698-21,770 samples and 74,124-4,373 events. Drug target MR of protein concentration was used to determine the on-target effects of CETP inhibition and compared to that of PCSK9 modulation. Fifteen eligible CETP inhibitor trials of four compounds were identified, enrolling 79,961 participants. There was a high degree of heterogeneity in effects on lipids, lipoproteins, blood pressure, and clinical events. For example, dalcetrapib and evacetrapib showed a neutral effect, torcetrapib increased, and anacetrapib decreased cardiovascular disease (CVD); heterogeneity p-value < 0.001. In drug target MR analysis, lower CETP concentration (per μg/ml) was associated with CHD (odds ratio 0.95; 95%CI 0.91; 0.99), heart failure (0.95; 95%CI 0.92; 0.99), chronic kidney disease (0.94 95%CI 0.91; 0.98), and age-related macular degeneration (1.69; 95%CI 1.44; 1.99). Lower PCSK9 concentration was associated with a lower risk of CHD, heart failure, atrial fibrillation and stroke, and increased risk of Alzheimer’s disease and asthma. In conclusion, previous failures of CETP inhibitors are likely compound related. CETP inhibition is expected to reduce risk of CHD, heart failure, and kidney disease, but potentially increase risk of age-related macular disease.

Mendelian randomisation (MR) analysis is an important tool to elucidate the causal relevance of environmental and biological risk factors for disease. However, causal inference is undermined if genetic variants used to instrument a risk factor also influence alternative disease-pathways (horizontal pleiotropy). Here we report how the ‘no horizontal pleiotropy assumption’ is strengthened when proteins are the risk factors of interest. Proteins are typically the proximal effectors of biological processes encoded in the genome. Moreover, proteins are the targets of most medicines, so MR studies of drug targets are becoming a fundamental tool in drug development. To enable such studies, we introduce a mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. We illustrate key model decisions and introduce an analytical framework for maximising power and evaluating the robustness of analyses., Mendelian randomisation (MR) analysis of drug targets has potential to aid drug development. Here, the authors introduce a mathematical framework to elucidate this specific application of MR.

Introduction: Hyperprolactinaemia, a common adverse effect of antipsychotic drugs, is primarily linked to blockade of dopamine D2 receptors in the pituitary gland. Certain antipsychotic drugs, such as, for example risperidone and paliperidone, are more likely to induce hyperprolactinaemia compared to others. This effect is probably caused by a relatively high blood/brain concentration ratio, a consequence of being a substrate of P-glycoprotein. Genetic variants of P-glycoprotein with changed functional activity might influence the potential of risperidone and paliperidone to cause hyperprolactinaemia as the altered blood/brain concentration ratio would lead to a reduced therapeutic drug level within essential brain areas making dose adaptations necessary. This increases exposure of dopamine D2 receptors within the pituitary gland. Aims: To investigate possible associations between MDR1/ABCB1 gene polymorphisms and antipsychotic drug-induced hyperprolactinaemia in Russian patients with schizophrenia and to determine possible differences between risperidone/paliperidone and other antipsychotics. Methods: In total, 446 patients with schizophrenia were included from 3 psychiatric hospitals in Siberia. Blood samples were obtained in a cross-sectional study design for DNA extraction and prolactin measurement. Associations between hyperprolactinaemia and 8 MDR1/ABCB1 gene-polymorphisms were assessed using logistic regression analysis accounting for covariates. The analysis was repeated in a patient subgroup using risperidone or paliperidone. Results: We did not observe an association between any of the 8 single nucleotide polymorphisms and the prevalence of antipsychotic-induced hyperprolactinaemia in the total patient population. However, in the risperidone/paliperidone subgroup, the single nucleotide polymorphism rs2032582 (G2677T) was found to be negatively associated with risperidone/paliperidone-induced hyperprolactinaemia. Conclusion: This study revealed a significant association between the ABCB1 gene polymorphism rs2032582 (G2677T) and risperidone/paliperidone-induced hyperprolactinaemia.

