The role of autonomic efferents and uncoupling protein 1 in the glucose-lowering effect of leptin therapy
Bibliographic record
Abstract
OBJECTIVE: Leptin reverses hyperglycemia in rodent models of type 1 diabetes (T1D). Direct application of leptin to the brain can lower blood glucose in diabetic rodents, and can activate autonomic efferents and non-shivering thermogenesis in brown adipose tissue (BAT). We investigated whether leptin reverses hyperglycemia through a mechanism that requires autonomic innervation, or uncoupling protein 1 (UCP1)-mediated thermogenesis. METHODS: To examine the role of parasympathetic and sympathetic efferents in the glucose-lowering action of leptin, mice with a subdiaphragmatic vagotomy or 6-hydroxydopamine induced chemical sympathectomy were injected with streptozotocin (STZ) to induce hyperglycemia, and subsequently leptin treated. To test whether the glucose-lowering action of leptin requires activation of UCP1-mediated thermogenesis in BAT, we administered leptin in STZ-diabetic Ucp1 knockout (Ucp1 (-/-)) mice and wildtype controls. RESULTS: Leptin ameliorated STZ-induced hyperglycemia in both intact and vagotomised mice. Similarly, mice with a partial chemical sympathectomy did not have an attenuated response to leptin-mediated glucose lowering relative to sham controls, and showed intact leptin-induced Ucp1 expression in BAT. Although leptin activated BAT thermogenesis in STZ-diabetic mice, the anti-diabetic effect of leptin was not blunted in Ucp1 (-/-) mice. CONCLUSIONS: These results suggest that leptin lowers blood glucose in insulin-deficient diabetes through a manner that does not require parasympathetic or sympathetic innervation, and thus imply that leptin lowers blood glucose through an alternative CNS-mediated mechanism or redundant target tissues. Furthermore, we conclude that the glucose lowering action of leptin is independent of UCP1-dependent thermogenesis.
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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".