Causal null hypotheses of sustained treatment strategies: What can be tested with an instrumental variable?

A causal relationship between distinct immune features and acute or chronic pancreatitis: results from a mendelian randomization analysis

OBJECTIVES

This study aimed to thoroughly examining the causal link between immune traits and four types of pancreatitis, using mendelian randomization.

METHODS

Data on 731 immune traits were collected from the genome-wide association study (GWAS) database as exposure. Information regarding acute pancreatitis (AP), alcohol-induced acute pancreatitis (AAP), chronic pancreatitis (CP), and alcohol-induced chronic pancreatitis (ACP) were acquired from the FinnGen Consortium as outcomes. Mendelian randomization (MR) using inverse variance weighting (IVW) evaluated the links between immune traits and pancreatitis. We evaluated the robustness of the IVW results through sensitivity analyses and validated them using meta-analysis with AP and CP data from the UK Biobank in the GWAS catalog.

RESULTS

A total of 36 immune traits showed significant associations with susceptibility of four types of pancreatitis, including AP (7 traits), AAP (8 traits), CP (14 traits), and ACP (7 traits). Twenty characteristics were found to be potential risk factors for pancreatitis, identified in B Cells (5 traits), conventional dendritic cells (cDCs, 2 traits), maturation stage of T cells (2 traits), monocytes (2 traits), myeloid cells (2 traits), T cells, B cells, natural killer cells (TBNK, 2 traits), and regulatory T cells (Treg cells, 5 traits). Multiple sensitivity analyses confirmed the validity of the findings. Meta-analysis confirmed a solid causal relationship between CX3CR1 on CD14- CD16-of monocyte panel and the susceptibility of CP.

CONCLUSIONS

Our MR study identified immune traits causally linked to acute and chronic pancreatitis, offering new insights for early clinical intervention and immune cell-targeted therapies.

Estimating Time-Varying Exposure Effects Through Continuous-Time Modelling in Mendelian Randomization

Mendelian randomization is an instrumental variable method that utilizes genetic information to investigate the causal effect of a modifiable exposure on an outcome. In most cases, the exposure changes over time. Understanding the time-varying causal effect of the exposure can yield detailed insights into mechanistic effects and the potential impact of public health interventions. Recently, a growing number of Mendelian randomization studies have attempted to explore time-varying causal effects. However, the proposed approaches oversimplify temporal information and rely on overly restrictive structural assumptions, limiting their reliability in addressing time-varying causal problems. This article considers a novel approach to estimate time-varying effects through continuous-time modelling by combining functional principal component analysis and weak-instrument-robust techniques. Our method effectively utilizes available data without making strong structural assumptions and can be applied in general settings where the exposure measurements occur at different timepoints for different individuals. We demonstrate through simulations that our proposed method performs well in estimating time-varying effects and provides reliable inference when the time-varying effect form is correctly specified. The method could theoretically be used to estimate arbitrarily complex time-varying effects. However, there is a trade-off between model complexity and instrument strength. Estimating complex time-varying effects requires instruments that are unrealistically strong. We illustrate the application of this method in a case study examining the time-varying effects of systolic blood pressure on urea levels.

Genome-wide association study of maternal plasma metabolites during pregnancy

Metabolites are key indicators of health and therapeutic targets, but their genetic underpinnings during pregnancy-a critical period for human reproduction-are largely unexplored. Using genetic data from non-invasive prenatal testing, we performed a genome-wide association study on 84 metabolites, including 37 amino acids, 24 elements, 13 hormones, and 10 vitamins, involving 34,394 pregnant Chinese women, with sample sizes ranging from 6,394 to 13,392 for specific metabolites. We identified 53 metabolite-gene associations, 23 of which are novel. Significant differences in genetic effects between pregnant and non-pregnant women were observed for 16.7%-100% of these associations, indicating gene-environment interactions. Additionally, 50.94% of genetic associations exhibited pleiotropy among metabolites and between six metabolites and eight pregnancy phenotypes. Mendelian randomization revealed potential causal relationships between seven maternal metabolites and 15 human traits and diseases. These findings provide new insights into the genetic basis of maternal plasma metabolites during pregnancy.

