Use Of Mendelian Randomization Research Articles

The association between inflammatory cytokines and sarcopenia-related traits: a bi-directional Mendelian randomization study.

Sarcopenia is among the most common musculoskeletal illnesses, yet its underlying biochemical mechanisms remain incompletely understood. Identifying the relationship of inflammatory cytokines with sarcopenia components would help understand the etiology of sarcopenia. We performed a bi-directional Mendelian randomization study to explore the causal relationship between 41 inflammatory cytokines and sarcopenia-related traits. The study was performed in two stages using bidirectional dual-sample Mendelian randomization. We obtained aggregated statistical data on inflammatory factors, low grip strength, and ALM from genome-wide association studies. To explore the causal association between exposure and outcomes, we primarily utilized the inverse variance weighted strategy. Furthermore, we conducted sensitivity analyses through the use of Mendelian randomization (MR) Egger, weighted median and simple mode methods. To evaluate robustness of the results and to identify and adjust for horizontal pleiotropy, we performed the MR Pleiotropy RESidual Sum and Outlier test, the MR Egger intercept test, and a leave-one-out analysis. The results displayed a potential association between interleukin-10 (OR: 1.046, 95% CI: 1.002-1.093, p = 0.042) and vascular endothelial growth factor (OR: 1.024, 95% CI: 1.001-1.047, p = 0.038) and the risk of low hand-grip strength. Moreover, interferon gamma-induced protein 10 (OR: 1.010, 95% CI: 1.000-1.019, p = 0.042) and macrophage colony-stimulating factor (OR: 1.010, 95% CI: 1.003-1.017, p = 0.003) were significantly linked to a higher risk of ALM. We identified a causal relationship between multiple inflammatory factors and sarcopenia-related traits. Our study offers valuable insights into innovative methods for the sarcopenia prevention and treatment by regulating inflammatory factors.

Using Mendelian Randomization to Study the Role of Iron in Health and Disease.

Iron has been shown to play a dual role in health and disease, with either a protective or harmful effect. Some of the contradictory findings from observational studies may be due to reverse causation, residual confounding, or small sample size. One approach that may overcome these limitations without the high cost of randomized control trials is the use of Mendelian randomization to examine the long-term role of iron in a variety of health outcomes. As there is emerging evidence employing Mendelian randomization as a method of assessing the role of micronutrients in health and disease, this narrative review will highlight recent Mendelian randomization findings examining the role of iron in cardiometabolic disorders, inflammation, neurological disorders, different cancers, and a number of other health-related outcomes.

Genetics of Intervertebral Disc Degeneration.

Intervertebral disc degeneration is a contributor to chronic back pain. While a part of the natural aging process, early or rapid intervertebral disc degeneration is highly heritable. In this review, we summarize recent progress towards unraveling the genetics associated with this degenerative process. Use of large cohorts of patient data to conduct genome-wide association studies (GWAS) for intervertebral disc disease, and to lesser extent for aspects of this process, such as disc height, has resulted in a large increase in our understanding of the genetic etiology. Genetic correlation suggests that intervertebral disc disease is pleiotropic with risk factors for other diseases such as osteoporosis. The use of Mendelian Randomization is slowly establishing what are the causal relationships between intervertebral disc disease and factors previously correlated with this disease. The results from these human genetic studies highlight the complex nature of this disease and have the potential to lead to improved clinical management of intervertebral disc disease. Much additional work should now be focused on characterizing the causative relationship various co-morbid conditions have with intervertebral disc degeneration and on finding interventions to slow or halt this disease.

