Commentary: Mendelian randomization with multiple exposures: the importance of thinking about time

Identification of Circulating Plasma Proteins as a Mediator of Hypertension-Driven Cardiac Remodeling: A Mediation Mendelian Randomization Study

BACKGROUND

This study focused on circulating plasma protein profiles to identify mediators of hypertension-driven myocardial remodeling and heart failure.

METHODS

A Mendelian randomization design was used to investigate the causal impact of systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure on 82 cardiac magnetic resonance traits and heart failure risk. Mediation analyses were also conducted to identify potential plasma proteins mediating these effects.

RESULTS

Genetically proxied higher SBP, DBP, and pulse pressure were causally associated with increased left ventricular myocardial mass and alterations in global myocardial wall thickness at end diastole. Elevated SBP and DBP were linked to increased regional myocardial radial strain of the left ventricle (basal anterior, mid, and apical walls), while higher SBP was associated with reduced circumferential strain in specific left ventricular segments (apical, mid-anteroseptal, mid-inferoseptal, and mid-inferolateral walls). Specific plasma proteins mediated the impact of blood pressure on cardiac remodeling, with FGF5 (fibroblast growth factor 5) contributing 2.96% (P=0.024) and 4.15% (P=0.046) to the total effect of SBP and DBP on myocardial wall thickness at end diastole in the apical anterior segment and leptin explaining 15.21% (P=0.042) and 23.24% (P=0.022) of the total effect of SBP and DBP on radial strain in the mid-anteroseptal segment. Additionally, FGF5 was the only mediator, explaining 4.19% (P=0.013) and 4.54% (P=0.032) of the total effect of SBP and DBP on heart failure susceptibility.

CONCLUSIONS

This mediation Mendelian randomization study provides evidence supporting specific circulating plasma proteins as mediators of hypertension-driven cardiac remodeling and heart failure.

Large-scale genome-wide association study to identify causal relationships and potential mediators between education and autoimmune diseases

Objectives

Epidemiological studies suggested a potential connection between education and autoimmune disorders. This study investigated the possible cause-and-effect relationship using a Mendelian randomization approach.

Methods

We explored the causality between four education traits (n = 257,841~1,131,881) and 22 autoimmune diseases. The mediating role of smoking (632,802 individuals), BMI (681,275 individuals), alcohol (335,394 individuals), and income (397,751 individuals) was also investigated. Transcriptome-wide association study (TWAS) and enriched signaling pathways analysis were used to investigate the underlying biological mechanisms.

Results

Especially, higher cognitive performance was protective for psoriasis (odds ratio (OR) = 0.69, 95% confidence interval (CI) = 0.60-0.79, p = 6.12×10-8), rheumatoid arthritis (RA) (OR = 0.75, 95% CI = 0.67-0.83, p = 4.62×10-6), and hypothyroidism (OR = 0.83, 95% CI = 0.77-0.90, p = 9.82×10-6). Higher levels of educational attainment decreased risks of psoriasis (OR = 0.61, 95% CI = 0.52-0.72, p = 1.12×10-9), RA (OR = 0.68, 95% CI = 0.59-0.79, p = 1.56×10-7), and hypothyroidism (OR = 0.80, 95% CI = 0.72-0.88, p = 5.00×10-6). The completion of highest-level math class genetically downregulates the incidence of psoriasis (OR = 0.66, 95% CI = 0.58-0.76, p = 2.47×10-9), RA (OR = 0.71, 95% CI = 0.63-0.81, p = 5.28×10-8), and hypothyroidism (OR = 0.85, 95% CI = 0.79-0.92, p = 8.88×10-5). Higher self-reported math ability showed protective effects on Crohn's disease (CD) (OR = 0.67, 95% CI = 0.55-0.81, p = 4.96×10-5), RA (OR = 0.76, 95% CI = 0.67-0.87, p = 5.21×10-5), and psoriasis (OR = 0.76, 95% CI = 0.65-0.88, p = 4.08×10-4). Protein modification and localization, response to arsenic-containing substances may participate in the genetic association of cognitive performance on UC, RA, psoriasis, and hypothyroidism. According to mediation analyses, BMI, smoking, and income served as significant mediators in the causal connection between educational traits and autoimmune diseases.