Nonlinear relationship between high-density lipoprotein cholesterol and cardiovascular disease: an observational and Mendelian randomization analysis

BACKGROUND

Clinical trials and Mendelian randomization (MR) studies reported null effects of high-density lipoprotein cholesterol (HDL-C) on risk of cardiovascular disease (CVD), which might have overlooked a nonlinear causal association. We aimed to investigate the dose-response relationship between circulating HDL-C concentrations and CVD in observational and MR frameworks.

METHODS

We included 348,636 participants (52,919 CVD cases and 295,717 non-cases) of European ancestry with genetic data from the UK Biobank (UKB) and acquired genome-wide association summary data for HDL-C of Europeans from the Global Lipids Genetics Consortium (GLGC). Observational analyses were conducted in the UKB. Stratified MR analyses were conducted combing genetic data for CVD from UKB and lipids from GLGC.

RESULTS

Observational analyses showed L-shaped associations of HDL-C with CVD, with no further risk reduction when HDL-C levels exceeded 70 mg/dL. Multivariable MR analyses across entire distribution of HDL-C found no association of HDL-C with CVD, after control of the pleiotropic effect on other lipids and unmeasured pleiotropism. However, in stratified MR analyses, significant inverse associations of HDL-C with CVD were observed in the stratum of participants with HDL-C ≤ 50 mg/dL (odds ratio per unit increase, 0.86; 95 % confidence interval, 0.79-0.94), while null associations were observed in any stratum above 50 mg/dL.

CONCLUSIONS

Our data suggest a potentially causal inverse association of HDL-C at low levels with CVD risks. These findings advance our knowledge about the role of HDL as a potential target in CVD prevention and therapy.

Two sample Mendelian Randomisation using an outcome from a multilevel model of disease progression

Identifying factors that are causes of disease progression, especially in neurodegenerative diseases, is of considerable interest. Disease progression can be described as a trajectory of outcome over time-for example, a linear trajectory having both an intercept (severity at time zero) and a slope (rate of change). A technique for identifying causal relationships between one exposure and one outcome in observational data whilst avoiding bias due to confounding is two sample Mendelian Randomisation (2SMR). We consider a multivariate approach to 2SMR using a multilevel model for disease progression to estimate the causal effect an exposure has on the intercept and slope. We carry out a simulation study comparing a naïve univariate 2SMR approach to a multivariate 2SMR approach with one exposure that effects both the intercept and slope of an outcome that changes linearly with time since diagnosis. The simulation study results, across six different scenarios, for both approaches were similar with no evidence against a non-zero bias and appropriate coverage of the 95% confidence intervals (for intercept 93.4-96.2% and the slope 94.5-96.0%). The multivariate approach gives a better joint coverage of both the intercept and slope effects. We also apply our method to two Parkinson's cohorts to examine the effect body mass index has on disease progression. There was no strong evidence that BMI affects disease progression, however the confidence intervals for both intercept and slope were wide.

Causal relationship between gut microbiota and risk of esophageal cancer: evidence from Mendelian randomization study

BACKGROUND

The causative implications remain ambiguous. Consequently, this study aims to evaluate the putative causal relationship between gut microbiota and Esophageal cancer (EC).

METHODS

The genome-wide association study (GWAS) pertaining to the microbiome, derived from the MiBioGen consortium-which consolidates 18,340 samples across 24 population-based cohorts-was utilized as the exposure dataset. Employing the GWAS summary statistics specific to EC patients sourced from the GWAS Catalog and leveraging the two-sample Mendelian randomization (MR) methodology, the principal analytical method applied was the inverse variance weighted (IVW) technique. Cochran's Q statistic was utilized to discern heterogeneity inherent in the data set. Subsequently, a reverse MR analysis was executed.

RESULTS

Findings derived from the IVW technique elucidated that the Family Porphyromonadaceae (P = 0.048) and Genus Candidatus Soleaferrea (P = 0.048) function as deterrents against EC development. In contrast, the Genus Catenibacterium (P = 0.044), Genus Eubacterium coprostanoligenes group (P = 0.038), Genus Marvinbryantia (P = 0.049), Genus Ruminococcaceae UCG010 (P = 0.034), Genus Ruminococcus1 (P = 0.047), and Genus Sutterella (P = 0.012) emerged as prospective risk contributors for EC. To assess reverse causal effect, we used EC as the exposure and the gut microbiota as the outcome, and this analysis revealed associations between EC and seven different types of gut microbiota. The robustness of the MR findings was substantiated through comprehensive heterogeneity and pleiotropy evaluations.

