Associations between the gut microbiota and its related metabolic pathways and uveitis: A bidirectional two-sample Mendelian randomization study
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Nutritional epidemiology aims to identify dietary and lifestyle causes for human diseases. Causality inference in nutritional epidemiology is largely based on evidence from studies of observational design, and may be distorted by unmeasured or residual confounding and reverse causation. Mendelian randomization is a recently developed methodology that combines genetic and classical epidemiological analysis to infer causality for environmental exposures, based on the principle of Mendel's law of independent assortment. Mendelian randomization uses genetic variants as proxies for environmental exposures of interest. Associations derived from Mendelian randomization analysis are less likely to be affected by confounding and reverse causation. During the past 5 years, a body of studies examined the causal effects of diet/lifestyle factors and biomarkers on a variety of diseases. The Mendelian randomization approach also holds considerable promise in the study of intrauterine influences on offspring health outcomes. However, the application of Mendelian randomization in nutritional epidemiology has some limitations.
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Lipoproteins are a major risk factor for atherosclerotic cardiovascular diseases (ASCVD). Among the lipoproteins, low-density lipoproteins (LDL) have been shown to be causally associated with ASCVD development. In contrast, triglycerides or triglyceride-rich lipoproteins receive less attention than LDL because there is little definite evidence from randomized controlled trials. A Mendelian randomization study has recently been published in which a causal association could be estimated with observational datasets. Using such Mendelian randomization studies, ranging from common to rare genetic variations, triglycerides seem to be causally associated with ASCVD outcomes independent of LDL. Although the "causal association" of serum triglycerides and ASCVD is difficult to assert, accumulated evidence from clinical and Mendelian randomization studies, using common and rare genetic variations, strongly supports such an association. In this article, we provide a summary of investigations focusing on important causal associations between serum triglycerides and ASCVD from the clinical point of view.
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Purpose of review The current review describes the fundamentals of the Mendelian randomization framework and its current application for causal inference in human nutrition and metabolism. Recent findings In the Mendelian randomization framework, genetic variants that are strongly associated with the potential risk factor are used as instrumental variables to determine whether the risk factor is a cause of the disease. Mendelian randomization studies are less susceptible to confounding and reverse causality compared with traditional observational studies. The Mendelian randomization study design has been increasingly used in recent years to appraise the causal associations of various nutritional factors, such as milk and alcohol intake, circulating levels of micronutrients and metabolites, and obesity with risk of different health outcomes. Mendelian randomization studies have confirmed some but challenged other nutrition-disease associations recognized by traditional observational studies. Yet, the causal role of many nutritional factors and intermediate metabolic changes for health and disease remains unresolved. Summary Mendelian randomization can be used as a tool to improve causal inference in observational studies assessing the role of nutritional factors and metabolites in health and disease. There is a need for more large-scale genome-wide association studies to identify more genetic variants for nutritional factors that can be utilized for Mendelian randomization analyses.
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Preclinical studies have indicated insulin-like growth factor 1 (IGF1) as a novel therapeutic target in the treatment of migraines. We aimed to investigate the causal effect of circulating IGF1 levels on migraine risk using the two-sample Mendelian randomization method.A total of 431 independent variants from 363,228 unrelated individuals in the UK Biobank were used as genetic instruments for circulating IGF1 levels. Summary-level data for migraines were obtained from two independent studies with 10,536 and 28,852 migraine cases, respectively.Mendelian randomization using inverse-variance weighting showed that increased IGF1 levels were significantly associated with decreased risk of migraines in both outcome datasets (odds ratio 0.905, 95% confidence interval 0.842-0.972, p = 0.006; odds ratio 0.929, 95% confidence interval 0.882-0.979, p = 0.006). Although some other robust Mendelian randomization methods did not demonstrate a significant association, no unbalanced horizontal pleiotropy was found by Mendelian randomization-Egger regression (p values for horizontal pleiotropy 0.232 and 0.435). The effect was confirmed in additional analyses including multivariable Mendelian randomization analyses.This two-sample Mendelian randomization study showed that genetically determined increased IGF1 levels are causally associated with decreased migraine risk. Future randomized controlled trials are warranted to confirm the benefits of IGF1 administration on migraines.
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A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables. However, in some cases, such as the case of triglyceride level as a risk factor for cardiovascular disease, it may be difficult to find a relevant genetic variant that is not also associated with related risk factors, such as other lipid fractions. Such a variant is known as pleiotropic. In this paper, we propose an extension of Mendelian randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome. This “multivariable Mendelian randomization” approach is similar to the simultaneous assessment of several treatments in a factorial randomized trial. In this paper, methods for estimating the causal effects are presented and compared using real and simulated data, and the assumptions necessary for a valid multivariable Mendelian randomization analysis are discussed. Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol.
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A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables. However, in some cases, such as the case of triglyceride level as a risk factor for cardiovascular disease, it may be difficult to find a relevant genetic variant that is not also associated with related risk factors, such as other lipid fractions. Such a variant is known as pleiotropic. In this paper, we propose an extension of Mendelian randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome. This "multivariable Mendelian randomization" approach is similar to the simultaneous assessment of several treatments in a factorial randomized trial. In this paper, methods for estimating the causal effects are presented and compared using real and simulated data, and the assumptions necessary for a valid multivariable Mendelian randomization analysis are discussed. Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol.
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Purpose of review Mendelian randomization is a technique for judging the causal impact of a risk factor on an outcome from observational data using genetic variants. Although evidence from Mendelian randomization for the effects of major lipids and lipoproteins on coronary heart disease (CHD) risk has been around for a relatively long time, new data resources and new methodological approaches have given fresh insight into these relationships. The lessons from these analyses are likely to be highly relevant when it comes to lipidomics and the analyses of lipid subspecies whose biology is less well understood. Recent findings Although analyses of low-density lipoprotein cholesterol and lipoprotein(a) are unambiguous as there are genetic variants that associate exclusively with these risk factors and have well understood biology, analyses for triglycerides, and high-density lipoprotein cholesterol (HDL-c) are less clear. For example, a subset of genetic variants having specific associations with HDL-c are not associated with CHD risk, but an allele score including all variants associated with HDL-c does associate with CHD risk. Recently developed methods, such as multivariable Mendelian randomization, Mendelian randomization-Egger, and a weighted median method, suggest that the relationship between HDL-c and CHD risk is null, thus confirming experimental evidence. Summary Robust methods for Mendelian randomization have important utility for understanding the causal relationships between major lipids and CHD risk, and are likely to play an important role in judging the causal relevance of lipid subspecies and other metabolites measured on high-dimensional phenotyping platforms.
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