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Causal relationships between blood lipids and depression phenotypes: a Mendelian randomisation analysis

Published online by Cambridge University Press:  24 April 2020

Hon-Cheong So Open the ORCID record for Hon-Cheong So [Opens in a new window] ,
Carlos Kwan-long Chau ,
Yu-ying Cheng  and
Pak C. Sham
Hon-Cheong So*
Affiliation:
School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Hong Kong, China CUHK Shenzhen Research Institute, Shenzhen, China Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Shatin, Hong Kong
Carlos Kwan-long Chau
Affiliation:
School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
Yu-ying Cheng
Affiliation:
School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
Pak C. Sham
Affiliation:
Depeartment of Psychiatry, University of Hong Kong, Pok Fu Lam, Hong Kong Center for Genomic Sciences, University of Hong Kong, Pok Fu Lam, Hong Kong
*
Author for correspondence: Hon-Cheong So, E-mail: hcso@cuhk.edu.hk
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Abstract

Background

The etiology of depression remains poorly understood. Changes in blood lipid levels were reported to be associated with depression and suicide, however study findings were mixed.

Methods

We performed a two-sample Mendelian randomisation (MR) analysis to investigate the causal relationship between blood lipids and depression phenotypes, based on large-scale GWAS summary statistics (N = 188 577/480 359 for lipid/depression traits respectively). Five depression-related phenotypes were included, namely major depression (MD; from PGC), depressive symptoms (DS; from SSGAC), longest duration and number of episodes of low mood, and history of deliberate self-harm (DSH)/suicide (from UK Biobank). MR was conducted with inverse-variance weighted (MR-IVW), Egger and Generalised Summary-data-based MR (GSMR) methods.

Results

There was consistent evidence that triglyceride (TG) is causally associated with DS (MR-IVW β for one-s.d. increase in TG = 0.0346, 95% CI 0.0114–0.0578), supported by MR-IVW and GSMR and multiple r2 clumping thresholds. We also observed relatively consistent associations of TG with DSH/suicide (MR-Egger OR = 2.514, CI 1.579–4.003). There was moderate evidence for positive associations of TG with MD and the number of episodes of low mood. For HDL-c, we observed moderate evidence for causal associations with DS and MD. LDL-c and TC did not show robust causal relationships with depression phenotypes, except for weak evidence that LDL-c is inversely related to DSH/suicide. We did not detect significant associations when depression phenotypes were treated as exposures.

Conclusions

This study provides evidence to a causal relationship between TG, and to a lesser extent, altered cholesterol levels with depression phenotypes. Further studies on its mechanistic basis and the effects of lipid-lowering therapies are warranted.