CONCLUSIONS

This research identified certain microbial taxa as either protective or detrimental elements for EC, potentially offering valuable biomarkers for asymptomatic diagnosis and prospective therapeutic interventions for EC.

The association between grip strength and carotid intima media thickness: A Mendelian randomization analysis of the Canadian Longitudinal Study on Aging

BACKGROUND

Several two-sample Mendelian randomization studies have reported discordant results concerning the association between grip strength and cardiovascular disease, possibly due to the number of instrumental variables used, pleiotropic bias, and/ or effect modification by age and sex.

METHODS

We conducted a sex- and age-stratified one-sample Mendelian randomization study in the Canadian Longitudinal Study on Aging. We investigated whether grip strength is associated with carotid intima media thickness (cIMT), a marker of vascular atherosclerosis event risk, using eighteen single nucleotide polymorphisms (SNP) identified as specifically associated with grip strength.

RESULTS

A total of 20,258 participants of self-reported European ancestry were included in the analytic sample. Our Mendelian randomization findings suggest a statistically significant association between grip strength and cIMT (MR coefficient of 0.02 (95% CI: 0.01, 0.04)). We found no statistically significant differences between sexes (p-value = 0.201), or age groups [(≤ 60 years old versus >60 years old); p-value = 0.421].

CONCLUSION

This study provides evidence that grip strength is inversely associated with cIMT. Our one-sample MR study design allowed us to demonstrate that there is no evidence of heterogeneity of effects according to age group or biological sex.

Guidelines for performing Mendelian randomization investigations: update for summer 2023

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.

Inhibition of tumor necrosis factor receptor 1 and the risk of periodontitis

Aim

To investigate the effect of genetically proxied inhibition of tumor necrosis factor receptor 1 (TNFR1) on the risk of periodontitis.

Materials and methods

Genetic instruments were selected from the vicinity of TNFR superfamily member 1A (TNFRSF1A) gene (chromosome 12; base pairs 6,437,923-6,451,280 as per GRCh37 assembly) based on their association with C-reactive protein (N= 575,531). Summary statistics of these variants were obtained from a genome-wide association study (GWAS) of 17,353 periodontitis cases and 28,210 controls to estimate the effect of TNFR1 inhibition on periodontitis using a fixed-effects inverse method.

Results

Considering rs1800693 as an instrument, we found no effect of TNFR1 inhibition on periodontitis risk (Odds ratio (OR) scaled per standard deviation increment in CRP: 1.57, 95% confidence interval (CI): 0.38;6.46). Similar results were derived from a secondary analysis that used three variants (rs767455, rs4149570, and rs4149577) to index TNFR1 inhibition.

Conclusions

We found no evidence of a potential efficacy of TNFR1 inhibition on periodontitis risk.

Estimation of time-varying causal effects with multivariable Mendelian randomization: some cautionary notes

INTRODUCTION

For many exposures present across the life course, the effect of the exposure may vary over time. Multivariable Mendelian randomization (MVMR) is an approach that can assess the effects of related risk factors using genetic variants as instrumental variables. Recently, MVMR has been used to estimate the effects of an exposure during distinct time periods.

METHODS

We investigated the behaviour of estimates from MVMR in a simulation study for different time-varying causal scenarios. We also performed an applied analysis to consider how MVMR estimates of body mass index on systolic blood pressure vary depending on the time periods considered.

RESULTS

Estimates from MVMR in the simulation study were close to the true values when the outcome model was correctly specified: i.e. when the outcome was a discrete function of the exposure at the precise time points at which the exposure was measured. However, in more realistic cases, MVMR estimates were misleading. For example, in one scenario, MVMR estimates for early life were clearly negative despite the true causal effect being constant and positive. In the applied example, estimates were highly variable depending on the time period in which genetic associations with the exposure were estimated.

CONCLUSIONS

The poor performance of MVMR to study time-varying causal effects can be attributed to model misspecification and violation of the exclusion restriction assumption. We would urge caution about quantitative conclusions from such analyses and even qualitative interpretations about the direction, or presence or absence, of a causal effect during a given time period.

