Mendelian Randomisation Study of Smoking, Alcohol, and Coffee Drinking in Relation to Parkinson’s Disease

Background: Previous studies showed that lifestyle behaviors (cigarette smoking, alcohol, coffee) are inversely associated with Parkinson’s disease (PD). The prodromal phase of PD raises the possibility that these associations may be explained by reverse causation. Objective: To examine associations of lifestyle behaviors with PD using two-sample Mendelian randomisation (MR) and the potential for survival and incidence-prevalence biases. Methods: We used summary statistics from publicly available studies to estimate the association of genetic polymorphisms with lifestyle behaviors, and from Courage-PD (7,369 cases, 7,018 controls; European ancestry) to estimate the association of these variants with PD. We used the inverse-variance weighted method to compute odds ratios (ORIVW) of PD and 95%confidence intervals (CI). Significance was determined using a Bonferroni-corrected significance threshold (p = 0.017). Results: We found a significant inverse association between smoking initiation and PD (ORIVW per 1-SD increase in the prevalence of ever smoking = 0.74, 95%CI = 0.60–0.93, p = 0.009) without significant directional pleiotropy. Associations in participants ≤67 years old and cases with disease duration ≤7 years were of a similar size. No significant associations were observed for alcohol and coffee drinking. In reverse MR, genetic liability toward PD was not associated with smoking or coffee drinking but was positively associated with alcohol drinking. Conclusion: Our findings are in favor of an inverse association between smoking and PD that is not explained by reverse causation, confounding, and survival or incidence-prevalence biases. Genetic liability toward PD was positively associated with alcohol drinking. Conclusions on the association of alcohol and coffee drinking with PD are hampered by insufficient statistical power.

Biological Essentialism Correlates with (But Doesn’t Cause?) Intergroup Bias

People with biological essentialist beliefs about social groups also tend to endorse biased beliefs about individuals in those groups, including stereotypes, prejudices, and intensified emphasis on the group. These correlations could be due to biological essentialism causing bias, and some experimental studies support this causal direction. Given this prior work, we expected to find that biological essentialism would lead to increased bias compared to a control condition and set out to extend this prior work in a new direction (regarding “value-based” essentialism). But although the manipulation affected essentialist beliefs and essentialist beliefs were correlated with stereotyping (Studies 1, 2a, and 2b), prejudice (Studies 2a), and group emphasis (Study 3), there was no evidence that biological essentialism caused these outcomes. Given these findings, our initial research question became moot, and the present work focuses on reexamining the relationship between essentialism and bias. We discuss possible moderators, reverse causation, and third variables.

1074The role of epidemiology in improving the health of people with disability

Focus and outcomes for participants The symposium will focus on the role of epidemiologists in building an evidence base to improve the health of the 15% of the world’s population with disability who currently experience vast health inequalities. Participants will be introduced to new ways of conceptualising disability in epidemiology; state of the art approaches to monitoring disability-related socio-economic and health inequalities; methodological challenges and solutions to address the biases due to misclassification, confounding and reverse causation; and the application of causal mediation analysis and natural experiments in identifying potential policy solutions. Participants will gain a greater understanding of how epidemiological methods can be applied to improve the health of people with disability, as well as insights and ideas for their research. A network of epidemiologists interested in this topic will be generated to foster ongoing communication and collaborative opportunities. Rationale for the symposium, including for its inclusion in the Congress The health of disabled people has largely been ignored by epidemiologists. This is despite emerging evidence that people with disability experience poorer health because of factors unrelated to their impairment, including socio-economic disadvantage, discrimination, and violence. However, turning epidemiologists’ efforts to the health of people with disability presents conceptual and methodological challenges, some of which are unique to the content area. Participants will be shown a suite of approaches that can be deployed to address these problems. Participatory methods and innovative graphical and statistical methods for analysing disability-related health inequalities, approaches rarely used in epidemiology, will be covered. The symposium will also concentrate on the application of methods to optimise causal inference in the presence of multiple potential biases, and methods that simulate randomised controlled trial conditions to model policy interventions. Presentation program The presentations are from researchers from the CRE-DH, funded through Australia’s National Health and Medical Research Council organised four themes. Theme 1: Conceptualisation of disability We will present findings from a scoping review of original articles in epidemiology journals and will argue that, while, disability is usually conceptualised in epidemiology as an outcome, reconceiving of disability as an exposure, mediator and/or effect modifier can provide important insights on the determinants of health of people with disability. Theme 2: Monitoring disability-related inequalities We will demonstrate how the CRE-DH has used participatory methods, where people with disability are ‘experts through lived experience’, to develop indicators to monitor disability-related inequalities and design a National Community Attitudes survey. We will demonstrate innovative ways to graphically illustrate prevalence, absolute and relative inequalities simultaneously, and discuss how hierarchical Bayesian methods can be used to overcome inadequate power due to disaggregation and assess inequalities under uncertainty. Theme 3: Approaches to minimising bias We will talk about how biases can affect estimates of disability prevalence and disability-outcome associations, including reverse causation, confounding and misclassification. We will discuss a range of approaches we have used to address these challenges including modelling incident (rather than prevalent) disability, using fixed effects models and propensity score approaches, and approaches to addressing misclassification bias drawing on examples from our program of research. Theme 4: Identification of policy interventions We will discuss methods that can be used to model the impact of policies on the health of people with disability using examples from our research. We will present the results of a causal mediation analysis modelling the impact of different employment policy interventions on mental health outcomes. We will illustrate the value of natural policy experiments for estimating effects of policy changes on employment and health of people with disability using two examples – the 2014 reassessment of Disability Support Pensioners under stricter impairment tables and the introduction of Australia’s National Disability Insurance Scheme. The symposium will conclude with a facilitated discussion focussed on how epidemiologists can come together internationally to grasp the opportunities and address the challenges in research focussed on the health of people with disabilities. Names of presenters Professor Anne Kavanagh, PhD Dr Nicola Fortune, PhD Dr George Disney, PhD Dr Zoe Aitken Dr Samia Badji, PhD

