Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-19T09:16:22.704Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation of bi-directional causal association between depression and cardiovascular diseases: a Mendelian randomization study

Published online by Cambridge University Press:  09 October 2020

Gloria Hoi-Yee Li Open the ORCID record for Gloria Hoi-Yee Li [Opens in a new window] ,
Ching-Lung Cheung Open the ORCID record for Ching-Lung Cheung [Opens in a new window] ,
Albert Kar-Kin Chung ,
Bernard Man-Yung Cheung ,
Ian Chi-Kei Wong ,
Marcella Lei Yee Fok ,
Philip Chun-Ming Au  and
Pak-Chung Sham
Gloria Hoi-Yee Li
Affiliation:
Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong
Ching-Lung Cheung*
Affiliation:
Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
Albert Kar-Kin Chung
Affiliation:
Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
Bernard Man-Yung Cheung
Affiliation:
Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
Ian Chi-Kei Wong
Affiliation:
Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
Marcella Lei Yee Fok
Affiliation:
Central and North West London NHS Foundation Trust, London, UK Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
Philip Chun-Ming Au
Affiliation:
Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
Pak-Chung Sham
Affiliation:
Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong
*
Author for correspondence: Ching-Lung Cheung, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Depression and cardiovascular disease (CVD) are associated with each other but their relationship remains unclear. We aim to determine whether genetic predisposition to depression are causally linked to CVD [including coronary artery disease (CAD), myocardial infarction (MI), stroke and atrial fibrillation (AF)].

Methods

Using summary statistics from the largest genome-wide association studies (GWAS) or GWAS meta-analysis of depression (primary analysis: n = 500 199), broad depression (help-seeking behavior for problems with nerves, anxiety, tension or depression; secondary analysis: n = 322 580), CAD (n = 184 305), MI (n = 171 875), stroke (n = 446 696) and AF (n = 1 030 836), genetic correlation was tested between two depression phenotypes and CVD [MI, stroke and AF (not CAD as its correlation was previously confirmed)]. Causality was inferred between correlated traits by Mendelian Randomization analyses.

Results

Both depression phenotypes were genetically correlated with MI (depression: rG = 0.169; p = 9.03 × 10−9; broad depression: rG = 0.123; p = 1 × 10−4) and AF (depression: rG = 0.112; p = 7.80 × 10−6; broad depression: rG = 0.126; p = 3.62 × 10−6). Genetically doubling the odds of depression was causally associated with increased risk of CAD (OR = 1.099; 95% CI 1.031–1.170; p = 0.004) and MI (OR = 1.146; 95% CI 1.070–1.228; p = 1.05 × 10−4). Adjustment for blood lipid levels/smoking status attenuated the causality between depression and CAD/MI. Null causal association was observed for CVD on depression. A similar pattern of results was observed in the secondary analysis for broad depression.

Conclusions

Genetic predisposition to depression may have positive causal roles on CAD/MI. Genetic susceptibility to self-awareness of mood problems may be a strong causal risk factor of CAD/MI. Blood lipid levels and smoking may potentially mediate the causal pathway. Prevention and early diagnosis of depression are important in the management of CAD/MI.

