The fractal nature of blood glucose fluctuations |
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| Institution: | 1. Department of Obstetrics & Gynecology, Rambam Health Care Campus, Haifa, Israel;2. Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel;3. Department of Physiology, Haifa, Israel;4. The Sohnis Family Stem Cells Center, Haifa, Israel;5. The Rappaport Family Institute for Research in the Medical Sciences, Haifa, Israel;1. Department of Ophthalmology and Visual Sciences, University of Michigan Medical School;2. Department of Internal Medicine, Division of Metabolism, Endocrinology, and Diabetes, University of Michigan Medical School, Ann Arbor, MI;3. University of Michigan Undergraduate Program, Ann Arbor, MI;1. Department of Epidemiology, New England Research Institutes, Inc., Watertown, MA, USA;2. Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA;1. Department of Nephrology, Medical University of Lublin, Lublin, Poland;2. Department of Cardiology, Medical University of Lublin, Lublin, Poland;1. Division of Diabetes and Metabolism, The Institute for Adult Diseases, Asahi Life Foundation, Tokyo, Japan;2. Department of Biostatistics, School of Public Health, University of Tokyo, Tokyo, Japan;3. Department of Public Health and Environmental Medicine, The Jikei University School of Medicine, Tokyo, Japan |
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| Abstract: | AimsFluctuations of blood glucose are generated by multiple external and internal factors continuously modifying glucose concentrations through complex feedback loops. This equilibrium may be perturbed during physiological or pathological conditions. The traditional theory suggests that physiological systems achieve homeostasis when disturbed and restore equilibrium through linear feedback loops. Complex systems on the other hand, may function nonlinearly with feedback loops that operate at different time scales, exhibiting chaotic or fractal behavior. We hypothesized that blood glucose fluctuations recorded for prolonged time periods show chaotic, fractal-like behavior that may be altered in diabetes.MethodsWe applied nonlinear analytical methods such as detrended fluctuation analysis to glucose data derived from continuous glucose monitoring devices for prolonged time periods in healthy volunteers, diabetes type 1 and pregnant diabetes type 1 patients.ResultsGlucose fluctuations extracted for prolonged time periods show fractal-like behavior and power law behavior of the system.ConclusionsHidden features underlying glucose fluctuations in health and in disease were revealed by using dynamic nonlinear analyses methods to discrete glucose readings extracted from continuous glucose monitoring devices. By using such methods we can enhance our understanding of the dynamics of blood glucose fluctuations in health and disease. |
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