Background We aimed at investigating the association of circulating fatty acids with coronary heart disease ( CHD ) and stroke risk. Methods and Results We conducted an individual‐participant data meta‐analysis of 5 UK ‐based cohorts and 1 matched case‐control study. Fatty acids (ie, omega‐3 docosahexaenoic acid, omega‐6 linoleic acid, monounsaturated and saturated fatty acids) were measured at baseline using an automated high‐throughput serum nuclear magnetic resonance metabolomics platform. Data from 3022 incident CHD cases (13 104 controls) and 1606 incident stroke cases (13 369 controls) were included. Logistic regression was used to model the relation between fatty acids and odds of CHD and stroke, adjusting for demographic and lifestyle variables only (ie, minimally adjusted model) or with further adjustment for other fatty acids (ie, fully adjusted model). Although circulating docosahexaenoic acid, but not linoleic acid, was related to lower CHD risk in the fully adjusted model (odds ratio, 0.85; 95% CI , 0.76–0.95 per standard unit of docosahexaenoic acid), there was evidence of high between‐study heterogeneity and effect modification by study design. Stroke risk was consistently lower with increasing circulating linoleic acid (odds ratio for fully adjusted model, 0.82; 95% CI , 0.75–0.90). Circulating monounsaturated fatty acids were associated with higher CHD risk across all models and with stroke risk in the fully adjusted model (odds ratio, 1.22; 95% CI , 1.03–1.44). Saturated fatty acids were not related to increased CHD risk in the fully adjusted model (odds ratio, 0.94; 95% CI , 0.82–1.09), or stroke risk. Conclusions We found consistent evidence that linoleic acid was associated with decreased risk of stroke and that monounsaturated fatty acids were associated with increased risk of CHD . The different pattern between CHD and stroke in terms of fatty acids risk profile suggests future studies should be cautious about using composite events. Different study designs are needed to assess which, if any, of the associations observed is causal.

Background: Brain-derived neurotrophic factor (BDNF) plays an important role in neuronal plasticity, and its dysregulation has been associated with the pathogenesis of mood and anxiety disorders. Prolactin (PRL) is a pituitary hormone which is also produced as a cytokine by immune cells and could be a neurotrophic factor regulating the functional activity of stress-related mechanisms. / Aim: To investigate the possible relationship between depressive state and BDNF and PRL genotypes or levels with special reference to severity of depression. / Methods: Participants of 18–70 years with a clinical diagnosis of depressive disorder of at least moderate severity were included. These patients had not been treated with antidepressant drugs before admission to hospital during the preceding period of the last 6 months, and 54.5% had never been treated with antidepressant drugs during their entire life. The DNA was genotyped for rs1341239 within the prolactin and for rs6265, rs7124442, and rs11030104 within the BDNF gene. Rs11030104 violated the Hardy-Weinberg equilibrium distribution and was excluded from further analyses. BDNF and prolactin concentration was measured in serum by MAGPIX multiplex analyzer (Luminex, USA) using MILLIPLEX® MAP kit (Merck, Germany). Genetic associations were determined by sequentially regressing prolactin, BDNF, 17-items Hamilton's Depression (HAMD-17) and Clinical Global Impression scale, Severity (CGI-S) ratings, and depression (absent/present) on the available SNPs. Genetic associations were evaluated assuming an additive model. / Results: A total of 186 depressed patients (of which 169 were women) and 94 healthy controls (67 women) were genotyped. After excluding subjects without genetic information on all three study SNPs, 217 remained of whom 138 suffered from depression. Within depressed patients we observed an association of rs6265 with HAMD-17: mean difference (MD) 2.33 (95%CI 0.49; 4.16; p = 0.014) and CGI-S: MD 0.38 (95%CI 0.09; 0.66; p = 0.011). No significant association was observed between the prolactin SNP rs1341239 and prolactin levels. Similarly the mean differences of BDNF SNPs did not show an association with BDNF: rs6265 −0.042 ln(pg/ml) (95%CI −0.198; 0.113), and rs7124442 0.006 ln(pg/ml) (95%CI −0.117; 0.130). No other association reached statistical significance. / Conclusion: We observed a significant association between BDNF gene variant rs6265 and the severity of depression in newly admitted, antidepressant treatment-free, depressed patients. Actual PRL and BDNF levels were not elevated sufficiently in depressed patients to reach statistical significance and were not associated with the studied genotypes.

