Divergent gonadotropin-gonadal dose-responsive coupling in healthy young and aging men
Bibliographic record
Abstract
The present study extends a recent composite model of in vivo interglandular signaling to assess the impact of age on 1) nonequilibrium exchange among diffusible and protein-bound testosterone (Te); 2) elimination of total and free Te; 3) basal and pulsatile Te secretion (sec); 4) the implicit feedforward function mediating luteinizing hormone (LH) concentration (con) drive of instantaneous Te sec; and 5) possible stochastic variability of the predicted LH con-Te sec dose-response linkage. To this end, we measured LH and Te con every 10 min for 24 h in healthy young (n = 13) and older men (n = 13). Statistical comparisons of analytic estimates revealed that elderly subjects manifest 1) reduced maximal burstlike LH-stimulated Te sec (impaired stimulus efficacy); 2) depressed half-maximally LH-stimulated Te sec (lower Leydig-cell responsivity); 3) decreased pulsatile and total Te sec; 4) elevated basal Te sec; 5) a prolonged half-life of total but not free Te con; and 6) delayed time evolution of LH and Te sec bursts. In contradistinction, age did not influence estimated LH-pulse potency (ED50), steroidogenic sensitivity (slope term), or stochastic variability of LH-Te coupling. On the basis of these data, we postulate that aging in the human male alters specific dose-response attributes linking LH con and Te sec and disrupts the time waveform of LH and Te sec bursts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".