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

Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy

Mendelian Randomization (MR) studies are threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large databases. Here we describe a suite of sensitivity analysis tools that enables investigators to quantify the robustness of their findings against such validity threats. Specifically, we propose the routine reporting of sensitivity statistics that reveal the minimal strength of violations necessary to explain away the MR results. We further provide intuitive displays of the robustness of the MR estimate to any degree of violation, and formal bounds on the worst-case bias caused by violations multiple times stronger than observed variables. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings by examining the effect of body mass index on diastolic blood pressure and Townsend deprivation index.

Mendelian randomization

Mendelian randomization (MR) is a term that applies to the use of genetic variation to address causal questions about how modifiable exposures influence different outcomes. The principles of MR are based on Mendel's laws of inheritance and instrumental variable estimation methods, which enable the inference of causal effects in the presence of unobserved confounding. In this Primer, we outline the principles of MR, the instrumental variable conditions underlying MR estimation and some of the methods used for estimation. We go on to discuss how the assumptions underlying an MR study can be assessed and give methods of estimation that are robust to certain violations of these assumptions. We give examples of a range of studies in which MR has been applied, the limitations of current methods of analysis and the outlook for MR in the future. The difference between the assumptions required for MR analysis and other forms of non-interventional epidemiological studies means that MR can be used as part of a triangulation across multiple sources of evidence for causal inference.

Mendelian Randomization: Concepts and Scope

Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure-outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.

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.

A Mendelian randomization study on the effect of 25-hydroxyvitamin D levels on periodontitis

BACKGROUND

25-hydroxy vitamin D (25OHD) levels have been proposed to protect against periodontitis based on in vitro and observational studies but evidence from long-term randomized controlled trials (RCTs) is lacking. This study tested whether genetically proxied 25OHD is associated with periodontitis using Mendelian randomization (MR).

METHOD

Genetic variants strongly associated with 25OHD in a genome-wide association study (GWAS) of 417,580 participants of European ancestry were used as instrumental variables, and linked to GWAS summary data of 17,353 periodontitis cases and 28,210 controls. In addition to the main analysis using an inverse variance weighted (IVW) model, we applied additional robust methods to control for pleiotropy. We also undertook sensitivity analyses excluding single nucleotide polymorphisms (SNPs) used as instruments with potential pleiotropic effects and used a second 25OHD GWAS for replication. We identified 288 SNPs to be genome-wide significant for 25OHD, explaining 7.0% of the variance of 25OHD levels and providing ≥90% power to detect an odds ratio (OR) of ≤ 0.97.

RESULTS

MR analysis suggested that a 1 standard deviation increase in natural log-transformed 25OHD was not associated with periodontitis risk (IVW OR = 1.04; 95% confidence interval (CI): 0.97-1.12; P-value = 0.297). The robust models, replication, and sensitivity analyses were coherent with the primary analysis.

CONCLUSIONS

Collectively, our findings suggest that 25OHD levels are unlikely to have a substantial effect on the risk of periodontitis, but large long-term RCTs are needed to derive definitive evidence on the causal role of 25OHD in periodontitis. This article is protected by copyright. All rights reserved.

Instrumental variable analysis using offspring BMI in childhood as an indicator of parental BMI in relation to mortality

Childhood BMI shows associations with adult mortality, but these may be influenced by effects of ill health in childhood on BMI and later mortality. To avoid this, we used offspring childhood BMI as an instrumental variable (IV) for own BMI in relation to mortality and compared it with conventional associations of own childhood BMI and own mortality. We included 36,097 parent–offspring pairs with measured heights and weights from the Copenhagen School Health Records Register and register-based information on death. Hazard ratios (HR) were estimated using adjusted Cox regression models. For all-cause mortality, per zBMI at age 7 the conventional HR = 1.07 (95%CI: 1.04–1.09) in women and 1.02 (95%CI: 0.92–1.14) in men, whereas the IV HR = 1.23 (95%CI: 1.15–1.32) in women and 1.05 (95%CI: 0.94–1.17) in men. Per zBMI at age 13, the conventional HR = 1.11 (95%CI: 1.08–1.15) in women and 1.03 (95%CI: 0.99–1.06) in men, whereas the IV HR = 1.30 (95%CI: 1.19–1.42) in women and 1.15 (95%CI: 1.04–1.29) in men. Only conventional models showed indications of J-shaped associations. Our IV analyses suggest that there is a causal relationship between BMI and mortality that is positive at both high and low BMI values.

