, 13:63 | Cite as

Habitual sleep and human plasma metabolomics

  • Qian Xiao
  • Andriy Derkach
  • Steven C. Moore
  • Wei Zheng
  • Xiao-Ou Shu
  • Fangyi Gu
  • Neil E. Caporaso
  • Joshua N. Sampson
  • Charles E. Matthews
Original Article



Sleep plays an important role in cardiometabolic health. The sleep-wake cycle is partially driven by the endogenous circadian clock, which governs a range of metabolic pathways. The association between sleep and cardiometabolic health may be mediated by alterations of the human metabolome.


To better understand the biological mechanism underlying the association between sleep and health, we examined human plasma metabolites in relation to sleep duration and sleep timing.


Using an untargeted approach, 329 fasting plasma metabolites were measured in 277 Chinese participants. We measured sleep timing (midpoint between bedtime and wake up time) using repeated time-use surveys (4 weeks during 1 year) and previous night sleep duration from questionnaires completed before sample donation.


We found 64 metabolites that were associated with sleep timing with a false discovery rate of 0.2 or lower, after adjusting for potential confounders. Notably, we found that later sleep timing was associated with higher levels of multiple metabolites in amino acid metabolism, including branched chain amino acids and their gamma-glutamyl dipeptides. We also found widespread associations between sleep timing and numerous metabolites in lipid metabolism, including bile acids, carnitines and fatty acids. In contrast, previous night sleep duration was not associated with plasma metabolites in our study.


Sleep timing was associated with a large number of metabolites across a variety of biochemical pathways. Some metabolite associations are consistent with a relationship between late chronotype and adverse effects on cardiometabolic health.


Sleep duration Sleep timing Metabolomics 



This research was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Department of Health and Human Services.

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11306_2017_1205_MOESM1_ESM.doc (3 mb)
Supplementary material 1 (DOC 3097 KB)


