The association of sleep with metabolic pathways and metabolites: evidence from the Dietary Approaches to Stop Hypertension (DASH)—sodium feeding study
Sleep is increasingly being viewed as an issue of public health concern, yet few epidemiologic studies have explored associations between sleep habits and metabolomic profile.
To assess the association between sleep and blood metabolites.
We examined the association between sleep and 891 fasting plasma metabolites in a subgroup of 106 participants from the Dietary Approaches to Stop Hypertension (DASH)—Sodium feeding trial (1997–1999). We produced two sleep variables to analyze, sleep midpoint (median time between bedtime and waketime) and sleep duration, as well as bedtime and wake time. Metabolites were measured using liquid and gas chromatography, coupled with mass spectrometry. We assessed associations between sleep variables and log transformed metabolites using linear mixed-effects models. We combined the resulting p-values using Fisher’s method to calculate associations between sleep and 38 metabolic pathways.
Sixteen pathways were associated (p < 0.05) with midpoint. Only the γ-glutamyl amino acid metabolism pathway reached Bonferroni-corrected threshold (0.0013). Eighty-three metabolites were associated with midpoint (FDR < 0.20). Similar associations were found for wake time. Neither bed time nor duration were strongly associated. The top metabolites (pathways given in brackets) associated with sleep were erythrulose (advanced glycation end-product) (positive association) and several γ-glutamyl pathway metabolites, including CMPF (fatty acid, dicarboxylate), isovalerate (valine, leucine and isoleucine and fatty acid metabolism) and HWESASXX (polypeptide) (inverse association).
Within our study, several metabolites that have previously been linked to inflammation and oxidative stress (processes involved in diseases such as cardiovascular disease and cancer) were found to be associated with sleep.
KeywordsSleep Metabolites Lifestyle Diet
RS-S conceived of the project idea. VG-D and IW undertook a literature review to support the study. All authors contributed to the analysis plan, the analysis undertaken, and the writing of the manuscript (as well as commenting upon and amending the final manuscript).
This work was supported by the Intramural Research Program of the National Institutes of Health, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services.
Compliance with ethical standards
Conflict of interest
Vanessa L. Z. Gordon-Dseagu, Andriy Derkach, Qian Xiao, Ishmael Williams, Joshua Sampson and Rachael Z. Stolzenberg-Solomon have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.
- Aho, V., Ollila, H. M., Kronholm, E., Bondia-Pons, I., Soininen, P., Kangas, A. J., et al. (2016). Prolonged sleep restriction induces changes in pathways involved in cholesterol metabolism and inflammatory responses. Scientific Reports. https://doi.org/10.1038/srep24828.CrossRefPubMedPubMedCentralGoogle Scholar
- Al Khatib, H. K., Hall, W. L., Creedon, A., Ooi, E., Masri, T., McGowan, L., et al. (2018). Sleep extension is a feasible lifestyle intervention in free-living adults who are habitually short sleepers: A potential strategy for decreasing intake of free sugars? A randomized controlled pilot study. The American Journal of Clinical Nutrition, 107(1), 43–53. https://doi.org/10.1093/ajcn/nqx030.CrossRefPubMedPubMedCentralGoogle Scholar
- Bass, A. J., Dabney, A., & Robinson, D. (n.d.). Q-value: Q-value estimation for false discovery rate control. R Package Version 260, 2015.Google Scholar
- Berentzen, N. E., Smit, H. A., Bekkers, M. B. M., Brunekreef, B., Koppelman, G. H., De Jongste, J. C., et al. (2014). Time in bed, sleep quality and associations with cardiometabolic markers in children: The Prevention and Incidence of Asthma and Mite Allergy birth cohort study. Journal of Sleep Research, 23(1), 3–12. https://doi.org/10.1111/jsr.12087.CrossRefPubMedGoogle Scholar
- Bridgewater, B. R. (2014). High resolution mass spectrometry improves data quantity and quality as compared to unit mass resolution mass spectrometry in high-throughput profiling metabolomics. Journal of Postgenomics Drug & Biomarker Development. https://doi.org/10.4172/2153-0769.1000132.
