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      Dried fruit intake and lower risk of type 2 diabetes: a two-sample mendelian randomization study

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          Abstract

          Background

          Previous studies have shown controversy about whether dried fruit intake is associated with type 2 diabetes. This study aimed to examine the potential causal effect of dried fruit intake on type 2 diabetes by conducting a two-sample Mendelian randomization study.

          Methods

          We used genome-wide association study (GWAS) summary statistics for MR analysis to explore the causal association of dried fruit intake with T2D. The inverse-variance weighted (IVW) method was used as the main analytical method for MR analysis. In addition, the MR-Egger method and the weighted median method were applied to supplement the IVW method. Furthermore, Cochrane’s Q test, MR-Egger intercept test, and leave-one-out analysis were used to perform sensitivity analysis. The funnel plot was used to assess publication bias.

          Results

          The results from the IVW analysis indicated that dried fruit intake could reduce the risk of T2D [odds ratio (OR) = 0.392, 95% confidence interval (CI): 0.241–0.636, p-value = 0.0001]. In addition, the result of additional method Weighted median is parallel to the effects estimated by IVW. Furthermore, the sensitivity analysis illustrates that our MR analysis was unaffected by heterogeneity and horizontal pleiotropy. Finally, the results of the leave-one-out method showed the robustness of our MR results. And the funnel plot shows a symmetrical distribution.

          Conclusion

          Our study provides evidence for the benefits of dried fruit intake on T2D. Therefore, a reasonable consumption of dried fruit may provide primary prevention.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12986-024-00813-z.

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          Most cited references43

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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
                15652386716@163.com
                360008222@qq.com
                Journal
                Nutr Metab (Lond)
                Nutr Metab (Lond)
                Nutrition & Metabolism
                BioMed Central (London )
                1743-7075
                10 July 2024
                10 July 2024
                2024
                : 21
                : 46
                Affiliations
                [1 ]Honghui Hospital, Xi’an Jiaotong University, ( https://ror.org/017zhmm22) Xi’an, Shannxi 710054 China
                [2 ]Truma Rehabilitation Department, Honghui Hospital, Xi’an Jiaotong University, ( https://ror.org/017zhmm22) Xi’an, Shannxi 710054 China
                Article
                813
                10.1186/s12986-024-00813-z
                11234600
                38987806
                99bf3ab4-9d0c-4be8-a9cd-8132978434c3
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 2 March 2024
                : 18 June 2024
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                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Nutrition & Dietetics
                type 2 diabetes,dried fruit,causal relationship,incidence risk,single-nucleotide polymorphisms,mendelian randomization

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