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A Multivariate Genome-wide Association Study of Psycho-cardiometabolic Multimorbidity

Vilte Baltramonaityte, Jean-Baptiste Pingault, Charlotte A. M. Cecil, Priyanka Choudhary, Marjo-Riitta Järvelin, Brenda W. J. H. Penninx, Janine Felix, Sylvain Sebert, Yuri Milaneschi, Esther Walton

Abstract

Coronary artery disease (CAD), type 2 diabetes (T2D) and depression are among the leading causes of chronic morbidity and mortality worldwide. Epidemiological studies indicate a substantial degree of multimorbidity, which may be explained by shared genetic influences. However, research exploring the presence of pleiotropic variants and genes common to CAD, T2D and depression is lacking. The present study aimed to identify genetic variants with effects on cross-trait liability to psycho-cardiometabolic diseases. We used genomic structural equation modelling to perform a multivariate genome-wide association study of multimorbidity (Neffective = 562,507), using summary statistics from univariate genome-wide association studies for CAD, T2D and major depression. CAD was moderately genetically correlated with T2D (rg = 0.39, P = 2e-34) and weakly correlated with depression (rg = 0.13, P = 3e-6). Depression was weakly correlated with T2D (rg = 0.15, P = 4e-15). The latent multimorbidity factor explained the largest proportion of variance in T2D (45%), followed by CAD (35%) and depression (5%).

Introduction

Depression, coronary artery disease (CAD) and type 2 diabetes (T2D) are important public health issues. Whilst each of these chronic disorders alone represent a major global burden, multimorbidity between them presents an additional challenge for healthcare systems [1–3]. Epidemiological studies suggest that individuals with depression have an 80–90% greater risk of cardiovascular morbidity and mortality [4] and a 32–60% higher risk of T2D [5,6] than individuals without depression. The reverse association has also been observed, with approximately 40% of people with CAD and 18–28% of people with diabetes either meeting the criteria for depression or experiencing depressive symptoms [7,8]. Notably, life expectancy in individuals with a diagnosis of depression is reduced [9], which may be partially accounted for by the co-occurrence with physical health diseases [10,11]. This emphasizes the importance of understanding the mechanisms through which mental and physical diseases may co-occur.

Materials and method

This research was conducted using the UK Biobank resource, application number 65769. The UK Biobank study was conducted under generic approval from the National Health Service (NHS) Research Ethics Service. The study protocol used by 23andMe was approved by an external Association for Accreditation of Human Research Protection Programs (AAHRPP)-accredited institutional review board. All cohorts contributing to the present study obtained written informed consent from all participants. Additionally, ethical approval for the present study was obtained from the University of Bath (PREC: 20–195).

Results

Heritability estimates (reported on the liability scale) were similar across all three univariate traits: 0.070 (SE = 0.005) for CAD, 0.162 (SE = 0.008) for T2D, and 0.064 (SE = 0.002) for depression. LD score regression identified a significant moderate correlation between CAD and T2D (rg = 0.39, SE = 0.03, P = 2e-34), and significant but weak correlations between CAD and depression (rg = 0.13, SE = 0.03, P = 3e-6), and between depression and T2D (rg = 0.15, SE = 0.02, P = 4e-15). For heritability Z scores, see S2 Table.

Discussion

The present study explored the multivariate genetic architecture of major depression, T2D, and CAD. Assessment of bivariate genetic correlations suggested a shared genetic architecture between all three disorders. The strongest correlation was observed between CAD and T2D (rg = 0.39), with weaker correlations detected between depression and CAD (rg = 0.13) and depression and T2D (rg = 0.15), suggesting a more distinct genetic basis. This was in line with findings from previous studies which reported genetic correlations of a similar magnitude [44–46].

Acknowledgments

We are grateful to the UK Biobank and all its voluntary participants. We also thank all 23andMe research participants who made this study possible.

Citation: Baltramonaityte V, Pingault J-B, Cecil CAM, Choudhary P, Järvelin M-R, Penninx BWJH, et al. (2023) A multivariate genome-wide association study of psycho-cardiometabolic multimorbidity. PLoS Genet 19(6): e1010508. https://doi.org/10.1371/journal.pgen.1010508

Editor: Heather J. Cordell, Newcastle University, UNITED KINGDOM

Received: November 3, 2022; Accepted: June 12, 2023; Published: June 30, 2023

Copyright: © 2023 Baltramonaityte et al. 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.

Data Availability: To access summary statistics for psycho-cardiometabolic multimorbidity, a data transfer agreement is required with 23andMe (dataset-request@23andMe.com) before making a request to the University of Bath Research Data Archive (https://doi.org/10.15125/BATH-01179). Further information regarding access to 23andMe is available at: https://research.23andme.com/collaborate/. Summary statistics for the top 10,000 SNPs generated during this study are available from the University of Bath Research Data Archive: https://doi.org/10.15125/BATH-01179. Summary statistics for coronary artery disease can be obtained from: http://www.cardiogramplusc4d.org. Summary statistics for type 2 diabetes can be obtained from: http://diagram-consortium.org/downloads.html. To access summary statistics for depression, a data transfer agreement is required from 23andMe (dataset-request@23andMe.com) before a request is made to David Howard (D.Howard@ed.ac.uk), as described in: https://www.nature.com/articles/s41593-018-0326-7. UK Biobank data can be accessed via an application process outlined here: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access. Code underlying our analyses can be found on GitHub: https://github.com/VilteBaltra/Psycho-cardiometabolic-multimorbidity.

Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 848158 (EarlyCause). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors declare no competing interests.

 

Source:  https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1010508#ack

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