The combination of sleep duration, television (TV) time and body mass index (BMI) may be related to the alteration of cardiometabolic risk. However, there are few studies that use these variables grouped, and showing the moderating role of age. This study aimed to verify if the combination of sleep duration, TV time and BMI is associated with cardiometabolic risk and the moderating role of age in this relationship in youth.
Cross-sectional study conducted with 1411 adolescents (611 male), aged 10–17 years. Sleep duration, TV time and BMI were assessed and grouped into eight categories. Cardiometabolic risk was assessed by a continuous metabolic risk score, including the following variables: low HDL-cholesterol, elevated triglycerides, dysglycemia, high systolic blood pressure, high waist circumference and low cardiorespiratory fitness. Generalized linear models were used to test moderation of age in the relationship between the eight categories of sleep duration/television time/BMI with cardiometabolic risk.
Cardiometabolic risk factor showed association with all overweight or obesity independent of sleep time and TV time. Age moderated the relationship between sleep duration/television time/BMI with cardiometabolic risk. This association was stronger in younger adolescents (11 and 13 years), indicating that individuals with inadequate sleep, prolonged TV time and overweight/obesity present higher cardiometabolic risk values when compared to 15-year-old adolescents.
Overweight/obesity, independently of sleep duration and TV time, is the main risk factor for cardiometabolic disorders in adolescence. When moderated by age, younger adolescents that presented the combination of risk factors had higher cardiometabolic risk.
Funding source: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
We thank the collaboration of the schools, our research group from Health Research Laboratory (LAPES), Professor Miria Suzana Burgos (in memoriam), who contributed to this study and for all her dedication to the research “Schoolchildren’s health”, as well as all the support of the University of Santa Cruz do Sul – UNISC and Higher Education Personnel Improvement Coordination - Brazil (CAPES).
Research funding: This work was carried out with the support of the Higher Education Personnel Improvement Coordination - Brazil (CAPES) - Financing Code 001.
Authors contributions: APS, CPR, JDPR participated in data organization and designed the study. APS, ARG, AFD, CB, JDPR and CPR performed the statistical analysis. All the authors contributed to the elaboration of the manuscript with critical comments about it.
Competing interests: The authors declare no conflict 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. In addition, this study has been conducted in accordance with Resolution 466/2012 of the National Council of Health in Brazil. Informed consent was obtained from all individual participants included in the study. The study was approved by the Research Ethics Committee of the University of Santa Cruz do Sul (UNISC) under Opinion No. 2936223.
2. Pedigão, C. Cardiometabolic risk - a concept that unites several specialties? Rev Factores Risco 2008;8:44–9.Search in Google Scholar
3. Barstad, LH, Júlíusson, PB, Johnson, LK, Hertel, JK, Lekhal, S, Hjelmesæth, J. Gender-related differences in cardiometabolic risk factors and lifestyle behaviors in treatment-seeking adolescents with severe obesity. BMC Pediatr 2018;18:1–8. https://doi.org/10.1186/s12887-018-1057-3.Search in Google Scholar PubMed PubMed Central
4. Fonseca, H. Prevention of cardiometabolic risk in children and adolescents. Rev Factores Risco 2010;17:58–61.Search in Google Scholar
5. Pogodina, A, Rychkova, L, Kravtzova, O, Klimkina, J, Kosovtzeva, A. Cardiometabolic risk factors and health-related quality of life in adolescents with obesity. Child Obes 2017;13:499–506. https://doi.org/10.1089/chi.2016.0330.Search in Google Scholar PubMed
6. Li, L, Pérez, A, Wu, L-T, Ranjit, N, Brown, HS, Kelder, SH. Cardiometabolic risk factors among severely obese children and adolescents in the United States, 1999–2012. Child Obes 2016;12:12–9. https://doi.org/10.1089/chi.2015.0136.Search in Google Scholar PubMed
7. Staiano, AE, Harrington, DM, Broyles, ST, Gupta, AK, Katzmarzyk, PT. Television, adiposity, and cardiometabolic risk in children and adolescents. Am J Prev Med 2013;44:40–7. https://doi.org/10.1016/j.amepre.2012.09.049.Search in Google Scholar PubMed PubMed Central