Conclusion

Higher levels of education-related factors have a protective effect on the risk of several autoimmune disorders. Reducing smoking and BMI and promoting income equality can mitigate health risks associated with low education levels.

Differentiating Associations of Glycemic Traits With Atherosclerotic and Thrombotic Outcomes: Mendelian Randomization Investigation

We conducted a Mendelian randomization analysis to differentiate associations of four glycemic indicators with a broad range of atherosclerotic and thrombotic diseases. Independent genetic variants associated with fasting glucose (FG), 2 h glucose after an oral glucose challenge (2hGlu), fasting insulin (FI), and glycated hemoglobin (HbA1c) at the genome-wide significance threshold were used as instrumental variables. Summary-level data for 12 atherosclerotic and 4 thrombotic outcomes were obtained from large genetic consortia and the FinnGen and UK Biobank studies. Higher levels of genetically predicted glycemic traits were consistently associated with increased risk of coronary atherosclerosis-related diseases and symptoms. Genetically predicted glycemic traits except HbA1c showed positive associations with peripheral artery disease risk. Genetically predicted FI levels were positively associated with risk of ischemic stroke and chronic kidney disease. Genetically predicted FG and 2hGlu were positively associated with risk of large artery stroke. Genetically predicted 2hGlu levels showed positive associations with risk of small vessel stroke. Higher levels of genetically predicted glycemic traits were not associated with increased risk of thrombotic outcomes. Most associations for genetically predicted levels of 2hGlu and FI remained after adjustment for other glycemic traits. Increase in glycemic status appears to increase risks of coronary and peripheral artery atherosclerosis but not thrombosis.

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.

Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization

With the increasing size and number of genome-wide association studies, individual single nucleotide polymorphisms are increasingly found to associate with multiple traits. Many different mechanisms could result in proposed genetic IVs for an exposure of interest being associated with multiple non-exposure traits, some of which could bias MR results. We describe and illustrate, through causal diagrams, a range of scenarios that could result in proposed IVs being related to non-exposure traits in MR studies. These associations could occur due to five scenarios: (i) confounding, (ii) vertical pleiotropy, (iii) horizontal pleiotropy, (iv) reverse causation and (v) selection bias. For each of these scenarios we outline steps that could be taken to explore the underlying mechanism and mitigate any resulting bias in the MR estimation. We recommend MR studies explore possible IV-non-exposure associations across a wider range of traits than is usually the case. We highlight the pros and cons of relying on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic or other biasing paths via known traits. We apply our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in UK Biobank.

The impact of education inequality on rheumatoid arthritis risk is mediated by smoking and body mass index: Mendelian randomization study

OBJECTIVE

To estimate the causal relationship between educational attainment-as a proxy for socioeconomic inequality-and risk of RA, and quantify the roles of smoking and BMI as potential mediators.

METHODS

Using the largest genome-wide association studies (GWAS), we performed a two-sample Mendelian randomization (MR) study of genetically predicted educational attainment (instrumented using 1265 variants from 766 345 individuals) and RA (14 361 cases, 43 923 controls). We used two-step MR to quantify the proportion of education's effect on RA mediated by smoking exposure (as a composite index capturing duration, heaviness and cessation, using 124 variants from 462 690 individuals) and BMI (517 variants, 681 275 individuals), and multivariable MR to estimate proportion mediated by both factors combined.

RESULTS

Each s.d. increase in educational attainment (4.2 years of schooling) was protective of RA (odds ratio 0.37; 95% CI: 0.31, 0.44). Higher educational attainment was also protective for smoking exposure (β = -0.25 s.d.; 95% CI: -0.26, -0.23) and BMI [β = -0.27 s.d. (∼1.3 kg/m2); 95% CI: -0.31, -0.24]. Smoking mediated 24% (95% CI: 13%, 35%) and BMI 17% (95% CI: 11%, 23%) of the total effect of education on RA. Combined, the two risk factors explained 47% (95% CI: 11%, 82%) of the total effect.

