Abstract :
Serial data of glucose and insulin values of individual patients vary over short periods of time; this phenomenon has been called biological variation. The classic homeostatic control model assumes that the physiological mechanisms maintaining the concentrations of glucose and insulin are linear. The only deviations over a short period of time one should observe are in relation to a glucose load or major hormonal disturbance. Otherwise, the values of these analytes should be constant and any variations seen are due to random disturbances. We investigated previously published serial data (three for glucose and one for insulin) with nonlinear analytical methods, such as embedding space, correlation dimension, Lyapunov exponents, singular value decomposition and phase portraits, as well as linear methods, such as power spectra and autocorrelation functions. The power spectra failed to show dominant frequencies, but the autocorrelation functions showed significant correlation, consistent with a deterministic process. The correlation dimension was finite, around 4.0, the first Lyapunov exponent was positive, indicative of a deterministic chaotic process. Furthermore, the phase portraits showed directional flow. Therefore, the short-term biological variation observed for analytes arises from nonlinear, deterministic chaotic behavior instead of random variation.
