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      Causal Relations between Exposome and Stroke: A Mendelian Randomization Study

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          Abstract

          Background and Purpose

          To explore the causal relationships of elements of the exposome with ischemic stroke and its subtypes at the omics level and to provide evidence for stroke prevention.

          Methods

          We conducted a Mendelian randomization study between exposure and any ischemic stroke (AIS) and its subtypes (large-artery atherosclerotic disease [LAD], cardioembolic stroke [CE], and small vessel disease [SVD]). The exposure dataset was the UK Biobank involving 361,194 subjects, and the outcome dataset was the MEGASTROKE consortium including 52,000 participants.

          Results

          We found that higher blood pressure (BP) (systolic BP: odds ratio [OR], 1.02; 95% confidence interval [CI], 1.01 to 1.04; diastolic BP: OR, 1.03; 95% CI, 1.01 to 1.05; pulse pressure: OR, 1.03; 95% CI, 1.00 to 1.06), atrial fibrillation (OR, 1.18; 95% CI, 1.13 to 1.25), and diabetes (OR, 1.13; 95% CI, 1.07 to 1.18) were significantly associated with ischemic stroke. Importantly, higher education (OR, 0.69; 95% CI, 0.60 to 0.79) decreased the risk of ischemic stroke. Higher systolic BP (OR, 1.06; 95% CI, 1.02 to 1.10), pulse pressure (OR, 1.08; 95% CI, 1.02 to 1.14), diabetes (OR, 1.28; 95% CI, 1.13 to 1.45), and coronary artery disease (OR, 1.58; 95% CI, 1.25 to 2.00) could cause LAD. Atrial fibrillation could cause CE (OR, 1.90; 95% CI, 1.71 to 2.11). For SVD, higher systolic BP (OR, 1.04; 95% CI, 1.00 to 1.07), diastolic BP (OR, 1.06; 95% CI, 1.01 to 1.12), and diabetes (OR, 1.22; 95% CI, 1.10 to 1.36) were causal factors.

          Conclusions

          The study revealed elements of the exposome causally linked to ischemic stroke and its subtypes, including conventional causal risk factors and novel protective factors such as higher education.

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          Most cited references32

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          Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

          Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
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            Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

            ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.
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              The UK Biobank resource with deep phenotyping and genomic data

              The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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                Author and article information

                Journal
                J Stroke
                J Stroke
                JOS
                Journal of Stroke
                Korean Stroke Society
                2287-6391
                2287-6405
                May 2022
                31 May 2022
                : 24
                : 2
                : 236-244
                Affiliations
                [a ]Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
                [b ]Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
                [c ]Genetics and Aging Research Unit, McCance Center for Brain Health, Mass General Institute for Neurodegenerative Diseases (MIND), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
                [d ]Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
                Author notes
                Correspondence: Jin-Tai Yu Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai 200040, China Tel: +86-21-52888163 Fax: +86-21-62483421 E-mail: yu-jintai@ 123456163.com
                Co-correspondence: Qiang Dong Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai 200040, China Tel: +86-21-52888163 Fax: +86-21-62483421 E-mail: dong_qiang@ 123456fudan.edu.cn
                [*]

                These authors contributed equally to the manuscript as first author.

                Author information
                http://orcid.org/0000-0002-4387-2461
                http://orcid.org/0000-0002-7686-0547
                Article
                jos-2021-01340
                10.5853/jos.2021.01340
                9194538
                35677978
                0c228310-6302-44e3-b6d1-9c29a7b0ddf3
                Copyright © 2022 Korean Stroke Society

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 April 2021
                : 29 October 2021
                : 2 November 2021
                Categories
                Original Article

                stroke,exposome,mendelian randomization analysis
                stroke, exposome, mendelian randomization analysis

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