Keywords

Cardiovascular diseasedepressiongeneticsMendelian randomization
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 9 , July 2022 , pp. 1765 - 1776
Check for updates
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahola-Olli, A. V., Wurtz, P., Havulinna, A. S., Aalto, K., Pitkanen, N., Lehtimaki, T., … Raitakari, O. T. (2017). Genome-wide association study identifies 27 loci influencing concentrations of circulating cytokines and growth factors. American Journal of Human Genetics, 100, 40–50. doi: 10.1016/j.ajhg.2016.11.007.CrossRefGoogle ScholarPubMed
Ambrose, J. A., & Barua, R. S. (2004). The pathophysiology of cigarette smoking and cardiovascular disease: An update. Journal of the American College of Cardiology, 43, 1731–1737. doi: 10.1016/j.jacc.2003.12.047.CrossRefGoogle ScholarPubMed
Atlantis, E., Lange, K., Goldney, R. D., Martin, S., Haren, M. T., Taylor, A., … Wittert, G. A. (2011). Specific medical conditions associated with clinically significant depressive symptoms in men. Social Psychiatry and Psychiatric Epidemiology, 46, 1303–1312. doi: 10.1007/s00127-010-0302-3.CrossRefGoogle ScholarPubMed
Bacon, S. L. (2019). Stress, psychiatric disorders, and cardiovascular disease. BMJ, 365, l1577. doi: 10.1136/bmj.l1577.CrossRefGoogle Scholar
Barlinn, K., Kepplinger, J., Puetz, V., Illigens, B. M., Bodechtel, U., & Siepmann, T. (2015). Exploring the risk-factor association between depression and incident stroke: A systematic review and meta-analysis. Neuropsychiatric Disease and Treatment, 11, 1–14. doi: 10.2147/NDT.S63904.Google ScholarPubMed
Berkman, L. F., Blumenthal, J., Burg, M., Carney, R. M., Catellier, D., & Cowan, M. J., … Enhancing Recovery in Coronary Heart Disease Patients, I. (2003). Effects of treating depression and low perceived social support on clinical events after myocardial infarction: The enhancing recovery in coronary heart disease patients (ENRICHD) randomized trial. JAMA, 289, 3106–3116. doi: 10.1001/jama.289.23.3106.Google ScholarPubMed
Bowden, J., Davey Smith, G., & Burgess, S. (2015). Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression. International Journal of Epidemiology, 44, 512–525. doi: 10.1093/ije/dyv080.CrossRefGoogle ScholarPubMed
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016). Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genetic Epidemiology, 40, 304–314. doi: 10.1002/gepi.21965.CrossRefGoogle ScholarPubMed
Brion, M. J., Shakhbazov, K., & Visscher, P. M. (2013). Calculating statistical power in Mendelian randomization studies. International Journal of Epidemiology, 42, 1497–1501. doi: 10.1093/ije/dyt179.CrossRefGoogle ScholarPubMed
Bulik-Sullivan, B., Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P. R., … Neale, B. M. (2015a). An atlas of genetic correlations across human diseases and traits. Nature Genetics, 47, 1236–1241. doi: 10.1038/ng.3406.CrossRefGoogle Scholar
Bulik-Sullivan, B. K., Loh, P. R., Finucane, H. K., Ripke, S., Yang, J., Schizophrenia Working Group of the Psychiatric Genomics Consortium, … Neale, B. M. (2015b). LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47, 291–295. doi: 10.1038/ng.3211.CrossRefGoogle Scholar
Burgess, S., Butterworth, A., & Thompson, S. G. (2013). Mendelian randomization analysis with multiple genetic variants using summarized data. Genetic Epidemiology, 37, 658–665. doi: 10.1002/gepi.21758.CrossRefGoogle ScholarPubMed
Burgess, S., Davies, N. M., & Thompson, S. G. (2016). Bias due to participant overlap in two-sample Mendelian randomization. Genetic Epidemiology, 40, 597–608. doi: 10.1002/gepi.21998.CrossRefGoogle ScholarPubMed
Burgess, S., & Labrecque, J. A. (2018). Mendelian randomization with a binary exposure variable: Interpretation and presentation of causal estimates. European Journal of Epidemiology, 33, 947–952. doi: 10.1007/s10654-018-0424-6.CrossRefGoogle ScholarPubMed
Burgess, S., & Thompson, S. G. (2015). Multivariable Mendelian randomization: The use of pleiotropic genetic variants to estimate causal effects. American Journal of Epidemiology, 181, 251–260. doi: 10.1093/aje/kwu283.CrossRefGoogle ScholarPubMed
