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      Causal relationship between breakfast skipping and bone mineral density: a two-sample Mendelian randomized study

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          Abstract

          Objective

          To explore the causal association between breakfast skipping and bone mineral density (BMD) through two-sample Mendelian randomisation (MR) analysis.

          Methods

          A two-sample MR approach was adopted to explore the causal relationship of breakfast skipping with BMDs (across three skeletal sites and five age groups). Publicly available genome-wide association study summary data were used for MR analysis. We used five methods to estimate the causal associations between breakfast skipping and BMDs: inverse-variance weighting (IVW), MR-Egger, weighted median, simple mode, and weighted mode. IVW was used for the main analysis and the remaining four methods were used as supplementary analyses. The heterogeneity of the MR results was determined using IVW and MR-Egger methods. The pleiotropy of the MR results was determined using MR-Egger intercept. Furthermore, a leave-one-out test was performed to determine whether the MR results were affected by a single nucleotide polymorphism.

          Results

          With the IVW method, we did not find any causal relationship between breakfast skipping and forearm, femoral neck, and lumbar spine BMD. Subsequently, when we included BMD data stratified by five different age groups in the analysis, the results showed that there was no apparent causal effect between breakfast skipping and age-stratified BMD. This finding was supported by all four supplementary methods (P > 0.05 for all methods). No heterogeneity or horizontal pleiotropy was detected in any of the analyses (P > 0.05). The leave-one-out tests conducted in the analyses did not identify any single nucleotide polymorphism that could have influenced the MR results, indicating the reliability of our findings.

          Conclusion

          No causal effect was found between breakfast skipping and BMD (across three skeletal sites and five age groups).

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          Most cited references64

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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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              Interpreting findings from Mendelian randomization using the MR-Egger method

              Mendelian randomization-Egger (MR-Egger) is an analysis method for Mendelian randomization using summarized genetic data. MR-Egger consists of three parts: (1) a test for directional pleiotropy, (2) a test for a causal effect, and (3) an estimate of the causal effect. While conventional analysis methods for Mendelian randomization assume that all genetic variants satisfy the instrumental variable assumptions, the MR-Egger method is able to assess whether genetic variants have pleiotropic effects on the outcome that differ on average from zero (directional pleiotropy), as well as to provide a consistent estimate of the causal effect, under a weaker assumption—the InSIDE (INstrument Strength Independent of Direct Effect) assumption. In this paper, we provide a critical assessment of the MR-Egger method with regard to its implementation and interpretation. While the MR-Egger method is a worthwhile sensitivity analysis for detecting violations of the instrumental variable assumptions, there are several reasons why causal estimates from the MR-Egger method may be biased and have inflated Type 1 error rates in practice, including violations of the InSIDE assumption and the influence of outlying variants. The issues raised in this paper have potentially serious consequences for causal inferences from the MR-Egger approach. We give examples of scenarios in which the estimates from conventional Mendelian randomization methods and MR-Egger differ, and discuss how to interpret findings in such cases. Electronic supplementary material The online version of this article (doi:10.1007/s10654-017-0255-x) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Journal
                Front Endocrinol (Lausanne)
                Front Endocrinol (Lausanne)
                Front. Endocrinol.
                Frontiers in Endocrinology
                Frontiers Media S.A.
                1664-2392
                07 November 2023
                2023
                : 14
                : 1200892
                Affiliations
                [1] 1 The First School of Clinical Medicine, Zhejiang Chinese Medical University , Hangzhou, Zhejiang, China
                [2] 2 Alberta Institute, Wenzhou Medical University , Wenzhou, China
                [3] 3 Orthopedic Department, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine) , Hangzhou, China
                Author notes

                Edited by: Giacomina Brunetti, University of Bari Aldo Moro, Italy

                Reviewed by: Sisi Cao, San Diego State University, United States; Flavia Prodam, University of Eastern Piedmont, Italy

                *Correspondence: Kun Tian, max612@ 123456163.com ; Rujie Zhuang, rujiezhuang@ 123456163.com

                †Present address: Rujie Zhuang, Orthopedic Department, Quzhou Traditional Chinese Medicine (TCM) Hospital at the Junction of Four Provinces Affiliated to Zhejiang Chinese Medical University, Quzhou, China

                ‡These authors have contributed equally to this work and share first authorship

                §These authors have contributed equally to this work and share senior authorship

                Article
                10.3389/fendo.2023.1200892
                10660815
                38027166
                1cc897eb-5d2f-4d60-9b19-fd447f68f80b
                Copyright © 2023 Yu, Zhuang, Guo, Zhou, Chen, Wang, Li, Zhu, Zhuang and Tian

                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.

                History
                : 05 April 2023
                : 11 October 2023
                Page count
                Figures: 4, Tables: 3, Equations: 0, References: 64, Pages: 11, Words: 4580
                Funding
                Funded by: Zhejiang Traditional Chinese Medicine Administration , doi 10.13039/501100012175;
                This work was supported by the national administration of traditional Chinese medicine, science and technology department and Zhejiang administration of traditional Chinese medicine, Jointly-funded program (Grant number: GZY-ZJ-KJ-23040).
                Categories
                Endocrinology
                Original Research
                Custom metadata
                Bone Research

                Endocrinology & Diabetes
                genetics,mendelian randomization,causal relationship,bone mineral density,breakfast skipping

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