Deciphering correlation and causation in risk factors for heart disease with Mendelian randomization

In this analysis, we studied the use of Mendelian randomization to identify the risk factors of coronary artery disease (CAD), a major cause of cardiovascular disease. Identifying risk factors of CAD are critical to understanding and managing the disease. Our analysis combined results from 28 genetic analyses from 12 unique studies. For each genetic variation, we obtained the variant ID, chromosome, base-pair position, reference and alternative alleles of the genetic variation, and estimated effect and p-value of the genetic variation on the outcome. We hypothesized that traits which are correlated with CAD outcomes will be causally associated with CAD risk in a genetic Mendelian randomization analysis. Our analysis showed that several traits such as blood pressure readings (systolic, OR 0.51 (95% CI: 0.34-0.69), p-value = 5.4x10-9) and (diastolic, OR 0.56 (95% CI: 0.41-0.71), p-value = 7.6x10-14), LDL cholesterol levels (OR 0.54 (95% CI: 0.47-0.60), p-value = 4.4x10-56), and BMI (OR 0.41 (95% I: 0.35-0.48), p-value = 6.30x10-33) were significant risk factors for CAD. C-reactive protein (OR -0.09 (95% CI: -0.18–0.00), p-value = 0.05) was a protective risk factor of CAD due to its negative odds ratio. In contrast, eosinophil count (OR -0.007 (95% CI: -0.06-0.04), p-value = 0.79) had no statistically significant association. Blood cells had weak associations with CAD, and uric acid’s role as a causal or reversible risk factor of CAD was inconclusive, requiring further study.

Use of Mendelian randomization to evaluate the effect of atrial fibrillation on cardiovascular diseases and cardiac death.

Several observational studies indicated that atrial fibrillation might aggravate other cardiovascular diseases apart from ischaemic stroke. However, it remains to be determined whether these associations reveal independent causation. Using Mendelian randomization (MR), we systematically investigated how genetically predicted atrial fibrillation affected other cardiovascular diseases and cardiac death. Summary-level data for atrial fibrillation and other cardiovascular diseases were obtained from public genome-wide association study data. The random inverse-variance weighted method was treated as the primary analysis. Sensitivity analyses (including weighted median, MR-Egger, and multivariable MR methods) were also performed. Atrial fibrillation was significantly associated with higher risks of heart failure [odds ratio (OR): 1.24; 95% confidence interval (CI): 1.19-1.28; P<0.001], ischaemic stroke (OR: 1.21; 95% CI: 1.17-1.25; P<0.001), transient ischaemic attack (OR: 1.10; 95% CI: 1.05-1.15; P<0.001), peripheral artery diseases (OR: 1.09; 95% CI: 1.03-1.15; P=0.002), cardiac death (OR: 1.08; 95% CI: 1.02-1.15; P=0.008), and hypertension (OR: 1.06; 95% CI: 1.01-1.11; P=0.010), without effects on coronary heart disease or pulmonary embolism. Associations for heart failure and ischaemic stroke remained robust to the sensitivity analyses. MR-Egger method (P>0.05) and funnel plot yielded no indication of directional pleiotropy. The leave-one-out analysis suggested that the causal associations were not driven by individual single nucleotide polymorphism. This comprehensive MR analysis verified the causal associations between atrial fibrillation and high risks of heart failure, ischaemic stroke, transient ischaemic attack, peripheral artery diseases, cardiac death, and hypertension. Interventions to reduce cardiovascular diseases beyond ischaemic stroke are warranted in patients with atrial fibrillation.

Genetically predicted phosphate and cardiovascular disease: A Mendelian randomization study.

BackgroundExtensive epidemiological studies have highlighted the correlation between serum phosphate and cardiovascular diseases. The present study aims to determine whether genetically predicted serum phosphate is causally associated with the distinct subtypes of cardiovascular events through the use of Mendelian randomization (MR) analysis.MethodsIndependent and strongly correlated single-nucleotide polymorphisms (SNPs) for serum phosphate were extracted from publicly available genome-wide association studies. Summary statistics of cardiovascular diseases were derived from large-scale consortiums, including HERMES and FinnGen biobank. MR-Egger, weighted median, inverse variance weighted, pleiotropy residual sum and outlier (MR-PRESSO) methods and MR using robust adjusted profile score (MR-RAPS) were employed to analyze causality. The sensitivity analyses comprised heterogeneity, horizontal pleiotropy, and leave-one-out approaches; these were used to ensure the stability of the results.ResultsOur study demonstrated that increased genetically predicted serum phosphate is causally associated with a higher risk of valvular heart disease (VHD) [For VHD including rheumatic fever: odds ratio (OR) = 2.45; 95% confidence interval (CI), 1.52–3.94; p = 0.0002; for non-rheumatic VHD: OR = 6.58; 95% CI, 2.50–17.32; p = 0.0001]. However, no causal association was detected between serum phosphate and other common cardiovascular diseases (including coronary heart disease, heart failure, atrial fibrillation, and essential hypertension).ConclusionsThe results indicate strong causality between serum phosphate and valvular heart disease. Serum phosphate-lowering therapy within the physiological range may represent a novel therapeutic method for valvular heart disease.