Testing the association between tobacco smoking, alcohol consumption, and risk of periodontitis: A Mendelian randomization study

AIM

To investigate the associations of tobacco smoking and alcohol consumption with periodontitis using Mendelian randomization (MR) analysis.

MATERIALS AND METHODS

We used 17 single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) for the number of cigarettes per day from a genome-wide association study (GWAS) of 337,334 individuals, 109 SNPs for a lifetime smoking index from GWAS of 462,690 participants, and 33 SNPs for the number of drinks per week from GWAS of 941,280 individuals. The periodontitis GWAS included 12,289 cases and 22,326 controls. Wald ratios were obtained by dividing the SNP-periodontitis effects by SNP-exposure effects and pooled using an inverse-variance weighted model.

RESULTS

Genetic liabilities for higher number of cigarettes per day (odds ratio [OR] per one standard deviation (1SD) increment = 1.56; 95% CI: 1.18-2.07, p-value = .0018, Q-value = .0054), lifetime smoking index (OR per 1SD = 1.26; 95% CI: 1.04-1.53, p-value = .0161, Q-value = .0242), and drinks per week (OR per 1SD = 1.41; 95% CI: 1.04-1.90, p-value = .0265, Q-value = .0265) were associated with increased odds of periodontitis. Estimates were consistent across robust and multivariable MR analyses.

CONCLUSIONS

The findings of this MR analysis suggest an association between tobacco smoking and alcohol consumption with periodontitis.

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.

Association of Genetic Variants for Plasma LRG1 With Rapid Decline in Kidney Function in Patients With Type 2 Diabetes

CONTEXT

Elevated levels of plasma leucine-rich α-2-glycoprotein 1 (LRG1), a component of transforming growth factor beta signaling, are associated with development and progression of chronic kidney disease in patients with type 2 diabetes (T2D). However, whether this relationship is causal is uncertain.

OBJECTIVES

To identify genetic variants associated with plasma LRG1 levels and determine whether genetically predicted plasma LRG1 contributes to a rapid decline in kidney function (RDKF) in patients with T2D.

DESIGN AND PARTICIPANTS

We performed a genome-wide association study of plasma LRG1 among 3694 T2D individuals [1881 (983 Chinese, 420 Malay, and 478 Indian) discovery from Singapore Study of Macro-angiopathy and Micro-vascular Reactivity in Type 2 Diabetes cohort and 1813 (Chinese) validation from Diabetic Nephropathy cohort]. One- sample Mendelian randomization analysis was performed among 1337 T2D Chinese participants with preserved glomerular filtration function [baseline estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2)]. RDKF was defined as an eGFR decline of 3 mL/min/1.73 m2/year or greater.

RESULTS

We identified rs4806985 variant near LRG1 locus robustly associated with plasma LRG1 levels (meta P = 6.66 × 10-16). Among 1337 participants, 344 (26%) developed RDKF, and the rs4806985 variant was associated with higher odds of RDKF (meta odds ratio = 1.23, P = 0.030 adjusted for age and sex). Mendelian randomization analysis provided evidence for a potential causal effect of plasma LRG1 on kidney function decline in T2D (P < 0.05).

CONCLUSION

We demonstrate that genetically influenced plasma LRG1 increases the risk of RDKF in T2D patients, suggesting plasma LRG1 as a potential treatment target. However, further studies are warranted to elucidate underlying pathways to provide insight into diabetic kidney disease prevention.

Using Mendelian randomization analysis to better understand the relationship between mental health and substance use: a systematic review

BACKGROUND

Poor mental health has consistently been associated with substance use (smoking, alcohol drinking, cannabis use, and consumption of caffeinated drinks). To properly inform public health policy it is crucial to understand the mechanisms underlying these associations, and most importantly, whether or not they are causal.

METHODS

In this pre-registered systematic review, we assessed the evidence for causal relationships between mental health and substance use from Mendelian randomization (MR) studies, following PRISMA. We rated the quality of included studies using a scoring system that incorporates important indices of quality, such as the quality of phenotype measurement, instrument strength, and use of sensitivity methods.

