Sexual Dimorphism and Regulation of Resistin, Adiponectin, and Leptin Expression in the Mouse
Bibliographic record
Abstract
OBJECTIVE: To examine gender differences and hormonal regulation of resistin, adiponectin, and leptin. RESEARCH METHODS AND PROCEDURES: Plasma levels were measured, and mRNA expression in perigonadal fat was quantified by RNase protection assays. RESULTS: Plasma resistin declined with age despite an increase in adiposity in both genders. In male mice, plasma leptin increased, whereas adiponectin levels were constant. In females, both adiponectin and leptin levels increased with age. Resistin mRNA levels were significantly higher in female than male mice at all ages, whereas leptin and adiponectin mRNA levels were similar in fat from 6-week-old male and female mice, and sexual dimorphism was apparent only in the older mice, with higher levels apparent in females. Castration did not abolish gender differences in plasma levels or resistin, adiponectin, or leptin mRNAs. Castration of male mice did not significantly change adipokine mRNA levels or plasma levels of resistin or leptin; however, adiponectin was significantly increased. Dihydrotestosterone treatment had no effect on adipokine mRNA expression or resistin and adiponectin levels but increased leptin levels. In contrast, ovariectomy significantly increased resistin mRNA abundance and decreased leptin and adiponectin mRNAs. Plasma leptin levels were also increased by ovariectomy, whereas resistin and adiponectin levels were unchanged. Estrogen replacement significantly reduced resistin mRNA and increased leptin and adiponectin mRNA levels but had no effect on plasma adipokine levels. DISCUSSION: The gender differences in adipokine mRNA expression and plasma levels were not ablated by castration and seem to be dependent on other factors in addition to gonadal steroids.
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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 | 0.002 | 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) | 0.003 | 0.001 |
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".