Burgess, S., Thompson, D. J., Rees, J. M. B., Day, F. R., Perry, J. R., & Ong, K. K. (2017). Dissecting causal pathways using Mendelian randomization with summarized genetic data: Application to Age at menarche and risk of breast cancer. Genetics, 207, 481–487. doi: 10.1534/genetics.117.300191.CrossRefGoogle ScholarPubMed
Carney, R. M., & Freedland, K. E. (2017). Depression and coronary heart disease. Nature Reviews Cardiology, 14, 145–155. doi: 10.1038/nrcardio.2016.181.CrossRefGoogle ScholarPubMed
Davies, N. M., Holmes, M. V., & Davey Smith, G. (2018). Reading Mendelian randomisation studies: A guide, glossary, and checklist for clinicians. BMJ, 362, k601. doi: 10.1136/bmj.k601.CrossRefGoogle ScholarPubMed
Emdin, C. A., Odutayo, A., Wong, C. X., Tran, J., Hsiao, A. J., & Hunn, B. H. (2016). Meta-analysis of anxiety as a risk factor for cardiovascular disease. The American Journal of Cardiology, 118, 511–519. doi: 10.1016/j.amjcard.2016.05.041.CrossRefGoogle ScholarPubMed
Enko, D., Brandmayr, W., Halwachs-Baumann, G., Schnedl, W. J., Meinitzer, A., & Kriegshauser, G. (2018). Prospective plasma lipid profiling in individuals with and without depression. Lipids in Health and Disease, 17, 149. doi: 10.1186/s12944-018-0796-3.CrossRefGoogle ScholarPubMed
Gafoor, R., Booth, H. P., & Gulliford, M. C. (2018). Antidepressant utilisation and incidence of weight gain during 10 years' follow-up: Population based cohort study. BMJ, 361, k1951. doi: 10.1136/bmj.k1951.CrossRefGoogle ScholarPubMed
Galling, B., Roldan, A., Nielsen, R. E., Nielsen, J., Gerhard, T., Carbon, M., … Correll, C. U. (2016). Type 2 diabetes mellitus in youth exposed to antipsychotics: A systematic review and meta-analysis. JAMA Psychiatry, 73, 247–259. doi: 10.1001/jamapsychiatry.2015.2923.CrossRefGoogle ScholarPubMed
Gan, Y., Gong, Y., Tong, X., Sun, H., Cong, Y., Dong, X., … Lu, Z. (2014). Depression and the risk of coronary heart disease: A meta-analysis of prospective cohort studies. BMC Psychiatry, 14, 371. doi: 10.1186/s12888-014-0371-z.CrossRefGoogle ScholarPubMed
Garg, P. K., O'Neal, W. T., Diez-Roux, A. V., Alonso, A., Soliman, E. Z., & Heckbert, S. (2019). Negative affect and risk of atrial fibrillation: MESA. Journal of the American Heart Association, 8, e010603. doi: 10.1161/JAHA.118.010603.CrossRefGoogle ScholarPubMed
GBD 2017 Disease and Injury Incidence and Prevalence Collaborators (2018). Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for the global burden of disease study 2017. Lancet (London, England), 392, 1789–1858. doi: 10.1016/S0140-6736(18)32279-7.CrossRefGoogle Scholar
Gill, D., James, N. E., Monori, G., Lorentzen, E., Fernandez-Cadenas, I., & Lemmens, R., … the, G. N. (2019). Genetically determined risk of depression and functional outcome after ischemic stroke. Stroke, 50, 2219–2222. doi: 10.1161/STROKEAHA.119.026089.CrossRefGoogle ScholarPubMed
Girardin, F. R., Gex-Fabry, M., Berney, P., Shah, D., Gaspoz, J. M., & Dayer, P. (2013). Drug-induced long QT in adult psychiatric inpatients: The 5-year cross-sectional ECG screening outcome in psychiatry study. The American Journal of Psychiatry, 170, 1468–1476. doi: 10.1176/appi.ajp.2013.12060860.CrossRefGoogle ScholarPubMed
Goren, A., Liu, X., Gupta, S., Simon, T. A., & Phatak, H. (2013). Quality of life, activity impairment, and healthcare resource utilization associated with atrial fibrillation in the US national health and wellness survey. PLoS One, 8, e71264. doi: 10.1371/journal.pone.0071264.CrossRefGoogle ScholarPubMed
Hartwig, F. P., Davies, N. M., Hemani, G., & Davey Smith, G. (2016). Two-sample Mendelian randomization: Avoiding the downsides of a powerful, widely applicable but potentially fallible technique. International Journal of Epidemiology, 45, 1717–1726. doi: 10.1093/ije/dyx028.CrossRefGoogle ScholarPubMed
Hoffmann, T. J., Ehret, G. B., Nandakumar, P., Ranatunga, D., Schaefer, C., Kwok, P. Y., … Risch, N. (2017). Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation. Nature Genetics, 49, 54–64. doi: 10.1038/ng.3715.CrossRefGoogle ScholarPubMed