RESULTS

Sixty-three studies were included for qualitative synthesis. The final quality rating was '-' for 16 studies, '- +' for 37 studies, and '+'for 10 studies. There was robust evidence that higher educational attainment decreases smoking and that there is a bi-directional, increasing relationship between smoking and (symptoms of) mental disorders. Another robust finding was that higher educational attainment increases alcohol use frequency, but decreases binge-drinking and alcohol use problems, and that mental disorders causally lead to more alcohol drinking without evidence for the reverse.

CONCLUSIONS

The current MR literature increases our understanding of the relationship between mental health and substance use. Bi-directional causal relationships are indicated, especially for smoking, providing further incentive to strengthen public health efforts to decrease substance use. Future MR studies should make use of large(r) samples in combination with detailed phenotypes, a wide range of sensitivity methods, and triangulate with other research methods.

Genetically increased circulating FUT3 level leads to reduced risk of Idiopathic Pulmonary Fibrosis: a Mendelian Randomisation Study

Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal fibrotic interstitial lung disease. Few circulating biomarkers have been identified to have causal effects on IPF.To identify candidate IPF-influencing circulating proteins, we undertook an efficient screen of circulating proteins by applying a two-sample Mendelian randomisation (MR) approach with existing publicly available data. For instruments we used genetic determinants of circulating proteins which reside cis to the encoded gene (cis-SNPs), identified by two genome-wide association studies (GWASs) in European individuals (3301 and 3200 subjects). We then applied MR methods to test if the levels of these circulating proteins influenced IPF susceptibility in the largest IPF GWAS (2668 cases and 8591 controls). We validated the MR results using colocalization analyses to ensure that both the circulating proteins and IPF shared a common genetic signal.MR analyses of 834 proteins found that a one sd increase in circulating FUT3 and FUT5 was associated with a reduced risk of IPF (OR: 0.81, 95%CI: 0.74-0.88, p=6.3×10^-7, and OR: 0.76, 95%CI: 0.68-0.86, p=1.1×10^-5). Sensitivity analyses including multiple-cis SNPs provided similar estimates both for FUT3 (inverse variance weighted [IVW] OR: 0.84, 95%CI: 0.78-0.91, p=9.8×10^-6, MR-Egger OR: 0.69, 95%CI: 0.50-0.97, p=0.03) and FUT5 (IVW OR: 0.84, 95%CI: 0.77-0.92, p=1.4×10^-4, MR-Egger OR: 0.59, 95%CI: 0.38-0.90, p=0.01) FUT3 and FUT5 signals colocalized with IPF signals, with posterior probabilities of a shared genetic signal of 99.9% and 97.7%. Further transcriptomic investigations supported the protective effects of FUT3 for IPF.An efficient MR scan of 834 circulating proteins provided evidence that genetically increased circulating FUT3 level is associated with reduced risk of IPF.

The Role of Mendelian Randomization Studies in Deciphering the Effect of Obesity on Cancer

Associations of obesity have been established for at least 11 cancer sites in observational studies, though some questions remain as to causality, strength of associations, and timing of associations throughout the life course. In recent years, Mendelian randomization (MR) has provided complementary information to traditional approaches, but the validity requires that the genetic instrumental variables be causally related to cancers only mediated by the exposure. We summarize and evaluate existing evidence from MR studies in comparison with conventional observational studies to provide insights into the complex relationship between obesity and multiple cancers. MR studies further establish the causality of adult obesity with esophageal adenocarcinoma, cancers of the colorectum, endometrium, ovary, kidney, and pancreas, as well as the inverse association of early life obesity with breast cancer. MR studies, which might account for lifelong adiposity, suggest that the associations in observational studies typically based on single measurement may underestimate the magnitude of the association. For lung cancer, MR studies find a positive association with obesity, supporting that the inverse association observed in some conventional observational studies likely reflects reverse causality (loss of lean body mass before diagnosis) and confounding by smoking. However, MR studies have not had sufficient power for gallbladder cancer, gastric cardia cancer, and multiple myeloma. In addition, more MR studies are needed to explore the effect of obesity at different time points on postmenopausal breast cancer and aggressive prostate cancer.