S1536 Iron Deficiency and the Development of Celiac Disease

Introduction: The prevalence of celiac disease is increasing; however, the cause for this increase is unknown. Iron deficiency is one suggested environmental trigger for celiac disease development. We aimed to evaluate this possible association through the use of Mendelian randomization (MR). MR capitalizes on the random allocation of single nucleotide polymorphisms (SNPs) at conception, and under certain assumptions can suggest causality. Methods: We conducted a 2-sample MR study examining the relationship between SNPs associated with iron status and the presence of celiac disease. The SNPs were drawn from a meta-analysis of 3 genome-wide association studies (GWAS). The GWAS summary statistics from the UK Biobank, consisting of 336,638 White British individuals of whom 1,855 participants had celiac disease, were used to assess the association between these SNPs and celiac disease. The assumptions of MR were tested to evaluate for possible causality. We also performed an MR Egger test for pleiotropy. Results: There are 4 SNPs strongly associated with systemic iron status. Using PhenoScanner, we found that these SNPs were not significantly associated with known risk factors for celiac disease. All 4 were available in the UK Biobank summary statistics, and utilizing these SNPs, we harmonized exposure and outcome associations. We found that higher iron status was negatively associated with risk of celiac disease (odds ratio per 1 standard deviation increase in serum iron: 0.65, 95% CI 0.47-0.91). We performed leave-one-out analyses and had consistent results and did not find one single variant to be driving this association. All 3 assumptions of MR appeared plausible. Conclusion: We found that genetically lower iron levels were associated with an increased risk of having celiac disease. If the assumptions of MR hold, this association suggests a causal role of iron deficiency in celiac disease development. While unlikely to be the only environmental trigger, iron deficiency is prevalent in both the general and celiac population. One possible mechanism for this pathogenic role is that the transferrin receptor 1, which is upregulated in the setting of iron deficiency, directly transports gliadin peptides across enterocytes – a key step in celiac disease development. Our findings highlight a potential opportunity for celiac disease prevention.

An Overview of Methods and Exemplars of the Use of Mendelian Randomisation in Nutritional Research.

Objectives: It is crucial to elucidate the causal relevance of nutritional exposures (such as dietary patterns, food intake, macronutrients intake, circulating micronutrients), or biomarkers in non-communicable diseases (NCDs) in order to find effective strategies for NCD prevention. Classical observational studies have found evidence of associations between nutritional exposures and NCD development, but such studies are prone to confounding and other biases. This has direct relevance for translation research, as using unreliable evidence can lead to the failure of trials of nutritional interventions. Facilitated by the availability of large-scale genetic data, Mendelian randomization studies are increasingly used to ascertain the causal relevance of nutritional exposures and biomarkers for many NCDs. Methods: A narrative overview was conducted in order to demonstrate and describe the utility of Mendelian randomization studies, for individuals with little prior knowledge engaged in nutritional epidemiological research. Results: We provide an overview, rationale and basic description of the methods, as well as strengths and limitations of Mendelian randomization studies. We give selected examples from the contemporary nutritional literature where Mendelian randomization has provided useful evidence on the potential causal relevance of nutritional exposures. Conclusions: The selected exemplars demonstrate the importance of well-conducted Mendelian randomization studies as a robust tool to prioritize nutritional exposures for further investigation.

