Disruption of Growth Hormone Receptor in Adipocytes Improves Insulin Sensitivity and Lifespan in Mice
Bibliographic record
Abstract
Growth hormone receptor knockout (GHRKO) mice have been used for 25 years to uncover some of the many actions of growth hormone (GH). Since they are extremely long-lived with enhanced insulin sensitivity and protected from multiple age-related diseases, they are often used to study healthy aging. To determine the effect that adipose tissue has on the GHRKO phenotype, our laboratory recently created and characterized adipocyte-specific GHRKO (AdGHRKO) mice, which have increased adiposity but appear healthy with enhanced insulin sensitivity. To test the hypothesis that removal of GH action in adipocytes might partially replicate the increased lifespan and healthspan observed in global GHRKO mice, we assessed adiposity, cytokines/adipokines, glucose homeostasis, frailty, and lifespan in aging AdGHRKO mice of both sexes. Our results show that disrupting the GH receptor gene in adipocytes improved insulin sensitivity at advanced age and increased lifespan in male AdGHRKO mice. AdGHRKO mice also exhibited increased fat mass, reduced circulating levels of insulin, c-peptide, adiponectin, resistin, and improved frailty scores with increased grip strength at advanced ages. Comparison of published mean lifespan data from GHRKO mice to that from AdGHRKO and muscle-specific GHRKO mice suggests that approximately 23% of lifespan extension in male GHRKO is due to GHR disruption in adipocytes vs approximately 19% in muscle. Females benefited less from GHR disruption in these 2 tissues with approximately 19% and approximately 0%, respectively. These data indicate that removal of GH's action, even in a single tissue, is sufficient for observable health benefits that promote long-term health, reduce frailty, and increase longevity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".