Monitoring Unobserved Gonadotropin Secretion

Lower incremental LH peak amplitude in older men can be dissected further mechanistically under the premise that fluctuating hormone concentrations are driven by specific (unobserved) secretion and kinetic processes. Deconvolution analysis is a family of computer-assisted methods designed to quantitate underlying secretion and/or elimination. Some deconvolution procedures allow one to determine (1) the frequency of secretory bursts, (2) the amount of hormone released within each burst (mass of hormone discharged within the event normalized to unit distribution volume),

(3) the subject- and condition-specific half-life of elimination of the hormone, and

(4) concomitant basal or (nonpulsatile) secretion.

There are several complementary deconvolution approaches to secretion analysis. Figure 8A illustrates one basic idea, wherein an unobserved Gaussian-shaped secretory burst creates each peak in serum hormone concentrations (102). In this waveform-dependent concept, hormone concentrations increase rapidly because of the sharp onset of an underlying secretory burst and then fall gradually after the burst as the hormone is eliminated slowly from the circulation. The Gaussian-burst model allows one to calculate the number, location, duration, and amplitude of secretory bursts and predict the hormone half-life at the same time (103,104). Application of this methodology in healthy men quantitates a lower mass and higher frequency of LH secretory events in older than young men but a comparable LH half-life (27,35,55). The lower mass (IU) of LH released per secretory burst in the elderly male results from attentuation of the amplitude (maximal secretion rate attained) rather than abbreviation of the duration (min) of release episodes.

A second-generation deconvolution technology defines secretion as an admixture of unknown basal output and superimposed events of any height, width, shape, and number (103) (see Fig. 8B). This deconvolution construct is, therefore, defined as waveform independent. For statistical reasons, optimal analysis in this methodology requires a priori knowledge of the two-component half-life of hormone elimination (105). Because the true waveform of secretory bursts is (definitionally) unknown, definitive pulse counting is analytically difficult, unless assumptions are made about event shape, smoothness, location, or number (104). However, this mathematical strategy predicts lower peak LH secretion rates in older men.

A third-generation deconvolution strategy uses coupled, stochastic differential equations to reconstruct simultaneously the number, shape, size, and timing of (unknown) secretory bursts, the basal release rate, biexponential disappearance kinetics, and random effects driving hormone release (82,83) (see Fig. 8C). This comprehensive statistical platform predicts a lower mass and higher frequency of LH secretory bursts in older than young men, with no age difference in LH kinetics (38,40,81). The diminution in mass and acceleration in frequency of LH pulses counterbalance closely, thus leaving the total and pulsatile daily LH secretion rate unchanged in aging individuals.

Contemporary secretion-based insights explain some earlier observations. For example, one method of discrete hormone peak detection, cluster analysis, identifies a pulse simply as a statistical jump and drop in the hormone concentration (34,106). This approach is termed model-free, because few assumptions are made in defining a pulse. Deconvolution analysis illustrates that the peak increment is proportionate to the mass of hormone secreted in the burst ceterus paribus (104,105,107). This point is important, because elderly men consistently exhibit a reduced incremental LH peak amplitude compared with young counterparts (28,81).

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