Changes in Leptin and Peptide YY Do Not Explain the Greater-Than-Predicted Decreases in Resting Energy Expenditure After Weight Loss
Bibliographic record
Abstract
CONTEXT: It is unknown whether leptin and peptide YY (PYY) influence changes in resting energy expenditure (REE), independently of fat mass (FM) and fat-free mass (FFM) in addition to changes in other energy expenditure (EE) components during weight loss. OBJECTIVE: The objective of the study was to examine the relationships between leptin, PYY, and body composition with different EE components before and after weight loss and whether changes in leptin and PYY were associated with differences in predicted vs measured REE after the intervention. DESIGN: This was a randomized controlled design. SETTING: The study was conducted in a laboratory. PARTICIPANTS: Participants were ninety-three overweight/obese postmenopausal women (aged 58.1 ± 4.8 y; body mass index 32.1 ± 4.3 kg/m(2)). INTERVENTION: Interventions included a 6-month caloric restriction diet alone or caloric restriction diet+resistance training. MAIN OUTCOME MEASURES: Body composition (dual energy x-ray absorptiometry), REE (indirect calorimetry), total EE (TEE; doubly labeled water), and fasting leptin and total PYY before and after weight loss were measured. RESULTS: Both interventions yielded significant decreases in weight, FFM, REE, and leptin, whereas a significant time × group interaction was noted for FM (greater decrease in FM in the diet+resistance training group) (P < .05 for all outcomes). No significant differences in TEE, physical activity EE, and PYY were noted between baseline and after the intervention. Age, FFM, leptin, and PYY were the best predictors of baseline REE (R(2) = 0.77; P = .0001), whereas age, FFM, and FM were associated with REE after the intervention (R(2) = 0.88; P = .0001). The same predictors, except for leptin, were significantly related to TEE at baseline (R(2) = 0.70; P = .0001) and after the intervention (R(2) = 0.29; P = .0001), whereas only PYY was a significant predictor of physical activity EE at baseline and after the intervention. Changes in FM and leptin accounted for 27% of the variance in ΔREE (P = .0001). Greater predicted vs measured REE was noted after the intervention (P = .02). However, Δ leptin and ΔPYY were not significant predictors of the differences between postintervention measurement and predicted REE. CONCLUSIONS: Δ Leptin and ΔFM were strong contributors to changes in REE. However, Δ leptin and ΔPYY were not significant predictors of the differences between predicted and measured REE after the intervention.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".