Causal relationships between body mass index, smoking and lung cancer: Univariable and multivariable Mendelian randomization

At the time of cancer diagnosis, body mass index (BMI) is inversely correlated with lung cancer risk, which may reflect reverse causality and confounding due to smoking behavior. We used two-sample univariable and multivariable Mendelian randomization (MR) to estimate causal relationships of BMI and smoking behaviors on lung cancer and histological subtypes based on an aggregated genome-wide association studies (GWASs) analysis of lung cancer in 29 266 cases and 56 450 controls. We observed a positive causal effect for high BMI on occurrence of small-cell lung cancer (odds ratio (OR) = 1.60, 95% confidence interval (CI) = 1.24-2.06, P = 2.70 × 10-4 ). After adjustment of smoking behaviors using multivariable Mendelian randomization (MVMR), a direct causal effect on small cell lung cancer (ORMVMR = 1.28, 95% CI = 1.06-1.55, PMVMR = .011), and an inverse effect on lung adenocarcinoma (ORMVMR = 0.86, 95% CI = 0.77-0.96, PMVMR = .008) were observed. A weak increased risk of lung squamous cell carcinoma was observed for higher BMI in univariable Mendelian randomization (UVMR) analysis (ORUVMR = 1.19, 95% CI = 1.01-1.40, PUVMR = .036), but this effect disappeared after adjustment of smoking (ORMVMR = 1.02, 95% CI = 0.90-1.16, PMVMR = .746). These results highlight the histology-specific impact of BMI on lung carcinogenesis and imply mediator role of smoking behaviors in the association between BMI and lung cancer.

Multivariable Mendelian Randomization and Mediation

Mendelian randomization (MR) is the use of genetic variants associated with an exposure to estimate the causal effect of that exposure on an outcome. Mediation analysis is the method of decomposing the effects of an exposure on an outcome, which act directly, and those that act via mediating variables. These effects are decomposed through the use of multivariable analysis to estimate the causal effects between three types of variables: exposures, mediators, and an outcome. Multivariable MR (MVMR) is a recent extension to MR that uses genetic variants associated with multiple, potentially related exposures to estimate the effect of each exposure on a single outcome. MVMR allows for equivalent analysis to mediation within the MR framework and therefore can also be used to estimate mediation effects. This approach retains the benefits of using genetic instruments for causal inference, such as avoiding bias due to confounding, while allowing for estimation of the different effects required for mediation analysis. This review explains MVMR, what is estimated when one exposure is a mediator of another in an MVMR estimation, and how MR and MVMR can therefore be used to estimate mediated effects. This review then goes on to consider the advantages and limitations of using MR and MVMR to conduct mediation analysis.

Blood Pressure and Risk of Cardiovascular Disease in UK Biobank: A Mendelian Randomization Study

This study aims to evaluate the causal association of blood pressure (BP) with cardiovascular diseases (CVDs). Two-sample Mendelian randomization was performed using a large genome-wide association study (n=299 024) and the UK Biobank cohort (n=375 256). We identified 327 and 364 single-nucleotide polymorphisms strongly and independently associated with systolic BP and diastolic BP, respectively, as genetic instruments to assess the causal association of BP with total CVD, CVD mortality, and 14 cardiovascular conditions. Nonlinearity was examined with nonlinear instrumental variable assumptions. Genetically predicted BP was significantly positively associated with total CVD (systolic BP, per 10 mm Hg: odds ratio [OR], 1.32 [95% CI, 1.25-1.40]; diastolic BP, per 5 mm Hg: OR, 1.20 [95% CI, 1.15-1.26]). Similar positive causal associations were observed for 14 cardiovascular conditions including ischemic heart disease (systolic BP, per 10 mm Hg: OR, 1.33 [95% CI, 1.24-1.41]; diastolic BP, per 5 mm Hg: OR, 1.20 [95% CI, 1.14-1.27]) and stroke (systolic BP, per 10 mm Hg: OR, 1.35 [95% CI, 1.24-1.48]; diastolic BP, per 5 mm Hg: OR, 1.20 [95% CI, 1.12-1.28]). Nonlinearity Mendelian randomization test demonstrated linear causal association of BP with these outcomes. Consistent estimates were observed in sensitivity analyses, suggesting robustness of the associations and minimal horizontal pleiotropy. The linear positive causal association of BP and CVD was consistent with previous findings that lower BP is better, thus consolidating clinical knowledge on hypertension management in CVD risk reduction.

