Table of Contents Author Guidelines Submit a Manuscript
Journal of Diabetes Research
Volume 2016 (2016), Article ID 9361958, 6 pages
http://dx.doi.org/10.1155/2016/9361958
Research Article

Delay in the Detrended Fluctuation Analysis Crossover Point as a Risk Factor for Type 2 Diabetes Mellitus

1Servicio de Medicina Interna, Hospital Universitario de Mostoles, Rio Jucar s/n, Mostoles, 28935 Madrid, Spain
2European University of Madrid, Villaviciosa de Odón, Spain

Received 3 February 2016; Revised 11 April 2016; Accepted 27 April 2016

Academic Editor: Janet H. Southerland

Copyright © 2016 Manuel Varela et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Ceriello, K. Esposito, L. Piconi et al., “Oscillating glucose is more deleterious to endothelial function and oxidative stress than mean glucose in normal and type 2 diabetic patients,” Diabetes, vol. 57, no. 5, pp. 1349–1354, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. L. Monnier, E. Mas, C. Ginet et al., “Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes,” The Journal of the American Medical Association, vol. 295, no. 14, pp. 1681–1687, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Muggeo, G. Zoppini, E. Bonora et al., “Fasting plasma glucose variability predicts 10-year survival of type 2 diabetic patients: the Verona Diabetes Study,” Diabetes Care, vol. 23, no. 1, pp. 45–50, 2000. View at Publisher · View at Google Scholar · View at Scopus
  4. R. M. Bergenstal, “Glycemic variability and diabetes complications: does it matter? simply put, there are better glycemic markers!,” Diabetes Care, vol. 38, no. 8, pp. 1615–1621, 2015. View at Publisher · View at Google Scholar
  5. R. Brunner, G. Adelsmayr, H. Herkner, C. Madl, and U. Holzinger, “Glycemic variability and glucose complexity in critically ill patients: a retrospective analysis of continuous glucose monitoring data,” Critical Care, vol. 16, no. 5, article R175, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Churruca, L. Vigil, E. Luna, J. Ruiz-Galiana, and M. Varela, “The route to diabetes: loss of complexity in the glycemic profile from health through the metabolic syndrome to type 2 diabetes,” Diabetes, Metabolic Syndrome and Obesity, vol. 1, pp. 3–11, 2008. View at Google Scholar
  7. K.-D. Kohnert, P. Heinke, L. Vogt, P. Augstein, and E. Salzsieder, “Declining β-cell function is associated with the lack of long-range negative correlation in glucose dynamics and increased glycemic variability: a retrospective analysis in patients with type 2 diabetes,” Journal of Clinical and Translational Endocrinology, vol. 1, no. 4, pp. 192–199, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. K. Lundelin, L. Vigil, S. Bua, I. Gomez-Mestre, T. Honrubia, and M. Varela, “Differences in complexity of glycemic profile in survivors and nonsurvivors in an intensive care unit: a pilot study,” Critical Care Medicine, vol. 38, no. 3, pp. 849–854, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Ogata, K. Tokuyama, S. Nagasaka et al., “Long-range negative correlation of glucose dynamics in humans and its breakdown in diabetes mellitus,” American Journal of Physiology—Regulatory Integrative and Comparative Physiology, vol. 291, no. 6, pp. R1638–R1643, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Ogata, K. Tokuyama, S. Nagasaka et al., “Long-range correlated glucose fluctuations in diabetes,” Methods of Information in Medicine, vol. 46, no. 2, pp. 222–226, 2007. View at Google Scholar · View at Scopus
  11. H. Ogata, K. Tokuyama, S. Nagasaka et al., “The lack of long-range negative correlations in glucose dynamics is associated with worse glucose control in patients with diabetes mellitus,” Metabolism: Clinical and Experimental, vol. 61, no. 7, pp. 1041–1050, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Varela, C. Rodriguez, L. Vigil, E. Cirugeda, A. Colas, and B. Vargas, “Glucose series complexity at the threshold of diabetes,” Journal of Diabetes, vol. 7, no. 2, pp. 287–293, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. L. Vigil, E. Condes, M. Varela et al., “Glucose series complexity in hypertensive patients,” Journal of the American Society of Hypertension, vol. 8, no. 9, pp. 630–636, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. N. Yamamoto, Y. Kubo, K. Ishizawa et al., “Detrended fluctuation analysis is considered to be useful as a new indicator for short-term glucose complexity,” Diabetes Technology and Therapeutics, vol. 12, no. 10, pp. 775–783, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Crenier, M. Lytrivi, A. Van Dalem, B. Keymeulen, and B. Corvilain, “Glucose complexity estimates insulin resistance in either nondiabetic individuals or in type 1 diabetes,” The Journal of Clinical Endocrinology & Metabolism, vol. 101, no. 4, pp. 1490–1497, 2016. View at Publisher · View at Google Scholar
  16. J.-L. Chen, P.-F. Chen, and H.-M. Wang, “Decreased complexity of glucose dynamics in diabetes: evidence from multiscale entropy analysis of continuous glucose monitoring system data,” American Journal of Physiology—Regulatory Integrative and Comparative Physiology, vol. 307, no. 2, pp. R179–R183, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. M. D. Costa, T. Henriques, M. N. Munshi, A. R. Segal, and A. L. Goldberger, “Dynamical glucometry: use of multiscale entropy analysis in diabetes,” Chaos, vol. 24, no. 3, p. 033139, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. C.-K. Peng, S. Havlin, H. E. Stanley, and A. L. Goldberger, “Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series,” Chaos, vol. 5, no. 1, pp. 82–87, 1995. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Bergman, “Pathophysiology of prediabetes and treatment implications for the prevention of type 2 diabetes mellitus,” Endocrine, vol. 43, no. 3, pp. 504–513, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Lilly and M. Godwin, “Treating prediabetes with metformin: systematic review and meta-analysis,” Canadian Family Physician, vol. 55, no. 4, pp. 363–369, 2009. View at Google Scholar · View at Scopus
  21. S. K. Malin, R. Gerber, S. R. Chipkin, and B. Braun, “Independent and combined effects of exercise training and metformin on insulin sensitivity in individuals with prediabetes,” Diabetes Care, vol. 35, no. 1, pp. 131–136, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. O. J. Phung, W. L. Baker, V. Tongbram, A. Bhardwaj, and C. I. Coleman, “Oral antidiabetic drugs and regression from prediabetes to normoglycemia: a meta-analysis,” Annals of Pharmacotherapy, vol. 46, no. 4, pp. 469–476, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. R. E. Ratner and A. Sathasivam, “Treatment recommendations for prediabetes,” Medical Clinics of North America, vol. 95, no. 2, pp. 385–395, 2011. View at Publisher · View at Google Scholar · View at Scopus