Leptin Therapy Reverses Hyperglycemia in Mice With Streptozotocin-Induced Diabetes, Independent of Hepatic Leptin Signaling
Bibliographic record
Abstract
OBJECTIVE: Leptin therapy has been found to reverse hyperglycemia and prevent mortality in several rodent models of type 1 diabetes. Yet the mechanism of leptin-mediated reversal of hyperglycemia has not been fully defined. The liver is a key organ regulating glucose metabolism and is also a target of leptin action. Thus we hypothesized that exogenous leptin administered to mice with streptozotocin (STZ)-induced diabetes reverses hyperglycemia through direct action on hepatocytes. RESEARCH DESIGN AND METHODS: After the induction of diabetes in mice with a high dose of STZ, recombinant mouse leptin was delivered at a supraphysiological dose for 14 days by an osmotic pump implant. We characterized the effect of leptin administration in C57Bl/6J mice with STZ-induced diabetes and then examined whether leptin therapy could reverse STZ-induced hyperglycemia in mice in which hepatic leptin signaling was specifically disrupted. RESULTS: Hyperleptinemia reversed hyperglycemia and hyperketonemia in diabetic C57Bl/6J mice and dramatically improved glucose tolerance. These effects were associated with reduced plasma glucagon and growth hormone levels and dramatically enhanced insulin sensitivity, without changes in glucose uptake by skeletal muscle. Leptin therapy also ameliorated STZ-induced hyperglycemia and hyperketonemia in mice with disrupted hepatic leptin signaling to a similar extent as observed in wild-type littermates with STZ-induced diabetes. CONCLUSIONS: These observations reveal that hyperleptinemia reverses the symptoms of STZ-induced diabetes in mice and that this action does not require direct leptin signaling in the liver.
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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) | 0.000 | 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 | 0.001 |
| 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".