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Review
. 2016 Jan;31(1):34-50.
doi: 10.1152/physiol.00027.2015.

Pulsatility of Hypothalamo-Pituitary Hormones: A Challenge in Quantification

Affiliations
Review

Pulsatility of Hypothalamo-Pituitary Hormones: A Challenge in Quantification

Daniel M Keenan et al. Physiology (Bethesda). 2016 Jan.

Abstract

Neuroendocrine systems control many of the most fundamental physiological processes, e.g., reproduction, growth, adaptations to stress, and metabolism. Each such system involves the hypothalamus, the pituitary, and a specific target gland or organ. In the quantification of the interactions among these components, biostatistical modeling has played an important role. In the present article, five key challenges to an understanding of the interactions of these systems are illustrated and discussed critically.

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Conflict of interest statement

No conflicts of interest, financial or otherwise, are declared by the author(s).

Figures

FIGURE 1.
FIGURE 1.
Schematics for three fundamental neuroendocrine systems and plots of homornal profiles from the three systems Top: schematics for three fundamental neuroendocrine systems that are responsible for the regulation of reproduction (male, female reproductive hormone axes), physiological stress (the stress axis), and growth [the growth hormone (GH) axis]. Bottom: plots of hormonal profiles from the three systems; LH and Te for young (blue) and older (red) males; ACTH concentrations for a normal subject and a subject with Cushing's disease (due to an ACTH-secreting tumor); and GH concentrations for normal and GH-tumor subjects. The concentrations in all three columns were sampled every 10 min for 24 h.
FIGURE 2.
FIGURE 2.
Results of the pulse detection algorithm Results of the pulse detection algorithm illustrated for older male LH concentrations (A) and younger male Te concentrations (B). Top: concentrations, with all of the local minima given as red asterisks. The resulting putative pulse-time sets (Tm) are displayed as the rows of the third subplot. The remaining plots show, progressively, the pulse times displayed with the concentrations, as the algorithm proceeds.
FIGURE 3.
FIGURE 3.
Final algorithmic results of the penalized maximum likelihood estimation Final algorithmic results of the penalized maximum likelihood estimation (MLE) for a young male LH and Te (A) and an older male LH and Te (B). C and D: the resulting estimates for four models of LH feedforward on Te: young male LH on Te (C) and older male LH on Te (D). The solid blue line is a logistic curve, denoting the estimated initial (before downregulation) mean dose-response across all the pulses considered over the 24-h profile; if there is downregulation, the mean dose-response, averaging over all pulses, is the solid red curve. Also presented are the individual pulse-by-pulse realizations of such curves, each with the allowed random effect in potency, sensitivity, or efficacy, before downregulation (blue dashed curves) and after downregulation (red dashed curves, the hysteresis effect for models 2–4).
FIGURE 4.
FIGURE 4.
Results of the pulse detection and the penalized MLE estimation Results of the pulse detection (A) and the penalized MLE estimation for the ACTH concentrations (B) of a Cushing's disease subject. The first three subpanels of B are 1) the model fit to the concentrations; 2) the estimated secretion rates; 3) the day and night Ψ function (Eq. 4), which describes a circadian rhythm modifying secretory-burst mass. The fourth subpanel is the resulting Weibull fit to the IPIk (the asterisks on the x-axis). C and D: the results of a Bayesian analysis of the Cushing's disease ACTH concentrations (see text for details).
FIGURE 5.
FIGURE 5.
Results of a proposed statistical model The results of a proposed statistical model that allows pulse-by-pulse variation in inferred secretory-burst shape for two GH profiles (A and B, normal; C and D, GH-pituitary tumor). A new model of putative pulse times is given in A and C, and the resulting fits and secretion rates are given in B and D.

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