title: Approximate entropy studies of hormone pulsatility from plasma concentration time series: influence of the kinetics assessed by simulation.
abstract: Approximate entropy (ApEn) is a method developed in the early nineties to quantify the "regularity" of a time series. In recent years, it has been vigorously employed to study the oscillatory/pulsatile secretory behavior of many hormones and found capable of successfully identifying pathological or prepathological states characterized by an enhanced secretion irregularity. Since hormone secretion rate is nonaccessible to direct measurement, ApEn is usually calculated from the time series of the hormone concentrations in plasma. However, the plasma concentration time course also reflects the whole-body kinetics of the hormone and can thus only provide a distorted portrait of the secretion rate at the gland level. In this paper, we investigate by simulation whether and how this distortion can influence the study of the regularity of hormone pulsatility by ApEn. Pulsatile secretion time series with different degrees of irregularity are simulated by varying the statistics of the random parameters which describe the secretory pulses. Then, plasma concentration time series are obtained by convolution with the hormone impulse response. Different degrees of impulse response smoothness are also considered in order to vary the amount of the distortion introduced. Results show that ApEn computed from secretion time series consistently discriminated better than ApEn calculated from plasma concentration time series among processes with different degrees of regularity. In addition, smoother impulse responses decreased the ApEn differences between plasma concentration time series corresponding to different degrees of secretion regularity. Therefore, the power of the ApEn index in the study of hormone pulsatility can potentially be enhanced by applying it to the hormone secretion time series.
authors.affiliation: Department of Electronics and Informatics, University of Padova, Italy.
authors.forename: G
authors.initials: G
authors.lastname: Sparacino
authors.forename: F
authors.initials: F
authors.lastname: Bardi
authors.forename: C
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D000465:Algorithms; D016477:Artifacts; D015982:Bias; D016002:Discriminant Analysis; D019277:Entropy; D006728:Hormones / Q000097:blood* / Q000378:metabolism*; D006801:Humans; D008954:Models, Biological; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D009991:Oscillometry; D011673:Pulsatile Flow / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013997:Time Factors
chemical_list: D006728:Hormones