Maxime M Bos1,2, Neil J Goulding3,4, Diana van Heemst1, Raymond Noordam5, Deborah A Lawlor6,7,8, Matthew A Lee3,4, Amy Hofman2, Mariska Bot9, René Pool10,11, Lisanne S Vijfhuizen12, Xiang Zhang13,14, Chihua Li15, Rima Mustafa16, Matt J Neville17,18, Ruifang Li-Gao19, Stella Trompet1, Marian Beekman20, Nienke R Biermasz21, Dorret I Boomsma10,11, Irene de Boer22, Constantinos Christodoulides18, Abbas Dehghan16,23,24, Ko Willems van Dijk12,21,25, Ian Ford26, Mohsen Ghanbari2, Bastiaan T Heijmans20, M Arfan Ikram2, J Wouter Jukema27,28, Dennis O Mook-Kanamori19,29, Fredrik Karpe17,18, Annemarie I Luik2, L H Lumey15,20, Arn M J M van den Maagdenberg12,22, Simon P Mooijaart1, Renée de Mutsert19, Brenda W J H Penninx9, Patrick C N Rensen21,25, Rebecca C Richmond3,4, Frits R Rosendaal19, Naveed Sattar30, Robert A Schoevers31, P Eline Slagboom20,32, Gisela M Terwindt22, Carisha S Thesing9, Kaitlin H Wade3,4, Carolien A Wijsman1, Gonneke Willemsen10,11, Aeilko H Zwinderman33. 1. Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. 2. Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands. 3. MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK. 4. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK. 5. Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. r.noordam@lumc.nl. 6. MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK. d.a.lawlor@bristol.ac.uk. 7. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK. d.a.lawlor@bristol.ac.uk. 8. NIHR Bristol Biomedical Research Centre, Bristol, UK. d.a.lawlor@bristol.ac.uk. 9. Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health research institute, Amsterdam, The Netherlands. 10. Amsterdam Public Health Research Institute, Amsterdam, The Netherlands. 11. Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. 12. Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands. 13. Department of Experimental Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. 14. Human and Animal Physiology, Wageningen University, Wageningen, The Netherlands. 15. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA. 16. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. 17. NIHR Oxford Biomedical Research Centre, Oxford University Hospitals Foundation Trust, Oxford, UK. 18. Radcliffe Department of Medicine, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, UK. 19. Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands. 20. Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands. 21. Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands. 22. Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands. 23. Dementia Research Institute at Imperial College London, London, W2 1PG, UK. 24. MRC Centre for Environment and Health, School of Public Health, Imperial College, London, UK. 25. Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands. 26. Robertson Center for Biostatistics, University of Glasgow, Glasgow, UK. 27. Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands. 28. Netherlands Heart Institute, Utrecht, The Netherlands. 29. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands. 30. BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, Glasgow, UK. 31. Department of Psychiatry, Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 32. Max Planck Institute for Biology of Ageing, Cologne, Germany. 33. Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Abstract
BACKGROUND: Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase coronary artery disease risk. We combined adjusted multivariable regression (AMV) and MR analyses of phenotypes of unfavourable sleep on 113 metabolomic traits to investigate possible biochemical mechanisms linking sleep to cardiovascular disease. METHODS: We used AMV (N = 17,368) combined with two-sample MR (N = 38,618) to examine effects of self-reported insomnia symptoms, total habitual sleep duration, and chronotype on 113 metabolomic traits. The AMV analyses were conducted on data from 10 cohorts of mostly Europeans, adjusted for age, sex, and body mass index. For the MR analyses, we used summary results from published European-ancestry genome-wide association studies of self-reported sleep traits and of nuclear magnetic resonance (NMR) serum metabolites. We used the inverse-variance weighted (IVW) method and complemented this with sensitivity analyses to assess MR assumptions. RESULTS: We found consistent evidence from AMV and MR analyses for associations of usual vs. sometimes/rare/never insomnia symptoms with lower citrate (- 0.08 standard deviation (SD)[95% confidence interval (CI) - 0.12, - 0.03] in AMV and - 0.03SD [- 0.07, - 0.003] in MR), higher glycoprotein acetyls (0.08SD [95% CI 0.03, 0.12] in AMV and 0.06SD [0.03, 0.10) in MR]), lower total very large HDL particles (- 0.04SD [- 0.08, 0.00] in AMV and - 0.05SD [- 0.09, - 0.02] in MR), and lower phospholipids in very large HDL particles (- 0.04SD [- 0.08, 0.002] in AMV and - 0.05SD [- 0.08, - 0.02] in MR). Longer total sleep duration associated with higher creatinine concentrations using both methods (0.02SD per 1 h [0.01, 0.03] in AMV and 0.15SD [0.02, 0.29] in MR) and with isoleucine in MR analyses (0.22SD [0.08, 0.35]). No consistent evidence was observed for effects of chronotype on metabolomic measures. CONCLUSIONS: Whilst our results suggested that unfavourable sleep traits may not cause widespread metabolic disruption, some notable effects were observed. The evidence for possible effects of insomnia symptoms on glycoprotein acetyls and citrate and longer total sleep duration on creatinine and isoleucine might explain some of the effects, found in MR analyses of these sleep traits on coronary heart disease, which warrant further investigation.
BACKGROUND: Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase coronary artery disease risk. We combined adjusted multivariable regression (AMV) and MR analyses of phenotypes of unfavourable sleep on 113 metabolomic traits to investigate possible biochemical mechanisms linking sleep to cardiovascular disease. METHODS: We used AMV (N = 17,368) combined with two-sample MR (N = 38,618) to examine effects of self-reported insomnia symptoms, total habitual sleep duration, and chronotype on 113 metabolomic traits. The AMV analyses were conducted on data from 10 cohorts of mostly Europeans, adjusted for age, sex, and body mass index. For the MR analyses, we used summary results from published European-ancestry genome-wide association studies of self-reported sleep traits and of nuclear magnetic resonance (NMR) serum metabolites. We used the inverse-variance weighted (IVW) method and complemented this with sensitivity analyses to assess MR assumptions. RESULTS: We found consistent evidence from AMV and MR analyses for associations of usual vs. sometimes/rare/never insomnia symptoms with lower citrate (- 0.08 standard deviation (SD)[95% confidence interval (CI) - 0.12, - 0.03] in AMV and - 0.03SD [- 0.07, - 0.003] in MR), higher glycoprotein acetyls (0.08SD [95% CI 0.03, 0.12] in AMV and 0.06SD [0.03, 0.10) in MR]), lower total very large HDL particles (- 0.04SD [- 0.08, 0.00] in AMV and - 0.05SD [- 0.09, - 0.02] in MR), and lower phospholipids in very large HDL particles (- 0.04SD [- 0.08, 0.002] in AMV and - 0.05SD [- 0.08, - 0.02] in MR). Longer total sleep duration associated with higher creatinine concentrations using both methods (0.02SD per 1 h [0.01, 0.03] in AMV and 0.15SD [0.02, 0.29] in MR) and with isoleucine in MR analyses (0.22SD [0.08, 0.35]). No consistent evidence was observed for effects of chronotype on metabolomic measures. CONCLUSIONS: Whilst our results suggested that unfavourable sleep traits may not cause widespread metabolic disruption, some notable effects were observed. The evidence for possible effects of insomnia symptoms on glycoprotein acetyls and citrate and longer total sleep duration on creatinine and isoleucine might explain some of the effects, found in MR analyses of these sleep traits on coronary heart disease, which warrant further investigation.
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