Leptin Levels, Leptin Receptor Gene Polymorphisms, and Energy Metabolism in Women
Bibliographic record
Abstract
OBJECTIVE: Resting metabolic rate (RMR) is mainly determined by fat-free mass and additionally by age, sex, hormones, and possibly genetic differences. We evaluated whether leptin levels and polymorphisms in the leptin receptor (LEPR) gene were associated with energy expenditure phenotypes. METHODS: RMR, body composition, and leptin levels were measured in 125 overweight and obese women. Three LEPR polymorphisms, Lys109Arg, Gln223Arg, and Lys656Asn, were typed on genomic DNA of another group of 192 women in whom RMR was measured. Fat, protein, and carbohydrate oxidation were calculated for 103 of these subjects. In 38 subjects, glucose-induced thermogenesis was measured over 3 hours. RESULTS: In the first study group, a negative correlation between RMR and leptin levels was found after controlling for fat and fat-free mass. In multiple regression analysis, leptin contributed significantly to RMR, independent of body composition. In the second study group, RMR was not associated with LEPR polymorphisms. Differences in substrate oxidation rates were found among genotypes at the Lys656Asn site. In fasting conditions, Lys656Lys showed a trend to oxidize more carbohydrates and less fat than Asn656 carriers, a trend which became significant after the glucose load when carbohydrate oxidation rate in Lys656Lys was 15% higher than in Asn656 carriers (p = 0.04), and fat oxidation rate was 44% lower (p = 0.02). DISCUSSION: These results suggest that DNA sequence variations in the LEPR gene could affect substrate oxidation. We hypothesize that this might be caused by differences in glucose levels, leading to differences in glucose oxidation rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".