Growth hormone receptor gene deficiency causes delayed insulin responsiveness in skeletal muscles without affecting compensatory islet cell overgrowth in obese mice
Bibliographic record
Abstract
Growth hormone (GH), acting through its receptor (GHR), is essential for somatic growth and development and maintaining metabolic homeostasis. GHR gene-deficient (GHR(-/-)) mice exhibit drastically diminished insulin-like growth factor-I (IGF-I) levels, proportional growth retardation, elevated insulin sensitivity, and reduced islet beta-cell mass. Unlike the liver, which is mostly unaffected by changes in IGF-I level, skeletal muscles express high levels of IGF-I receptor (IGF-IR). The net result of a concurrent deficiency in the actions of both GH and IGF-I, which exert opposite influences on insulin responsiveness, has not been evaluated. We studied insulin-stimulated early responses in the insulin receptor (IR), insulin receptor substrate-1 (IRS-1), and p85 subunit of phosphatidylinositol 3-kinase. Upon in vivo insulin stimulation, skeletal muscles of GHR(-/-) mice exhibit transient delayed responses in IR and IRS-1 phosphorylation but normal levels of p85 association with IRS-1. This is in contrast to normal/elevated insulin responses in hepatocytes and indicates tissue-specific effects of GHR gene deficiency. In addition to stimulating normal islet cell growth, GH may participate in islet cell overgrowth, which compensates for insulin resistance induced by obesity. To determine whether the islet cell overgrowth is dependent on GH signaling, we studied the response of male GHR(-/-) mice to high-fat diet (HFD)-induced obesity. After 17 wk on a HFD, GHR(-/-) mice became more significantly obese than wild-type mice and exhibited increased beta-cell mass to a slightly higher extent. These data demonstrate that GH signaling is not required for compensatory islet growth. Thus, in both muscle insulin responsiveness and islet growth compensation, normal levels of GH signals do not seem to play a dominant role.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".