scholarly journals Alcohol consumption in relation to cardiovascular diseases and mortality: a systematic review of Mendelian randomization studies

2021 ◽  
Author(s):  
Inge A. T. van de Luitgaarden ◽  
Sabine van Oort ◽  
Emma J. Bouman ◽  
Linda J. Schoonmade ◽  
Ilse C. Schrieks ◽  
...  
Keyword(s):  
Systematic Review ◽  
Alcohol Consumption ◽  
Instrumental Variable ◽  
Methodological Quality ◽  
Mendelian Randomization ◽  
Mr Studies ◽  
Cardiometabolic Health ◽  
Instrumental Variable Analysis ◽  
All Cause Mortality ◽  
Variable Analysis

AbstractThe causal effects of alcohol-in-moderation on cardiometabolic health are continuously debated. Mendelian randomization (MR) is an established method to address causal questions in observational studies. We performed a systematic review of the current evidence from MR studies on the association between alcohol consumption and cardiometabolic diseases, all-cause mortality and cardiovascular risk factors. We performed a systematic search of the literature, including search terms on type of design and exposure. We assessed methodological quality based on key elements of the MR design: use of a full instrumental variable analysis and validation of the three key MR assumptions. We additionally looked at exploration of non-linearity. We reported the direction of the studied associations. Our search yielded 24 studies that were eligible for inclusion. A full instrumental variable analysis was performed in 17 studies (71%) and 13 out of 24 studies (54%) validated all three key assumptions. Five studies (21%) assessed potential non-linearity. In general, null associations were reported for genetically predicted alcohol consumption with the primary outcomes cardiovascular disease (67%) and diabetes (75%), while the only study on all-cause mortality reported a detrimental association. Considering the heterogeneity in methodological quality of the included MR studies, it is not yet possible to draw conclusions on the causal role of moderate alcohol consumption on cardiometabolic health. As MR is a rapidly evolving field, we expect that future MR studies, especially with recent developments regarding instrument selection and non-linearity methodology, will further substantiate this discussion.

scholarly journals Two-step Randomisation: Applying the Results of Small Feasibility Studies of Interventions to Large-scale Mendelian Randomisation Studies to Robustly Infer Causal Effects on Clinical Endpoints

2019 ◽  
Author(s):  
Meda R Sandu ◽  
Rhona Beynon ◽  
Rebecca Richmond ◽  
Diana L. Santos Ferreira ◽  
Lucy Hackshaw-McGeagh ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Green Tea ◽  
Instrumental Variable ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Two Stage ◽  
Instrumental Variable Analysis ◽  
Metabolic Traits ◽  
Randomization Analysis ◽  
Variable Analysis

Background: Feasibility trials are preliminary trials that assess the viability and acceptability of intervention studies and the effects of the intervention on intermediate endpoints. Due to their short duration, they are unable to establish the effects of the intervention on long-term clinical outcomes. We propose a novel method that could transform the interpretation of feasibility trials using modified two-stage randomisation analyses. Methods In this two-stage process, we explored the effects of a 6-month feasibility factorial randomised controlled trial (RCT) of lycopene and green tea dietary interventions (ProDiet) on 159 serum metabolic traits in 133 men with raised PSA levels but prostate cancer (PCA) free. In the first stage, we conducted an intention-to-treat analysis, using linear regression to examine the effects of the interventions on metabolic traits, compared to the placebo group and instrumental variable analysis to assess the causal effect of the intervention on the outcomes. In the second stage, we used a two-sample Mendelian Randomization (MR) approach to assess the causal effect of metabolic traits altered by the interventions, on PCA risk, using summary statistics data from an international PCA consortium of 44,825 cancer cases and 27,904 controls. ResultsThe systemic effects of lycopene and green tea supplementation on serum metabolic profile were comparable to the effects of the respective dietary advice interventions (R2= 0.65 and 0.76 for lycopene and green tea respectively). Metabolites which were altered in response to lycopene supplementation were acetate (standard deviation difference versus placebo (β)): 0.69; 95% CI= 0.24, 1.15; p=0.003), valine (β: -0.62; -1.03, -0.02; p=0.004), pyruvate (β: -0.56; -0.95, -0.16; p=0.006), and docosahexaenoic acid (β: -0.50; -085, -0.14; p=0.006). The instrumental variable analysis showed there was no evidence that green tea altered the metabolome, but lycopene was associated with an increase in acetate (β=2.13; p=0.006) and decreases in pyruvate (β=-1.90; p=0.009), valine (β=-1.79; p=0.023), diacylglycerol (β=-1.81; p=0.026), alanine (β=-1.55; p=0.015) and DHA (p=0.097), where the regression coefficient represents the standard deviation (SD) difference in metabolite measures per unit change in lycopene (µmol/L) or EGCG (nM).Using MR, a genetically instrumented SD increase in pyruvate increased the odds of PCA by 1.29 (1.03, 1.62; p=0.027). Conclusion Using a two-stage randomization analysis in a feasibility RCT, we found that lycopene lowered levels of pyruvate, which our Mendelian randomization analysis suggests may be causally related to reduced PCA risk.