Background: Studies examining the role of factor V Leiden among patients at higher risk of atherothrombotic events, such as those with established coronary heart disease (CHD), are lacking. Given that coagulation is involved in the thrombus formation stage on atherosclerotic plaque rupture, we hypothesized that factor V Leiden may be a stronger risk factor for atherothrombotic events in patients with established CHD. Methods: We performed an individual-level meta-analysis including 25 prospective studies (18 cohorts, 3 case-cohorts, 4 randomized trials) from the GENIUS-CHD (Genetics of Subsequent Coronary Heart Disease) consortium involving patients with established CHD at baseline. Participating studies genotyped factor V Leiden status and shared risk estimates for the outcomes of interest using a centrally developed statistical code with harmonized definitions across studies. Cox proportional hazards regression models were used to obtain age- and sex-adjusted estimates. The obtained estimates were pooled using fixed-effect meta-analysis. The primary outcome was composite of myocardial infarction and CHD death. Secondary outcomes included any stroke, ischemic stroke, coronary revascularization, cardiovascular mortality, and all-cause mortality. Results: The studies included 69 681 individuals of whom 3190 (4.6%) were either heterozygous or homozygous (n=47) carriers of factor V Leiden. Median follow-up per study ranged from 1.0 to 10.6 years. A total of 20 studies with 61 147 participants and 6849 events contributed to analyses of the primary outcome. Factor V Leiden was not associated with the combined outcome of myocardial infarction and CHD death (hazard ratio, 1.03 [95% CI, 0.92–1.16]; I 2 =28%; P -heterogeneity=0.12). Subgroup analysis according to baseline characteristics or strata of traditional cardiovascular risk factors did not show relevant differences. Similarly, risk estimates for the secondary outcomes including stroke, coronary revascularization, cardiovascular mortality, and all-cause mortality were also close to identity. Conclusions: Factor V Leiden was not associated with increased risk of subsequent atherothrombotic events and mortality in high-risk participants with established and treated CHD. Routine assessment of factor V Leiden status is unlikely to improve atherothrombotic events risk stratification in this population.

OBJECTIVE: To examine whether genetic variation affecting the expression or function of lipid-lowering drug targets is associated with Alzheimer disease (AD) risk, to evaluate the potential impact of long-term exposure to corresponding therapeutics. METHODS: We conducted Mendelian randomization analyses using variants in genes that encode the protein targets of several approved lipid-lowering drug classes: HMGCR (encoding the target for statins), PCSK9 (encoding the target for PCSK9 inhibitors, eg, evolocumab and alirocumab), NPC1L1 (encoding the target for ezetimibe), and APOB (encoding the target of mipomersen). Variants were weighted by associations with low-density lipoprotein cholesterol (LDL-C) using data from lipid genetics consortia (n up to 295,826). We meta-analyzed Mendelian randomization estimates for regional variants weighted by LDL-C on AD risk from 2 large samples (total n = 24,718 cases, 56,685 controls). RESULTS: Models for HMGCR, APOB, and NPC1L1 did not suggest that the use of related lipid-lowering drug classes would affect AD risk. In contrast, genetically instrumented exposure to PCSK9 inhibitors was predicted to increase AD risk in both of the AD samples (combined odds ratio per standard deviation lower LDL-C inducible by the drug target = 1.45, 95% confidence interval = 1.23-1.69). This risk increase was opposite to, although more modest than, the degree of protection from coronary artery disease predicted by these same methods for PCSK9 inhibition. INTERPRETATION: We did not identify genetic support for the repurposing of statins, ezetimibe, or mipomersen for AD prevention. Notwithstanding caveats to this genetic evidence, pharmacovigilance for AD risk among users of PCSK9 inhibitors may be warranted. ANN NEUROL 2020;87:30-39.

Despite the availability of effective drug therapies reducing LDL-cholesterol (LDL-C), cardiovascular disease (CVD) remains a significant source of mortality and morbidity. Additional LDL-C reduction may be warranted, especially in patients that are unresponsive to or unable to take existing LDL-C reducing therapies.1 Monoclonal antibodies against PCSK9 (PCSK9 inhibitors) may provide such additional LDL-C reduction. In this synopsis, we summarise findings from a recent Cochrane systematic review2 on the safety and effectiveness of PCSK9 inhibitors. Here we particularly focus on the relative effectiveness of PCSK9 inhibitors compared to existing treatments such as statins and/or ezetimibe and report on the (perceived) quality of the evidence. The following databases were systematically searched for eligible randomised controlled trials (RCTs): Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, Web of Science, Clinicaltrials.gov and the International Clinical Trials Registry Platform. Parallel-group and factorial RCTs with at least 24 weeks of follow-up were included. The 20 identified randomised trials (68 341 participants) predominantly selected high-risk patients (box 1): 7% having familial hypercholesterolaemia, 89% a history of CVD and 39% a type 2 diabetes mellitus (T2DM) diagnosis. The PCSK9 inhibitor alirocumab was evaluated in 13 trials, evolocumab in 3 trials, bococizumab (discontinued agent) in 3 trials and RG7652 by a single trial. Comparisons were made against placebo in 13 trials, ezetimibe and statins in …