The Causal Relationship between Type 2 Diabetes Mellitus and Ovarian Cancer: Two-Sample Mendelian Randomization

Aims: This research aimed at determining the causal relationship between type 2 diabetes mellitus (T2DM) and ovarian cancer using two-sample Mendelian randomization technique. This is because there is an assumption that type 2 diabetes mellitus (T2DM) has a causal relationship with ovarian cancer due to the alarming rising incidence statistics. Study design: This study used a two-sample Mendelian Randomization (MR) design to undertake the causal relationship investigation. Mendelian randomization technique uses genetic variants as instrumental variables, which undergo random allocation at conception and are non-modifiable. This makes it not to be affected by confounding factors and reverse causation. The MR techniques employed are MR-Egger and Inverse Variance Weighted (IVW.) Data sources: The outcome (ovarian cancer) summary statistics was retrieved from Ovarian Cancer Association Consortium (OCAC), which has 66,450 samples (number of cases=25,509, number of controls=40,941) of European population. The exposure (T2DM) summary genetic data came from DIAGRAM plus Metabochip consortium which involved approximately 149,821 samples (number of cases=34,840, number of controls=114,981) of mixed population. Results: The study indicated that there was no evidence of causal relationship between T2DM and ovarian cancer (MR-Egger: b= -0.0476, se = 0.0619, p-value = 0.4479, IVW: b = -0.0165, se = 0.0257, p-value = 0.5217). The odds ratios indicated that the two-sample Mendelian randomization had the power to detect 0.0464 and 0.0164 decrease in variability per 1 SD for MR-Egger and IVW respectively (MR-Egger: OR = 0.9536, CI: 0.8447, 1.0765, IVW: OR = 0.9836, CI: 0.9352, 1.0345). Conclusion: This approach alleviated the usual problem of reverse causation and confounding factors hence depicting clearly that there is no causal relationship between T2DM and Ovarian cancer.

Impact of lung function on cardiovascular diseases and cardiovascular risk factors: a two sample bidirectional Mendelian randomisation study