Association of PCSK9 Variants With the Risk of Atherosclerotic Cardiovascular Disease and Variable Responses to PCSK9 Inhibitor Therapy

Genetic polymorphisms or variations, randomly distributed in a population, may cause drug-gene response variations. Investigation into these polymorphisms may identify novel mechanisms contributing to a specific disease process. Such investigation necessitates the use of Mendelian randomization, an analytical method that uses genetic variants as instrumental variables for modifiable risk factors that affect population health.1 In the past decade, advances in our understanding of genetic polymorphisms have enabled the identification of genetic variants in candidate genes that impact low-density lipoprotein cholesterol (LDL-C) regulating pathways and cardiovascular disease (CVD) outcomes. A specific candidate gene of interest is that of the LDL receptor degrading protein, PCSK9. In fact, loss-of-function genetic variants for the PCSK9 gene are what first highlighted this pathway as a candidate for pharmacologic inhibition. PCSK9 inhibitors (PCSK9i) are a class of cholesterol-lowering medications that provide significant reductions in LDL by inhibiting the degradation of LDL receptors (LDLR). These inhibitors have also been found to reduce production and enhance clearance of lipoprotein A (Lp[a]), an LDL-like particle currently under study as a separate risk factor for atherosclerotic CVD. Here, we discuss the promise of personalized medicine in developing a more efficacious and individualized pharmacogenomics-based approach for the use of PCSK9i that considers genetic variation and targets different patient populations. This review explores the pharmacogenomics of PCSK9i in the context of PCSK9 allele variants related to drug-metabolizing enzymes and responses since more studies are demonstrating that some patients are hyporesponsive or non-responsive to PCSK9i.2 In summary, the pharmacogenomics of PCSK9 are a promising therapeutic target and genetic information from prospective randomized clinical trials is warranted to gain a full understanding of the efficacy and cost-effectiveness of such allele and/or gene-guided PCSK9i therapy.

Time to Think Differently About Sarcomere-Negative Hypertrophic Cardiomyopathy.

Time to Think Differently About Sarcomere-Negative Hypertrophic Cardiomyopathy.

Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development.

Drug development in cardiovascular disease is stagnating, with lack of efficacy and adverse effects being barriers to innovation. Human genetics can provide compelling evidence of causation through approaches such as Mendelian randomization, with genetic support for causation increasing the probability of a clinical trial succeeding. Mendelian randomization applied to quantitative traits can identify risk factors for disease that are both causal and amenable to therapeutic modification. However, important differences exist between genetic investigations of a biomarker (such as HDL cholesterol) and a drug target aimed at modifying the same biomarker of interest (such as cholesteryl ester transfer protein), with implications for the methodology, interpretation and application of Mendelian randomization to drug development. Differences include the comparative nature of the genetic architecture - that is, biomarkers are typically polygenic, whereas protein drug targets are influenced by either cis-acting or trans-acting genetic variants - and the potential for drug targets to show disease associations that might differ from those of the biomarker that they are intended to modify (target-mediated pleiotropy). In this Review, we compare and contrast the use of Mendelian randomization to evaluate potential drug targets versus quantitative traits. We explain how genetic epidemiological studies can be used to assess the aetiological roles of biomarkers in disease and to prioritize drug targets, including designing their evaluation in clinical trials.

The Role of the Microbiome in Driving RA-Related Autoimmunity.

