Consistent sleep onset and maintenance of body weight after weight loss: An analysis of data from the NoHoW trial
Autoři:
Sofus C. Larsen aff001; Graham Horgan aff002; Marie-Louise K. Mikkelsen aff001; Antonio L. Palmeira aff003; Sarah Scott aff004; Cristiana Duarte aff004; Inês Santos aff003; Jorge Encantado aff003; Ruairi O'Driscoll aff004; Jake Turicchi aff004; Joanna Michalowska aff004; R. James Stubbs aff004; Berit L. Heitmann aff001
Působiště autorů:
Research Unit for Dietary Studies, The Parker Institute, Bispebjerg and Frederiksberg Hospital, The Capital Region, Denmark
aff001; Biomathematics and Statistics Scotland, Aberdeen, United Kingdom
aff002; Centro Interdisciplinar para o Estudo da Performance Humana, Faculdade de Motricidade Humana, Universidade de Lisboa, Lisbon, Portugal
aff003; School of Psychology, Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom
aff004; Laboratório de Nutrição, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
aff005; The Boden Institute of Obesity, Nutrition, Exercise & Eating Disorders, The University of Sydney, Sydney, Australia
aff006; Department of Public Health, Section for General Practice, University of Copenhagen, Copenhagen, Denmark
aff007
Vyšlo v časopise:
Consistent sleep onset and maintenance of body weight after weight loss: An analysis of data from the NoHoW trial. PLoS Med 17(7): e32767. doi:10.1371/journal.pmed.1003168
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pmed.1003168
Souhrn
Background
Several studies have suggested that reduced sleep duration and quality are associated with an increased risk of obesity and related metabolic disorders, but the role of sleep in long-term weight loss maintenance (WLM) has not been thoroughly explored using prospective data.
Methods and findings
The present study is an ancillary study based on data collected on participants from the Navigating to a Healthy Weight (NoHoW) trial, for which the aim was to test the efficacy of an evidence-based digital toolkit, targeting self-regulation, motivation, and emotion regulation, on WLM among 1,627 British, Danish, and Portuguese adults. Before enrolment, participants had achieved a weight loss of ≥5% and had a BMI of ≥25 kg/m2 prior to losing weight. Participants were enrolled between March 2017 and March 2018 and followed during the subsequent 12-month period for change in weight (primary trial outcome), body composition, metabolic markers, diet, physical activity, sleep, and psychological mediators/moderators of WLM (secondary trial outcomes). For the present study, a total of 967 NoHoW participants were included, of which 69.6% were women, the mean age was 45.8 years (SD 11.5), the mean baseline BMI was 29.5 kg/m2 (SD 5.1), and the mean weight loss prior to baseline assessments was 11.4 kg (SD 6.4). Objectively measured sleep was collected using the Fitbit Charge 2 (FC2), from which sleep duration, sleep duration variability, sleep onset, and sleep onset variability were assessed across 14 days close to baseline examinations. The primary outcomes were 12-month changes in body weight (BW) and body fat percentage (BF%). The secondary outcomes were 12-month changes in obesity-related metabolic markers (blood pressure, low- and high-density lipoproteins [LDL and HDL], triglycerides [TGs], and glycated haemoglobin [HbA1c]). Analysis of covariance and multivariate linear regressions were conducted with sleep-related variables as explanatory and subsequent changes in BW, BF%, and metabolic markers as response variables. We found no evidence that sleep duration, sleep duration variability, or sleep onset were associated with 12-month weight regain or change in BF%. A higher between-day variability in sleep onset, assessed using the standard deviation across all nights recorded, was associated with weight regain (0.55 kg per hour [95% CI 0.10 to 0.99]; P = 0.016) and an increase in BF% (0.41% per hour [95% CI 0.04 to 0.78]; P = 0.031). Analyses of the secondary outcomes showed that a higher between-day variability in sleep duration was associated with an increase in HbA1c (0.02% per hour [95% CI 0.00 to 0.05]; P = 0.045). Participants with a sleep onset between 19:00 and 22:00 had the greatest reduction in diastolic blood pressure (DBP) (P = 0.02) but also the most pronounced increase in TGs (P = 0.03). The main limitation of this study is the observational design. Hence, the observed associations do not necessarily reflect causal effects.
