Altered Growth Hormone, Cortisol, and Leptin Secretion in Healthy Elderly Persons With Sarcopenia and Mixed Body Composition Phenotypes
Bibliographic record
Abstract
BACKGROUND: Obese phenotypes and aging are independently associated with hypothalamic-pituitary-adrenocortical (HPA) axis and leptin secretion alterations. However, leptin secretion and HPA axis function in elderly persons with other body composition phenotypes is largely unknown. METHODS: Forty-five healthy elderly participants were classified normal lean (NL), sarcopenic (SS), sarcopenic-obese (SO), or obese (OO) using dual-energy x-ray absorptiometry. Growth hormone (GH), cortisol, and leptin secretion were evaluated during a free-running night, and oral glucocorticoid suppression test (dexamethasone DEX). Diurnal cortisol secretion was assessed by hourly salivary samples with timed meals. Data were analyzed using cluster, deconvolution, and approximate entropy (ApEn) analyses. RESULTS: GH area, total secretion, and mean concentration during the free-running night was lower in the SO and OO groups verses the SS and NL groups (p <.02, Wilcoxon test). GH mean concentration and total secretion significantly increased in all groups during DEX (overall p <.05) except the SO group, in which ApEn increased (p =.03). Pre- and postbreakfast peak salivary cortisol (p =.004) and area under the curve (p =.03) was greatest in the SS group. Baseline leptin (11:00 pm) was significantly higher in the SO, OO, and SS groups verses the NL group (p =.01). Appendicular skeletal muscle mass was independently and negatively correlated with leptin in all groups, even after adjusting for percentage body fat (p =.001). CONCLUSIONS: In the presence of obesity, GH secretion was depressed with a blunted and disorderly response to oral glucocorticoid suppression in SO participants. Sarcopenic participants had concomitantly elevated leptin and cortisol relative to their low body fat mass. Complex or dysregulated neuroendocrine feedback systems appear to be operating in elderly persons with specific body composition phenotypes.
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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 | 0.001 | 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".