ABC | Volume 111, Nº1, July 2018

Original Article Oliveira et al Nonlinear dynamics in young adults with type 1 diabetes Arq Bras Cardiol. 2018; 111(1):94-101 1. Sociedade Brasileira de Diabetes. (SBD). Diretrizes Sociedade Brasileira de Diabetes.DiabetesMellitotipo1etipo2.SãoPaulo:ACFarmacêutica;2015. 2. Rolim LC, Sá JR, Chacra AR, Dib SA. Diabetic cardiovascular autonomic neuropathy:risk factors,clinical impactandearlydiagnosis.ArqBrasCardiol. 2008;90(4):e23-31. 3. Vinik AI, Ziegler D. Diabetic cardiovascular autonomic neuropathy. Circulation. 2007;115(3):387-97. 4. Vanderlei LC, Pastre CM, Hoshi RA, Carvalho TD, Godoy MF. Basic notions of heart rate variability and its clinical applicability. Rev Bras Cir Cardiovasc. 2009;24(2):205-17. 5. Roy B, Ghatak S. Nonlinearmethods to assess changes in heart rate variability in type 2 diabetic patients. Arq Bras Cardiol. 2013;101(4):317-27. 6. Guzzetti S, Borroni E, Garbelli PE, Ceriani E, Della Bella P, Montano N, et al. Symbolic dynamics of heart rate variability: A probe to investigate cardiac autonomic modulation. Circulation. 2005;112(4):465-70. 7. Porta A, Tobaldini E, Guzzetti S, Furlan R, Montano N, Gnecchi-Ruscone T. Assessment of cardiac autonomicmodulation during graded head-up tilt by symbolic analysis of heart rate variability. Am J Physiol Heart Circ Physiol. 2007;293(1):H702-8. 8. Porta A, Guzzetti S, Furlan R, Gnecchi-Ruscone T, Montano N, Malliani A. Complexity and non linearity in short-term heart period variability : comparison of methods based on local non linear prediction. IEEE Trans Biomed Eng. 2007;54(1):94-106. 9. Porta A, Guzzetti S, Montano N, Furlan R, Pagani M, Malliani A, et al. Entropy, entropy rate, and pattern classification as tools to typify complexity in short heart period variability series. IEEE Trans Biomed Eng. 2001;48(11):1282-91. 10. Moura-TonelloSC,TakahashiAC,FranciscoCO,LopesSL,DelValeAM,Borghi- Silva A, et al. Influence of type 2 diabetes on symbolic analysis and complexity of heart rate variability inmen. Diabetol Metab Syndr. 2014;6(1):13. 11. Khandoker AH, JelinekHF, Palaniswami M. Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis. Biomed Eng Online. 2009 Jan 29;8:3. 12. GodoyMF,TakakuraIT,CorreaPR.Therelevanceofnonlineardynamicanalysis (ChaosTheory)topredictmorbidityandmortalityinpatientsundergoingsurgical myocardial revascularization. Arq Ciênc Saúde. 2005;12(4):167-71. 13. Souza NM, Pastre CM, Kastelianne A, Fernanda A, Bernardo B, Vanderlei FM, et al. Geometric indexes of heart rate of variability identifies autonomic alterations in young patients with type 1 diabetes mellitus. Curr Res Cardiol. 2016;3(2):38-42. 14. Brazilian Association for the Study of Obesity and metabolic syndrome. ABESO. III Brazilian guidelines on obesity. 3rd ed. São Paulo: AC Pharmaceuticals; 2009. 15. Vanderlei LC, Silva RA, Pastre CM, Azevedo FM, GodoyMF. Comparison of the Polar S810i monitor and the ECG for the analysis of heart rate variability inthetimeand frequencydomains.Braz JMedBiolRes.2008;41(10):854-9. 16. Ewing DJ, Neilson JM, Shapiro CM, Stewart JA, Reid W. Twenty four hour heart rate variability: effects of posture, sleep, and time of day in healthy controlsandcomparisonwithbedsidetestsofautonomic function indiabetic patients. Br Heart J. 1991;65(5):239-44. 17. Tarvainen MP, Niskanen JP, Lipponen JA, Ranta-Aho PO, Karjalainen PA. Kubios HRV - Heart rate variability analysis software. Comput Methods Programs Biomed. 2014;113(1):210-20. 18. Maher JM, Markey JC, Ebert-May D. The other half of the story: Effect size analysis in quantitative research. CBE Life Sci Educ. 2013;12(3):345-51. References symbolic analysis, further research should be encouraged so that more information can be disseminated about this method in other age groups and populations. Conclusion The results show that type 1 diabetes mellitus influences linear indexes (time and frequency domains) and the symbolic analysis; however, it does not yet influence the heart rate variability complexity. Symbolic analysis correlates with linear indexes of heart rate variability. Author contributions Conception and design of the research and analysis and interpretation of the data: Oliveira EA, Silva AKF, Christofaro DGD, Vanderlei FM, Vanderlei LCM; Acquisition of data: Oliveira EA, Silva AKF, Vanzella LM, Gomes RL; Statistical analysis: Oliveira EA, Silva AKF, Christofaro DGD, Vanderlei LCM; Obtaining financing: Oliveira EA, Silva AKF, Vanderlei LCM; Writing of the manuscript: Oliveira EA, Silva AKF, Vanzella LM, Gomes RL, Vanderlei FM, Vanderlei LCM; Critical revision of the manuscript for intellectual content: Oliveira EA, Silva AKF, Christofaro DGD, Vanzella LM, Gomes RL, Vanderlei FM, Vanderlei LCM. Potential Conflict of Interest No potential conflict of interest relevant to this article was reported. Sources of Funding This study was funded by FAPESP, process number 2013/19055-0 and partially funded by PIBIC. Study Association This article is part of the thesis of master submitted by Anne Kastelianne França da Silva, from Faculdade de Ciências e Tecnologia and UNESP-Presidente Prudente. Ethics approval and consent to participate This study was approved by the Ethics Committee of the Faculdade de Ciências e Tecnologia (FCT/UNESP), Campus Presidente Prudente under the protocol number CAAE: 22530813.9.0000.5402; opinion 417.031. All the procedures in this study were in accordance with the 1975 Helsinki Declaration, updated in 2013. Informed consent was obtained from all participants included in the study. 100