Conclusion
Our results suggest that maintaining a consistent sleep onset is associated with improved WLM and body composition. Sleep onset and variability in sleep duration may be associated with subsequent change in different obesity-related metabolic markers, but due to multiple-testing, the secondary exploratory outcomes should be interpreted cautiously.
Trial registration
The trial was registered with the ISRCTN registry (ISRCTN88405328).
Klíčová slova:
Alcohol consumption – Blood pressure – Body weight – Emotions – Obesity – Physical activity – Sleep – Weight loss
Zdroje
1. Bhaskaran K, dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3.6 million adults in the UK. Lancet Diabetes Endocrinol. 2018;6(12):944‐953. doi: 10.1016/S2213-8587(18)30288-2 30389323
2. Aune D, Sen A, Prasad M, Norat T, Janszky I, Tonstad S, et al. BMI and all cause mortality: systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3.74 million deaths among 30.3 million participants. 2016;353:i2156. doi: 10.1136/bmj.i2156 27146380
3. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014.
4. Franz MJ, VanWormer JJ, Crain AL, Boucher JL, Histon T, Caplan W, et al. Weight-Loss Outcomes: A Systematic Review and Meta-Analysis of Weight-Loss Clinical Trials with a Minimum 1-Year Follow-Up. Journal of the American Dietetic Association. 2007;107(10):1755–67. doi: 10.1016/j.jada.2007.07.017 17904936
5. Dombrowski SU, Knittle K, Avenell A, Araújo-Soares V, Sniehotta FF. Long term maintenance of weight loss with non-surgical interventions in obese adults: systematic review and meta-analyses of randomised controlled trials. 2014;348:g2646. doi: 10.1136/bmj.g2646 25134100
6. Varkevisser RDM, van Stralen MM, Kroeze W, Ket JCF, Steenhuis IHM. Determinants of weight loss maintenance: a systematic review. 2019;20(2):171–211. doi: 10.1111/obr.12772 30324651
7. Ross KM, Graham Thomas J, Wing RRJJoBM. Successful weight loss maintenance associated with morning chronotype and better sleep quality. 2016;39(3):465–71. doi: 10.1007/s10865-015-9704-8 26660638
8. Yannakoulia M, Anastasiou CA, Karfopoulou E, Pehlivanidis A, Panagiotakos DB, Vgontzas A. Sleep quality is associated with weight loss maintenance status: the MedWeight study. Sleep Medicine. 2017;34:242–5. doi: 10.1016/j.sleep.2017.01.023 28476339
9. Zuraikat FM, Thomas E, Roeshot D, Gallagher D, St-Onge M-P. Can Healthy Sleep Improve Long-Term Bariatric Surgery Outcomes? Results of a Pilot Study and Call for Further Research. Obesity. 2019;27(11):1769–71. doi: 10.1002/oby.22601 31565843
10. Knutson KL, Spiegel K, Penev P, Van CE. The metabolic consequences of sleep deprivation. Sleep Med Rev. 2007;11(3):163–78. doi: 10.1016/j.smrv.2007.01.002 17442599
11. Wu Y, Zhai L, Zhang D. Sleep duration and obesity among adults: a meta-analysis of prospective studies. Sleep Medicine. 2014;15(12):1456–62. doi: 10.1016/j.sleep.2014.07.018 25450058
12. Patel SR, Hu FB. Short Sleep Duration and Weight Gain: A Systematic Review. Obesity. 2008;16(3):643–53. doi: 10.1038/oby.2007.118 18239586
13. Currie A, Cappuccio FP, Stranges S, Taggart FM, Miller MA, Kandala NB, et al. Meta-Analysis of Short Sleep Duration and Obesity in Children and Adults. Sleep. 2008;31(5):619–26. doi: 10.1093/sleep/31.5.619 18517032
