Reduced Fat Oxidation During Exercise in Post-Menopausal Overweight-Obese Women with Higher Lipid Accumulation Product Index
Bibliographic record
Abstract
Abstract Background and Aims The main aim of this study was to analyze how the lipid accumulation affects the whole-body fat oxidation over a range of intensities during a submaximal incremental exercise test in post-menopausal overweight-obese women. Patients and Methods The maximal fat oxidation (MFO), the intensity where the MFO occurs (FatMax), fat oxidation were measured over a range of intensities during a submaximal incremental exercise test through indirect calorimetry in 60 postmenopausal overweight-obese women (aged>49 years; body mass index 28.0 to 39.0 kg/m²). The metabolic profile of participants was evaluated and the LAP index was calculated (waist-58×triglycerides [mmol/L]). A cutoff point of 34.5 was adopted and participant were designed as low LAP index (n=30) or high LAP index (n=30). Results During submaximal exercise postmenopausal overweight-obese women with low LAP index showed a higher fat oxidation at 50% (0.53±0.05 vs. 0.45±0.12 g/min; p=0.01), 60% (0.40±0.06 vs. 0.31±0.16 g/min; p=0.02) and 70% (0.34±0.08 vs. 0.25±0.15 g/min; p=0.03) of VO2Peak than those with high LAP index. No significant difference was observed in carbohydrate oxidation between groups (p>0.05) during exercise. Moreover, a significant difference in absolute MFO (p=0.018), MFO relative to free fat mass (p=0.043) and FatMax (p=0.002) was identified. Conclusion Postmenopausal overweight-obese women who showed unhealthy metabolic phenotype evaluated through LAP index presented low fat oxidation during a submaximal incremental exercise.
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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 | 0.000 | 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".