Association between total body bone mineral density and periodontitis: A Mendelian randomization study

BACKGROUND

The purpose of the study was to examine the association between total body bone mineral density (BMD) and periodontitis using Mendelian randomization (MR) analysis.

METHODS AND MATERIALS

We used 81 single nucleotide polymorphisms (SNPs) associated with BMD at a p-value of < 5 × 10^-8 from a genome-wide association study (GWAS) of 66,628 individuals of European descent. The GWAS for periodontitis was derived from a meta-analysis of seven cohort studies that included 17,353 cases and 28,210 controls of European ancestry.

RESULTS

MR showed no association between BMD and periodontitis (odds ratio per standard deviation increment in genetically predicted BMD = 1.00; 95% confidence interval: 0.92-1.08). Leave-one-out analyses and pleiotropy-robust methods did not indicate any bias.

CONCLUSIONS

The MR study provided no evidence that BMD might be causally linked to periodontitis. Hence it may be concluded as the key finding that BMD depletion does not increase the risk of periodontitis.

Prenatal exposure to trans fatty acids and head growth in fetal life and childhood: triangulating confounder-adjustment and instrumental variable approaches

Dietary trans fatty acids (TFAs) are primarily industrially produced and remain abundant in processed food, particularly in low- and middle-income countries. Although TFAs are a cause of adverse cardiometabolic outcomes, little is known about exposure to TFAs in relation to brain development. We aimed to investigate the effect of maternal TFA concentration during pregnancy on offspring head growth in utero and during childhood. In a prospective population-based study in Rotterdam, the Netherlands, with 6900 mother–child dyads, maternal plasma TFA concentration was assessed using gas chromatography in mid-gestation. Offspring head circumference (HC) was measured in the second and third trimesters using ultrasonography; childhood brain morphology was assessed using magnetic resonance imaging at age 10 years. We performed regression analyses adjusting for sociodemographic and lifestyle confounders and instrumental variable (IV) analyses. Our IV analysis leveraged a national policy change that led to a substantial reduction in TFA and occurred mid-recruitment. After adjusting for covariates, maternal TFA concentration during pregnancy was inversely related to fetal HC in the third trimester (mean difference per 1% wt:wt increase: − 0.33, 95% CI − 0.51, − 0.15, cm) and to fetal HC growth from the second to the third trimester (− 0.04, 95% CI − 0.06, − 0.02, cm/week). Consistent findings were obtained with IV analyses, strengthening a causal interpretation. Association between prenatal TFA exposure and HC in the second trimester or global brain volume at age 10 years was inconclusive. Our findings are of important public health relevance as TFA levels in food remain high in many countries.

Interpretation of Mendelian randomization using a single measure of an exposure that varies over time

BACKGROUND

Mendelian randomization (MR) is a powerful tool through which the causal effects of modifiable exposures on outcomes can be estimated from observational data. Most exposures vary throughout the life course, but MR is commonly applied to one measurement of an exposure (e.g. weight measured once between ages 40 and 60 years). It has been argued that MR provides biased causal effect estimates when applied to one measure of an exposure that varies over time.

METHODS

We propose an approach that emphasizes the liability that causes the entire exposure trajectory. We demonstrate this approach using simulations and an applied example.

RESULTS

We show that rather than estimating the direct or total causal effect of changing the exposure value at a given time, MR estimates the causal effect of changing the underlying liability for the exposure, scaled to the effect of the liability on the exposure at that time. As such, results from MR conducted at different time points are expected to differ (unless the effect of the liability on exposure is constant over time), as we illustrate by estimating the effect of body mass index measured at different ages on systolic blood pressure.

CONCLUSION

Univariable MR results should not be interpreted as time-point-specific direct or total causal effects, but as the effect of changing the liability for the exposure. Estimates of how the effects of a genetic variant on an exposure vary over time, together with biological knowledge that provides evidence regarding likely effective exposure periods, are required to interpret time-point-specific causal effects.

Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner. Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.