Using instrumental variables to estimate the attributable fraction

2019 ◽  
Vol 29 (8) ◽  
pp. 2063-2073
Author(s):  
Elisabeth Dahlqwist ◽  
Zoltán Kutalik ◽  
Arvid Sjölander
Keyword(s):  
Coronary Heart Disease ◽  
Risk Factor ◽  
Heart Disease ◽  
Instrumental Variables ◽  
Instrumental Variable ◽  
Mendelian Randomization ◽  
Attributable Fraction ◽  
Two Stage ◽  
Instrumental Variable Analysis ◽  
Variable Analysis

In order to design efficient interventions aimed to improve public health, policy makers need to be provided with reliable information of the health burden of different risk factors. For this purpose, we are interested in the proportion of cases that could be prevented had some harmful exposure been eliminated from the population, i.e. the attributable fraction. The attributable fraction is a causal measure; thus, to estimate the attributable fraction from observational data, we have to make appropriate adjustment for confounding. However, some confounders may be unobserved, or even unknown to the investigator. A possible solution to this problem is to use instrumental variable analysis. In this work, we present how the attributable fraction can be estimated with instrumental variable methods based on the two-stage estimator or the G-estimator. One situation when the problem of unmeasuredconfounding may be particularly severe is when assessing the effect of low educational qualifications on coronary heart disease. By using Mendelian randomization, a special case of instrumental variable analysis, it has been claimed that low educational qualifications is a causal risk factor for coronary heart disease. We use Mendelian randomization to estimate the causal risk ratio and causal odds ratio of low educational qualifications as a risk factor for coronary heart disease with data from the UK Biobank. We compare the two-stage and G-estimator as well as the attributable fraction based on the two estimators. The plausibility of drawing causal conclusion in this analysis is thoroughly discussed and alternative genetic instrumental variables are tested.

scholarly journals Mendelian Randomization using Public Data from Genetic Consortia

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
John R. Thompson ◽  
Cosetta Minelli ◽  
Fabiola Del Greco M
Keyword(s):  
Risk Score ◽  
Instrumental Variable ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Regression Coefficients ◽  
Risk Scores ◽  
Instrumental Variable Analysis ◽  
Genome Wide ◽  
Public Data ◽  
Variable Analysis

AbstractMendelian randomization (MR) is a technique that seeks to establish causation between an exposure and an outcome using observational data. It is an instrumental variable analysis in which genetic variants are used as the instruments. Many consortia have meta-analysed genome-wide associations between variants and specific traits and made their results publicly available. Using such data, it is possible to derive genetic risk scores for one trait and to deduce the association of that same risk score with a second trait. The properties of this approach are investigated by simulation and by evaluating the potentially causal effect of birth weight on adult glucose level. In such analyses, it is important to decide whether one is interested in the risk score based on a set of estimated regression coefficients or the score based on the true underlying coefficients. MR is primarily concerned with the latter. Methods designed for the former question will under-estimate the variance if used for MR. This variance can be corrected but it needs to be done with care to avoid introducing bias. MR based on public data sources is useful and easy to perform, but care must be taken to avoid false precision or bias.

scholarly journals Reporting methodological issues of the mendelian randomization studies in health and medical research: a systematic review

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Shabab Noor Islam ◽  
Tanvir Ahammed ◽  
Aniqua Anjum ◽  
Olayan Albalawi ◽  
Md. Jamal Uddin
Keyword(s):  
Systematic Review ◽  
Instrumental Variable ◽  
Mendelian Randomization ◽  
Least Square ◽  
Mr Studies ◽  
Risk Scores ◽  
Methodological Issues ◽  
Exclusion Criteria ◽  
Prisma Statement ◽  
Modelling Approach

Abstract Background Mendelian randomization (MR) studies using Genetic risk scores (GRS) as an instrumental variable (IV) have increasingly been used to control for unmeasured confounding in observational healthcare databases. However, proper reporting of methodological issues is sparse in these studies. We aimed to review published papers related to MR studies and identify reporting problems. Methods We conducted a systematic review using the clinical articles published between 2009 and 2019. We searched PubMed, Scopus, and Embase databases. We retrieved information from every MR study, including the tests performed to evaluate assumptions and the modelling approach used for estimation. Using our inclusion/exclusion criteria, finally, we identified 97 studies to conduct the review according to the PRISMA statement. Results Only 66 (68%) of the studies empirically verified the first assumption (Relevance assumption), and 40 (41.2%) studies reported the appropriate tests (e.g., R2, F-test) to investigate the association. A total of 35.1% clearly stated and discussed theoretical justifications for the second and third assumptions. 30.9% of the studies used a two-stage least square, and 11.3% used the Wald estimator method for estimating IV. Also, 44.3% of the studies conducted a sensitivity analysis to illuminate the robustness of estimates for violations of the untestable assumptions. Conclusions We found that incompleteness of the justification of the assumptions for the instrumental variable in MR studies was a common problem in our selected studies. This may misdirect the findings of the studies.

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