IntroductionObservational studies suggested lung function is inversely associated with cardiovascular disease (CVD) although these studies could be confounded. We conducted a two sample Mendelian randomisation study using summary statistics from genome-wide association studies (GWAS) to clarify the role of lung function in CVD and its risk factors, and conversely the role of CVD in lung function.MethodsWe obtained genetic instruments for forced expiratory volume in 1 s (FEV1: 260) and forced vital capacity (FVC: 320) from publicly available UK Biobank summary statistics (n=421 986) and applied to GWAS summary statistics for coronary artery disease (CAD) (n=184 305), stroke (n=446 696), atrial fibrillation (n=1 030 836) and heart failure (n=977 320) and cardiovascular risk factors. Inverse variance weighting was used to assess the impact of lung function on these outcomes, with various sensitivity analyses. Bidirectional Mendelian randomisation was used to assess reverse causation.ResultsFEV1 and FVC were inversely associated with CAD (OR per SD increase, 0.72 (95% CI 0.63 to 0.82) and 0.70 (95%CI 0.62 to 0.78)), overall stroke (0.87 (95%CI 0.77 to 0.97), 0.90 (95% CI 0.82 to 1.00)) and some stroke subtypes. FEV1 and FVC were inversely associated with type 2 diabetes and systolic blood pressure. Sensitivity analyses produced similar findings although the association with CAD was attenuated after adjusting for height (eg, OR for 1SD FEV10.95 (0.75 to 1.19), but not for stroke or type 2 diabetes. There was no strong evidence for reverse causation.ConclusionHigher lung function likely protect against CAD and stroke.

Methods to Address Self-Selection and Reverse Causation in Studies of Neighborhood Environments and Brain Health

Preliminary evidence suggests that neighborhood environments, such as socioeconomic disadvantage, pedestrian and physical activity infrastructure, and availability of neighborhood destinations (e.g., parks), may be associated with late-life cognitive functioning and risk of Alzheimer’s disease and related disorders (ADRD). The supposition is that these neighborhood characteristics are associated with factors such as mental health, environmental exposures, health behaviors, and social determinants of health that in turn promote or diminish cognitive reserve and resilience in later life. However, observed associations may be biased by self-selection or reverse causation, such as when individuals with better cognition move to denser neighborhoods because they prefer many destinations within walking distance of home, or when individuals with deteriorating health choose residences offering health services in neighborhoods in rural or suburban areas (e.g., assisted living). Research on neighborhood environments and ADRD has typically focused on late-life brain health outcomes, which makes it difficult to disentangle true associations from associations that result from reverse causality. In this paper, we review study designs and methods to help reduce bias due to reverse causality and self-selection, while drawing attention to the unique aspects of these approaches when conducting research on neighborhoods and brain aging.

How well can we assess the validity of non-randomised studies of medications? A systematic review of assessment tools

ObjectiveTo determine whether assessment tools for non-randomised studies (NRS) address critical elements that influence the validity of NRS findings for comparative safety and effectiveness of medications.DesignSystematic review and Delphi survey.Data sourcesWe searched PubMed, Embase, Google, bibliographies of reviews and websites of influential organisations from inception to November 2019. In parallel, we conducted a Delphi survey among the International Society for Pharmacoepidemiology Comparative Effectiveness Research Special Interest Group to identify key methodological challenges for NRS of medications. We created a framework consisting of the reported methodological challenges to evaluate the selected NRS tools.Study selectionChecklists or scales assessing NRS.Data extractionTwo reviewers extracted general information and content data related to the prespecified framework.ResultsOf 44 tools reviewed, 48% (n=21) assess multiple NRS designs, while other tools specifically addressed case–control (n=12, 27%) or cohort studies (n=11, 25%) only. Response rate to the Delphi survey was 73% (35 out of 48 content experts), and a consensus was reached in only two rounds. Most tools evaluated methods for selecting study participants (n=43, 98%), although only one addressed selection bias due to depletion of susceptibles (2%). Many tools addressed the measurement of exposure and outcome (n=40, 91%), and measurement and control for confounders (n=40, 91%). Most tools have at least one item/question on design-specific sources of bias (n=40, 91%), but only a few investigate reverse causation (n=8, 18%), detection bias (n=4, 9%), time-related bias (n=3, 7%), lack of new-user design (n=2, 5%) or active comparator design (n=0). Few tools address the appropriateness of statistical analyses (n=15, 34%), methods for assessing internal (n=15, 34%) or external validity (n=11, 25%) and statistical uncertainty in the findings (n=21, 48%). None of the reviewed tools investigated all the methodological domains and subdomains.ConclusionsThe acknowledgement of major design-specific sources of bias (eg, lack of new-user design, lack of active comparator design, time-related bias, depletion of susceptibles, reverse causation) and statistical assessment of internal and external validity is currently not sufficiently addressed in most of the existing tools. These critical elements should be integrated to systematically investigate the validity of NRS on comparative safety and effectiveness of medications.Systematic review protocol and registrationhttps://osf.io/es65q.