Once referred to as “normal commensal flora” the human microbiome plays an integral role between health and disease. The host mucosal surface replete with a multitude of immune cells is a vast arena constantly sensing and responding to antigen presentation and microbial by-products. It is this key role that may allow the microbiome to prime or protect the host from autoimmune disease. Rheumatoid arthritis (RA) is a chronic, disabling inflammatory condition characterized by a complex multifactorial etiology. The presence of certain genetic markers has been proven to increase susceptibility to RA however it does not guarantee disease development. Given low concordance rates demonstrated in monozygotic twin studies there is a clear implication for the involvement of external players in RA pathogenesis. Since the historical description of rheumatoid factor, numerous additional autoantibodies have been described in the sera of RA patients. The presence of anti-cyclic citrullinated protein antibody is now a standard test, and is associated with a more severe disease course. Interestingly these antibodies are detectable in patient’s sera long before the clinical signs of RA occur. The production of autoantibodies is driven by the lack of tolerance of the immune system, and how tolerance is broken is a crucial question for understanding RA development. Here we review current literature on the role of the microbiome in RA development including periodontal, gut and lung mucosa, with particular focus on proposed mechanisms of host microbiome interactions. We discuss the use of Mendelian randomization to assign causality to the microbiome and present considerations for future studies.

The use of Mendelian randomisation to identify causal cancer risk factors: promise and limitations.

The use of observational analyses, such as classical epidemiological studies or randomised controlled trials (RCTs), to infer causality in cancer may be problematic due to both ethical reasons and technical issues, such as confounding variables and reverse causation. Mendelian randomisation (MR) is an epidemiological technique that uses genetic variants as proxies for exposures in an attempt to determine whether there is a causal link between an exposure and an outcome. Given that genetic variants are randomly assigned during meiosis according to Mendel's first and second laws of heritability, MR may be thought of as a 'natural' RCT and is therefore less vulnerable to the aforementioned problems. MR has the potential to help identify new, and validate or disprove previously implicated, modifiable risk factors in cancer, but it is not without limitations. This review provides a brief description of the history and principles of MR, as well as a guide to basic MR methodology. The bulk of the review then examines various limitations of MR in more detail, discussing some of the proposed solutions to these problems. The review ends with a brief section detailing the practical implementation of MR, with examples of its use in the study of cancer, and an assessment of its utility in identifying cancer predisposition traits. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Use of Mendelian Randomization to Examine Causal Inference in Osteoporosis.

Epidemiological studies have identified many risk factors for osteoporosis, however it is unclear whether these observational associations reflect true causal effects, or the effects of latent confounding or reverse causality. Mendelian randomization (MR) enables causal relationships to be evaluated, by examining the relationship between genetic susceptibility to the risk factor in question, and the disease outcome of interest. This has been facilitated by the development of two-sample MR analysis, where the exposure and outcome are measured in different studies, and by exploiting summary result statistics from large well-powered genome-wide association studies that are available for thousands of traits. Though MR has several inherent limitations, the field is rapidly evolving and at least 14 methodological extensions have been developed to overcome these. The present paper aims to discuss some of the limitations in the MR analytical framework, and how this method has been applied to the osteoporosis field, helping to reinforce conclusions about causality, and discovering potential new regulatory pathways, exemplified by our recent MR study of sclerostin.

Use of Mendelian Randomization for Identifying Risk Factors for Brain Tumors.

Gliomas are a group of primary brain tumors, the most common and aggressive subtype of which is glioblastoma. Glioblastoma has a median survival of just 15 months after diagnosis. Only previous exposure to ionizing radiation and particular inherited genetic syndromes are accepted risk factors for glioma; the vast majority of cases are thought to occur spontaneously. Previous observational studies have described associations between several risk factors and glioma, but studies are often conflicting and whether these associations reflect true casual relationships is unclear because observational studies may be susceptible to confounding, measurement error and reverse causation. Mendelian randomization (MR) is a form of instrumental variable analysis that can be used to provide supporting evidence for causal relationships between exposures (e.g., risk factors) and outcomes (e.g., disease onset). MR utilizes genetic variants, such as single nucleotide polymorphisms (SNPs), that are robustly associated with an exposure to determine whether there is a causal effect of the exposure on the outcome. MR is less susceptible to confounding, reverse causation and measurement errors as it is based on the random inheritance during conception of genetic variants that can be relatively accurately measured. In previous studies, MR has implicated a genetically predicted increase in telomere length with an increased risk of glioma, and found little evidence that obesity related factors, vitamin D or atopy are causal in glioma risk. In this review, we describe MR and its potential use to discover and validate novel risk factors, mechanistic factors, and therapeutic targets in glioma.

