Liver Triglyceride Secretion and Lipid Oxidative Metabolism Are Rapidly Altered by Leptin in Vivo
Bibliographic record
Abstract
Leptin has potent lipid-lowering effects in peripheral tissues and plasma that are proposed to be important for the prevention of cellular lipotoxicity and insulin resistance. The current study addressed in vivo the effects of acute leptin delivery on liver triglyceride (TG) metabolism, the consequence of hepatic leptin action on whole-body TG homeostasis, and the mechanisms of leptin action. A 120-min iv leptin infusion (plasma leptin, approximately 14 ng/ml) decreased liver TG levels (53 +/- 3%; P = 0.001), but not skeletal muscle TG levels, and increased liver phosphatidylinositol 3-kinase activity (341 +/- 95%; P = 0.01) in lean rats. Leptin had no effect on liver TG levels or phosphatidylinositol 3-kinase activity in diet-induced obese rats. In lean animals, leptin decreased the plasma TG concentration (20 +/- 7%; P = 0.017), the rate of TG accumulation in plasma after tyloxapol administration (26 +/- 6%; P = 0.003), and TG secretion from isolated liver (51 +/- 8%; P = 0.004). To determine possible metabolic fates of depleted hepatic TG, we assessed leptin effects on liver oxidative metabolism. Leptin increased hepatic acetyl-coenzyme A carboxylase phosphorylation (85 +/- 13%; P = 0.006), fatty acid oxidation (49 +/- 7%; P = 0.001) and ketogenesis (69 +/- 15%; P = 0.004). Finally, intracerebroventricular delivery of leptin for 120 min had no effect on liver TG levels, but did increase signal transducer and activator of transcription 3 phosphorylation (162 +/- 40%; P = 0.02). These data present in vivo evidence for a role for leptin in the acute regulation of hepatic TG metabolism, and whole body TG homeostasis. A likely contributing mechanism for these effects is leptin-induced partitioning of TG into oxidative pathways.
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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 | 0.000 | 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) | 0.002 | 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".