ABSTRACT
BACKGROUND AND OBJECTIVES: There has been considerable interest in statins because of their pleiotropic effects beyond their lipid-lowering properties. Many of these pleiotropic effects are predominantly ascribed to Rho small guanosine triphosphatases (Rho GTPases) proteins. We aimed to genetically investigate the role of lipids and statin interventions on multiple sclerosis (MS) risk and severity. METHOD: We used two-sample Mendelian randomization (MR) to investigate (1) the causal role of genetically mimic both cholesterol-dependent (through low-density lipoprotein cholesterol (LDL-C) and cholesterol biosynthesis pathway) and cholesterol-independent (through Rho GTPases) effects of statins on MS risk and MS severity, (2) the causal link between lipids (high-density lipoprotein cholesterol [HDL-C] and triglycerides [TG]) levels and MS risk and severity, and (3) the reverse causation between lipid fractions and MS risk. We used summary statistics from the Global Lipids Genetics Consortium (GLGC), eQTLGen Consortium, and the International MS Genetics Consortium (IMSGC) for lipids, expression quantitative trait loci, and MS, respectively (GLGC: n = 188,577; eQTLGen: n = 31,684; IMSGC (MS risk): n = 41,505; IMSGC (MS severity): n = 7,069). RESULTS: The results of MR using the inverse-variance weighted method show that genetically predicted RAC2, a member of cholesterol-independent pathway (OR 0.86 [95% CI 0.78-0.95], p-value 3.80E-03), is implicated causally in reducing MS risk. We found no evidence for the causal role of LDL-C and the member of cholesterol biosynthesis pathway on MS risk. The MR results also show that lifelong higher HDL-C (OR 1.14 [95% CI 1.04-1.26], p-value 7.94E-03) increases MS risk but TG was not. Furthermore, we found no evidence for the causal role of lipids and genetically mimicked statins on MS severity. There is no evidence of reverse causation between MS risk and lipids. DISCUSSION: Evidence from this study suggests that RAC2 is a genetic modifier of MS risk. Because RAC2 has been reported to mediate some of the pleiotropic effects of statins, we suggest that statins may reduce MS risk through a cholesterol-independent pathway (that is, RAC2-related mechanism(s)). MR analyses also support a causal effect of HDL-C on MS risk.
Subject(s)
Hydroxymethylglutaryl-CoA Reductase Inhibitors , Multiple Sclerosis , Humans , Hydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic use , Cholesterol, LDL , Triglycerides , Mendelian Randomization Analysis , Multiple Sclerosis/epidemiology , Multiple Sclerosis/genetics , Cholesterol , Cholesterol, HDL , rho GTP-Binding Proteins/genetics , Genome-Wide Association Study , Polymorphism, Single NucleotideABSTRACT
OBJECTIVE: The objective of this study was to explore the potential causal associations of body mass index, height, weight, fat mass, fat percentage and non-fat mass in the whole body, arms, legs and trunk (henceforth, 'anthropometric measures') with multiple sclerosis (MS) risk and severity. We also investigated the potential for reverse causation between anthropometric measures and MS risk. METHODS: We conducted a two-sample univariable, multivariable and bidirectional Mendelian randomisation (MR) analysis. RESULTS: A range of features linked to obesity (body mass index, weight, fat mass and fat percentage) were risk factors for MS development and worsened the disease's severity in MS patients. Interestingly, we were able to demonstrate that height and non-fat mass have no association with MS risk or MS severity. We demonstrated that the association between anthropometric measures and MS is not subject to bias from reverse causation. CONCLUSIONS: Our findings provide evidence from human genetics that a range of features linked to obesity is an important contributor to MS development and MS severity, but height and non-fat mass are not. Importantly, these findings also identify a potentially modifiable factor that may reduce the accumulation of further disability and ameliorate MS severity.
Subject(s)
Multiple Sclerosis , Adipose Tissue , Body Mass Index , Humans , Mendelian Randomization Analysis , Multiple Sclerosis/complications , Multiple Sclerosis/epidemiology , Multiple Sclerosis/genetics , Obesity/epidemiology , Obesity/genetics , Polymorphism, Single NucleotideABSTRACT
Parkinson's disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson's disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson's disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson's disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson's disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson's disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson's disease drug development.
Subject(s)
Genome, Human , Mendelian Randomization Analysis , Parkinson Disease/drug therapy , Parkinson Disease/genetics , Brain/metabolism , Brain/pathology , Case-Control Studies , Cohort Studies , Disease Progression , Gene Expression Regulation , Genetic Predisposition to Disease , Genetic Variation , Humans , Parkinson Disease/blood , Quantitative Trait Loci/genetics , Risk FactorsABSTRACT
BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative movement disorder. Observational studies suggest higher levels of plasma urate may protect against Parkinson's risk and progression; however, causality cannot be established. OBJECTIVES: This study set out to determine whether there is a true causal association between urate levels and PD age at onset (AAO) and progression severity using recently released PD AAO and progression genome-wide association study (GWAS) data. METHODS: A large two-sample Mendelian randomization design was employed, using genetic variants underlying urate levels and the latest GWAS data for PD outcomes. RESULTS: This study found no causal association between urate levels and Parkinson's risk, AAO, or progression severity. CONCLUSIONS: Our results predict increasing urate levels as a therapeutic strategy is unlikely to benefit PD patients. © 2021 International Parkinson and Movement Disorder Society.
Subject(s)
Parkinson Disease , Disease Progression , Genome-Wide Association Study , Humans , Mendelian Randomization Analysis , Parkinson Disease/genetics , Uric AcidABSTRACT
Neurodegenerative diseases are etiologically and clinically heterogeneous conditions, often reflecting a spectrum of disease rather than well-defined disorders. The underlying molecular complexity of these diseases has made the discovery and validation of useful biomarkers challenging. The search of characteristic genetic and transcriptomic indicators for preclinical disease diagnosis, prognosis, or subtyping is an area of ongoing effort and interest. The next generation of biomarker studies holds promise by implementing meaningful longitudinal and multi-modal approaches in large scale biobank and healthcare system scale datasets. This work will only be possible in an open science framework. This review summarizes the current state of genetic and transcriptomic biomarkers in Parkinson's disease, Alzheimer's disease, and amyotrophic lateral sclerosis, providing a comprehensive landscape of recent literature and future directions.
Subject(s)
Biomarkers/metabolism , Neurodegenerative Diseases/genetics , Transcriptome/genetics , Humans , Multifactorial Inheritance/genetics , Mutation/geneticsABSTRACT
Common neurodegenerative diseases are thought to arise from a combination of environmental and genetic exposures. Mendelian randomization is a powerful way to leverage existing genetic data to investigate causal relationships between risk factors and disease. In recent years, Mendelian randomization has gathered considerable traction in neurodegenerative disease research, providing valuable insights into the aetiology of these conditions. This review aims to evaluate the impact of Mendelian randomization studies on translational medicine for neurodegenerative diseases, highlighting the advances made and challenges faced. We will first describe the fundamental principles and limitations of Mendelian randomization and then discuss the lessons from Mendelian randomization studies of environmental risk factors for neurodegeneration. We will illustrate how Mendelian randomization projects have used novel resources to study molecular pathways of neurodegenerative disease and discuss the emerging role of Mendelian randomization in drug development. Finally, we will conclude with our view of the future of Mendelian randomization in these conditions, underscoring unanswered questions in this field.