- Cespedes, E. M., Hu, F. B., Redline, S., Rosner, B., Alcantara, C., Cai, J., et al. (2016). Comparison of self-reported sleep duration with actigraphy: Results from the hispanic community health study/study of Latinos Sueño Ancillary study. American Journal of Epidemiology, 183(6), 561–573. https://doi.org/10.1093/aje/kwv251.CrossRefPubMedPubMedCentralGoogle Scholar
- Derkach, A., Sampson, J., Joseph, J., Playdon, M. C., & Stolzenberg-Solomon, R. Z. (2017). Effects of dietary sodium on metabolites: The Dietary Approaches to Stop Hypertension (DASH)-Sodium Feeding Study. The American Journal of Clinical Nutrition. https://doi.org/10.3945/ajcn.116.150136.CrossRefPubMedPubMedCentralGoogle Scholar
- Evans, A. M., DeHaven, C. D., Barrett, T., Mitchell, M., & Milgram, E. (2009). Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Analytical Chemistry, 81(16), 6656–6667. https://doi.org/10.1021/ac901536h.CrossRefPubMedGoogle Scholar
- Fatima, Y., Doi, S. a. R., & Mamun, A. A. (2015). Longitudinal impact of sleep on overweight and obesity in children and adolescents: A systematic review and bias-adjusted meta-analysis. Obesity Reviews: An Official Journal of the International Association for the Study of Obesity, 16(2), 137–149. https://doi.org/10.1111/obr.12245.CrossRefGoogle Scholar
- Freidin, M. B., Wells, H. R. R., Potter, T., Livshits, G., Menni, C., & Williams, F. M. K. (2018). Metabolomic markers of fatigue: Association between circulating metabolome and fatigue in women with chronic widespread pain. Biochimica et Biophysica Acta (BBA)—Molecular Basis of Disease, 1864(2), 601–606. https://doi.org/10.1016/j.bbadis.2017.11.025.CrossRefGoogle Scholar
- Iftikhar, I. H., Donley, M. A., Mindel, J., Pleister, A., Soriano, S., & Magalang, U. J. (2015). Sleep duration and metabolic syndrome. An updated dose–risk metaanalysis. Annals of the American Thoracic Society, 12(9), 1364–1372. https://doi.org/10.1513/AnnalsATS.201504-190OC.CrossRefPubMedPubMedCentralGoogle Scholar
- Kanbay, A., Kaya, E., Buyukoglan, H., Ozdogan, N., Kaya, M. G., Oymak, F. S., et al. (2011). Serum gamma-glutamyl transferase activity is an independent predictor for cardiovascular disease in obstructive sleep apnea syndrome. Respiratory Medicine, 105(4), 637–642. https://doi.org/10.1016/j.rmed.2010.12.003.CrossRefPubMedGoogle Scholar
- Kinuhata, S., Hayashi, T., Sato, K. K., Uehara, S., Oue, K., Endo, G., et al. (2014). Sleep duration and the risk of future lipid profile abnormalities in middle-aged men: The Kansai Healthcare Study. Sleep Medicine, 15(11), 1379–1385. https://doi.org/10.1016/j.sleep.2014.06.011.CrossRefPubMedGoogle Scholar
- Knutson, K. L., Wu, D., Patel, S. R., Loredo, J. S., Redline, S., Cai, J., et al. (2017). Association between sleep timing, obesity, diabetes: The hispanic community health study/study of latinos (hchs/sol) cohort study. Sleep. https://doi.org/10.1093/sleep/zsx014.
- Menni, C., Migaud, M., Glastonbury, C. A., Beaumont, M., Nikolaou, A., Small, K. S., et al. (2016). Metabolomic profiling to dissect the role of visceral fat in cardiometabolic health: Visceral fat in cardiometabolic health. Obesity, 24(6), 1380–1388. https://doi.org/10.1002/oby.21488.CrossRefPubMedGoogle Scholar
- Merikanto, I., Lahti, T., Puolijoki, H., Vanhala, M., Peltonen, M., Laatikainen, T., et al. (2013). Associations of chronotype and sleep with cardiovascular diseases and type 2 diabetes. Chronobiology International, 30(4), 470–477. https://doi.org/10.3109/07420528.2012.741171.CrossRefPubMedGoogle Scholar
- R Core Team. (2017). R: A language and environment for statistical computing. Vienna, Austria. https://www.R-project.org/.