Mendelian randomisation analysis has emerged as an important tool to elucidate the causal relevance of a range of environmental and biological risk factors for human disease. However, inference on cause is undermined if the genetic variants used to instrument a risk factor of interest also associate with other traits that open alternative pathways to the disease (horizontal pleiotropy). We show how the ‘no horizontal pleiotropy assumption’ in MR analysis is strengthened when proteins are the risk factors of interest. Proteins are the proximal effectors of biological processes encoded in the genome, and are becoming assayable on an-omics scale. Moreover, proteins are the targets of most medicines, so Mendelian randomization (MR) studies of drug targets are becoming a fundamental tool in drug development. To enable such studies we introduce a formal mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. Finally, we illustrate key model decisions and introduce an analytical framework for maximizing power and elucidating the robustness of drug target MR analyses.

BackgroundIncreasingly, genetic analyses are conducted using information from subjects with established disease, who often receive concomitant treatment. We determined when treatment may bias genetic associations with a quantitative trait.MethodsGraph theory and simulated data were used to explore the impact of drug prescriptions on (longitudinal) genetic effect estimates. Analytic derivations of longitudinal genetic effects are presented, accounting for the following scenarios: 1) treatment allocated independently of a genetic variant, 2) treatment that mediates the genetic effect, 3) treatment that modifies the genetic effect. We additionally evaluate treatment modelling strategies on bias, the root mean squared error (RMSE), coverage, and rejection rate.ResultsWe show that in the absence of treatment by gene effect modification or mediation, genetic effect estimates will be unbiased. In simulated data we found that conditional models accounting for treatment, confounding, and effect modification were generally unbiased with appropriate levels of confidence interval coverage. Ignoring the longitudinal nature of treatment prescription, however (e.g. because of incomplete records in longitudinal data), biased these conditional models to a similar degree (or worse) as simply ignoring treatment.ConclusionThe mere presence of (drug) treatment affecting a GWAS phenotype is insufficient to bias genetic associations with quantitative traits. While treatment may bias associations through effect modification and mediation, this might not occur frequently enough to warrant general concern at the presence of treated subjects in GWAS. Should treatment by gene effect modification or mediation be present however, current GWAS approaches attempting to adjust for treatment insufficiently account for the multivariable and longitudinal nature of treatment trajectories and hence genetic estimates may still be biased.

Background and objective: Applying results from clinical studies to individual patients can be a difficult process. Using the concept of treatment effect modification (also referred to as interaction), defined as a difference in treatment response between patient groups, we discuss whether and how treatment effects can be tailored to better meet patients' needs. Results: First we argue that contrary to how most studies are designed, treatment effect modification should be expected. Second, given this expected heterogeneity, a small number of clinically relevant subgroups should be a priori selected, depending on the expected magnitude of effect modification, and prevalence of the patient type. Third, by defining generalizability as the absence of treatment effect modification we show that generalizability can be evaluated within the usual statistical framework of equivalence testing. Fourth, when equivalence cannot be confirmed, we address the need for further analyses and studies tailoring treatment towards groups of patients with similar response to treatment. Fifth, we argue that to properly frame, the entire body of evidence on effect modification should be quantified in a prior probability.

BackgroundWe characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.MethodsPublished and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Fourteen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentrationResultsThe PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95%CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95%CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95%CI 0.57; 1.22) for the GS, compared to 0.85 (95%CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95%CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer’s disease – outcomes for which large-scale trial data were unavailable.ConclusionsGenetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. Apparent discordance between genetic associations and trial outcome for T2DM might be explained lack by a of statistical precision, or differences in the nature and duration of genetic versus pharmacological perturbation of PCSK9.FundingThis research was funded by the British Heart Foundation (SP/13/6/30554, RG/10/12/28456, FS/18/23/33512), UCL Hospitals NIHR Biomedical Research Centre, by the Rosetrees and Stoneygate Trusts.Condensed abstractEvidence on the long-term efficacy and safety of therapeutic inhibition of PCSK9 is lacking. To explore potential long-term effects of PCSK9 inhibition, we characterised the phenotypic consequence of LDL-cholesterol lowering variants at the PCSK9 locus. A PCSK9 gene score comprising 4 SNPs recapitulated the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and risk of myocardial infarction, and was associated with an increased risk of type 2 diabetes. No associations with safety outcomes such as cancer, COPD, Alzheimer’s disease or atrial fibrillation were identified. Our findings suggest PCSK9 inhibition may be safe and effective during prolonged use.