14. Fatima Y, Doi SAR, Mamun AA. Sleep quality and obesity in young subjects: a meta-analysis. 2016;17(11):1154–66.
15. Lian Y, Yuan Q, Wang G, Tang F. Association between sleep quality and metabolic syndrome: A systematic review and meta-analysis. Psychiatry Research. 2019;274:66–74. doi: 10.1016/j.psychres.2019.01.096 30780064
16. Shan Z, Ma H, Xie M, Yan P, Guo Y, Bao W, et al. Sleep Duration and Risk of Type 2 Diabetes: A Meta-analysis of Prospective Studies. 2015;38(3):529–37. doi: 10.2337/dc14-2073 25715415
17. Ogilvie RP, Patel SR. The epidemiology of sleep and obesity. Sleep Health. 2017;3(5):383–8. doi: 10.1016/j.sleh.2017.07.013 28923198
18. Papandreou C, Bulló M, Díaz-López A, Martínez-González MA, Corella D, Castañer O, et al. High sleep variability predicts a blunted weight loss response and short sleep duration a reduced decrease in waist circumference in the PREDIMED-Plus Trial. International Journal of Obesity. 2019.
19. Scott SE, Duarte C, Encantado J, Evans EH, Harjumaa M, Heitmann BL, et al. The NoHoW protocol: a multicentre 2×2 factorial randomised controlled trial investigating an evidence-based digital toolkit for weight loss maintenance in European adults. BMJ Open. 2019;9(9):e029425. doi: 10.1136/bmjopen-2019-029425 31575569
20. Fitbit Charge 2. User Manual: Version 1.2. 2019 [cited 2020 Jun 28]. Available from: https://staticcs.fitbit.com/content/assets/help/manuals/manual_charge_2_en_US.pdf
21. Fitbit Help: What should I know about sleep stages? 2019 [cited 2020 Jun 28]. Available from: https://help.fitbit.com/articles/en_US/Help_article/2163
22. de Zambotti M, Goldstone A, Claudatos S, Colrain IM, Baker FC. A validation study of Fitbit Charge 2 compared with polysomnography in adults. Chronobiol Int. 2018;35(4):465–76. doi: 10.1080/07420528.2017.1413578 29235907
23. Larsen SC, Horgan G, Mikkelsen M-LK, Palmeira AL, Scott S, Duarte C, et al. Association between objectively measured sleep duration, adiposity and weight loss history. Int J Obes (Lond). 2020: doi: 10.1038/s41366-020-0537-3 31937906
24. Moissl UM, Wabel P, Chamney PW, Bosaeus I, Levin NW, Bosy-Westphal A, et al. Body fluid volume determination via body composition spectroscopy in health and disease. 2006;27(9):921–33.
25. Wood JR, Kaminski BM, Kollman C, Beck RW, Hall CA, Yun JP, et al. Accuracy and Precision of the Axis-Shield Afinion Hemoglobin A1c Measurement Device. 2012;6(2):380–6. doi: 10.1177/193229681200600224 22538150
26. Jain A, Rao N, Sharifi M, Bhatt N, Patel P, Nirmal D, et al. Evaluation of the point of care Afinion AS100 analyser in a community setting. 2017;54(3):331–41.
27. Statistics UIf. International Standard Classification of Education: ISCED 2011. UNESCO Institute for Statistics. 2012 2012.
28. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24(4):385–96. 6668417
29. Harding JL, Backholer K, Williams ED, Peeters A, Cameron AJ, Hare MJ, et al. Psychosocial stress is positively associated with body mass index gain over 5 years: evidence from the longitudinal AusDiab study. Obesity (Silver Spring). 2014;22(1):277–86.
30. Patel SR, Hayes AL, Blackwell T, Evans DS, Ancoli-Israel S, Wing YK, et al. The association between sleep patterns and obesity in older adults. International Journal of Obesity. 2014;38(9):1159–64. doi: 10.1038/ijo.2014.13 24458262
31. Liu Q, Shi J, Duan P, Liu B, Li T, Wang C, et al. Is shift work associated with a higher risk of overweight or obesity? A systematic review of observational studies with meta-analysis. International Journal of Epidemiology. 2018;47(6):1956–71. doi: 10.1093/ije/dyy079 29850840
32. Sun M, Feng W, Wang F, Li P, Li Z, Li M, et al. Meta-analysis on shift work and risks of specific obesity types. Obes Rev. 2018;19(1):28–40. doi: 10.1111/obr.12621 28975706
33. Teixeira PJ, Carraça EV, Marques MM, Rutter H, Oppert J-M, De Bourdeaudhuij I, et al. Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Medicine. 2015;13(1):84.
