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      Causality between Sarcopenia and Depression: A Bidirectional Mendelian Randomization Study

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          Abstract

          Background:

          Numerous observational studies have suggested a correlation between sarcopenia and depression, but the nature of this relationship requires further investigation.

          Methods:

          This study employed bidirectional Mendelian randomization to explore this connection. Data from genome-wide association studies were used, encompassing measures of sarcopenia and mental factors, including depression and emotional states. The initial analysis concentrated on the impact of depression on sarcopenia, and then it examined the reverse relationship. The same methodology was applied to emotional data for validation.

          Results:

          The results indicated a reciprocal causation between sarcopenia and depression, even when emotional state data were considered. Various emotions can impact sarcopenia, and in turn, sarcopenia can affect emotions, except subjective well-being. These findings highlight a cyclic deterioration between sarcopenia and depression, with a link to negative emotions and a partially ameliorative effect of subjective well-being on sarcopenia.

          Conclusions:

          In summary, this study sheds light on the interplay between psychiatric factors and sarcopenia, offering insights into intervention and prevention strategies.

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          Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

          Jack Bowden, George Davey Smith, Stephen Burgess (2016)
          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

            Jack Bowden, George Davey Smith, Philip C. Haycock … (2017)
            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

              Marie Verbanck, Chia-Yen Chen, Benjamin M. Neale … (2018)
              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

                Journal
                Journal ID (nlm-ta): Actas Esp Psiquiatr
                Journal ID (iso-abbrev): Actas Esp Psiquiatr
                Journal ID (publisher-id): stme
                Title: Actas Españolas de Psiquiatría
                Publisher: Actas Españolas de Psiquiatría
                ISSN (Print): 1139-9287
                ISSN (Electronic): 1578-2735
                Publication date Collection: Jul-Aug 2024
                Publication date (Electronic): 05 August 2024
                Volume: 52
                Issue: 4
                Pages: 394-404
                Affiliations
                [1] 1Orthopedics and Traumatology Department II, Shangluo Traditional Chinese Medicine Hospital, 726000 Shangluo, Shaanxi, China
                [2] 2Spinal Ward of Orthopedic Hospital, The Affiliated Hospital of Shaanxi University of Chinese Medicine, 712000 Xianyang, Shaanxi, China
                [3] 3Nursing Department, The Affiliated Hospital of Shaanxi University of Chinese Medicine, 712000 Xianyang, Shaanxi, China
                Author notes
                *Corresponding author details: Xiaojie Sun, Spinal Ward of Orthopedic Hospital, The Affiliated Hospital of Shaanxi University of Chinese Medicine, 712000 Xianyang, Shaanxi, China. Email: sunxiaojie0925@ 123456163.com
                Article
                Publisher ID: 10.62641/aep.v52i4.1679
                DOI: 10.62641/aep.v52i4.1679
                PMC ID: 11319753
                PubMed ID: 39129686
                SO-VID: d422601d-a4b4-46c5-93c1-84184bd8c685
                Copyright © © 2024 Actas Españolas de Psiquiatría
                License:

                This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.

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                Subject: Article

                Keywords: mendelian randomization analysis,genome-wide association study,sarcopenia,depression,mental health,negative emotion,causation
                Data availability:
                Keywords: mendelian randomization analysis, genome-wide association study, sarcopenia, depression, mental health, negative emotion, causation

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