Tianyi Huang1,2, Oana A Zeleznik1, Elizabeth M Poole1, Clary B Clish3, Amy A Deik3, Justin M Scott3, Céline Vetter4, Eva S Schernhammer1,5, Robert Brunner6, Lauren Hale7, JoAnn E Manson8, Frank B Hu1,2,9, Susan Redline10, Shelley S Tworoger9,11, Kathryn M Rexrode8,12. 1. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. 2. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 3. Broad Institute of MIT and Harvard, Boston, MA, USA. 4. Department of Integrative Physiology, University of Colorado at Boulder, Boulder, CO, USA. 5. Department of Epidemiology, Center for Public Health, University of Vienna, Vienna, Austria. 6. School of Medicine, University of Nevada, Reno, NV, USA. 7. Program in Public Health, Department of Family, Population, and Preventive Medicine, Stony Brook Medicine, Stony Brook, NY, USA. 8. Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. 9. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 10. Departments of Medicine, Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. 11. Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 12. Division of Women's Health, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Abstract
BACKGROUND: Epidemiologic studies suggest a strong link between poor habitual sleep quality and increased cardiovascular disease risk. However, the underlying mechanisms are not entirely clear. Metabolomic profiling may elucidate systemic differences associated with sleep quality that influence cardiometabolic health. METHODS: We explored cross-sectional associations between sleep quality and plasma metabolites in a nested case-control study of coronary heart disease (CHD) in the Women's Health Initiative (WHI; n = 1956) and attempted to replicate the results in an independent sample from the Nurses' Health Study II (NHSII; n = 209). A sleep-quality score (SQS) was derived from self-reported sleep problems asked in both populations. Plasma metabolomics were assayed using LC-MS with 347 known metabolites. General linear regression was used to identify individual metabolites associated with continuous SQS (false-discovery rate <0.05). Using least absolute shrinkage and selection operator (LASSO) algorithms, a metabolite score was created from replicated metabolites and evaluated with CHD risk in the WHI. RESULTS: After adjusting for age, race/ethnicity, body mass index (BMI) and smoking, we identified 69 metabolites associated with SQS in the WHI (59 were lipids). Of these, 16 were replicated in NHSII (15 were lipids), including 6 triglycerides (TAGs), 4 phosphatidylethanolamines (PEs), 3 phosphatidylcholines (PCs), 1 diglyceride (DAG), 1 lysophosphatidylcholine and N6-acetyl-L-lysine (a product of histone acetylation). These metabolites were consistently higher among women with poorer sleep quality. The LASSO selection resulted in a nine-metabolite score (TAGs 45: 1, 48: 1, 50: 4; DAG 32: 1; PEs 36: 4, 38: 5; PCs 30: 1, 40: 6; N6-acetyl-L-lysine), which was positively associated with CHD risk (odds ratio per SD increase in the score: 1.16; 95% confidence interval: 1.05, 1.28; p = 0.0003) in the WHI after adjustment for matching factors and conventional CHD risk factors. CONCLUSIONS: Differences in lipid metabolites may be an important pathogenic pathway linking poor habitual sleep quality and CHD risk.
BACKGROUND: Epidemiologic studies suggest a strong link between poor habitual sleep quality and increased cardiovascular disease risk. However, the underlying mechanisms are not entirely clear. Metabolomic profiling may elucidate systemic differences associated with sleep quality that influence cardiometabolic health. METHODS: We explored cross-sectional associations between sleep quality and plasma metabolites in a nested case-control study of coronary heart disease (CHD) in the Women's Health Initiative (WHI; n = 1956) and attempted to replicate the results in an independent sample from the Nurses' Health Study II (NHSII; n = 209). A sleep-quality score (SQS) was derived from self-reported sleep problems asked in both populations. Plasma metabolomics were assayed using LC-MS with 347 known metabolites. General linear regression was used to identify individual metabolites associated with continuous SQS (false-discovery rate <0.05). Using least absolute shrinkage and selection operator (LASSO) algorithms, a metabolite score was created from replicated metabolites and evaluated with CHD risk in the WHI. RESULTS: After adjusting for age, race/ethnicity, body mass index (BMI) and smoking, we identified 69 metabolites associated with SQS in the WHI (59 were lipids). Of these, 16 were replicated in NHSII (15 were lipids), including 6 triglycerides (TAGs), 4 phosphatidylethanolamines (PEs), 3 phosphatidylcholines (PCs), 1 diglyceride (DAG), 1 lysophosphatidylcholine and N6-acetyl-L-lysine (a product of histone acetylation). These metabolites were consistently higher among women with poorer sleep quality. The LASSO selection resulted in a nine-metabolite score (TAGs 45: 1, 48: 1, 50: 4; DAG 32: 1; PEs 36: 4, 38: 5; PCs 30: 1, 40: 6; N6-acetyl-L-lysine), which was positively associated with CHD risk (odds ratio per SD increase in the score: 1.16; 95% confidence interval: 1.05, 1.28; p = 0.0003) in the WHI after adjustment for matching factors and conventional CHD risk factors. CONCLUSIONS: Differences in lipid metabolites may be an important pathogenic pathway linking poor habitual sleep quality and CHD risk.
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