  1. Adams, S. H. (2011). Emerging perspectives on essential amino acid metabolism in obesity and the insulin-resistant state. Advances in Nutrition: An International Review Journal, 2(6), 445–456.CrossRefGoogle Scholar
  2. Ang, J. E., Revell, V., Mann, A., Mantele, S., Otway, D. T., Johnston, J. et al. (2012). Identification of human plasma metabolites exhibiting time-of-day variation using an untargeted liquid chromatography-mass spectrometry metabolomic approach. Chronobiology International, 29, 868–881.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bailey, S. M., Udoh, U. S., & Young, M. E. (2014). Circadian regulation of metabolism. Journal of Endocrinology, 222, R75–R96.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Bass, J., & Takahashi, J. S. (2010). Circadian integration of metabolism and energetics. Science, 330, 1349–1354.CrossRefPubMedPubMedCentralGoogle Scholar
  5. Batch, B. C., Hyland, K., & Svetkey, L. P. (2014). Branch chain amino acids: Biomarkers of health and disease. Current Opinion in Clinical Nutrition and Metabolic Care, 17, 86–89.PubMedGoogle Scholar
  6. Bell, L. N., Kilkus, J. M., Booth, J. N., Bromley, L. E., 3rd, Imperial, J. G., & Penev, P. D. (2013). Effects of sleep restriction on the human plasma metabolome. Physiology and Behavior, 122, 25–31.CrossRefPubMedGoogle Scholar
  7. Brinton, E. A. (2008). Novel pathways for glycaemic control in type 2 diabetes: Focus on bile acid modulation. Diabetes Obesity and Metabolism, 10, 1004–1011.CrossRefGoogle Scholar
  8. Cappuccio, F. P., Cooper, D., D’elia, L., Strazzullo, P., & Miller, M. A. (2011). Sleep duration predicts cardiovascular outcomes: A systematic review and meta-analysis of prospective studies. European Heart Journal, 32, 1484–1492.CrossRefPubMedGoogle Scholar
  9. Cappuccio, F. P., D’elia, L., Strazzullo, P., & MILLER, M. A. (2010). Quantity and quality of sleep and incidence of type 2 diabetes: A systematic review and meta-analysis. Diabetes Care, 33, 414–420.CrossRefPubMedGoogle Scholar
  10. Dallmann, R., Viola, A. U., Tarokh, L., Cajochen, C., & Brown, S. A. (2012). The human circadian metabolome. Proceedings of the National Academy of Sciences of the United States of America, 109, 2625–2629.CrossRefPubMedPubMedCentralGoogle Scholar
  11. Davies, S. K., Ang, J. E., Revell, V. L., Holmes, B., MANN, A., Robertson, F. P., et al. (2014). Effect of sleep deprivation on the human metabolome. Proceedings of the National Academy of Sciences of the United States of America, 111, 10761–10766.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Dehaven, C. D., Evans, A. M., Dai, H. P., & Lawton, K. A. (2010). Organization of GC/MS and LC/MS metabolomics data into chemical libraries. Journal of Cheminformatics, 2, 9.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Dinis-Oliveira, R. J. (2016). Oxidative and non-oxidative metabolomics of ethanol. Current Drug Metabolism, 17(4), 327–335.CrossRefPubMedGoogle Scholar
  14. Duffield, G. E., Best, J. D., Meurers, B. H., Bittner, A., Loros, J. J., & Dunlap, J. C. (2002). Circadian programs of transcriptional activation, signaling, and protein turnover revealed by microarray analysis of mammalian cells. Current Biology, 12, 551–557.CrossRefPubMedGoogle Scholar
  15. 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.CrossRefPubMedGoogle Scholar
  16. Farkkila, M. A., Kairemo, K. J., Taavitsainen, M. J., Strandberg, T. A., & Miettinen, T. A. (1996). Plasma lathosterol as a screening test for bile acid malabsorption due to ileal resection: Correlation with (75)SeHCAT test and faecal bile acid excretion. Clinical Science, 90, 315–319.CrossRefPubMedGoogle Scholar
  17. Ferrannini, E., Natali, A., Camastra, S., Nannipieri, M., Mari, A., Adam, K. P., et al. (2013). Early metabolic markers of the development of dysglycemia and type 2 diabetes and their physiological significance. Diabetes, 62, 1730–1737.CrossRefPubMedPubMedCentralGoogle Scholar
  18. Gall, W. E., Beebe, K., Lawton, K. A., Adam, K. P., Mitchell, M. W., Nakhle, P. J., et al. (2010). Alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS ONE, 5, e10883.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Gan, Y., Yang, C., Tong, X., Sun, H., Cong, Y., Yin, X., et al. (2015). Shift work and diabetes mellitus: A meta-analysis of observational studies. Occupational and Environmental Medicine, 72, 72–78.CrossRefPubMedGoogle Scholar
  20. Gooley, J. J., & Chua, E. C. P. (2014). Diurnal regulation of lipid metabolism and applications of circadian lipidomics. Journal of Genetics and Genomics, 41(5), 231–250.CrossRefPubMedGoogle Scholar
  21. Guertin, K. A., Loftfield, E., Boca, S. M., Sampson, J. N., Moore, S. C., Xiao, Q., & Sinha, R. (2015). Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer. The American Journal of Clinical Nutrition, 101(5), 1000–1011.CrossRefPubMedPubMedCentralGoogle Scholar
  22. Guertin, K. A., Moore, S. C., Sampson, J. N., Huang, W. Y., Xiao, Q., Stolzenberg-Solomon, R. Z., et al. (2014). Metabolomics in nutritional epidemiology: Identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations. The American Journal of Clinical Nutrition, 100(1), 208-217.CrossRefPubMedCentralGoogle Scholar
  23. Hughes, M. E., Ditacchio, L., Hayes, K. R., Vollmers, C., Pulivarthy, S., Baggs, J. E., et al. (2009). Harmonics of circadian gene transcription in mammals. PLoS Genetics, 5, e1000442.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Kantermann, T., Sung, H., & Burgess, H. J. (2015). Comparing the morningness-eveningness questionnaire and munich chronotype questionnaire to the dim light melatonin onset. Journal of Biological Rhythms, 30, 449–453.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Lynch, C. J., & Adams, S. H. (2014). Branched-chain amino acids in metabolic signalling and insulin resistance. Nature Reviews Endocrinology, 10(12), 723–736.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Matthan, N. R., Zhu, L., Pencina, M., D’Agostino, R. B., Schaefer, E. J., & Lichtenstein, A. H. (2013). Sex-specific differences in the predictive value of cholesterol homeostasis markers and 10-year cardiovascular disease event rate in framingham offspring study participants. Journal of the American Heart Association, 2(1), e005066.CrossRefPubMedPubMedCentralGoogle Scholar