- Rey-López, J. P., de Carvalho, H. B., de Moraes, A. C. F., Ruiz, J. R., Sjöström, M., Marcos, A., et al. (2014). Sleep time and cardiovascular risk factors in adolescents: The HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) study. Sleep Medicine, 15(1), 104–110. https://doi.org/10.1016/j.sleep.2013.07.021.CrossRefPubMedGoogle Scholar
- Romani, C., Palermo, L., MacDonald, A., Limback, E., Hall, S. K., & Geberhiwot, T. (2017). The impact of phenylalanine levels on cognitive outcomes in adults with phenylketonuria: Effects across tasks and developmental stages. Neuropsychology, 31(3), 242–254. https://doi.org/10.1037/neu0000336.CrossRefPubMedPubMedCentralGoogle Scholar
- Sacks, F. M., Svetkey, L. P., Vollmer, W. M., Appel, L. J., Bray, G. A., Harsha, D., et al. (2001). Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. DASH-Sodium Collaborative Research Group. The New England Journal of Medicine, 344(1), 3–10. https://doi.org/10.1056/NEJM200101043440101.CrossRefPubMedGoogle Scholar
- Sánchez-Armengol, A., Villalobos-López, P., Caballero-Eraso, C., Carmona-Bernal, C., Asensio-Cruz, M., Barbé, F., & Capote, F. (2015). Gamma glutamyl transferase and oxidative stress in obstructive sleep apnea: A study in 1744 patients. Sleep and Breathing, 19(3), 883–890. https://doi.org/10.1007/s11325-014-1115-5.CrossRefPubMedGoogle Scholar
- Smuda, M., Henning, C., Raghavan, C. T., Johar, K., Vasavada, A. R., Nagaraj, R. H., & Glomb, M. A. (2015). Comprehensive analysis of maillard protein modifications in human lenses: Effect of age and cataract. Biochemistry, 54(15), 2500–2507. https://doi.org/10.1021/bi5013194.CrossRefPubMedPubMedCentralGoogle Scholar
- Svetkey, L. P., Sacks, F. M., Obarzanek, E., Vollmer, W. M., Appel, L. J., Lin, P.-H., et al. (1999). The DASH diet, sodium intake and blood pressure trial (DASH-Sodium). Journal of the American Dietetic Association, 99(8), S96–S104. https://doi.org/10.1016/S0002-8223(99)00423-X.CrossRefPubMedGoogle Scholar
- van den Berg, R., Mook-Kanamori, D. O., Donga, E., van Dijk, M., van Dijk, J. G., Lammers, G.-J., et al. (2016). A single night of sleep curtailment increases plasma acylcarnitines: Novel insights in the relationship between sleep and insulin resistance. Archives of Biochemistry and Biophysics, 589, 145–151. https://doi.org/10.1016/j.abb.2015.09.017.CrossRefPubMedGoogle Scholar
- Wan Mahmood, W. A., Yusoff, D., Behan, M. S., Di Perna, L. A., Tun, A. Kyaw, McDermott, T., & Sreenan, S. (2013). Association between sleep disruption and levels of lipids in Caucasians with type 2 diabetes. International Journal of Endocrinology, 2013, 1–7. https://doi.org/10.1155/2013/341506.CrossRefGoogle Scholar
- Xiao, Q., Keadle, S. K., Hollenbeck, A. R., & Matthews, C. E. (2014). Sleep duration and total and cause-specific mortality in a large US cohort: interrelationships with physical activity, sedentary behavior, and body mass index. American Journal of Epidemiology, 180(10), 997–1006. https://doi.org/10.1093/aje/kwu222.CrossRefPubMedPubMedCentralGoogle Scholar
- Zhan, S., Wu, Y., Sun, P., Lin, H., Zhu, Y., & Han, X. (2016). Decrease in circulating fatty acids is associated with islet dysfunction in chronically sleep-restricted rats. International Journal of Molecular Sciences, 17(12), 2102. https://doi.org/10.3390/ijms17122102.CrossRefPubMedCentralGoogle Scholar
- Zhan, Y., Zhang, F., Lu, L., Wang, J., Sun, Y., Ding, R., et al. (2014). Prevalence of dyslipidemia and its association with insomnia in a community based population in China. BMC Public Health. https://doi.org/10.1186/1471-2458-14-1050.
- Zhang, S., Chen, P., Jin, H., Yi, J., Xie, X., Yang, M., et al. (2017). Circulating 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) levels are associated with hyperglycemia and β cell dysfunction in a Chinese population. Scientific Reports, 7(1), 3114. https://doi.org/10.1038/s41598-017-03271-1.CrossRefPubMedPubMedCentralGoogle Scholar