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      The relationship between emotional disorders and heart rate variability: A Mendelian randomization study

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      Journal: PLOS ONE
      Publisher: Public Library of Science

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          Abstract

          Objective

          Previous studies have shown that emotional disorders are negatively associated with heart rate variability (HRV), but the potential causal relationship between genetic susceptibility to emotional disorders and HRV remains unclear. We aimed to perform a Mendelian randomization (MR) study to investigate the potential association between emotional disorders and HRV.

          Methods

          The data used for this study were obtained from publicly available genome-wide association study datasets. Five models, including the inverse variance weighted model (IVW), the weighted median estimation model (WME), the weighted model-based method (WM), the simple model (SM) and the MR–Egger regression model (MER), were utilized for MR. The leave-one-out sensitivity test, MR pleiotropy residual sum and outlier test (MR-PRESSO) and Cochran’s Q test were used to confirm heterogeneity and pleiotropy.

          Results

          MR analysis revealed that genetic susceptibility to broad depression was negatively correlated with HRV (pvRSA/HF) (OR = 0.380, 95% CI 0.146–0.992; p = 0.048). However, genetic susceptibility to irritability was positively correlated with HRV (pvRSA/HF, SDNN) (OR = 2.017, 95% CI 1.152–3.534, p = 0.008) (OR = 1.154, 95% CI 1.000–1.331, p = 0.044). Genetic susceptibility to anxiety was positively correlated with HRV (RMSSD) (OR = 2.106, 95% CI 1.032–4.299; p = 0.041). No significant directional pleiotropy or heterogeneity was detected. The accuracy and robustness of these findings were confirmed through a sensitivity analysis.

          Conclusions

          Our MR study provides genetic support for the causal effects of broad depression, irritable mood, and anxiety on HRV.

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          Most cited references54

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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

                Contributors
                Xu Luo: Role: Writing – original draftRole: Writing – review & editing
                Rui Wang: Role: Data curation
                YunXiang Zhou: Role: Methodology
                Wen Xie:
                ORCID: https://orcid.org/0009-0003-6198-4293
                Role: ConceptualizationRole: Writing – review & editing
                Simone Battaglia: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS One
                Journal ID (publisher-id): plos
                Title: PLOS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 7 March 2024
                Publication date Collection: 2024
                Volume: 19
                Issue: 3
                Electronic Location Identifier: e0298998
                Affiliations
                [1 ] College of Clinical Medicine, University of Traditional Chinese Medicine, Chengdu, Sichuan, China
                [2 ] Department of Cardiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
                University of Bologna: Universita di Bologna, ITALY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0009-0003-6198-4293
                Article
                Publisher ID: PONE-D-23-27049
                DOI: 10.1371/journal.pone.0298998
                PMC ID: 10919610
                PubMed ID: 38451975
                SO-VID: 206ab5b1-380d-45c7-aee4-1504616e923e
                Copyright © © 2024 Luo et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 23 August 2023
                Date accepted : 4 February 2024
                Page count
                Figures: 2, Tables: 3, Pages: 13
                Funding
                Funded by: the Key Project of Sichuan Provincial Department of Science and Technology
                Award ID: 2022YFS0395
                Funded by: State Administration of Traditional Chinese Medicine
                Award ID: 2023MS522
                This research was financially supported by the Key Project of Sichuan Provincial Department of Science and Technology’funding (NO. 2022YFS0395). State Administration of Traditional Chinese Medicine (NO. 2023MS522.
                Categories
                Subject: Research Article
                Subject: Biology and Life Sciences
                Subject: Psychology
                Subject: Emotions
                Subject: Social Sciences
                Subject: Psychology
                Subject: Emotions
                Subject: Medicine and Health Sciences
                Subject: Mental Health and Psychiatry
                Subject: Mood Disorders
                Subject: Depression
                Subject: Medicine and Health Sciences
                Subject: Cardiology
                Subject: Heart Rate
                Subject: Medicine and Health Sciences
                Subject: Mental Health and Psychiatry
                Subject: Neuropsychiatric Disorders
                Subject: Anxiety Disorders
                Subject: Medicine and Health Sciences
                Subject: Mental Health and Psychiatry
                Subject: Neuroses
                Subject: Anxiety Disorders
                Subject: Biology and Life Sciences
                Subject: Computational Biology
                Subject: Genome Analysis
                Subject: Genome-Wide Association Studies
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Genomics
                Subject: Genome Analysis
                Subject: Genome-Wide Association Studies
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Human Genetics
                Subject: Genome-Wide Association Studies
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Single Nucleotide Polymorphisms
                Subject: Medicine and Health Sciences
                Subject: Mental Health and Psychiatry
                Subject: Mood Disorders
                Subject: Bipolar Disorder
                Subject: Research and Analysis Methods
                Subject: Mathematical and Statistical Techniques
                Subject: Statistical Methods
                Subject: Instrumental Variable Analysis
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Statistics
                Subject: Statistical Methods
                Subject: Instrumental Variable Analysis
                Custom metadata
                Data Availability All relevant datas are within the manuscript and its Supporting Information files.

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