8. Kobayashi, D, Kuriyama, N, Osugi, Y, Arioka, H, Takahashi, O. Longitudinal relationships between cardiovascular events, risk factors, and time-dependent sleep duration. Cardiol J 2018;25:229–35. https://doi.org/10.5603/CJ.a2017.0088.Search in Google Scholar PubMed
9. Krittanawong, C, Tunhasiriwet, A, Wang, Z, Zhang, H, Farrell, AM, Chirapongsathorn, S, et al.. Association between short and long sleep durations and cardiovascular outcomes: a systematic review and meta-analysis. Eur Hear J Acute Cardiovasc Care 2017. https://doi.org/10.1177/2048872617741733.Search in Google Scholar PubMed
10. Liu, TZ, Xu, C, Rota, M, Cai, H, Zhang, C, Shi, MJ, et al.. Sleep duration and risk of all-cause mortality: a flexible, non-linear, meta-regression of 40 prospective cohort studies. Sleep Med Rev 2017;32:28–36. https://doi.org/10.1016/j.smrv.2016.02.005.Search in Google Scholar PubMed
11. Grandner, M, Sands-Lincoln, Pak, Garland. Sleep duration, cardiovascular disease, and proinflammatory biomarkers. Nat Sci Sleep 2013;5:93–107. https://doi.org/10.2147/nss.s31063.Search in Google Scholar PubMed PubMed Central
12. Carson, V, Tremblay, MS, Chaput, J-P, Chastin, SFM. Associations between sleep duration, sedentary time, physical activity, and health indicators among Canadian children and youth using compositional analyses. Appl Physiol Nutr Metabol 2016;41:S294–302. https://doi.org/10.1139/apnm-2016-0026.Search in Google Scholar PubMed
14. Carson, V, Hunter, S, Kuzik, N, Gray, CE, Poitras, VJ, Chaput, J-P, et al.. Systematic review of sedentary behaviour and health indicators in school-aged children and youth: an update. Appl Physiol Nutr Metabol 2016;41:S240–265. https://doi.org/10.1139/apnm-2016-0026.Search in Google Scholar PubMed
15. Chastin, SFM, Palarea-Albaladejo, J, Dontje, ML, Skelton, DA. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel compositional data analysis approach. PloS One 2015;10:1–37. https://doi.org/10.1371/journal.pone.0139984.Search in Google Scholar PubMed PubMed Central
16. Dumuid, D, Stanford, TE, Ž, Pedišić, Maher, C, Lewis, LK, Martín-Fernández, JA, et al.. Adiposity and the isotemporal substitution of physical activity, sedentary time and sleep among school-aged children: a compositional data analysis approach. BMC Publ Health 2018;18:1–10. https://doi.org/10.1186/s12889-018-5207-1.Search in Google Scholar PubMed PubMed Central
17. Wiklund, P, Törmäkangas, T, Shi, Y, Wu, N, Vainionpää, A, Alen, M, et al.. Normal-weight obesity and cardiometabolic risk: a 7-year longitudinal study in girls from prepuberty to early adulthood. Obesity 2017;25:1077–82. https://doi.org/10.1002/oby.21838.Search in Google Scholar PubMed
18. Suglia, SF, Koenen, KC, Boynton-Jarrett, R, Chan, PS, Clark, CJ, Danese, A, et al.. Childhood and adolescent adversity and cardiometabolic outcomes: a scientific statement from the American heart association. Circulation 2018;137:e15–28. https://doi.org/10.1161/cir.0000000000000536.Search in Google Scholar
20. Faul, F, Erdfelder, E, Buchner, A, Lang, AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods 2009;41:1149–60. https://doi.org/10.3758/brm.41.4.1149.Search in Google Scholar
22. Hirshkowitz, M, Whiton, K, Albert, SM, Alessi, C, Bruni, O, DonCarlos, L, et al.. National Sleep Foundation’s sleep time duration recommendations: methodology and results summary. Sleep Heal 2015;1:40–3. https://doi.org/10.1016/j.sleh.2014.12.010.Search in Google Scholar PubMed
24. SBC. Brazilian Society of Hypertension. VII Brazilian guideline for hypertension. Arq Bras Cardiol 2016;107:1–103.Search in Google Scholar
25. ADA. American Diabetes Association. Standards of Medical Care in Diabetes – 2015 [Internet]. Diabetes Care; 2015:1–94 pp. Available from: http://care.diabetesjournals.org/cgi/doi/10.2337/dc15-S001.Search in Google Scholar
26. NHLBI. National Heart Lung and Blood Institute Expert. Expert panel on integrated guidelines for cardiovascular Health and risk reduction in children and adolescents summary report. United States: National Institutes of Health; 2012:1–83 p.Search in Google Scholar
27. Fernández, JR, Redden, DT, Pietrobelli, A, Allison, DB. Waist circumference percentiles in nationally representative samples of African-American, European-American, and Mexican-American children and adolescents. J Pediatr 2004;145:439–44. https://doi.org/10.1016/j.jpeds.2004.06.044.Search in Google Scholar PubMed