Mendelian Randomization With Repeated Measures of a Time-varying Exposure: An Application of Structural Mean Models

Mendelian randomization (MR) is often used to estimate effects of time-varying exposures on health outcomes using observational data. However, MR studies typically use a single measurement of exposure and apply conventional instrumental variable (IV) methods designed to handle time-fixed exposures. As such, MR effect estimates for time-varying exposures are often biased, and interpretations are unclear. We describe the instrumental conditions required for IV estimation with a time-varying exposure, and the additional conditions required to causally interpret MR estimates as a point effect, a period effect or a lifetime effect depending on whether researchers have measurements at a single or multiple time points. We propose methods to incorporate time-varying exposures in MR analyses based on g-estimation of structural mean models, and demonstrate its application by estimating the period effect of alcohol intake, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol on intermediate coronary heart disease outcomes using data from the Framingham Heart Study. We use this data example to highlight the challenges of interpreting MR estimates as causal effects, and describe other extensions of structural mean models for more complex data scenarios.

Understanding the consequences of educational inequalities on periodontitis: Mendelian randomization study

AIM

Higher educational attainment is associated with a lower risk of periodontitis, but the extent to which this association is causal and mediated by intermediate factors is unclear.

METHODS AND MATERIALS

Using summary data from genetic association studies from up to 1.1 million participants of European descent, univariable and multivariable Mendelian randomization analyses were performed to infer the total effect of educational attainment on periodontitis and to estimate the degree to which income, smoking, alcohol consumption, and body mass index mediate the association.

RESULTS

The odds ratio of periodontitis per one standard deviation increment in genetically predicted education was 0.78 (95% CI: 0.68-0.89). The proportions mediated of the total effect of genetically predicted education on periodontitis were 64%, 35%, 15%, and 46% for income, smoking, alcohol consumption, and body mass index, respectively.

CONCLUSIONS

Using a genetic instrumental variable approach, this study triangulated evidence from existing observational epidemiological studies and suggested that higher educational attainment lowers periodontitis risk. Measures to reduce the burden of educational disparities in periodontitis risk may tackle downstream risk factors, particularly income, smoking, and obesity.

Mendelian randomization and pleiotropy analysis

BACKGROUND

Mendelian randomization (MR) analysis has become popular in inferring and estimating the causality of an exposure on an outcome due to the success of genome wide association studies. Many statistical approaches have been developed and each of these methods require specific assumptions.

RESULTS

In this article, we review the pros and cons of these methods. We use an example of high-density lipoprotein cholesterol on coronary artery disease to illuminate the challenges in Mendelian randomization investigation.

CONCLUSION

The current available MR approaches allow us to study causality among risk factors and outcomes. However, novel approaches are desirable for overcoming multiple source confounding of risk factors and an outcome in MR analysis.

Application of the Instrumental Inequalities to a Mendelian Randomization Study With Multiple Proposed Instruments

Supplemental Digital Content is available in the text.

Investigators often support the validity of Mendelian randomization (MR) studies, an instrumental variable approach proposing genetic variants as instruments, via. subject matter knowledge. However, the instrumental variable model implies certain inequalities, offering an empirical method of falsifying (but not verifying) the underlying assumptions. Although these inequalities are said to detect only extreme assumption violations in practice, to our knowledge they have not been used in settings with multiple proposed instruments.

Directed Acyclic Graphs, Effect Measure Modification, and Generalizability

Directed acyclic graphs (DAGs) have had a major impact on the field of epidemiology by providing straightforward graphical rules for determining when estimates are expected to lack causally interpretable internal validity. Much less attention has been paid, however, to what DAGs can tell researchers about effect measure modification and external validity. In this work, we describe 2 rules based on DAGs related to effect measure modification. Rule 1 states that if a variable, $P$, is conditionally independent of an outcome, $Y$, within levels of a treatment, $X$, then $P$ is not an effect measure modifier for the effect of $X$ on $Y$ on any scale. Rule 2 states that if $P$ is not conditionally independent of $Y$ within levels of $X$, and there are open causal paths from $X$ to $Y$ within levels of $P$, then $P$ is an effect measure modifier for the effect of $X$ on $Y$ on at least 1 scale (given no exact cancelation of associations). We then show how Rule 1 can be used to identify sufficient adjustment sets to generalize nested trials studying the effect of $X$ on $Y$ to the total source population or to those who did not participate in the trial.