Meta-analyses based on systematic literature reviews are commonly used to obtain a quantitative summary of the available evidence on a given topic. Despite its attractive simplicity, and its established position at the summit of the evidence-based medicine hierarchy, the reliability of any meta-analysis is largely constrained by the quality of its constituent studies. One major limitation is small study effects, whose presence can often easily be detected, but not so easily adjusted for. Here, robust methods of estimation based on the median and mode are proposed as tools to increase the reliability of findings in a meta-analysis. By re-examining data from published meta-analyses, and by conducting a detailed simulation study, we show that these two simple methods offer notable robustness to a range of plausible bias mechanisms, without making any explicit modelling assumptions. In conclusion, when performing a meta-analysis with suspected small study effects, we recommend reporting the mean, median and modal pooled estimates as a simple but informative sensitivity analyses.

Background: The MR-Egger (MRE) estimator has been proposed to correct for directional pleiotropic effects of genetic instruments in an instrumental variable (IV) analysis. The power of this method is considerably lower than that of conventional estimators, limiting its applicability. Here we propose a novel Bayesian implementation of the MR-Egger estimator (BMRE) and explore the utility of applying weakly informative priors on the intercept term (the pleiotropy estimate) to increase power of the IV (slope) estimate.Methods: This was a simulation study to compare the performance of different IV estimators. Scenarios differed in the presence of a causal effect, the presence of pleiotropy, the proportion of pleiotropic instruments and degree of 'Instrument Strength Independent of Direct Effect' (InSIDE) assumption violation. Based on empirical plasma urate data, we present an approach to elucidate a prior distribution for the amount of pleiotropy.Results: A weakly informative prior on the intercept term increased power of the slope estimate while maintaining type 1 error rates close to the nominal value of 0.05. Under the InSIDE assumption, performance was unaffected by the presence or absence of pleiotropy. Violation of the InSIDE assumption biased all estimators, affecting the BMRE more than the MRE method.Conclusions: Depending on the prior distribution, the BMRE estimator has more power at the cost of an increased susceptibility to InSIDE assumption violations. As such the BMRE method is a compromise between the MRE and conventional IV estimators, and may be an especially useful approach to account for observed pleiotropy.

Background— Studies of recurrent or subsequent disease events may be susceptible to bias caused by selection of subjects who both experience and survive the primary indexing event. Currently, the magnitude of any selection bias, particularly for subsequent time-to-event analysis in genetic association studies, is unknown. Methods and Results— We used empirically inspired simulation studies to explore the impact of selection bias on the marginal hazard ratio for risk of subsequent events among those with established coronary heart disease. The extent of selection bias was determined by the magnitudes of genetic and nongenetic effects on the indexing (first) coronary heart disease event. Unless the genetic hazard ratio was unrealistically large (>1.6 per allele) and assuming the sum of all nongenetic hazard ratios was Conclusions— In most empirical settings, selection bias is expected to have a limited impact on genetic effect estimates of subsequent event risk. Nevertheless, because of undercoverage increasing with sample size, most confidence intervals will be over precise (not wide enough). When there is no effect modification by history of coronary heart disease, the false-positive rates of association tests will be close to nominal.

Genome-wide association studies (GWAS) in coronary artery disease (CAD) had identified 66 loci at 'genome-wide significance' (P < 5 × 10(-8)) at the time of this analysis, but a much larger number of putative loci at a false discovery rate (FDR) of 5% (refs. 1,2,3,4). Here we leverage an interim release of UK Biobank (UKBB) data to evaluate the validity of the FDR approach. We tested a CAD phenotype inclusive of angina (SOFT; ncases = 10,801) as well as a stricter definition without angina (HARD; ncases = 6,482) and selected cases with the former phenotype to conduct a meta-analysis using the two most recent CAD GWAS. This approach identified 13 new loci at genome-wide significance, 12 of which were on our previous list of loci meeting the 5% FDR threshold, thus providing strong support that the remaining loci identified by FDR represent genuine signals. The 304 independent variants associated at 5% FDR in this study explain 21.2% of CAD heritability and identify 243 loci that implicate pathways in blood vessel morphogenesis as well as lipid metabolism, nitric oxide signaling and inflammation.

