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Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors that influence on disease susceptibility. Studies of disease progression feed directly into therapeutics for disease, whereas studies of incidence inform prevention strategies. However, studies of disease progression are known to be affected by collider (also known as “index event”) bias since the disease progression phenotype can only be observed for individuals who have the disease. This applies equally to observational and genetic studies, including genome-wide association studies and Mendelian randomisation (MR) analyses. In this paper, our aim is to review several statistical methods that can be used to detect and adjust for index event bias in studies of disease progression, and how they apply to genetic and MR studies using both individual- and summary-level data. Methods to detect the presence of index event bias include the use of negative controls, a comparison of associations between risk factors for incidence in individuals with and without the disease, and an inspection of Miami plots. Methods to adjust for the bias include inverse probability weighting (with individual-level data), or Slope-Hunter and Dudbridge et al.’s index event bias adjustment (when only summary-level data are available). We also outline two approaches for sensitivity analysis. We then illustrate how three methods to minimise bias can be used in practice with two applied examples. Our first example investigates the effects of blood lipid traits on mortality from coronary heart disease, while our second example investigates genetic associations with breast cancer mortality.

Therapeutic targets for halting the progression of Alzheimer’s disease pathology are lacking. Recent evidence suggests that APOE4, but not APOE3, activates the Cyclophilin-A matrix metalloproteinase-9 (CypA-MMP9) pathway, leading to an accelerated breakdown of the blood–brain barrier (BBB) and thereby causing neuronal and synaptic dysfunction. Furthermore, blockade of the CypA-MMP9 pathway in APOE4 knock-in mice restores BBB integrity and subsequently normalizes neuronal and synaptic function. Thus, CypA has been suggested as a potential target for treating APOE4 mediated neurovascular injury and the resulting neuronal dysfunction and degeneration. The odds of drug targets passing through clinical trials are greatly increased if they are supported by genomic evidence. We found little evidence to suggest that CypA or MMP9 affects the risk of Alzheimer’s disease or cognitive impairment using two-sample Mendelian randomization and polygenic risk score analysis in humans. This casts doubt on whether they are likely to represent effective drug targets for cognitive impairment in human APOE4 carriers.

Mendelian randomization (MR) uses genetic information to strengthen causal inference concerning the effect of exposures on outcomes. This method has a broad range of applications, including investigating risk factors and appraising potential targets for intervention. MR-Base has become established as a freely accessible, online platform, which combines a database of complete genome-wide association study results with an interface for performing Mendelian randomization and sensitivity analyses. This allows the user to explore millions of potentially causal associations. MR-Base is available as a web application or as an R package . The technical aspects of the tool have previously been documented in the literature. The present article is complimentary to this as it focuses on the applied aspects. Specifically, we describe how MR-Base can be used in several ways, including to perform novel causal analyses, replicate results and enable transparency, amongst others. We also present three use cases, which demonstrate important applications of Mendelian randomization and highlight the benefits of using MR-Base for these types of analyses.

Background Drugs for dementia have been available in England since 1997. Since their launch, there have been several changes to national guidelines and initiatives that may have influenced prescribing. These include changes in National Institute for Health and Care Excellence (NICE) guidance, several government dementia strategies, the addition of dementia to the Quality and Outcomes Framework (QOF), and the expiry of drug patents. Despite this, there has been little research into the effect of these events on prescribing. This paper examines prescribing trends in England using data from the U.K. Clinical Practice Research Datalink since the launch of drugs for dementia up to 1st January 2016. Methods We considered the monthly proportion of patients eligible for treatment, with a diagnosis of probable Alzheimer’s disease, receiving their first prescription for each drug class—namely, acetylcholinesterase (AChE) inhibitors (donepezil, rivastigmine, galantamine) and N -methyl- d -aspartate (NMDA) receptor antagonists (memantine). Trend analysis using joinpoint models was then applied to identify up to two trend changes per treatment of interest. Results The overall trend was for increasing prescriptions in each drug class over the period in which they were studied. This was indicated by the average monthly percentage change, which was 6.0% (95% CI, − 6.4 to 19.9; June 1997 to December 2015) for AChE inhibitors and 15.4% (95% CI, − 77.1 to 480.9; January 2003 to December 2015) for NMDA receptor antagonists. Prescriptions of AChE inhibitors increased at the end of 2012, probably in response to the patent expiry of these drugs earlier that year. The Prime Minister’s Dementia Challenge launched in May 2012 may also have contributed to the observed increase. However, neither this strategy nor patent expiry appeared to influence prescriptions of NMDA receptor antagonists. Instead trend changes in this drug class were driven by NICE guidance released in 2011 that allowed access to these drugs outside of clinical trials. Conclusions Dementia drug prescribing does not always respond to factors such as regulatory guidance, recommendations, or patent expiry, and when it does, not necessarily in a predictable way. This suggests that communication with clinicians may need to be improved to use drugs for dementia more cost-effectively.