CONCLUSION

Higher educational attainment has a protective effect on RA risk. Interventions to reduce smoking and excess adiposity at a population level may reduce this risk, but a large proportion of education's effect on RA remains unexplained. Further research into other risk factors that act as potentially modifiable mediators are required.

Causal mediation analysis with multiple causally non-ordered and ordered mediators based on summarized genetic data

Causal mediation analysis investigates the mechanism linking exposure and outcome. Dealing with the impact of unobserved confounders among exposure, mediator and outcome is an issue of great concern. Moreover, when multiple mediators exist, this causal pathway intertwines with other causal pathways, rendering it difficult to estimate the path-specific effects. In this study, we propose a method (PSE-MR) to identify and estimate path-specific effects of an exposure (e.g. education) on an outcome (e.g. osteoarthritis risk) through multiple causally ordered and non-ordered mediators (e.g. body mass index and pack-years of smoking) using summarized genetic data, when the sequential ignorability assumption is violated. Specifically, PSE-MR requires a specific rank condition in which the number of instrumental variables is larger than the number of mediators. Furthermore, we illustrate the utility of PSE-MR by providing guidance for practitioners and exploring the mediation effects of body mass index and pack-years of smoking in the causal pathways from education to osteoarthritis risk. Additionally, the results of simulation reveal that the causal estimates of path-specific effects are almost unbiased with good coverage and Type I error properties. Also, we summarize the least number of instrumental variables for the specific number of mediators to achieve 80% power.

Evaluating the direct effects of childhood adiposity on adult systemic metabolism: a multivariable Mendelian randomization analysis

BACKGROUND

Individuals who are obese in childhood have an elevated risk of disease in adulthood. However, whether childhood adiposity directly impacts intermediate markers of this risk, independently of adult adiposity, is unclear. In this study, we have simultaneously evaluated the effects of childhood and adulthood body size on 123 systemic molecular biomarkers representing multiple metabolic pathways.

METHODS

Two-sample Mendelian randomization (MR) was conducted to estimate the causal effect of childhood body size on a total of 123 nuclear magnetic resonance-based metabolic markers using summary genome-wide association study (GWAS) data from up to 24 925 adults. Multivariable MR was then applied to evaluate the direct effects of childhood body size on these metabolic markers whilst accounting for adult body size. Further MR analyses were undertaken to estimate the potential mediating effects of these circulating metabolites on the risk of coronary artery disease (CAD) in adulthood using a sample of 60 801 cases and 123 504 controls.

RESULTS

Univariable analyses provided evidence that childhood body size has an effect on 42 of the 123 metabolic markers assessed (based on P < 4.07 × 10-4). However, the majority of these effects (35/42) substantially attenuated when accounting for adult body size using multivariable MR. We found little evidence that the biomarkers that were potentially influenced directly by childhood body size (leucine, isoleucine and tyrosine) mediate this effect onto adult disease risk. Very-low-density lipoprotein markers provided the strongest evidence of mediating the long-term effect of adiposity on CAD risk.

CONCLUSIONS

Our findings suggest that childhood adiposity predominantly exerts its detrimental effect on adult systemic metabolism along a pathway that involves adulthood body size.

Mendelian randomisation for mediation analysis: current methods and challenges for implementation

Mediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Traditional, non-instrumental variable methods for mediation analysis experience a number of methodological difficulties, including bias due to confounding between an exposure, mediator and outcome and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable MR (MVMR) and two-step MR. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, interactions between exposures and mediators and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although MR relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our simulations demonstrate that these methods are unaffected by confounders of the exposure or mediator and the outcome and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR. MR mediation methods require different assumptions to be made, compared with non-instrumental variable mediation methods. Where these assumptions are more plausible, MR can be used to improve causal inference in mediation analysis.

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.