Key result
Nonlinear analytical methods applied to serial glucose and insulin data revealed a finite correlation dimension of around 4.0 and a positive Lyapunov exponent, indicating deterministic chaotic behavior.
Population
Previously published serial data of glucose and insulin values of individual patients
Design
Other
Authors
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Challenges linear glucose homeostasis models; hypothesis-generating and requires validation before clinical or research adoption.
Short-term biological variation in glucose and insulin levels is driven by deterministic chaotic processes rather than random fluctuations.
Martin H. Kroll (1999) studied Biological variation of glucose and insulin. Nonlinear analytical methods vs. Linear methods was evaluated on Deterministic chaotic behavior. Nonlinear analytical methods applied to serial glucose and insulin data revealed a finite correlation dimension of around 4.0 and a positive Lyapunov exponent, indicating deterministic chaotic behavior.
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