Leptin contributes to the beneficial effects of insulin treatment in streptozotocin-diabetic male mice
Bibliographic record
Abstract
It was long thought that the only hormone capable of reversing the catabolic consequences of diabetes was insulin. However, various studies have demonstrated that the adipocyte-derived hormone leptin can robustly lower blood glucose levels in rodent models of insulin-deficient diabetes. In addition, it has been suggested that some of the metabolic manifestations of insulin-deficient diabetes are due to hypoleptinemia as opposed to hypoinsulinemia. Because insulin therapy increases leptin levels, we sought to investigate the contribution of leptin to the beneficial effects of insulin therapy. To do this, we tested insulin therapy in streptozotocin (STZ) diabetic mice that were either on an ob/ ob background or that were given a leptin antagonist to determine if blocking leptin action would blunt the glucose-lowering effects of insulin therapy. We found that STZ diabetic ob/ ob mice have a diminished blood glucose-lowering effect in response to insulin therapy compared with STZ diabetic controls and exhibited more severe weight loss post-STZ injection. In addition, STZ diabetic mice administered a leptin antagonist through daily injection or plasmid expression respond less robustly to insulin therapy as assessed by both fasting blood glucose levels and blood glucose levels during an oral glucose tolerance test. However, leptin antagonism did not prevent the insulin-induced reduction in β-hydroxybutyrate and triglyceride levels. Therefore, we conclude that elevated leptin levels can contribute to the glucose-lowering effect of insulin therapy in insulin-deficient 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".