Epidemiological studies have reported conflicting findings on the potential adverse effects of long-term antihypertensive medication use on cancer risk. Naturally occurring variation in genes encoding antihypertensive drug targets can be used as proxies for these targets to examine the effect of their long-term therapeutic inhibition on disease outcomes. We performed a mendelian randomization analysis to examine the association between genetically proxied inhibition of 3 antihypertensive drug targets and risk of 4 common cancers (breast, colorectal, lung, and prostate). Single-nucleotide polymorphisms (SNPs) in ACE, ADRB1, and SLC12A3 associated (P < 5.0 × 10-8) with systolic blood pressure (SBP) in genome-wide association studies (GWAS) were used to proxy inhibition of angiotensin-converting enzyme (ACE), β-1 adrenergic receptor (ADRB1), and sodium-chloride symporter (NCC), respectively. Summary genetic association estimates for these SNPs were obtained from GWAS consortia for the following cancers: breast (122,977 cases, 105,974 controls), colorectal (58,221 cases, 67,694 controls), lung (29,266 cases, 56,450 controls), and prostate (79,148 cases, 61,106 controls). Replication analyses were performed in the FinnGen consortium (1,573 colorectal cancer cases, 120,006 controls). Cancer GWAS and FinnGen consortia data were restricted to individuals of European ancestry. Inverse-variance weighted random-effects models were used to examine associations between genetically proxied inhibition of these drug targets and risk of cancer. Multivariable mendelian randomization and colocalization analyses were employed to examine robustness of findings to violations of mendelian randomization assumptions. Genetically proxied ACE inhibition equivalent to a 1-mm Hg reduction in SBP was associated with increased odds of colorectal cancer (odds ratio (OR) 1.13, 95% CI 1.06 to 1.22; P = 3.6 × 10-4). This finding was replicated in the FinnGen consortium (OR 1.40, 95% CI 1.02 to 1.92; P = 0.035). There was little evidence of association of genetically proxied ACE inhibition with risk of breast cancer (OR 0.98, 95% CI 0.94 to 1.02, P = 0.35), lung cancer (OR 1.01, 95% CI 0.92 to 1.10; P = 0.93), or prostate cancer (OR 1.06, 95% CI 0.99 to 1.13; P = 0.08). Genetically proxied inhibition of ADRB1 and NCC were not associated with risk of these cancers. The primary limitations of this analysis include the modest statistical power for analyses of drug targets in relation to some less common histological subtypes of cancers examined and the restriction of the majority of analyses to participants of European ancestry. In this study, we observed that genetically proxied long-term ACE inhibition was associated with an increased risk of colorectal cancer, warranting comprehensive evaluation of the safety profiles of ACE inhibitors in clinical trials with adequate follow-up. There was little evidence to support associations across other drug target-cancer risk analyses, consistent with findings from short-term randomized controlled trials for these medications.

Aims/hypothesisType 2 diabetes and atherosclerotic CVD share many risk factors. This study aimed to systematically assess a broad range of continuous traits to separate their direct effects on coronary and peripheral artery disease from those mediated by type 2 diabetes.MethodsOur main analysis was a two-step Mendelian randomisation for mediation to quantify the extent to which the associations observed between continuous traits and liability to atherosclerotic CVD were mediated by liability to type 2 diabetes. To support this analysis, we performed several univariate Mendelian randomisation analyses to examine the associations between our continuous traits, liability to type 2 diabetes and liability to atherosclerotic CVD.ResultsEight traits were eligible for the two-step Mendelian randomisation with liability to coronary artery disease as the outcome and we found similar direct and total effects in most cases. Exceptions included fasting insulin and hip circumference where the proportion mediated by liability to type 2 diabetes was estimated as 56% and 52%, respectively. Six traits were eligible for the analysis with liability to peripheral artery disease as the outcome. Again, we found limited evidence to support mediation by liability to type 2 diabetes for all traits apart from fasting insulin (proportion mediated: 70%).Conclusions/interpretationMost traits were found to affect liability to atherosclerotic CVD independently of their relationship with liability to type 2 diabetes. These traits are therefore important for understanding atherosclerotic CVD risk regardless of an individual’s liability to type 2 diabetes.

