An appetite for growth: The role of the hypothalamic – pituitary – growth hormone axis in energy balance
Bibliographic record
Abstract
Links between the regulation of growth and energy balance are clear; to fuel growth, there must be consumption of energy. Therefore, it is perhaps intuitive that interactions between the hypothalamic - pituitary - growth hormone axis (growth axis) and pathways that drive metabolic processes exist. Overproduction of growth hormone has been associated with diabetes and metabolic disease for decades and the opposing effects of growth hormone and insulin have been studied since early experiments almost a century ago. The relationship between neuroendocrine axes can be complex and the growth axis is no exception, interacting with energy balance in several organ systems, both in the periphery and centrally in hypothalamic nuclei. Much is known about peripheral interactions between growth axis hormones and processes such as glucose homeostasis and adipogenesis. More is still being learned about the molecular actions of growth axis hormones in adipose and other metabolically active tissues, and recent findings are discussed in this perspective. However, less is known about interactions with central energy balance pathways in the hypothalamus. This perspective aims to summarise what is known about these interactions, taking lessons from human studies and animal genetic and seasonal models, and discusses what this may mean in an evolving landscape of personalised medicine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".