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.

The role of cortisol in ischemic heart disease, ischemic stroke, type 2 diabetes, and cardiovascular disease risk factors: a bi-directional Mendelian randomization study

BACKGROUND

Cortisol, a steroid hormone frequently used as a biomarker of stress, is associated with cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM). To clarify whether cortisol causes these outcomes, we assessed the role of cortisol in ischemic heart disease (IHD), ischemic stroke, T2DM, and CVD risk factors using a bi-directional Mendelian randomization (MR) study.

METHODS

Single nucleotide polymorphisms (SNPs) strongly (P < 5 × 10-6) and independently (r2 < 0.001) predicting cortisol were obtained from the CORtisol NETwork (CORNET) consortium (n = 12,597) and two metabolomics genome-wide association studies (GWAS) (n = 7824 and n = 2049). They were applied to GWAS of the primary outcomes (IHD, ischemic stroke and T2DM) and secondary outcomes (adiposity, glycemic traits, blood pressure and lipids) to obtain estimates using inverse variance weighting, with weighted median, MR-Egger, and MR-PRESSO as sensitivity analyses. Conversely, SNPs predicting IHD, ischemic stroke, and T2DM were applied to the cortisol GWAS.

RESULTS

Genetically predicted cortisol (based on 6 SNPs from CORNET; F-statistic = 28.3) was not associated with IHD (odds ratio (OR) 0.98 per 1 unit increase in log-transformed cortisol, 95% confidence interval (CI) 0.93-1.03), ischemic stroke (0.99, 95% CI 0.91-1.08), T2DM (1.00, 95% CI 0.96-1.04), or CVD risk factors. Genetically predicted IHD, ischemic stroke, and T2DM were not associated with cortisol.

CONCLUSIONS

Contrary to observational studies, genetically predicted cortisol was unrelated to IHD, ischemic stroke, T2DM, or CVD risk factors, or vice versa. Our MR results find no evidence that cortisol plays a role in cardiovascular risk, casting doubts on the cortisol-related pathway, although replication is warranted.

Plasma lipids and risk of aortic valve stenosis: a Mendelian randomization study

AIMS

Aortic valve stenosis is commonly considered a degenerative disorder with no recommended preventive intervention, with only valve replacement surgery or catheter intervention as treatment options. We sought to assess the causal association between exposure to lipid levels and risk of aortic stenosis.

METHODS AND RESULTS

Causality of association was assessed using two-sample Mendelian randomization framework through different statistical methods. We retrieved summary estimations of 157 genetic variants that have been shown to be associated with plasma lipid levels in the Global Lipids Genetics Consortium that included 188 577 participants, mostly European ancestry, and genetic association with aortic stenosis as the main outcome from a total of 432 173 participants in the UK Biobank. Secondary negative control outcomes included aortic regurgitation and mitral regurgitation. The odds ratio for developing aortic stenosis per unit increase in lipid parameter was 1.52 [95% confidence interval (CI) 1.22-1.90; per 0.98 mmol/L] for low density lipoprotein (LDL)-cholesterol, 1.03 (95% CI 0.80-1.31; per 0.41 mmol/L) for high density lipoprotein (HDL)-cholesterol, and 1.38 (95% CI 0.92-2.07; per 1 mmol/L) for triglycerides. There was no evidence of a causal association between any of the lipid parameters and aortic or mitral regurgitation.

CONCLUSION

Lifelong exposure to high LDL-cholesterol increases the risk of symptomatic aortic stenosis, suggesting that LDL-lowering treatment may be effective in its prevention.

Role of the Metabolic Profile in Mediating the Relationship Between Body Mass Index and Left Ventricular Mass in Adolescents: Analysis of a Prospective Cohort Study