Keywords

Causal inferencedepressiongenome-wide association studylipidsMendelian randomisation
Type
Original Article
Information
Psychological Medicine , Volume 51 , Issue 14 , October 2021 , pp. 2357 - 2369
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate – a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B-Methodological, 57, 289–300.Google Scholar
Benjamini, Y., & Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29, 1165–1188.CrossRefGoogle Scholar
Bennett, D. A., & Holmes, M. V. (2017). Mendelian randomisation in cardiovascular research: An introduction for clinicians. Heart (British Cardiac Society), 103, 1400–1407.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.CrossRefGoogle ScholarPubMed
Bulik-Sullivan, B., Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P. R., … Neale, B. M. (2015). An atlas of genetic correlations across human diseases and traits. Nature Genetics, 47, 1236–1241.CrossRefGoogle ScholarPubMed
Burgess, S., Butterworth, A., & Thompson, S. G. (2013). Mendelian randomization analysis with multiple genetic variants using summarized data. Genetic Epidemiology, 37, 658–665.CrossRefGoogle ScholarPubMed
Burgess, S., Dudbridge, F., & Thompson, S. G. (2015). Re: “multivariable Mendelian randomization: The use of pleiotropic genetic variants to estimate causal effects”. American Journal of Epidemiology, 181, 290–291.CrossRefGoogle Scholar
Burgess, S., Dudbridge, F., & Thompson, S. G. (2016). Combining information on multiple instrumental variables in Mendelian randomization: Comparison of allele score and summarized data methods. Statistics in Medicine, 35, 1880–1906.CrossRefGoogle ScholarPubMed
Burgess, S., Smith, G. D., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., … Relton, C. L. (2019). Guidelines for performing Mendelian randomization investigations. Wellcome Open Research, 4, 186.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.CrossRefGoogle ScholarPubMed
Burgess, S., & Thompson, S. G. (2017). Interpreting findings from Mendelian randomization using the MR-Egger method. European Journal of Epidemiology, 32, 377–389.CrossRefGoogle ScholarPubMed
Burgess, S., Zuber, V., Valdes-Marquez, E., Sun, B. B., & Hopewell, J. C. (2017). Mendelian randomization with fine-mapped genetic data: Choosing from large numbers of correlated instrumental variables. Genetic Epidemiology, 41, 714–725.CrossRefGoogle ScholarPubMed
Chang, S. C., Glymour, M. M., Walter, S., Liang, L., Koenen, K. C., Tchetgen, E. J., … Kubzansky, L. D. (2014). Genome-wide polygenic scoring for a 14-year long-term average depression phenotype. Brain and Behavior, 4, 298–311.CrossRefGoogle ScholarPubMed
Dhar, A. K., & Barton, D. A. (2016). Depression and the link with cardiovascular disease. Frontiers in Psychiatry, 7, 33.CrossRefGoogle ScholarPubMed
Elovainio, M., Pulkki-Raback, L., Kivimaki, M., Jokela, M., Viikari, J., Raitakari, O. T., … Keltikangas-Jarvinen, L. (2010). Lipid trajectories as predictors of depressive symptoms: The Young Finns Study. Health Psychology, 29, 237–245.CrossRefGoogle ScholarPubMed
Engelberg, H. (1992). Low serum cholesterol and suicide. Lancet (London, England), 339, 727–729.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 Diseases, 17, 149.CrossRefGoogle ScholarPubMed
Gieger, C., Radhakrishnan, A., Cvejic, A., Tang, W. H., Porcu, E., Pistis, G., … Soranzo, N. (2011). New gene functions in megakaryopoiesis and platelet formation. Nature, 480, 201–208.CrossRefGoogle ScholarPubMed
Gkatzionis, A., & Burgess, S. (2019). Contextualizing selection bias in Mendelian randomization: How bad is it likely to be? International Journal of Epidemiology, 48, 691–701.CrossRefGoogle ScholarPubMed
Glueck, C. J., Tieger, M., Kunkel, R., Tracy, T., Speirs, J., Streicher, P., & Illig, E. (1993). Improvement in symptoms of depression and in an Index of life stressors accompany treatment of severe hypertriglyceridemia. Biological Psychiatry, 34, 240–252.CrossRefGoogle Scholar
Hemani, G., Bowden, J., & Davey Smith, G. (2018a). Evaluating the potential role of pleiotropy in Mendelian randomization studies. Human Molecular Genetics, 27, R195–R208.CrossRefGoogle Scholar
