The role of the growth hormone/IGF-I system on islet cell growth and insulin action /
Bibliographic record
Abstract
The study of diabetes mellitus is vital in this day and age because its incidence is increasing at an alarming rate. Diabetes results in the loss of function of beta-cells within the pancreas. Insulin resistance contributes to diabetes but the human body can compensate in various ways such as increasing the islet cell mass, glucose disposal and insulin secretion, in order to prevent the onset of diabetes. Growth hormone (GH) and insulin-like growth factor-I (IGF-I) are two integral hormones important in both glucose homeostasis and islet cell growth. Early studies using cultured islet cells have demonstrated positive regulation of beta-cell growth by both GH and IGF-I. To evaluate their relevance on normal beta-cell growth, compensatory growth, as well as in insulin responsiveness, we have used two mouse models that represent opposite manipulations of the GH/IGF-I axis. Specifically, the growth hormone receptor gene deficient (GHR-/-) and the IGF-I overexpression (MT-IGF) mice, to help understand the role of glucose homeostasis and islet cell growth in the GH/IGF-I axis. GH is essential for somatic growth and development as well as maintaining metabolic homeostasis. It is known that GH stimulates normal islet cell growth. Moreover, GH may also participate in islet cell overgrowth and compensate for insulin resistance induced by obesity. To determine whether the islet cell overgrowth is dependent on GH signaling, we studied the response of GHR-/- mice to high-fat diet (HFD)-induced obesity. We also studied the insulin responsiveness in GHR-/- mice. On the other hand, IGF-I promotes embryonic development, postnatal growth and the maturation of various organ systems. The notion that IGF-I stimulates islet cell growth has been challenged in recent years by results from IGF-I and receptor gene targeted models. We have characterized MT-IGF mice which overexpress the IGF-I gene. The results of our studies indicate that (1) GH is essential for normal islet cell growth, but not required for compensatory overgrowth of the islets in response to obesity, (2) GHR gene deficiency caused delayed insulin responsiveness in skeletal muscle; in contrast to elevated insulin sensitivity in the liver; (3) although overexpression does not stimulate islet cell growth, a chronic IGF-I elevation caused significant hypoglycemia, hypoinsulinemia, and improved glucose tolerance, (4) finally IGF-I overexpression mice are resistant to experimental diabetes.
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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 | 0.001 |
| 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".