Background We aimed to quantify the role of the plasma metabolic profile in explaining the effect of adiposity on cardiac structure. Methods and Results Body mass index (BMI) was measured at age 11 in the Avon Longitudinal Study of Parents and Children. Left ventricular mass indexed to height^2.7 (LVMI) was assessed by echocardiography at age 17. The metabolic profile was quantified via ¹H-nuclear magnetic resonance spectroscopy at age 15. Multivariable confounder (maternal age, parity, highest qualification, maternal smoking, prepregnancy BMI, prepregnancy height, household social class, adolescent birthweight, adolescent smoking, fruit and vegetable consumption, and physical activity)-adjusted linear regression estimated the association of BMI with LVMI and mediation by metabolic traits. We considered 156 metabolomic traits individually and jointly as principal components explaining 95% of the variance in the nuclear magnetic resonance platform and assessed whether the principal components for the metabolic traits added to the proportion of the association explained by putative cardiovascular risk factors (systolic and diastolic blood pressures, insulin, triglycerides, low-density lipoprotein cholesterol, and glucose). A 1 kg/m² higher BMI was associated with a 0.70 g/m^2.7 (95% CI, 0.53-0.88 g/m^2.7) and 0.66 g/m^2.7 (95% CI, 0.53-0.79 g/m^2.7) higher LVMI in males (n=437) and females (n=536), respectively. Putative risk factors explained 3% (95% CI, 2%-5%) of this association in males, increasing to 10% (95% CI, 8%-13%) when including metabolic principal components. In females, the standard risk factors explained 3% (95% CI, 2%-5%) of the association and did not increase when including the metabolic principal components. Conclusions The addition of the nuclear magnetic resonance-measured metabolic traits appears to mediate more of the association of BMI on LVMI than the putative risk factors alone in adolescent males, but not females.

Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: a Mendelian randomisation study

BACKGROUND

Smoking prevalence is higher amongst individuals with schizophrenia and depression compared with the general population. Mendelian randomisation (MR) can examine whether this association is causal using genetic variants identified in genome-wide association studies (GWAS).

METHODS

We conducted two-sample MR to explore the bi-directional effects of smoking on schizophrenia and depression. For smoking behaviour, we used (1) smoking initiation GWAS from the GSCAN consortium and (2) we conducted our own GWAS of lifetime smoking behaviour (which captures smoking duration, heaviness and cessation) in a sample of 462690 individuals from the UK Biobank. We validated this instrument using positive control outcomes (e.g. lung cancer). For schizophrenia and depression we used GWAS from the PGC consortium.

RESULTS

There was strong evidence to suggest smoking is a risk factor for both schizophrenia (odds ratio (OR) 2.27, 95% confidence interval (CI) 1.67-3.08, p < 0.001) and depression (OR 1.99, 95% CI 1.71-2.32, p < 0.001). Results were consistent across both lifetime smoking and smoking initiation. We found some evidence that genetic liability to depression increases smoking (β = 0.091, 95% CI 0.027-0.155, p = 0.005) but evidence was mixed for schizophrenia (β = 0.022, 95% CI 0.005-0.038, p = 0.009) with very weak evidence for an effect on smoking initiation.

CONCLUSIONS

These findings suggest that the association between smoking, schizophrenia and depression is due, at least in part, to a causal effect of smoking, providing further evidence for the detrimental consequences of smoking on mental health.

Physical activity and risk of Alzheimer disease: A 2-sample mendelian randomization study

OBJECTIVE

Evidence from observational studies for the effect of physical activity on the risk of Alzheimer disease (AD) is inconclusive. We performed a 2-sample mendelian randomization analysis to examine whether physical activity is protective for AD.

METHODS

Summary data of genome-wide association studies on physical activity and AD were used. The primary study population included 21,982 patients with AD and 41,944 cognitively normal controls. Eight single nucleotide polymorphisms (SNPs) known at p < 5 × 10-8 to be associated with average accelerations and 8 SNPs associated at p < 5 × 10-7 with vigorous physical activity (fraction of accelerations >425 milligravities) served as instrumental variables.

RESULTS

There was no association between genetically predicted average accelerations with the risk of AD (inverse variance weighted odds ratio [OR] per SD increment: 1.03, 95% confidence interval 0.97-1.10, p = 0.332). Genetic liability for fraction of accelerations >425 milligravities was unrelated to AD risk.

CONCLUSION

The present study does not support a causal association between physical activity and risk of AD.

Factorial Mendelian randomization: using genetic variants to assess interactions

BACKGROUND

Factorial Mendelian randomization is the use of genetic variants to answer questions about interactions. Although the approach has been used in applied investigations, little methodological advice is available on how to design or perform a factorial Mendelian randomization analysis. Previous analyses have employed a 2 × 2 approach, using dichotomized genetic scores to divide the population into four subgroups as in a factorial randomized trial.