Howard, D. M., Adams, M. J., Clarke, T. K., Hafferty, J. D., Gibson, J., Shirali, M., … McIntosh, A. M. (2019). Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature Neuroscience, 22, 343–352. doi: 10.1038/s41593-018-0326-7.CrossRefGoogle ScholarPubMed
Howard, D. M., Adams, M. J., Shirali, M., Clarke, T. K., Marioni, R. E., Davies, G., … McIntosh, A. M. (2018). Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nature Communications, 9, 1470. doi: 10.1038/s41467-018-03819-3.CrossRefGoogle ScholarPubMed
Howren, M. B., Lamkin, D. M., & Suls, J. (2009). Associations of depression with C-reactive protein, IL-1, and IL-6: A meta-analysis. Psychosomatic Medicine, 71, 171–186. doi: 10.1097/PSY.0b013e3181907c1b.CrossRefGoogle ScholarPubMed
Hyde, C. L., Nagle, M. W., Tian, C., Chen, X., Paciga, S. A., Wendland, J. R., … Winslow, A. R. (2016). Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nature Genetics, 48, 1031–1036. doi: 10.1038/ng.3623.CrossRefGoogle ScholarPubMed
Jostins, L. (2017). Mangrove: Risk prediction on trees. https://cran.r-project.org/web/packages/Mangrove/index.html.Google Scholar
Kamphuis, M. H., Geerlings, M. I., Tijhuis, M. A., Giampaoli, S., Nissinen, A., Grobbee, D. E., & Kromhout, D. (2007). Physical inactivity, depression, and risk of cardiovascular mortality. Medicine & Science in Sports & Exercise, 39, 1693–1699. doi: 10.1249/mss.0b013e3180f6109f.CrossRefGoogle ScholarPubMed
Kannel, W. B., & McGee, D. L. (1979). Diabetes and cardiovascular disease. The Framingham study. JAMA, 241, 2035–2038. doi: 10.1001/jama.241.19.2035.CrossRefGoogle ScholarPubMed
Khan, S. S., Ning, H., Wilkins, J. T., Allen, N., Carnethon, M., Berry, J. D., … Lloyd-Jones, D. M. (2018). Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity. JAMA Cardiology, 3, 280–287. doi: 10.1001/jamacardio.2018.0022.CrossRefGoogle ScholarPubMed
Larsson, S. C., & Markus, H. S. (2019). Genetic liability to insomnia and cardiovascular disease risk. Circulation, 140, 796–798. doi: 10.1161/CIRCULATIONAHA.119.041830.CrossRefGoogle ScholarPubMed
Lawlor, D. A. (2016). Commentary: Two-sample Mendelian randomization: Opportunities and challenges. International Journal of Epidemiology, 45, 908–915. doi: 10.1093/ije/dyw127.CrossRefGoogle ScholarPubMed
Lewington, S., Clarke, R., Qizilbash, N., Peto, R., Collins, R., & Prospective Studies, C. (2002). Age-specific relevance of usual blood pressure to vascular mortality: A meta-analysis of individual data for one million adults in 61 prospective studies. Lancet (London, England), 360, 1903–1913. doi: 10.1016/s0140-6736(02)11911-8.Google ScholarPubMed
Libby, P. (2006). Inflammation and cardiovascular disease mechanisms. The American Journal of Clinical Nutrition, 83, 456S–460S. doi: 10.1093/ajcn/83.2.456S.CrossRefGoogle ScholarPubMed
Lippi, G., Montagnana, M., Favaloro, E. J., & Franchini, M. (2009). Mental depression and cardiovascular disease: A multifaceted, bidirectional association. Seminars in Thrombosis and Hemostasis, 35, 325–336. doi: 10.1055/s-0029-1222611.CrossRefGoogle ScholarPubMed
Luppino, F. S., de Wit, L. M., Bouvy, P. F., Stijnen, T., Cuijpers, P., Penninx, B. W., & Zitman, F. G. (2010). Overweight, obesity, and depression: A systematic review and meta-analysis of longitudinal studies. Archives of General Psychiatry, 67, 220–229. doi: 10.1001/archgenpsychiatry.2010.2.CrossRefGoogle ScholarPubMed
Nikpay, M., Goel, A., Won, H. H., Hall, L. M., Willenborg, C., Kanoni, S., … Farrall, M. (2015). A comprehensive 1000 genomes-based genome-wide association meta-analysis of coronary artery disease. Nature Genetics, 47, 1121–1130. doi: 10.1038/ng.3396.Google ScholarPubMed
Nutt, D., Wilson, S., & Paterson, L. (2008). Sleep disorders as core symptoms of depression. Dialogues in Clinical Neuroscience, 10, 329–336.CrossRefGoogle ScholarPubMed
Parekh, A., Smeeth, D., Milner, Y., & Thure, S. (2017). The role of lipid biomarkers in Major depression. Healthcare (Basel), 5(1), 5–21. doi: 10.3390/healthcare5010005.CrossRefGoogle ScholarPubMed
