Metabolic effects of leptin receptor knockdown or reconstitution in adipose tissues
Bibliographic record
Abstract
Abstract The relative contribution of peripheral and central leptin signalling to the regulation of metabolism and the mechanisms through which leptin affects glucose homeostasis have not been fully elucidated. We generated complementary lines of mice with either leptin receptor (Lepr) knockdown or reconstitution in adipose tissues using Cre-lox methodology. Lepr knockdown mice were modestly lighter and had lower plasma insulin concentrations following an oral glucose challenge compared to controls, despite similar insulin sensitivity. We rendered male mice diabetic using streptozotocin (STZ) and found that upon prolonged leptin therapy, Lepr knockdown mice had an accelerated decrease in blood glucose compared to controls that was associated with higher plasma concentrations of leptin and leptin receptor. Mice with transcriptional blockade of Lepr (LeprloxTB/loxTB) were obese and hyperglycemic and reconstitution of Lepr in adipose tissues of LeprloxTB/loxTB mice resulted in males reaching a higher maximal body weight. Although mice with adipose tissue Lepr reconstitution had lower blood glucose levels at several ages, their plasma insulin concentrations during an oral glucose test were elevated. Thus, attenuation or restoration of Lepr in adipocytes alters the plasma insulin profile following glucose ingestion, modifies the glucose-lowering effect of prolonged leptin therapy in insulin-deficient diabetes, and may modulate weight gain.
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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 | 0.001 | 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".