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      Causality between Celiac disease and kidney disease: A Mendelian Randomization Study

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          Abstract

          Celiac disease, characterized as an autoimmune disorder, possesses the capacity to affect multiple organs and systems. Earlier research has indicated an increased risk of kidney diseases associated with celiac disease. However, the potential causal relationship between genetic susceptibility to celiac disease and the risk of kidney diseases remains uncertain. We conducted Mendelian randomization analysis using nonoverlapping European population data, examining the link between celiac disease and 10 kidney traits in whole-genome association studies. We employed the inverse variance-weighted method to enhance statistical robustness, and results’ reliability was reinforced through rigorous sensitivity analysis. Mendelian randomization analysis revealed a genetic susceptibility of celiac disease to an increased risk of immunoglobulin A nephropathy (OR = 1.44; 95% confidence interval [CI] = 1.17–1.78; P = 5.7 × 10 −4), chronic glomerulonephritis (OR = 1.15; 95% CI = 1.08–1.22; P = 2.58 × 10 −5), and a decline in estimated glomerular filtration rate (beta = −0.001; P = 2.99 × 10 −4). Additionally, a potential positive trend in the causal relationship between celiac disease and membranous nephropathy (OR = 1.37; 95% CI = 1.08–1.74; P = 0.01) was observed. Sensitivity analysis indicated the absence of pleiotropy. This study contributes novel evidence establishing a causal link between celiac disease and kidney traits, indicating a potential association between celiac disease and an elevated risk of kidney diseases. The findings provide fresh perspectives for advancing mechanistic and clinical research into kidney diseases associated with celiac disease.

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          Most cited references50

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          The MR-Base platform supports systematic causal inference across the human phenome

          Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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            Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data

            Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene–gene interactions, linkage disequilibrium, and ‘weak instruments’ on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized data give similar estimates and precision to the two-stage least squares method for individual-level data, even when there are gene–gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P-value in a linear regression of the risk factor for each variant is less than , then weak instrument bias will be small. We use these methods to estimate the causal association of low-density lipoprotein cholesterol (LDL-C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL-C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual-level data, although the necessary assumptions cannot be so fully assessed.
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              Avoiding bias from weak instruments in Mendelian randomization studies.

              Mendelian randomization is used to test and estimate the magnitude of a causal effect of a phenotype on an outcome by using genetic variants as instrumental variables (IVs). Estimates of association from IV analysis are biased in the direction of the confounded, observational association between phenotype and outcome. The magnitude of the bias depends on the F-statistic for the strength of relationship between IVs and phenotype. We seek to develop guidelines for the design and analysis of Mendelian randomization studies to minimize bias. IV analysis was performed on simulated and real data to investigate the effect on bias of size of study, number and choice of instruments and method of analysis. Bias is shown to increase as the expected F-statistic decreases, and can be reduced by using parsimonious models of genetic association (i.e. not over-parameterized) and by adjusting for measured covariates. Using data from a single study, the causal estimate of a unit increase in log-transformed C-reactive protein on fibrinogen (μmol/l) is shown to increase from -0.005 (P = 0.99) to 0.792 (P = 0.00003) due to injudicious choice of instrument. Moreover, when the observed F-statistic is larger than expected in a particular study, the causal estimate is more biased towards the observational association and its standard error is smaller. This correlation between causal estimate and standard error introduces a second source of bias into meta-analysis of Mendelian randomization studies. Bias can be alleviated in meta-analyses by using individual level data and by pooling genetic effects across studies. Weak instrument bias is of practical importance for the design and analysis of Mendelian randomization studies. Post hoc choice of instruments, genetic models or data based on measured F-statistics can exacerbate bias. In particular, the commonly cited rule of thumb that F > 10 avoids bias in IV analysis is misleading.
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                Author and article information

                Contributors
                Journal
                Medicine (Baltimore)
                Medicine (Baltimore)
                MD
                Medicine
                Lippincott Williams & Wilkins (Hagerstown, MD )
                0025-7974
                1536-5964
                30 August 2024
                30 August 2024
                : 103
                : 35
                : e39465
                Affiliations
                [a ]Clinical College of Chinese Medicine, Hubei University of Chinese Medicine, Hubei, Wuhan, China
                [b ]Department of Traditional Chinese Medicine, Renmin Hospital of Wuhan University, Hubei, Wuhan, China
                [c ]Department of Nephrology, Renmin Hospital of Wuhan University, Hubei, Wuhan, China
                [d ]First Clinical College, Hubei University of Chinese Medicine, Hubei, Wuhan, China.
                Author notes
                [* ]Correspondence: Jun Yuan, Wuchang District, Wuhan, Hubei, China (e-mail: yjun_92@ 123456hbtcm.edu.cn ).
                Author information
                https://orcid.org/0000-0002-9634-5761
                Article
                MD-D-24-06162 00060
                10.1097/MD.0000000000039465
                11365674
                39213254
                6c158565-932a-49df-89e5-14883c860159
                Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

                This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 06 June 2024
                : 03 August 2024
                : 06 August 2024
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Award ID: 82074364
                Award Recipient : Jun Yuan
                Funded by: Wuhan Science and Technology Project, doi 10.13039/501100018583;
                Award ID: 2022020801020506
                Award Recipient : Jun Yuan
                Categories
                5200
                Research Article
                Observational Study
                Custom metadata
                TRUE
                T

                causal relationship,celiac disease,genome-wide association studies,kidney disease,mendelian randomization analysis

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