Habitual sleep quality, plasma metabolites and risk of coronary heart disease in post-menopausal women. (26th October 2018)

• Record Type: Journal Article
• Authors: Huang, Tianyi; Zeleznik, Oana A; Poole, Elizabeth M; Clish, Clary B; Deik, Amy A; Scott, Justin M; Vetter, Céline; Schernhammer, Eva S; Brunner, Robert; Hale, Lauren; Manson, JoAnn E; Hu, Frank B; Redline, Susan; Tworoger, Shelley S; Rexrode, Kathryn M
• Is Part Of: International journal of epidemiology. Volume 48:Number 4(2019)
• Page Start: 1262
• Page End: 1274
• Publication Date: 2018-10-26
• Subjects: coronary heart disease -- epidemiology -- metabolomics -- sleep -- women; Epidemiology -- Periodicals; 614.4
• DOI: 10.1093/ije/dyy234
• Languages: English
• ISSNs: 0300-5771

Abstract

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.