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      Mendelian randomization reveals association between retinal thickness and non-motor symptoms of Parkinson’s disease

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          Abstract

          Retinal thickness is related to Parkinson’s disease (PD), but its association with the severity of PD is still unclear. We conducted a Mendelian randomized (MR) study to explore the association between retinal thickness and PD. For the two-sample MR analysis, the summary statistics obtained from genome-wide association studies on the thickness of Retinal nerve fiber layer (RNFL) and ganglion cell inner plexiform layer (GCIPL) were employed as exposure, while the summary statistics associated with PD were used as the outcome. The primary approach utilized was inverse variance weighted. To correct for multiple testing, the false discovery rate (FDR) was employed. For sensitivity analysis, an array of robust MR methods was utilized. We found genetically predicted significant association between reduced RNFL thickness and a reduced risk of constipation in PD (odds ratio [OR] = 0.854, 95% confidence interval [CI] (0.782, 0.933), P < 0.001, FDR-corrected P = 0.018). Genetically predicted reduced RNFL thickness was associated with a reduced Unified Parkinson’s Disease Rating Scale total score ( β = −0.042, 95% CI (−0.079, 0.005), P = 0.025), and reduced GCIPL thickness was associated with a lower risk of constipation (OR = 0.901, 95% CI (0.821, 0.988), P = 0.027) but a higher risk of depression (OR = 1.103, 95% CI (1.016, 1.198), P = 0.020), insomnia (OR = 1.090, 95% CI (1.013, 1.172), P = 0.021), and rapid eye movement sleep behaviour disorder (RBD) (OR = 1.198, 95% CI (1.061, 1.352), P = 0.003). In conclusion, we identify an association between retinal thickness and non-motor symptoms (constipation, depression, insomnia and RBD) in PD, highlighting the potential of retinal thickness as a biomarker for PD nonmotor symptoms.

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          Most cited references78

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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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              Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases

              Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the ‘no horizontal pleiotropy’ assumption can cause severe bias in MR. We developed the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that MR-PRESSO is best suited when horizontal pleiotropy occurs in <50% of instruments. Next, we applied MR-PRESSO, along with several other MR tests to complex traits and diseases, and found that horizontal pleiotropy: (i) was detectable in over 48% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from −131% to 201%; (iii) induced false positive causal relationships in up to 10% of relationships; and (iv) can be corrected in some but not all instances.
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                Author and article information

                Contributors
                tan.eng.king@sgh.com.sg
                wqdennis@hotmail.com , denniswq@yahoo.com
                Journal
                NPJ Parkinsons Dis
                NPJ Parkinsons Dis
                NPJ Parkinson's Disease
                Nature Publishing Group UK (London )
                2373-8057
                13 December 2023
                13 December 2023
                2023
                : 9
                : 163
                Affiliations
                [1 ]Department of Neurology, Zhujiang Hospital of Southern Medical University, ( https://ror.org/02mhxa927) Guangzhou, Guangdong 510282 P.R. China
                [2 ]Clinical Research Centre, Orthopedic Centre, Zhujiang Hospital of Southern Medical University, ( https://ror.org/02mhxa927) Guangzhou, Guangdong 510282 P.R. China
                [3 ]School of Medical, Indigenous and Health Sciences, and Molecular Horizons, University of Wollongong, ( https://ror.org/00jtmb277) Wollongong, Australia
                [4 ]GRID grid.437123.0, ISNI 0000 0004 1794 8068, Centre of Reproduction, Development & Aging and Institute of Translation Medicine, Faculty of Health Sciences, , University of Macau, ; Avenida de Universidade, Taipa, Macau China
                [5 ]Division of Biostatistics and Health Services Research, Department of Population and Quantitative Health Sciences, T.H. Chan School of Medicine, UMass Chan Medical School, ( https://ror.org/0464eyp60) Massachusetts, 01605 USA
                [6 ]Department of Neurology, National Taiwan University Hospital, ( https://ror.org/03nteze27) Taipei, Taiwan
                [7 ]GRID grid.428397.3, ISNI 0000 0004 0385 0924, Department of Neurology, National Neuroscience Institute, , Singapore General Hospital, Singapore; Duke-NUS Medical School, ; Singapore, Singapore
                Author information
                http://orcid.org/0000-0003-1147-5741
                http://orcid.org/0000-0001-8566-7573
                http://orcid.org/0000-0003-2986-4980
                http://orcid.org/0000-0001-9928-4805
                http://orcid.org/0000-0002-3882-6540
                Article
                611
                10.1038/s41531-023-00611-z
                10719335
                38092812
                759879e1-c3b7-4153-9661-930c62718638
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 25 June 2023
                : 24 November 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 82071414
                Award ID: 82171253
                Award Recipient :
                Funded by: Scientific Research Foundation of Guangzhou(Grant Reference Number:202206010005)
                Funded by: National Science Foundation of Guangdong Province of Chian(Grant Reference Number: 2022A1515010522)
                Funded by: Guangdong Medical Science and Technology Research Foundation Project(Grant Reference Number:A2020305)
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                © Springer Nature Limited 2023

                neurodegeneration,parkinson's disease,epidemiology
                neurodegeneration, parkinson's disease, epidemiology

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