BACKGROUND: Despite the availability of effective drug therapies that reduce low‐density lipoprotein (LDL)‐cholesterol (LDL‐C), cardiovascular disease (CVD) remains an important cause of mortality and morbidity. Therefore, additional LDL‐C reduction may be warranted, especially for people who are unresponsive to, or unable to take, existing LDL‐C‐reducing therapies. By inhibiting the proprotein convertase subtilisin/kexin type 9 (PCSK9) enzyme, monoclonal antibodies (PCSK9 inhibitors) reduce LDL‐C and CVD risk. OBJECTIVES: Primary To quantify the effects of PCSK9 inhibitors on CVD, all‐cause mortality, myocardial infarction, and stroke, compared to placebo or active treatment(s) for primary and secondary prevention. Secondary To quantify the safety of PCSK9 inhibitors, with specific focus on the incidence of influenza, hypertension, type 2 diabetes, and cancer, compared to placebo or active treatment(s) for primary and secondary prevention. SEARCH METHODS: We identified studies by systematically searching CENTRAL, MEDLINE, Embase, and Web of Science in December 2019. We also searched ClinicalTrials.gov and the International Clinical Trials Registry Platform in August 2020 and screened the reference lists of included studies. This is an update of the review first published in 2017. SELECTION CRITERIA: All parallel‐group and factorial randomised controlled trials (RCTs) with a follow‐up of at least 24 weeks and adult participants with or without a history of CVD were eligible if they compared PCSK9 inhibitors alirocumab or evolocumab to placebo or active treatments such as statins, ezetimibe, or a combination of these. DATA COLLECTION AND ANALYSIS: Two review authors independently reviewed and extracted data. Where data were available, we calculated pooled effect estimates. We used GRADE to assess certainty of evidence and in 'Summary of findings' tables. MAIN RESULTS: We included 24 studies with data on 60,997 participants. Eighteen trials randomised participants to alirocumab and six to evolocumab. All participants received background lipid‐lowering treatment or lifestyle counselling. Six alirocumab studies used an active treatment comparison group (the remaining used placebo), compared to three evolocumab active comparison trials. Follow‐up ranged from 6 to 36 months for the comparisons with placebo and from 6 to 12 months for comparisons with active treatment. Most of the available studies preferentially enrolled people with either established CVD or at a high risk already, and evidence in low‐ to medium‐risk settings is minimal. Alirocumab compared with placebo decreased the risk of CVD events, with an absolute risk difference (RD) of –2% (odds ratio (OR) 0.87, 95% confidence interval (CI) 0.80 to 0.94; 10 studies, 23,868 participants; high‐certainty evidence), decreased the risk of mortality (RD –1%; OR 0.83, 95% CI 0.72 to 0.96; 12 studies, 24,797 participants; high‐certainty evidence), and MI (RD –2%; OR 0.86, 95% CI 0.79 to 0.94; 9 studies, 23,352 participants; high‐certainty evidence) and for any stroke (RD 0%; OR 0.73, 95% CI 0.58 to 0.91; 8 studies, 22,835 participants; high‐certainty evidence). Alirocumab compared with ezetimibe and statins: for CVD, the RD was 1% (OR 1.37, 95% CI 0.65 to 2.87; 3 studies, 1379 participants; low‐certainty evidence); for mortality, RD was –1% (OR 0.51, 95% CI 0.18 to 1.40; 5 studies, 1333 participants; low‐certainty evidence); for MI, RD was 1% (OR 1.45, 95% CI 0.64 to 3.28, 5 studies, 1734 participants; low‐certainty evidence); and for any stroke, RD was less than 1% (OR 0.85, 95% CI 0.13 to 5.61; 5 studies, 1734 participants; low‐certainty evidence). Evolocumab compared with placebo: for CVD, the RD was –2% (OR 0.84, 95% CI 0.78 to 0.91; 3 studies, 29,432 participants; high‐certainty evidence); for mortality, RD was less than 1% (OR 1.04, 95% CI 0.91 to 1.19; 3 studies, 29,432 participants; high‐certainty evidence); for MI, RD was –1% (OR 0.72, 95% CI 0.64 to 0.82; 3 studies, 29,432 participants; high‐certainty evidence); and for any stroke RD was less than –1% (OR 0.79, 95% CI 0.65 to 0.94; 2 studies, 28,531 participants; high‐certainty evidence). Evolocumab compared with ezetimibe and statins: for any CVD event RD was less than –1% (OR 0.66, 95% CI 0.14 to 3.04; 1 study, 218 participants; very low‐certainty evidence); for all‐cause mortality, the RD was less than 1% (OR 0.43, 95% CI 0.14 to 1.30; 3 studies, 5223 participants; very low‐certainty evidence); and for MI, RD was less than 1% (OR 0.66, 95% CI 0.23 to 1.85; 3 studies, 5003 participants; very low‐certainty evidence). There were insufficient data on any stroke. AUTHORS' CONCLUSIONS: The evidence for the clinical endpoint effects of evolocumab and alirocumab versus placebo were graded as high. There is a strong evidence base for the benefits of PCSK9 monoclonal antibodies to people who might not be eligible for other lipid‐lowering drugs, or to people who cannot meet their lipid goals on more traditional therapies, which was the main patient population of the available trials. The evidence base of PCSK9 inhibitors compared with ezetimibe and statins is much weaker (low very‐ to low‐certainty evidence) and it is unclear whether evolocumab or alirocumab might be effectively used as replacement therapies. Finally, there is very limited evidence on any potential safety issues of both evolocumab and alirocumab. While the current evidence synthesis does not reveal any adverse signals, neither does it provide evidence against such signals. This suggests careful consideration of alternative lipid lowering treatments before prescribing PCSK9 inhibitors.