Hemani, G., Tilling, K., & Davey Smith, G. (2017). Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genetics, 13, e1007081.CrossRefGoogle ScholarPubMed
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., … Haycock, P. C. (2018b). The MR-base platform supports systematic causal inference across the human phenome. Elife, 7, e34408.CrossRefGoogle Scholar
Holmes, M. V., & Davey Smith, G. (2018). Challenges in interpreting multivariable Mendelian randomization: Might “Good Cholesterol” be good after all? American Journal of Kidney Diseases, 71, 149–153.CrossRefGoogle Scholar
Lehto, S. M., Niskanen, L., Tolmunen, T., Hintikka, J., Viinamaki, H., Heiskanen, T., … Koivumaa-Honkanen, H. (2010). Low serum HDL-cholesterol levels are associated with long symptom duration in patients with major depressive disorder. Psychiatry and Clinical Neurosciences, 64, 279–283.CrossRefGoogle ScholarPubMed
Lloyd-Jones, L. R., Robinson, M. R., Yang, J., & Visscher, P. M. (2018). Transformation of summary statistics from linear mixed model association on all-or-none traits to odds ratio. Genetics, 208, 1397–1408.CrossRefGoogle ScholarPubMed
McIntyre, R. S., Soczynska, J. K., Konarski, J. Z., & Kennedy, S. H. (2006). The effect of antidepressants on glucose homeostasis and insulin sensitivity: Synthesis and mechanisms. Expert Opinion on Drug Safety, 5, 157–168.CrossRefGoogle ScholarPubMed
McTaggart, F., & Jones, P. (2008). Effects of statins on high-density lipoproteins: A potential contribution to cardiovascular benefit. Cardiovascular Drugs and Therapy, 22, 321–338.CrossRefGoogle ScholarPubMed
Mercado, C., DeSimone, A. K., Odom, E., Gillespie, C., Ayala, C., & Loustalot, F. (2015). Prevalence of cholesterol treatment eligibility and medication use among adults – United States, 2005–2012. Morbidity and Mortality Weekly Report, 64, 1305–1311.CrossRefGoogle ScholarPubMed
Milaneschi, Y., Lamers, F., Peyrot, W. J., Abdellaoui, A., Willemsen, G., Hottenga, J. J., … Penninx, B. W. (2016). Polygenic dissection of major depression clinical heterogeneity. Molecular Psychiatry, 21, 516–522.CrossRefGoogle ScholarPubMed
Moskvina, V., Holmans, P., Schmidt, K. M., & Craddock, N. (2005). Design of case-controls studies with unscreened controls. Annals of Human Genetics, 69, 566–576.CrossRefGoogle ScholarPubMed
Muldoon, M. F., Manuck, S. B., & Matthews, K. A. (1990). Lowering cholesterol concentrations and mortality: A quantitative review of primary prevention trials. BMJ, 301, 309–314.CrossRefGoogle ScholarPubMed
Munafo, M. R., Tilling, K., Taylor, A. E., Evans, D. M., & Smith, G. D. (2018). Collider scope: When selection bias can substantially influence observed associations. International Journal of Epidemiology, 47, 226–235.CrossRefGoogle ScholarPubMed
Nunes, S. O., Piccoli de Melo, L. G., Pizzo de Castro, M. R., Barbosa, D. S., Vargas, H. O., Berk, M., & Maes, M. (2015). Atherogenic index of plasma and atherogenic coefficient are increased in major depression and bipolar disorder, especially when comorbid with tobacco use disorder. Journal of Affective Disorders, 172, 55–62.CrossRefGoogle ScholarPubMed
Oh, J., & Kim, T. S. (2017). Serum lipid levels in depression and suicidality: The Korea National Health and Nutrition Examination Survey (KNHANES) 2014. Journal of Affective Disorders, 213, 51–58.CrossRefGoogle ScholarPubMed
Okbay, A., Baselmans, B. M., De Neve, J. E., Turley, P., Nivard, M. G., Fontana, M. A., … Cesarini, D. (2016). Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nature Genetics, 48, 624–633.CrossRefGoogle ScholarPubMed
Parekh, A., Smeeth, D., Milner, Y., & Thure, S.. (2017). The role of lipid biomarkers in major depression. In Healthcare (Vol. 5, No. 1, p. 5). Multidisciplinary Digital Publishing Institute.CrossRefGoogle Scholar
Persons, J. E., & Fiedorowicz, J. G. (2016). Depression and serum low-density lipoprotein: A systematic review and meta-analysis. Journal of Affective Disorders, 206, 55–67.CrossRefGoogle ScholarPubMed
