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      Mendelian randomization study of diabetes and dementia in the Million Veteran Program

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          Abstract

          Introduction

          Diabetes and dementia are diseases of high health‐care burden worldwide. Individuals with diabetes have 1.4 to 2.2 times higher risk of dementia. Our objective was to evaluate evidence of causality between these two common diseases.

          Methods

          We conducted a one‐sample Mendelian randomization (MR) analysis in the US Department of Veterans Affairs Million Veteran program. The study included 334,672 participants ≥65 years of age with type 2 diabetes and dementia case‐control status and genotype data.

          Results

          For each standard deviation increase in genetically predicted diabetes, we found increased odds of three dementia diagnoses in non‐Hispanic White participants (all‐cause: odds ratio [OR] = 1.07 [1.05–1.08], P = 3.40E‐18; vascular: OR = 1.11 [1.07–1.15], P = 3.63E‐09, Alzheimer's disease [AD]: OR = 1.06 [1.02–1.09], P = 6.84E‐04) and non‐Hispanic Black participants (all‐cause: OR = 1.06 [1.02–1.10], P = 3.66E‐03, vascular: OR = 1.11 [1.04–1.19], P = 2.20E‐03, AD: OR = 1.12 [1.02–1.23], P = 1.60E‐02) but not in Hispanic participants (all P > 0.05).

          Discussion

          We found evidence of causality between diabetes and dementia using a one‐sample MR study, with access to individual level data, overcoming limitations of prior studies using two‐sample MR techniques.

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          Most cited references45

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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
                (View ORCID Profile)
                Journal
                Alzheimer's & Dementia
                Alzheimer's &amp; Dementia
                Wiley
                1552-5260
                1552-5279
                October 2023
                July 07 2023
                October 2023
                : 19
                : 10
                : 4367-4376
                Affiliations
                [1 ] VA Eastern Colorado Healthcare System Aurora Colorado USA
                [2 ] Department of Biomedical Informatics University of Colorado Anschutz Medical Campus Aurora Colorado USA
                [3 ] Department of Epidemiology University of Colorado Anschutz Medical Campus Aurora Colorado USA
                [4 ] National Center for PTSD Behavioral Sciences Division VA Boston Healthcare System Boston Massachusetts USA
                [5 ] Boston University Schools of Medicine and Public Health Boston Massachusetts USA
                [6 ] VA Boston Healthcare System Boston Massachusetts USA
                [7 ] Salt Lake City VA VA Informatics &amp; Computing Infrastructure Salt Lake City Utah USA
                [8 ] School of Medicine University of Utah Salt Lake City Utah USA
                [9 ] Corporal Michael J. Crescenz VA Medical Center Philadelphia Pennsylvania USA
                [10 ] University of Pennsylvania Philadelphia Pennsylvania USA
                [11 ] Atlanta VA Health Care System Decatur Georgia USA
                [12 ] Division of Endocrinology and Metabolism Department of Medicine Emory University School of Medicine Atlanta Georgia USA
                [13 ] Center of Excellence for Stress and Mental Health VA San Diego Healthcare System San Diego California USA
                [14 ] Center for Behavior Genetics of Aging School of Medicine University of California San Diego, La Jolla California USA
                Article
                10.1002/alz.13373
                37417779
                78704afb-6805-4369-acbe-1b03bc6a8f20
                © 2023

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