Background: \ud \ud Lipoprotein(a) concentrations in plasma are associated with cardiovascular risk in the general population. Whether lipoprotein(a) concentrations or LPA genetic variants predict long-term mortality in patients with established coronary heart disease remains less clear.\ud \ud Methods: \ud \ud We obtained data from 3313 patients with established coronary heart disease in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. We tested associations of tertiles of lipoprotein(a) concentration in plasma and two LPA single-nucleotide polymorphisms ([SNPs] rs10455872 and rs3798220) with all-cause mortality and cardiovascular mortality by Cox regression analysis and with severity of disease by generalised linear modelling, with and without adjustment for age, sex, diabetes diagnosis, systolic blood pressure, BMI, smoking status, estimated glomerular filtration rate, LDL-cholesterol concentration, and use of lipid-lowering therapy. Results for plasma lipoprotein(a) concentrations were validated in five independent studies involving 10 195 patients with established coronary heart disease. Results for genetic associations were replicated through large-scale collaborative analysis in the GENIUS-CHD consortium, comprising 106 353 patients with established coronary heart disease and 19 332 deaths in 22 studies or cohorts.\ud \ud Findings: \ud \ud The median follow-up was 9·9 years. Increased severity of coronary heart disease was associated with lipoprotein(a) concentrations in plasma in the highest tertile (adjusted hazard radio [HR] 1·44, 95% CI 1·14–1·83) and the presence of either LPA SNP (1·88, 1·40–2·53). No associations were found in LURIC with all-cause mortality (highest tertile of lipoprotein(a) concentration in plasma 0·95, 0·81–1·11 and either LPA SNP 1·10, 0·92–1·31) or cardiovascular mortality (0·99, 0·81–1·2 and 1·13, 0·90–1·40, respectively) or in the validation studies.\ud \ud Interpretation: \ud \ud In patients with prevalent coronary heart disease, lipoprotein(a) concentrations and genetic variants showed no associations with mortality. We conclude that these variables are not useful risk factors to measure to predict progression to death after coronary heart disease is established.\ud \ud Funding: \ud \ud Seventh Framework Programme for Research and Technical Development (AtheroRemo and RiskyCAD), INTERREG IV Oberrhein Programme, Deutsche Nierenstiftung, Else-Kroener Fresenius Foundation, Deutsche Stiftung für Herzforschung, Deutsche Forschungsgemeinschaft, Saarland University, German Federal Ministry of Education and Research, Willy Robert Pitzer Foundation, and Waldburg-Zeil Clinics Isny.

It may not always be possible to blind participants of a randomized controlled trial for treatment allocation. As a result, estimators of the actual treatment effect may be biased. In this paper, we will extend a novel method, originally introduced in genetic research, for instrumental variable meta-analysis, adjusting for bias due to unblinding of trial participants. Using simulation studies, this novel method, “Egger Correction for non-Adherence”, is introduced and compared to the performance of the “intention-to-treat,” “as-treated,” and conventional “instrumental variable” estimators. Scenarios considered (time-varying) non-adherence, confounding, and between-study heterogeneity. The effect of treatment on a binary endpoint was quantified by means of a risk difference. In all scenarios with unblinded treatment allocation, the Egger Correction for non-Adherence method was the least biased estimator. However, unless the variation in adherence was relatively large, precision was lacking, and power did not surpass 0.50. As a comparison, in a meta-analysis of blinded randomized controlled trials, power of the conventional IV estimator was 1.00 versus at most 0.14 for the Egger Correction for non-Adherence estimator. Due to this lack of precision and power, we suggest to use this method mainly as a sensitivity analysis.