Persons, J. E., Robinson, J. G., Coryell, W. H., Payne, M. E., & Fiedorowicz, J. G. (2016). Longitudinal study of low serum LDL cholesterol and depressive symptom onset in postmenopause. Journal of Clinical Psychiatry, 77, 212–220.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.CrossRefGoogle ScholarPubMed
Salagre, E., Fernandes, B. S., Dodd, S., Brownstein, D. J., & Berk, M. (2016). Statins for the treatment of depression: A meta-analysis of randomized, double-blind, placebo-controlled trials. Journal of Affective Disorders, 200, 235–242.CrossRefGoogle ScholarPubMed
Schmidt, A. F., & Dudbridge, F. (2018). Mendelian randomization with Egger pleiotropy correction and weakly informative Bayesian priors. International Journal of Epidemiology, 47(4), 1217–1228.CrossRefGoogle ScholarPubMed
Shin, J. Y., Suls, J., & Martin, R. (2008). Are cholesterol and depression inversely related? A meta-analysis of the association between two cardiac risk factors. Annals of Behavioral Medicine, 36, 33–43.CrossRefGoogle ScholarPubMed
Smith, G. D., & Ebrahim, S. (2003). ‘Mendelian randomization’: Can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology, 32, 1–22.CrossRefGoogle ScholarPubMed
Virtanen, M., Ferrie, J. E., Akbaraly, T., Tabak, A., Jokela, M., Ebmeier, K. P., … Kivimaki, M. (2017). Metabolic syndrome and symptom resolution in depression: A 5-year follow-up of older adults. Journal of Clinica Psychiatry, 78, e1–e7.CrossRefGoogle ScholarPubMed
Vrablik, M., & Ceska, R. (2015). Treatment of hypertriglyceridemia: A review of current options. Physiological Research, 64(Suppl 3), S331–S340.CrossRefGoogle ScholarPubMed
Wadhera, R. K., Steen, D. L., Khan, I., Giugliano, R. P., & Foody, J. M. (2016). A review of low-density lipoprotein cholesterol, treatment strategies, and its impact on cardiovascular disease morbidity and mortality. Journal of Clinical Lipidology, 10, 472–489.CrossRefGoogle ScholarPubMed
Wei, Y. G., Cai, D. B., Liu, J., Liu, R. X., Wang, S. B., Tang, Y. Q., … Wang, F. (2020). Cholesterol and triglyceride levels in first-episode patients with major depressive disorder: A meta-analysis of case-control studies. Journal of Affective Disorders, 266, 465–472.CrossRefGoogle ScholarPubMed
Willer, C. J., Schmidt, E. M., Sengupta, S., Peloso, G. M., Gustafsson, S., & Kanoni, S., … Global Lipids Genetics, C. (2013). Discovery and refinement of loci associated with lipid levels. Nature Genetics 45, 1274–1283.Google ScholarPubMed
Wong, B. C., Chau, C. K., Ao, F. K., Mo, C. H., Wong, S. Y., Wong, Y. H., & So, H. C. (2019). Differential associations of depression-related phenotypes with cardiometabolic risks: Polygenic analyses and exploring shared genetic variants and pathways. Depression and Anxiety, 36, 330–344.CrossRefGoogle ScholarPubMed
World Health Organization (2017). Depression and other common mental disorders: global health estimates (No. WHO/MSD/MER/2017.2).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.CrossRefGoogle ScholarPubMed
Wu, S., Ding, Y., Wu, F., Xie, G., Hou, J., & Mao, P. (2016). Serum lipid levels and suicidality: A meta-analysis of 65 epidemiological studies. Journal of Psychiatry & Neuroscience, 41, 56–69.CrossRefGoogle ScholarPubMed
Yatham, M. S., Yatham, K. S., Ravindran, A. V., & Sullivan, F. (2019). Do statins have an effect on depressive symptoms? A systematic review and meta-analysis. Journal of Affective Disorders, 257, 55–63.CrossRefGoogle ScholarPubMed
Yavorska, O. O., & Burgess, S. (2017). Mendelian randomization: An R package for performing Mendelian randomization analyses using summarized data. International Journal of Epidemiology, 46, 1734–1739.CrossRefGoogle Scholar
You, H., Lu, W., Zhao, S. P., Hu, Z. P., & Zhang, J. N. (2013). The relationship between statins and depression: A review of the literature. Expert Opinion on Pharmacotherapy, 14, 1467–1476.CrossRefGoogle ScholarPubMed
Zhu, Z., Zheng, Z., Zhang, F., Wu, Y., Trzaskowski, M., Maier, R., … Yang, J. (2018). Causal associations between risk factors and common diseases inferred from GWAS summary data. Nature Communications, 9, 224.CrossRefGoogle ScholarPubMed
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