Chronic Central Leptin Decreases Food Intake and Improves Glucose Tolerance in Diet-Induced Obese Mice Independent of Hypothalamic Malonyl CoA Levels and Skeletal Muscle Insulin Sensitivity
Bibliographic record
Abstract
Although acute leptin administration in the hypothalamus decreases food intake and increases peripheral energy metabolism, the peripheral actions of central chronic leptin administration are less understood. In this study, we investigated what effects chronic (7 d) intracerebroventricular (ICV) administration of leptin has on energy metabolism and insulin sensitivity in diet-induced obese mice. C57/BL mice were fed a low-fat diet (LFD; 10% total calories) or high-fat diet (HFD; 60% total calories) for 8 wk after which leptin was administered ICV for 7 consecutive days. Mice fed a HFD showed signs of insulin resistance, as evidenced by an impaired glucose tolerance test. Chronic leptin treatment resulted in a decrease in food intake and body weight and normalization of glucose clearance but no improvement in insulin sensitivity. Chronic ICV leptin increased hypothalamic signal transducer and activator of transcription-3 and AMP-activated protein kinase phosphorylation but did not change hypothalamic malonyl CoA levels in HFD fed and LFD-fed mice. In the gastrocnemius muscles, the levels of malonyl CoA in both leptin-treated groups were lower than their respective control groups, suggesting an increase in fatty acid oxidation. However, only in the muscles of ICV leptin-treated LFD mice was there a decrease in lipid metabolites including diacylglycerol, triacylglycerol, and ceramide. Our results suggest that chronic ICV leptin decreases food consumption and body weight via a mechanism different from acute ICV leptin administration. Although chronic ICV leptin treatment in HFD mice improves glucose tolerance, this occurs independent of changes in insulin sensitivity in the muscles of HFD 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".