IntroductionCurrent treatments for Alzheimer's and other neurodegenerative diseases have only limited effectiveness meaning that there is an urgent need for new medications that could influence disease incidence and progression. We will investigate the potential of a selection of commonly prescribed drugs, as a more efficient and cost-effective method of identifying new drugs for the prevention or treatment of Alzheimer's disease, non-Alzheimer's disease dementias, Parkinson's disease and amyotrophic lateral sclerosis. Our research will focus on drugs used for the treatment of hypertension, hypercholesterolaemia and type 2 diabetes, all of which have previously been identified as potentially cerebroprotective and have variable levels of preclinical evidence that suggest they may have beneficial effects for various aspects of dementia pathology.Methods and analysisWe will conduct a hypothesis testing observational cohort study using data from the Clinical Practice Research Datalink (CPRD). Our analysis will consider four statistical methods, which have different approaches for modelling confounding. These are multivariable adjusted Cox regression; propensity matched regression; instrumental variable analysis and marginal structural models. We will also use an intention-to-treat analysis, whereby we will define all exposures based on the first prescription observed in the database so that the target parameter is comparable to that estimated by a randomised controlled trial.Ethics and disseminationThis protocol has been approved by the CPRD's Independent Scientific Advisory Committee (ISAC). We will publish the results of the study as open-access peer-reviewed publications and disseminate findings through national and international conferences as are appropriate.

Mendelian randomization (MR) estimates the causal effect of exposures on outcomes by exploiting genetic variation to address confounding and reverse causation. This method has a broad range of applications, including investigating risk factors and appraising potential targets for intervention. MR-Base has become established as a freely accessible, online platform, which combines a database of complete genome-wide association study results with an interface for performing Mendelian randomization and sensitivity analyses. This allows the user to explore millions of potentially causal associations. MR-Base is available as a web application or as an R package . The technical aspects of the tool have previously been documented in the literature. The present article is complementary to this as it focuses on the applied aspects. Specifically, we describe how MR-Base can be used in several ways, including to perform novel causal analyses, replicate results and enable transparency, amongst others. We also present three use cases, which demonstrate important applications of Mendelian randomization and highlight the benefits of using MR-Base for these types of analyses.

Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors that influence on disease susceptibility. Studies of disease progression feed directly into therapeutics for disease, whereas studies of incidence inform prevention strategies. However, studies of disease progression are known to be affected by collider (also known as “index event”) bias since the disease progression phenotype can only be observed for individuals who have the disease. This applies equally to observational and genetic studies, including genome-wide association studies and Mendelian randomisation (MR) analyses. In this paper, our aim is to review several statistical methods that can be used to detect and adjust for index event bias in studies of disease progression, and how they apply to genetic and MR studies using both individual- and summary-level data. Methods to detect the presence of index event bias include the use of negative controls, a comparison of associations between risk factors for incidence in individuals with and without the disease, and an inspection of Miami plots. Methods to adjust for the bias include inverse probability weighting (with individual-level data), or Slope-Hunter and Dudbridge et al.’s index event bias adjustment (when only summary-level data are available). We also outline two approaches for sensitivity analysis. We then illustrate how three methods to minimise bias can be used in practice with two applied examples. Our first example investigates the effects of blood lipid traits on mortality from coronary heart disease, while our second example investigates genetic associations with breast cancer mortality.

Evidence concerning the potential repurposing of antihypertensives for Alzheimer’s disease prevention is inconclusive. We used Mendelian randomization, which can be more robust to confounding by indication and patient characteristics, to investigate the effects of lowering systolic blood pressure (SBP), via different antihypertensive drug classes, on Alzheimer’s disease. We used summary statistics from genome wide association studies of SBP (from UK Biobank) and Alzheimer’s disease (from the International Genomics of Alzheimer’s Project) in a two-sample Mendelian randomization analysis. We identified single nucleotide polymorphisms (SNPs) that mimic the action of antihypertensive targets and estimated the effect of lowering SBP, via antihypertensive drug classes, on Alzheimer’s disease. We also report the effect of lowering SBP on Alzheimer’s disease by combining all drug targets and without consideration of the associated drugs. There was limited evidence that lowering SBP, via antihypertensive drug classes, affected Alzheimer’s disease risk. For example, calcium channel blockers had an odds ratio (OR) per 10mmHg lower SBP of 1.53 (95% confidence interval (CI): 0.94 to 2.49; p=0.09; SNPs=17). We also found limited evidence for an effect of lowering SBP on Alzheimer’s disease when combining all drug targets (OR per 10mmHg lower SBP: 1.14; 95%CI: 0.83 to 1.56; p=0.41; SNPs=59) and without consideration of the associated drug targets (OR per 10mmHg lower SBP: 1.04; 95%CI: 0.95 to 1.13; p=0.45; SNPs=153). Lowering SBP itself is unlikely to affect risk of developing Alzheimer’s disease. Consequently, if specific antihypertensive drug classes do affect risk of Alzheimer’s disease, they are unlikely to do so via SBP. This is the first study to use Mendelian randomization to estimate the effects of the twelve most common antihypertensive drug classes on Alzheimer’s disease. Lowering systolic blood pressure itself is unlikely to affect risk of developing Alzheimer’s disease. If specific antihypertensive drug classes do affect Alzheimer’s disease risk, they are unlikely to do so via systolic blood pressure.