Leptin Increases Hepatic Insulin Sensitivity and Protein Tyrosine Phosphatase 1B Expression
Bibliographic record
Abstract
Leptin has been shown to improve insulin sensitivity and glucose metabolism in obese diabetic ob/ob mice, yet the mechanisms remain poorly defined. We found that 2 d of leptin treatment improved fasting but not postprandial glucose homeostasis, suggesting enhanced hepatic insulin sensitivity. Consistent with this hypothesis, leptin improved in vivo insulin receptor (IR) activation in liver, but not in skeletal muscle or fat. To explore the cellular mechanism by which leptin up-regulates hepatic IR activation, we examined the expression of the protein tyrosine phosphatase PTP1B, recently implicated as an important negative regulator of insulin signaling. Unexpectedly, liver PTP1B protein abundance was increased by leptin to levels similar to lean controls, whereas levels in muscle and fat remained unchanged. The ability of leptin to augment liver IR activation and PTP1B expression was also observed in vitro in human hepatoma cells (HepG2). However, overexpression of PTP1B in HepG2 cells led to diminished insulin-induced IR phosphorylation, supporting the role of PTP1B as a negative regulator of IR activation in hepatocytes. Collectively, our results suggest that leptin acutely improves hepatic insulin sensitivity in vivo with concomitant increases in PTP1B expression possibly serving to counterregulate insulin action and to maintain insulin signaling in proper balance.
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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 | 0.000 |
| 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".