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      Causal relationship among obesity and body fat distribution and epilepsy subtypes


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          The observational studies indicate an association between obesity and epilepsy, but it is unclear whether such an association responds to causality. The objective of this study was to determine the causal relationship between obesity and fat distribution and epilepsy subtypes based on waist circumference, hip circumference (HP), waist-hip ratio (WHR), and body mass index (BMI).


          A two-sample Mendelian randomization study was conducted separately for the four indicators of obesity and epilepsy and its seven subtypes, with reverse Mendelian randomization and multivariate Mendelian randomization for significant outcomes.


          A two-sample Mendelian randomized analysis informed us that waist circumference was a risk factor for juvenile myoclonic epilepsy (beta = 0.0299, P = 4.60 × 10 −3). The increase in hip circumference increased the risk of juvenile myoclonic epilepsy and epilepsy, with effect values of 0.0283 ( P = 2.01 × 10 −3) and 0.0928 ( P = 1.40 × 10 −2), respectively. Furthermore, children with a higher BMI exhibit a higher risk of epilepsy (beta = 0.0148 P = 1.05 × 10 −3). The reverse Mendelian randomization study revealed that childhood absence epilepsy increased its BMI (beta = 0.8980, P = 7.52 × 10 −7), and juvenile myoclonic epilepsy increased its waist circumference (beta = 0.7322, P = 3.26 × 10 −2). Multivariate Mendelian randomization revealed that an increase in hip circumference and waist-hip ratio increased the risk of juvenile myoclonic epilepsy, with an effect value of 0.1051 ( P = 9.75 × 10 −4) and 0.1430 ( P = 3.99 × 10 −3), respectively, while an increase in BMI and waist circumference instead decreased their risk, with effect values of −0.0951 ( P = 3.14 × 10 −2) and−0.0541 ( P = 1.71 × 10 −2). In contrast, multivariate Mendelian randomization for childhood absence epilepsy and epilepsy did not identify any independent risk factors.


          Our findings provide novel evidence in favor of obesity as a risk factor for epilepsy and waist circumference as a risk factor for juvenile myoclonic epilepsy. Increased hip circumference confers an elevated risk of juvenile myoclonic epilepsy and epilepsy (all documented cases), and a high BMI increases the risk of childhood absence epilepsy. With this, new insights are provided into the energy metabolism of epilepsy, which supports further nutritional interventions and the search for new therapeutic targets.

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          Most cited references28

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          Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians

          Mendelian randomisation uses genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data. As with all epidemiological approaches, findings from Mendelian randomisation studies depend on specific assumptions. We provide explanations of the information typically reported in Mendelian randomisation studies that can be used to assess the plausibility of these assumptions and guidance on how to interpret findings from Mendelian randomisation studies in the context of other sources of evidence
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            Mendelian randomization: using genes as instruments for making causal inferences in epidemiology.

            Observational epidemiological studies suffer from many potential biases, from confounding and from reverse causation, and this limits their ability to robustly identify causal associations. Several high-profile situations exist in which randomized controlled trials of precisely the same intervention that has been examined in observational studies have produced markedly different findings. In other observational sciences, the use of instrumental variable (IV) approaches has been one approach to strengthening causal inferences in non-experimental situations. The use of germline genetic variants that proxy for environmentally modifiable exposures as instruments for these exposures is one form of IV analysis that can be implemented within observational epidemiological studies. The method has been referred to as 'Mendelian randomization', and can be considered as analogous to randomized controlled trials. This paper outlines Mendelian randomization, draws parallels with IV methods, provides examples of implementation of the approach and discusses limitations of the approach and some methods for dealing with these.
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              Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry

              Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ∼250000 European participants have led to the discovery of ∼700 and ∼100 nearly independent single nucleotide polymorphisms (SNPs) associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ∼450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N ∼700000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3290 and 941 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of P < 1 × 10-8), including 1185 height-associated SNPs and 751 BMI-associated SNPs located within loci not previously identified by these two GWAS. The near-independent genome-wide significant SNPs explain ∼24.6% of the variance of height and ∼6.0% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were ∼0.44 and ∼0.22, respectively. From analyses of integrating GWAS and expression quantitative trait loci (eQTL) data by summary-data-based Mendelian randomization, we identified an enrichment of eQTLs among lead height and BMI signals, prioritizing 610 and 138 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by the discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow-up studies.

                Author and article information

                Front Neurol
                Front Neurol
                Front. Neurol.
                Frontiers in Neurology
                Frontiers Media S.A.
                26 October 2022
                : 13
                : 984824
                Key Laboratory of Neurology of Hebei Province, Department of Neurology, The Second Hospital of Hebei Medical University , Shijiazhuang, China
                Author notes

                Edited by: Kette D. Valente, University of São Paulo, Brazil

                Reviewed by: Silvia Vincentiis, University of São Paulo, Brazil; Christos Panagiots Lisgaras, New York University, United States

                *Correspondence: Zhenzhen Qu quzhzh1986@ 123456126.com

                This article was submitted to Epilepsy, a section of the journal Frontiers in Neurology

                Copyright © 2022 Zhou, Yang, Chen, Wang and Qu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 02 July 2022
                : 13 October 2022
                Page count
                Figures: 2, Tables: 2, Equations: 3, References: 28, Pages: 8, Words: 4851

                obesity,fat distribution,epilepsy,mendelian randomization analysis,causation
                obesity, fat distribution, epilepsy, mendelian randomization analysis, causation


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