Leptin Administration Enhances Islet Transplant Performance in Diabetic Mice
Bibliographic record
Abstract
Islet transplantation is an effective method to obtain long-term glycemic control for patients with type 1 diabetes, yet its widespread use is limited by an inadequate supply of donor islets. The hormone leptin has profound glucose-lowering and insulin-sensitizing action in type 1 diabetic rodent models. We hypothesized that leptin administration could reduce the dose of transplanted islets required to achieve metabolic control in a mouse model of type 1 diabetes. We first performed a leptin dose-response study in C57Bl/6 mice with streptozotocin (STZ)-induced diabetes to determine a leptin dose insufficient to reverse hyperglycemia. Subsequently, we compared the ability of suboptimal islet transplants of 50 or 125 syngeneic islets to achieve glycemic control in STZ-induced diabetic C57Bl/6 mice treated with or without this dose of leptin. The dose-response study revealed that leptin reverses STZ-induced diabetes in a dose-dependent manner. Supraphysiological leptin levels were necessary to restore euglycemia but simultaneously increased risk of hypoglycemia, and also lost efficacy after 12 days of administration. In contrast, 1 µg/day leptin only modestly reduced blood glucose but maintained efficacy throughout the study duration. We then administered 1 µg/day leptin to diabetic mice that underwent transplantation of 50 or 125 islets. Although these islet doses were insufficient to ameliorate hyperglycemia alone, coadministration of leptin with islet transplantation robustly improved control of glucose and lipid metabolism, without increasing circulating insulin levels. This study reveals that low-dose leptin administration can reduce the number of transplanted islets required to achieve metabolic control in STZ-induced diabetic mice.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".