28. Gaya, A, Gaya, A. Testing and evaluation manual for the project sport Brazil - PROESP-BR [Internet]. Porto Alegre: UFRGS; 2016:26 p. Available from: https://www.ufrgs.br/proesp/arquivos/manual-proesp-br-2016.pdf.Search in Google Scholar
29. Bergmann, G, Bergmann, M, Castro, A, Lorenzi, T, Pinheiro, E, Moreira, R, et al.. Use of the 6-minute walk/run test to predict peak oxygen uptake in adolescents. Rev Bras Atividade Física Saúde 2014;19:64–73. https://doi.org/10.12820/rbafs.v.19n1p64.Search in Google Scholar
30. Reuter, CP, Andersen, LB, de Moura Valim, AR, Reuter, ÉM, Borfe, L, Renner, JDP, et al.. Cutoff points for continuous metabolic risk score in adolescents from southern Brazil. Am J Hum Biol 2019;31:1–5. https://doi.org/10.1002/ajhb.23211.Search in Google Scholar PubMed
31. Stavnsbo, M, Resaland, GK, Anderssen, SA, Steene-Johannessen, J, Domazet, SL, Skrede, T, et al.. Reference values for cardiometabolic risk scores in children and adolescents: suggesting a common standard. Atherosclerosis 2018;278:299–306. https://doi.org/10.1016/j.atherosclerosis.2018.10.003.Search in Google Scholar PubMed
32. Andersen, LB, Lauersen, JB, Brønd, JC, Anderssen, SA, Sardinha, LB, Steene-Johannessen, J, et al.. A new approach to define and diagnose cardiometabolic disorder in children. J Diabetes Res 2015;2015:1–10. https://doi.org/10.1155/2015/539835.Search in Google Scholar PubMed PubMed Central
33. Guerra, PH, Farias Júnior, JC, Florindo, AA, de Farias Júnior, JC, Florindo, AA. Sedentary behavior in Brazilian children and adolescents: a systematic review. Rev Saude Publica 2016;50:1–9. https://doi.org/10.1590/s1518-8787.2016050006307.Search in Google Scholar
34. Mozafarian, N, Motlagh, ME, Heshmat, R, Karimi, S, Mansourian, M, Mohebpour, F, et al.. Factors associated with screen time in Iranian children and adolescents: the CASPIAN-IV study. Int J Prev Med 2017;8:1–8. https://doi.org/10.4103/ijpvm.IJPVM_36_17.Search in Google Scholar PubMed PubMed Central
35. Martinez, SM, Blanco, E, Burrows, R, Lozoff, B, Gahagan, S. Mechanisms linking childhood weight status to metabolic risk in adolescence. Pediatr Diabetes 2020;21:203–9. https://doi.org/10.1111/pedi.12972.Search in Google Scholar PubMed
36. Prado, CV, Rech, CR, Hino, AAF, Reis, RS. Percepção de segurança no bairro e tempo despendido em frente à tela por adolescentes de Curitiba, Brasil. Rev Bras Epidemiol 2017;20:688–701. https://doi.org/10.1590/1980-5497201700040011.Search in Google Scholar PubMed
37. Chaput, J, Gray, CE, Poitras, VJ, Carson, V, Gruber, R, Olds, T, et al.. Systematic review of the relationships between sleep duration and health indicators in school-aged children and youth. Appl Physiol Nutr Metabol 2016;41:S266–82. https://doi.org/10.1139/apnm-2015-0627.Search in Google Scholar PubMed
38. Álvarez, C, Lucia, A, Ramírez‐Campillo, R, Martínez‐Salazar, C, Delgado‐Floody, P, Cadore, EL, et al.. Low sleep time is associated with higher levels of blood pressure and fat mass in Amerindian schoolchildren. Am J Hum Biol 2019;1–11.10.1002/ajhb.23303Search in Google Scholar PubMed
39. Sehn, AP, Gaya, AR, Dias, AF, Brand, C, Mota, J, Pfeiffer, KA, et al.. Relationship between sleep duration and TV time with cardiometabolic risk in adolescents. Environ Health Prev Med 2020;25:42. https://doi.org/10.1186/s12199-020-00880-7.Search in Google Scholar PubMed PubMed Central
40. Norman, GJ, Carlson, JA, Patrick, K, Kolodziejczyk, JK, Godino, JG, Huang, J, et al.. Sedentary behavior and cardiometabolic health associations in obese 11–13-year olds. Child Obes 2017;13:425–32. https://doi.org/10.1089/chi.2017.0048.Search in Google Scholar PubMed PubMed Central
41. Ruiz, LD, Zuelch, ML, Dimitratos, SM, Scherr, RE. Adolescent obesity: diet quality, psychosocial health, and cardiometabolic risk factors. Nutrients 2020;12:1–22.10.3390/nu12010043Search in Google Scholar PubMed PubMed Central
42. Tanrikulu, MA, Agirbasli, M, Berenson, G. Primordial prevention of cardiometabolic risk in childhood. Adv Exp Med Biol 2017;2017:489–96. https://doi.org/10.1007/5584_2016_172.Search in Google Scholar PubMed
The online version of this article offers supplementary material (https://doi.org/10.1515/jpem-2020-0399).
© 2020 Walter de Gruyter GmbH, Berlin/Boston