  27. McCoin, C. S., Knotts, T. A., & Adams, S. H. (2015). Acylcarnitines [mdash] old actors auditioning for new roles in metabolic physiology. Nature Reviews Endocrinology, 11, 617–625.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Moore, S. C., Matthews, C. E., Sampson, J. N., Stolzenberg-Solomon, R. Z., Zheng, W., Cai, Q., et al. (2014). Human metabolic correlates of body mass index. Metabolomics, 10, 259–269.CrossRefPubMedGoogle Scholar
  29. Natale, V., Plazzi, G., & Martoni, M. (2009). Actigraphy in the assessment of insomnia: A quantitative approach. Sleep, 32, 767–771.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Panda, S., Antoch, M. P., Miller, B. H., Su, A. I., Schook, A. B., Straume, M., et al. (2002). Coordinated transcription of key pathways in the mouse by the circadian clock. Cell, 109(3), 307–320.CrossRefPubMedGoogle Scholar
  31. Peters, T. M., Moore, S. C., Xiang, Y. B., Yang, G., Shu, X. O., Ekelund, U., et al. (2010). Accelerometer-measured physical activity in Chinese adults. American Journal of Preventive Medicine, 38(6), 583–591.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Roehrs, T., & Roth, T. (2008). Caffeine: Sleep and daytime sleepiness. Sleep Medicine Reviews, 12(2), 153–162.CrossRefPubMedGoogle Scholar
  33. Roenneberg, T., Kuehnle, T., Juda, M., Kantermann, T., Allebrandt, K., Gordijn, M., et al. (2007). Epidemiology of the human circadian clock. Sleep Medicine Reviews, 11(6), 429–438.CrossRefPubMedGoogle Scholar
  34. Roenneberg, T., Wirz-Justice, A., & Merrow, M. (2003). Life between clocks: Daily temporal patterns of human chronotypes. Journal of Biological Rhythms, 18(1), 80–90.CrossRefPubMedGoogle Scholar
  35. Sampson, J. N., Boca, S. M., Shu, X. O., Stolzenberg-Solomon, R. Z., Matthews, C. E., Hsing, A. W., et al. (2013). Metabolomics in epidemiology: Sources of variability in metabolite measurements and implications. Cancer Epidemiology and Prevention Biomarkers, 22(4), 631–640.CrossRefGoogle Scholar
  36. Shan, Z., Ma, H., Xie, M., Yan, P., Guo, Y., Bao, W., et al. (2015). Sleep duration and risk of type 2 diabetes: A meta-analysis of prospective studies. Diabetes care, 38(3), 529–537.CrossRefPubMedGoogle Scholar
  37. Shu, X. O., Li, H., Yang, G., Gao, J., Cai, H., Takata, Y., et al. (2015). Cohort profile: The shanghai men’s health study. International Journal of Epidemiology, 44(3), 810–818.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Tom, A., & Nair, K. S. (2006). Assessment of branched-chain amino acid status and potential for biomarkers. The Journal of Nutrition, 136(1), 324S–330S.PubMedGoogle Scholar
  39. Van Drongelen, A., Boot, C. R., Merkus, S. L., Smid, T., & Van Der Beek, A. J. (2011). The effects of shift work on body weight change—a systematic review of longitudinal studies. Scandinavian Journal of Work, Environment and Health, 37, 263–275.CrossRefPubMedGoogle Scholar
  40. Vyas, M. V., Garg, A. X., Iansavichus, A. V., Costella, J., Donner, A., Laugsand, L. E., et al. (2012). Shift work and vascular events: Systematic review and meta-analysis. BMJ (Clinical Research ed.), 345, e4800.Google Scholar
  41. Wang, F., Zhang, L., Zhang, Y., Zhang, B., He, Y., Xie, S., et al. (2014). Meta-analysis on night shift work and risk of metabolic syndrome. Occupational and Environmental Medicine, 71(1), A78–A78.CrossRefGoogle Scholar
  42. Weljie, A. M., Meerlo, P., Goel, N., Sengupta, A., Kayser, M. S., Abel, T., et al. (2015). Oxalic acid and diacylglycerol 36:3 are cross-species markers of sleep debt. Proceedings of the National Academy of Sciences, 112(8), 2569–2574.CrossRefGoogle Scholar
  43. Wittmann, M., Dinich, J., Merrow, M., & Roenneberg, T. (2006). Social jetlag: Misalignment of biological and social time. Chronobiology International, 23(1–2), 497–509.CrossRefPubMedGoogle Scholar
  44. Wong, P. M., Hasler, B. P., Kamarck, T. W., Muldoon, M. F., & Manuck, S. B. (2015). Social jetlag, chronotype, and cardiometabolic risk. The Journal of Clinical Endocrinology & Metabolism, 100(12), 4612–4620.CrossRefGoogle Scholar
  45. Wu, Y., Zhai, L., & Zhang, D. (2014). Sleep duration and obesity among adults: A meta-analysis of prospective studies. Sleep Medicine, 15(12), 1456–1462.CrossRefPubMedGoogle Scholar
  46. Xiao, Q., Moore, S. C., Keadle, S. K., Xiang, Y. B., Zheng, W., Peters, T. M., et al. (2016). Objectively measured physical activity and plasma metabolomics in the Shanghai Physical Activity Study. International Journal of Epidemiology, 45(5), 1433-1444.CrossRefPubMedGoogle Scholar
  47. Zheng, W., Chow, W.H., Yang, G., Jin, F., Rothman, N., Blair, A., et al. (2005). The Shanghai Women’s Health Study: Rationale, study design, and baseline characteristics. American Journal of Epidemiology, 162, 1123–1131.CrossRefPubMedGoogle Scholar
  48. Zou, H., Hastie, T., & Tibshirani, R. (2006). Sparse principal component analysis. Journal of Computational and Graphical Statistics, 15, 265–286.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Health and Human PhysiologyUniversity of IowaIowa CityUSA
  2. 2.Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer InstituteNational Institutes of HealthRockvilleUSA
  3. 3.Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer InstituteNational Institutes of HealthRockvilleUSA
  4. 4.Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer CenterVanderbilt University School of MedicineNashvilleUSA
  5. 5.Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer InstituteNational Institutes of HealthRockvilleUSA

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