Mendelian randomisation approaches to the study of prenatal exposures: A systematic review

BACKGROUND

Mendelian randomisation (MR) designs apply instrumental variable techniques using genetic variants to study causal effects. MR is increasingly used to evaluate the role of maternal exposures during pregnancy on offspring health.

OBJECTIVES

We review the application of MR to prenatal exposures and describe reporting of methodologic challenges in this area.

DATA SOURCES

We searched PubMed, EMBASE, Medline Ovid, Cochrane Central, Web of Science, and Google Scholar.

STUDY SELECTION AND DATA EXTRACTION

Eligible studies met the following criteria: (a) a maternal pregnancy exposure; (b) an outcome assessed in offspring of the pregnancy; and (c) a genetic variant or score proposed as an instrument or proxy for an exposure.

SYNTHESIS

We quantified the frequency of reporting of MR conditions stated, techniques used to examine assumption plausibility, and reported limitations.

RESULTS

Forty-three eligible studies were identified. When discussing challenges or limitations, the most common issues described were known potential biases in the broader MR literature, including population stratification (n = 29), weak instrument bias (n = 18), and certain types of pleiotropy (n = 30). Of 22 studies presenting point estimates for the effect of exposure, four defined their causal estimand. Twenty-four studies discussed issues unique to prenatal MR, including selection on pregnancy (n = 1) and pleiotropy via postnatal exposure (n = 10) or offspring genotype (n = 20).

CONCLUSIONS

Prenatal MR studies frequently discuss issues that affect all MR studies, but rarely discuss problems specific to the prenatal context, including selection on pregnancy and effects of postnatal exposure. Future prenatal MR studies should report and attempt to falsify their assumptions, with particular attention to issues specific to prenatal MR. Further research is needed to evaluate the impacts of biases unique to prenatal MR in practice.

Guidelines for performing Mendelian randomization investigations

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.

Guidelines for performing Mendelian randomization investigations

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.

Refractive Error Has Minimal Influence on the Risk of Age-Related Macular Degeneration: A Mendelian Randomization Study

PURPOSE

To test the hypothesis that refractive errors such as myopia and hyperopia cause an increased risk of age-related macular degeneration (AMD) and to quantify the degree of risk.

DESIGN

Two-sample Mendelian randomization analysis of data from a genome-wide association study.

PARTICIPANTS

As instrumental variables for refractive error, 126 genome-wide significant genetic variants identified by the Consortium for Refractive Error and Myopia and 23andMe Inc. were chosen. The association with refractive error for the 126 variants was obtained from a published study for a sample of 95,505 European ancestry participants from UK Biobank. Association with AMD for the 126 genetic variants was determined from a genome-wide association study (GWAS) published by the International Age-related Macular Degeneration Genomics consortium of 33,526 (16,144 cases and 17,832 controls) European ancestry participants.

METHODS

Two-sample Mendelian randomization (MR) analysis was used to assess the causal role of refractive error on AMD risk, using the 126 genetic variants associated with refractive error as instrumental variables, under the assumption that the relationship between refractive error and AMD risk is linear.

MAIN OUTCOME MEASUREMENT

the risk AMD was caused by a 1-diopter (D) change in refractive error.

RESULTS

MR analysis suggested that refractive error had very limited influence on the risk of AMD. Specifically, 1 D more hyperopic refractive error was associated with an odds ratio (OR) of 1.080 (95% confidence interval [CI], 1.021-1.142; P = 0.007) increased risk of AMD. MR-Egger, MR pleiotropy residual sum and outlier, weighted median, and Phenoscanner-based sensitivity analyses detected minimal evidence to suggest that this result was biased by horizontal pleiotropy.

CONCLUSIONS

Under the assumption of a linear relationship between refractive error and the risk of AMD, myopia and hyperopia only minimally influence the causal risk for AMD. Thus, inconsistently reported strong associations between refractive error and AMD are likely to be the result of noncausal factors such as stochastic variation, confounding, or selection bias.

An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings

BACKGROUND

Mendelian randomization (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilizing genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome.

METHODS AND RESULTS

We use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single-sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK Biobank to estimate the effect of education and cognitive ability on body mass index.

CONCLUSION

MVMR analysis consistently estimates the direct causal effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual- or summary-level data.