METHODS

We describe two distinct contexts for factorial Mendelian randomization: investigating interactions between risk factors, and investigating interactions between pharmacological interventions on risk factors. We propose two-stage least squares methods using all available genetic variants and their interactions as instrumental variables, and using continuous genetic scores as instrumental variables rather than dichotomized scores. We illustrate our methods using data from UK Biobank to investigate the interaction between body mass index and alcohol consumption on systolic blood pressure.

RESULTS

Simulated and real data show that efficiency is maximized using the full set of interactions between genetic variants as instruments. In the applied example, between 4- and 10-fold improvement in efficiency is demonstrated over the 2 × 2 approach. Analyses using continuous genetic scores are more efficient than those using dichotomized scores. Efficiency is improved by finding genetic variants that divide the population at a natural break in the distribution of the risk factor, or else divide the population into more equal-sized groups.

CONCLUSIONS

Previous factorial Mendelian randomization analyses may have been underpowered. Efficiency can be improved by using all genetic variants and their interactions as instrumental variables, rather than the 2 × 2 approach.

Effects of body mass index on relationship status, social contact and socio-economic position: Mendelian randomization and within-sibling study in UK Biobank

BACKGROUND

We assessed whether body mass index (BMI) affects social and socio-economic outcomes.

METHODS

We used Mendelian randomization (MR), non-linear MR and non-genetic and MR within-sibling analyses, to estimate relationships of BMI with six socio-economic and four social outcomes in 378 244 people of European ancestry in UK Biobank.

RESULTS

In MR of minimally related individuals, higher BMI was related to higher deprivation, lower income, fewer years of education, lower odds of degree-level education and skilled employment. Non-linear MR suggested both low (bottom decile, <22 kg/m2) and high (top seven deciles, >24.6 kg/m2) BMI, increased deprivation and reduced income. Non-genetic within-sibling analysis supported an effect of BMI on socio-economic position (SEP); precision in within-sibling MR was too low to draw inference about effects of BMI on SEP. There was some evidence of pleiotropy, with MR Egger suggesting limited effects of BMI on deprivation, although precision of these estimates is also low. Non-linear MR suggested that low BMI (bottom three deciles, <23.5 kg/m2) reduces the odds of cohabiting with a partner or spouse in men, whereas high BMI (top two deciles, >30.7 kg/m2) reduces the odds of cohabitation in women. Both non-genetic and MR within-sibling analyses supported this sex-specific effect of BMI on cohabitation. In men only, higher BMI was related to lower participation in leisure and social activities. There was little evidence that BMI affects visits from friends and family or having someone to confide in.

CONCLUSIONS

BMI may affect social and socio-economic outcomes, with both high and low BMI being detrimental for SEP, although larger within-family MR studies may help to test the robustness of MR results in unrelated individuals. Triangulation of evidence across MR and within-family analyses supports evidence of a sex-specific effect of BMI on cohabitation.

Use of genetic variation to separate the effects of early and later life adiposity on disease risk: mendelian randomisation study

OBJECTIVE

To evaluate whether body size in early life has an independent effect on risk of disease in later life or whether its influence is mediated by body size in adulthood.

DESIGN

Two sample univariable and multivariable mendelian randomisation.

SETTING

The UK Biobank prospective cohort study and four large scale genome-wide association studies (GWAS) consortiums.

PARTICIPANTS

453 169 participants enrolled in UK Biobank and a combined total of more than 700 000 people from different GWAS consortiums.

EXPOSURES

Measured body mass index during adulthood (mean age 56.5) and self-reported perceived body size at age 10.

MAIN OUTCOME MEASURES

Coronary artery disease, type 2 diabetes, breast cancer, and prostate cancer.