Contains fulltext : 118275.pdf (Publisher’s version ) (Open Access) OBJECTIVE: To determine the comparability of subgroup-specific and interaction effects (differences between subgroups) between different study designs. STUDY DESIGN AND SETTING: We compared effects of interventions based on observational studies, randomized clinical trials (RCTs), and individual patient data meta-analyses (IPDMAs) of RCTs (reference) on three clinical topics: (1) mammography screening and breast cancer mortality, (2) coronary artery bypass surgery (CABG) and all-cause mortality, and (3) statins and incidence of major coronary events. Main, subgroup-specific, and interaction effects were compared. RESULTS: Main and subgroup-specific effects were comparable with respect to the direction of the effects. Differences in the magnitude of subgroup-specific effects in observational studies yielded different interactions compared with those in IPDMA. In the mammography example, the ratio of risk ratios (RRR) (i.e., interaction effect) among observational studies was 1.46 [95% confidence interval (CI): 1.09, 1.96] compared with an IPDMA effect of 1.10 (95% CI: 0.89, 1.37). For the CABG studies, the observational RRR was 1.03 (95% CI: 0.84, 1.26), whereas in the IPDMA, this was 1.40 (95% CI: 1.08, 1.1.81). Finally, in the statin example, the RRR was 1.35 (95% CI: 1.13, 1.61) and 0.90 (95% CI: 0.84, 0.97) for observational studies and IPDMA, respectively. CONCLUSION: Main and subgroup-specific effects based on observational data were similar to main and subgroup-specific effects in IPDMAs based on RCTs, yet interactions differed.

It has been widely accepted that most randomized control trials (RCTs) include patient groups that are not a representative sample of the patients who will receive the intervention in daily practice [1–3]. This has raised concerns about the generalizability of RCT results. Recently, Pressler and Kaizar [4] enriched the discussion by asserting that the bias that results from a lack of generalizability can be quantified. They define two populations: the first consisting of subjects fulfilling the inclusion criteria of an RCT (in their notation group I) and the second group (group E) comprising subjects not meeting the inclusion criteria. They propose to estimate the treatment effects in both groups ( O .I / and O .E/, respectively) using nonrandomized (i.e., observational) data. Assuming equal amounts of confounding in both groups, Ǒ D O .I / O .E/ provides an unbiased estimate of how much treatment effect modification there exists between included and excluded subjects. If the interest is in estimating the ‘population average treatment effect’ (PATE), weighing Ǒ by the proportion of E among the total population of interest O D nE n provides an estimate O D O Ǒ of how much ‘generalizability bias’ is created by relying on O .I / to estimate the PATE. This weighing of Ǒ is necessary because if there is treatment effect modification between groups I and E, the PATE is dependent on the proportionate size of both groups. While we acknowledge the relevance of the approach suggested by Pressler and Kaizar, we wish to touch upon some concerns and discuss alternative strategies for exploring generalizability. First, Pressler and Kaizar fail to address why one would be interested in the treatment effect in group E. For example, if we explore the effectiveness of a new antihypertensive drug and E comprises subjects without hypertension, it seems illogical to try to estimate treatment effect modification between groups I and E. Second, using nonrandomized data to estimate O or Ǒ only results in an unbiased estimate if the amount of confounding is equal in both groups E and I. This assumption is not testable, as the authors acknowledge, and results in a problem encountered in virtually all nonrandomized studies, that

Melanocyte lineage-specific antigens, like gp100, have been shown to be recognized by tumour-infiltrating lymphocytes isolated from melanoma patients. Therefore these antigens might be used as targets for specific anti-melanoma immunotherapy. To investigate this potential in syngeneic mouse models, we cloned and characterized the murine homologue of gp100 from a B16 murine melanoma cDNA library. The isolated cDNA clone encodes a protein of 626 amino acids, sharing 79.7% sequence homology with human gp100. Expression of murine gp100 was restricted to cells of the melanocyte lineage, as determined by Northern and Western analysis. However, like human gp100, reverse transcription-polymerase chain reaction (RT-PCR) analysis revealed low levels of transcription of the murine gp100 gene in all the cell lines and normal tissues tested. In contrast, murine tyrosinase, another melanocyte differentiation antigen, mimics human tyrosinase expression and did show absolute melanocyte lineage-specific expression. The characterization of murine gp100 will allow investigation of anti-gp100 immune responses in C57BL/6 mice using the syngeneic B16 tumour model and the possible adverse effects of such immunity on normal melanocytes.