Genetic predictors of testosterone and their associations with cardiovascular disease and risk factors: A Mendelian randomization investigation

BackgroundTestosterone supplementation has been linked to increased cardiovascular disease risk in some observational studies. The causal role of testosterone can be investigated using a Mendelian randomization approach. Methods and resultsWe assessed genetic associations of variants in two gene regions (SHBG and JMJD1C) with several cardiovascular risk factors (lipids, adiponectin, blood pressure, anthropometric traits) plus male pattern baldness, including control outcomes and potential mediators. We assessed genetic associations with coronary artery disease (CAD) risk in the CARDIoGRAMplusC4D consortium (171,191 individuals including 60,801 cases), and associations with CAD and ischaemic stroke risk in the UK Biobank (367,643 individuals including 25,352 CAD cases and 3650 ischaemic stroke cases).Genetic predictors of increased serum testosterone were associated with lipids, blood pressure, and height. There was some evidence of an association with risk of CAD (SHBG gene region: odds ratio (OR) 0.95 per 1 unit increase in log-transformed testosterone [95% confidence interval: 0.81–1.12, p = 0.55]; JMJD1C gene region: OR 1.24 [1.01–1.51, p = 0.04]) and ischaemic stroke both overall (SHBG: OR 1.05 [0.64, 1.73, p = 0.83]; JMJD1C: OR 2.52 [1.33, 4.77, p = 0.005]) and in men. However, associations with some control outcomes were in the opposite direction to that expected. ConclusionsSex hormone-related mechanisms appear to be relevant to cardiovascular risk factors and for stroke (particularly for men). However, the extent that these findings are specifically informative about endogenous testosterone or testosterone supplementation is unclear. These findings underline a fundamental limitation for the use of Mendelian randomization where biological knowledge about the function of genetic variants is uncertain.

Biological Insights Into Muscular Strength: Genetic Findings in the UK Biobank

We performed a large genome-wide association study to discover genetic variation associated with muscular strength, and to evaluate shared genetic aetiology with and causal effects of muscular strength on several health indicators. In our discovery analysis of 223,315 individuals, we identified 101 loci associated with grip strength (P <5 × 10−8). Of these, 64 were associated (P < 0.01 and consistent direction) also in the replication dataset (N = 111,610). eQTL analyses highlighted several genes known to play a role in neuro-developmental disorders or brain function, and the results from meta-analysis showed a significant enrichment of gene expression of brain-related transcripts. Further, we observed inverse genetic correlations of grip strength with cardiometabolic traits, and positive correlation with parents’ age of death and education. We also showed that grip strength had shared biological pathways with indicators of frailty, including cognitive performance scores. By use of Mendelian randomization, we provide evidence that higher grip strength is protective of both coronary heart disease (OR = 0.69, 95% CI 0.60–0.79, P < 0.0001) and atrial fibrillation (OR = 0.75, 95% CI 0.62–0.90, P = 0.003). In conclusion, our results show shared genetic aetiology between grip strength, and cardiometabolic and cognitive health; and suggest that maintaining muscular strength could prevent future cardiovascular events.

Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies.

Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.

Insight into rheumatological cause and effect through the use of Mendelian randomization.

Insight into rheumatological cause and effect through the use of Mendelian randomization.

Insight into rheumatological cause and effect through the use of Mendelian randomization.

Establishing causality of risk factors is important to determine the pathogenetic mechanisms underlying rheumatic diseases, and can facilitate the design of interventions to improve care for affected patients. The presence of unmeasured confounders, as well as reverse causation, is a challenge to the assignment of causality in observational studies. Alleles for genetic variants are randomly inherited at meiosis. Mendelian randomization analysis uses these genetic variants to test whether a particular risk factor is causal for a disease outcome. In this Review of the Mendelian randomization technique, we discuss published results and potential applications in rheumatology, as well as the general clinical utility and limitations of the approach.