RESULTS

Having a larger genetically predicted body size in early life was associated with an increased odds of coronary artery disease (odds ratio 1.49 for each change in body size category unless stated otherwise, 95% confidence interval 1.33 to 1.68) and type 2 diabetes (2.32, 1.76 to 3.05) based on univariable mendelian randomisation analyses. However, little evidence was found of a direct effect (ie, not through adult body size) based on multivariable mendelian randomisation estimates (coronary artery disease: 1.02, 0.86 to 1.22; type 2 diabetes:1.16, 0.74 to 1.82). In the multivariable mendelian randomisation analysis of breast cancer risk, strong evidence was found of a protective direct effect for larger body size in early life (0.59, 0.50 to 0.71), with less evidence of a direct effect of adult body size on this outcome (1.08, 0.93 to 1.27). Including age at menarche as an additional exposure provided weak evidence of a total causal effect (univariable mendelian randomisation odds ratio 0.98, 95% confidence interval 0.91 to 1.06) but strong evidence of a direct causal effect, independent of early life and adult body size (multivariable mendelian randomisation odds ratio 0.90, 0.85 to 0.95). No strong evidence was found of a causal effect of either early or later life measures on prostate cancer (early life body size odds ratio 1.06, 95% confidence interval 0.81 to 1.40; adult body size 0.87, 0.70 to 1.08).

CONCLUSIONS

The findings suggest that the positive association between body size in childhood and risk of coronary artery disease and type 2 diabetes in adulthood can be attributed to individuals remaining large into later life. However, having a smaller body size during childhood might increase the risk of breast cancer regardless of body size in adulthood, with timing of puberty also putatively playing a role.

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.

Assessing causal links between metabolic traits, inflammation and schizophrenia: a univariable and multivariable, bidirectional Mendelian-randomization study

BACKGROUND

Blood immunoreactive biomarkers, such as C-reactive protein (CRP), and metabolic abnormalities have been associated with schizophrenia. Studies comprehensively and bidirectionally probing possible causal links between such blood constituents and liability to schizophrenia are lacking.

METHODS

To disentangle putative causal links between CRP blood levels and schizophrenia in both directions, we conducted multiple univariable Mendelian-randomization (MR) analyses, ranging from fixed-effect to inverse variance-weighted (IVW), weighted-median, MR Egger and generalized summary-data-based Mendelian-randomization (GSMR) models. To prioritize metabolic risk factors for schizophrenia, a novel multivariable approach was applied: multivariable Mendelian-randomization-Bayesian model averaging (MR-BMA).

RESULTS

All forward univariable MR analyses consistently showed that CRP has a protective effect on schizophrenia, whereas reverse MR analyses consistently suggested absent causal effects of schizophrenia liability on CRP blood levels. Using MR-BMA, as the top protective factors for schizophrenia we prioritized leucine and as the prime risk-factor triglycerides in medium very-low-density lipoprotein (VLDL). The five best-performing MR-BMA models provided one additional risk factor: triglycerides in large VLDL; and two additional protective factors: citrate and lactate.

CONCLUSIONS

Our results add to a growing body of literature hinting at metabolic changes-in particular of triglycerides-independently of medication status in schizophrenia. We also highlight the absent effects of genetic liability to schizophrenia on CRP levels.

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.

MR-pheWAS with stratification and interaction: Searching for the causal effects of smoking heaviness identified an effect on facial aging

Mendelian randomization (MR) is an established approach to evaluate the effect of an exposure on an outcome. The gene-by-environment (GxE) study design can be used to determine whether the genetic instrument affects the outcome through pathways other than via the exposure of interest (horizontal pleiotropy). MR phenome-wide association studies (MR-pheWAS) search for the effects of an exposure, and can be conducted in UK Biobank using the PHESANT package. In this proof-of-principle study, we introduce the novel GxE MR-pheWAS approach, that combines MR-pheWAS with the use of GxE interactions. This method aims to identify the presence of effects of an exposure while simultaneously investigating horizontal pleiotropy. We systematically test for the presence of causal effects of smoking heaviness-stratifying on smoking status (ever versus never)-as an exemplar. If a genetic variant is associated with smoking heaviness (but not smoking initiation), and this variant affects an outcome (at least partially) via tobacco intake, we would expect the effect of the variant on the outcome to differ in ever versus never smokers. We used PHESANT to test for the presence of effects of smoking heaviness, instrumented by genetic variant rs16969968, among never and ever smokers respectively, in UK Biobank. We ranked results by the strength of interaction between ever and never smokers. We replicated previously established effects of smoking heaviness, including detrimental effects on lung function. Novel results included a detrimental effect of heavier smoking on facial aging. We have demonstrated how GxE MR-pheWAS can be used to identify potential effects of an exposure, while simultaneously assessing whether results may be biased by horizontal pleiotropy.