Gender differences in whole-body fat oxidation kinetics during exercise
Bibliographic record
Abstract
Discrepancies appear in studies comparing fat oxidation between men and women. Therefore, this study aimed to quantitatively describe and compare whole-body fat oxidation kinetics between genders during exercise, using a sinusoidal (SIN) model. Twelve men and 11 women matched for age, body mass index, and aerobic fitness (maximal oxygen uptake and maximal power output per kilogram of fat-free mass (FFM)) performed submaximal incremental tests (Incr) with 5-min stages and a 7.5% maximal power output increment on a cycle ergometer. Fat oxidation rates were determined using indirect calorimetry, and plotted as a function of exercise intensity. The SIN model, which includes 3 independent variables (dilatation, symmetry, translation) that account for the main quantitative characteristics of kinetics, was used to mathematically describe fat oxidation kinetics and to determine the intensity (Fatmax) eliciting the maximal fat oxidation (MFO). During Incr, women exhibited greater fat oxidation rates from 35% to 85% maximal oxygen uptake, MFO (6.6 ± 0.9 vs. 4.5 ± 0.3 mg·kg FFM-1·min-1), and Fatmax (58.1% ± 1.9% vs. 50.0% ± 2.7% maximal oxygen uptake) than men (p < 0.05). While men and women showed similar global shapes of fat oxidation kinetics in terms of dilatation and symmetry (p > 0.05), the fat oxidation curve tended to be shifted toward higher exercise intensities in women (rightward translation, p = 0.08). These results support the idea that women have a greater reliance on fat oxidation than men during submaximal exercise, but also indicate that this greater fat oxidation is shifted toward higher exercise intensities in women than in men.
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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 | 0.001 | 0.001 |
| 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".