Pepys, M. B., & Hirschfield, G. M. (2003). C-reactive protein: A critical update. Journal of Clinical Investigation, 111, 1805–1812. doi: 10.1172/JCI18921.CrossRefGoogle ScholarPubMed
Pierce, B. L., & Burgess, S. (2013). Efficient design for Mendelian randomization studies: Subsample and 2-sample instrumental variable estimators. American Journal of Epidemiology, 178, 1177–1184. doi: 10.1093/aje/kwt084.CrossRefGoogle ScholarPubMed
Prins, B. P., Kuchenbaecker, K. B., Bao, Y., Smart, M., Zabaneh, D., Fatemifar, G., … Zeggini, E. (2017). Genome-wide analysis of health-related biomarkers in the UK household longitudinal study reveals novel associations. Scientific Reports, 7, 11008. doi: 10.1038/s41598-017-10812-1.CrossRefGoogle ScholarPubMed
Rees, J. M. B., Wood, A. M., & Burgess, S. (2017). Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy. Statistics in Medicine, 36, 4705–4718. doi: 10.1002/sim.7492.CrossRefGoogle ScholarPubMed
Shapiro, P. A. (2013). Depression treatment and coronary artery disease outcomes: Time for reflection. Journal of Psychosomatic Research, 74, 4–5. doi: 10.1016/j.jpsychores.2012.11.008.CrossRefGoogle ScholarPubMed
Song, H., Fang, F., Arnberg, F. K., Mataix-Cols, D., Fernandez de la Cruz, L., Almqvist, C., … Valdimarsdottir, U. A. (2019). Stress related disorders and risk of cardiovascular disease: Population based, sibling controlled cohort study. BMJ, 365, l1255. doi: 10.1136/bmj.l1255.CrossRefGoogle ScholarPubMed
Sparrenberger, F., Cichelero, F. T., Ascoli, A. M., Fonseca, F. P., Weiss, G., Berwanger, O., … Fuchs, F. D. (2009). Does psychosocial stress cause hypertension? A systematic review of observational studies. Journal of Human Hypertension, 23, 12–19. doi: 10.1038/jhh.2008.74.CrossRefGoogle ScholarPubMed
Staley, J. R., Blackshaw, J., Kamat, M. A., Ellis, S., Surendran, P., Sun, B. B., … Butterworth, A. S. (2016). Phenoscanner: A database of human genotype-phenotype associations. Bioinformatics (Oxford, England), 32, 3207–3209. doi: 10.1093/bioinformatics/btw373.CrossRefGoogle ScholarPubMed
Vancampfort, D., Mitchell, A. J., De Hert, M., Sienaert, P., Probst, M., Buys, R., & Stubbs, B. (2015). Type 2 diabetes in patients with major depressive disorder: A meta-analysis of prevalence estimates and predictors. Depression and Anxiety, 32, 763–773. doi: 10.1002/da.22387.CrossRefGoogle ScholarPubMed
Verbanck, M., Chen, C. Y., Neale, B., & Do, R. (2018). Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature Genetics, 50, 693–698. doi: 10.1038/s41588-018-0099-7.CrossRefGoogle ScholarPubMed
Welty, F. K. (2013). How do elevated triglycerides and low HDL-cholesterol affect inflammation and atherothrombosis? Current Cardiology Reports, 15, 400. doi: 10.1007/s11886-013-0400-4.CrossRefGoogle ScholarPubMed
Wootton, R. E., Richmond, R. C., Stuijfzand, B. G., Lawn, R. B., Sallis, H. M., Taylor, G. M. J., … Munafo, M. R. (2019). Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: A Mendelian randomisation study. Psychological Medicine, 1–9.Google ScholarPubMed
World Health Organization (2017). “Depression: let's talk” says WHO, as depression tops list of causes of ill health. pp. News Release: https://www.who.int/news-room/detail/30-03-2017--depression-let-s-talk-says-who-as-depression-tops-list-of-causes-of-ill-health.Google Scholar
Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., & Abdellaoui, A., … Major Depressive Disorder Working Group of the Psychiatric Genomics, C. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics, 50, 668–681. doi: 10.1038/s41588–018-0090-3.CrossRefGoogle ScholarPubMed
Zuidersma, M., Conradi, H. J., van Melle, J. P., Ormel, J., & de Jonge, P. (2013). Depression treatment after myocardial infarction and long-term risk of subsequent cardiovascular events and mortality: A randomized controlled trial. Journal of Psychosomatic Research, 74, 25–30. doi: 10.1016/j.jpsychores.2012.08.015.CrossRefGoogle ScholarPubMed
Supplementary material: File

Li et al. supplementary material

Li et al. supplementary material

Download Li et al. supplementary material(File)
File 2 MB