34. Bei B, Wiley JF, Trinder J, Manber R. Beyond the mean: A systematic review on the correlates of daily intraindividual variability of sleep/wake patterns. Sleep Medicine Reviews. 2016;28:108–24. doi: 10.1016/j.smrv.2015.06.003 26588182
35. Stunkard AJ, Faith MS, Allison KC. Depression and obesity. Biological Psychiatry. 2003;54(3):330–7. doi: 10.1016/s0006-3223(03)00608-5 12893108
36. Bei BD, Manber R, Allen NB, Trinder J, Wiley JF. Too Long, Too Short, or Too Variable? Sleep Intraindividual Variability and Its Associations With Perceived Sleep Quality and Mood in Adolescents During Naturalistically Unconstrained Sleep. Sleep. 2016;40(2).
37. Chaput J-P, Tremblay A. Insufficient Sleep as a Contributor to Weight Gain: An Update. Current Obesity Reports. 2012;1(4):245–56.
38. Barclay JL, Husse J, Bode B, Naujokat N, Meyer-Kovac J, Schmid SM, et al. Circadian Desynchrony Promotes Metabolic Disruption in a Mouse Model of Shiftwork. PLoS ONE. 2012;7(5):e37150. doi: 10.1371/journal.pone.0037150 22629359
39. Shi S-q, Ansari TS, McGuinness Owen P, Wasserman David H, Johnson Carl H. Circadian Disruption Leads to Insulin Resistance and Obesity. Current Biology. 2013;23(5):372–81. doi: 10.1016/j.cub.2013.01.048 23434278
40. Turek FW, Joshu C, Kohsaka A, Lin E, Ivanova G, McDearmon E, et al. Obesity and Metabolic Syndrome in Circadian <em>Clock</em> Mutant Mice. Science. 2005;308(5724):1043. doi: 10.1126/science.1108750 15845877
41. Alhussain MH, Macdonald IA, Taylor MA. Irregular meal-pattern effects on energy expenditure, metabolism, and appetite regulation: a randomized controlled trial in healthy normal-weight women. The American Journal of Clinical Nutrition. 2016;104(1):21–32. doi: 10.3945/ajcn.115.125401 27305952
42. Pot GK, Almoosawi S, Stephen AM. Meal irregularity and cardiometabolic consequences: results from observational and intervention studies. Proceedings of the Nutrition Society. 2016;75(4):475–86. doi: 10.1017/S0029665116000239 27327128
43. Hernández-García J, Navas-Carrillo D, Orenes-Piñero E. Alterations of circadian rhythms and their impact on obesity, metabolic syndrome and cardiovascular diseases. Critical Reviews in Food Science and Nutrition. 2019:1–10.
44. Nakajima H, Kaneita Y, Yokoyama E, Harano S, Tamaki T, Ibuka E, et al. Association between sleep duration and hemoglobin A1c level. Sleep Medicine. 2008;9(7):745–52. doi: 10.1016/j.sleep.2007.07.017 17921062
45. Aggarwal B, Makarem N, Shah R, Emin M, Wei Y, St‐Onge MP, et al. Effects of Inadequate Sleep on Blood Pressure and Endothelial Inflammation in Women: Findings From the American Heart Association Go Red for Women Strategically Focused Research Network. 2018;7(12):e008590.
46. Li Y, Vgontzas AN, Fernandez-Mendoza J, Bixler EO, Sun Y, Zhou J, et al. Insomnia With Physiological Hyperarousal Is Associated With Hypertension. 2015;65(3):644–50. doi: 10.1161/HYPERTENSIONAHA.114.04604 25624338
47. Vyas MV, Garg AX, Iansavichus AV, Costella J, Donner A, Laugsand LE, et al. Shift work and vascular events: systematic review and meta-analysis. 2012;345:e4800.
48. Nisar M, Mohammad R, Arshad A, Hashmi I, Yousuf S, Baig S. Influence of Dietary Intake on Sleeping Patterns of Medical Students. Cureus. 2019;11.
49. DiNicolantonio JJ, O’Keefe JH. Effects of dietary fats on blood lipids: a review of direct comparison trials. Open Heart. 2018;5(2):e000871. doi: 10.1136/openhrt-2018-000871 30094038
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