Epidemiology, genetic epidemiology and Mendelian randomisation: more need than ever to attend to detail

In the current era, with increasing availability of results from genetic association studies, finding genetic instruments for inferring causality in observational epidemiology has become apparently simple. Mendelian randomisation (MR) analyses are hence growing in popularity and, in particular, methods that can incorporate multiple instruments are being rapidly developed for these applications. Such analyses have enormous potential, but they all rely on strong, different, and inherently untestable assumptions. These have to be clearly stated and carefully justified for every application in order to avoid conclusions that cannot be replicated. In this article, we review the instrumental variable assumptions and discuss the popular linear additive structural model. We advocate the use of tests for the null hypothesis of ‘no causal effect’ and calculation of the bounds for a causal effect, whenever possible, as these do not rely on parametric modelling assumptions. We clarify the difference between a randomised trial and an MR study and we comment on the importance of validating instruments, especially when considering them for joint use in an analysis. We urge researchers to stand by their convictions, if satisfied that the relevant assumptions hold, and to interpret their results causally since that is the only reason for performing an MR analysis in the first place.

Body mass index and all cause mortality in HUNT and UK Biobank studies: linear and non-linear mendelian randomisation analyses

EDITOR'S NOTE

Please see the Editor's Note (doi: https://doi.org/10.1136/bmj.l1042) on Methodological Criticism and an Updated Analysis

OBJECTIVE

To investigate the shape of the causal relation between body mass index (BMI) and mortality.

DESIGN

Linear and non-linear mendelian randomisation analyses.

SETTING

Nord-Trøndelag Health (HUNT) Study (Norway) and UK Biobank (United Kingdom).

PARTICIPANTS

Middle to early late aged participants of European descent: 56 150 from the HUNT Study and 366 385 from UK Biobank.

MAIN OUTCOME MEASURES

All cause and cause specific (cardiovascular, cancer, and non-cardiovascular non-cancer) mortality.

RESULTS

12 015 and 10 344 participants died during a median of 18.5 and 7.0 years of follow-up in the HUNT Study and UK Biobank, respectively. Linear mendelian randomisation analyses indicated an overall positive association between genetically predicted BMI and the risk of all cause mortality. An increase of 1 unit in genetically predicted BMI led to a 5% (95% confidence interval 1% to 8%) higher risk of mortality in overweight participants (BMI 25.0-29.9) and a 9% (4% to 14%) higher risk of mortality in obese participants (BMI ≥30.0) but a 34% (16% to 48%) lower risk in underweight (BMI <18.5) and a 14% (-1% to 27%) lower risk in low normal weight participants (BMI 18.5-19.9). Non-linear mendelian randomisation indicated a J shaped relation between genetically predicted BMI and the risk of all cause mortality, with the lowest risk at a BMI of around 22-25 for the overall sample. Subgroup analyses by smoking status, however, suggested an always-increasing relation of BMI with mortality in never smokers and a J shaped relation in ever smokers.

CONCLUSIONS

The previously observed J shaped relation between BMI and risk of all cause mortality appears to have a causal basis, but subgroup analyses by smoking status revealed that the BMI-mortality relation is likely comprised of at least two distinct curves, rather than one J shaped relation. An increased risk of mortality for being underweight was only evident in ever smokers.

Interpretation and Potential Biases of Mendelian Randomization Estimates With Time-Varying Exposures

Mendelian randomization (MR) is used to answer a variety of epidemiologic questions. One stated advantage of MR is that it estimates a "lifetime effect" of exposure, though this term remains vaguely defined. Instrumental variable analysis, on which MR is based, has focused on estimating the effects of point or time-fixed exposures rather than "lifetime effects." Here we use an empirical example with data from the Rotterdam Study (Rotterdam, the Netherlands, 2009-2013) to demonstrate how confusion can arise when estimating "lifetime effects." We provide one possible definition of a lifetime effect: the average change in outcome measured at time t when the entire exposure trajectory from conception to time t is shifted by 1 unit. We show that MR only estimates this type of lifetime effect under specific conditions-for example, when the effect of the genetic variants used on exposure does not change over time. Lastly, we simulate the magnitude of bias that would result in realistic scenarios that use genetic variants with effects that change over time. We recommend that investigators in future MR studies carefully consider the effect of interest and how genetic variants whose effects change with time may impact the interpretability and validity of their results.