Increased thermogenic response to food and fat oxidation in female athletes: relationship withV˙<scp>o</scp><sub>2 max</sub>
Bibliographic record
Abstract
The thermogenic response to food (TRF) and substrate oxidation were studied in 12 endurance-trained and 13 untrained female subjects. Energy expenditure and substrate oxidation were calculated by indirect calorimetry before and for 6 h after an oral test meal and after the same meal given intragastrically on a separate occasion. The TRF was calculated after the oral meal, the obligatory component after the intragastric meal (OTRF), and the facultative component from the difference between the two. VO(2 max) was measured on a treadmill and body composition by underwater weighing. The TRF and OTRF were significantly higher in trained than in untrained subjects: 223 +/- 63 vs. 185 +/- 50 kJ/6 h (P < 0.03) and 174 +/- 38 vs. 131 +/- 37 kJ/6 h (P < 0.01) for the TRF and OTRF in trained vs. untrained subjects, respectively. Multiple regression analysis showed that maximum O(2) consumption (VO(2 max)), but not percentage of body fat, was significantly related to OTRF (r =0.68, P < 0.01). Trained subjects had higher fatty acid oxidation than untrained subjects before (0.6 vs. 0.4 mg. kg(-1). min(-1), P < 0.05) and after the oral meal (13 +/- 6 vs. 8 +/- 4 g/6 h P < 0.05). These results demonstrate that 1) TRF is higher in trained than in untrained women; 2) this is due to a higher cost of nutrient digestion, absorption and storage; 3) the difference is related to higher VO(2 max); and 4) fatty acid oxidation is greater in trained women in both the postabsorptive and postprandial states. These observations suggest that endurance training induces metabolic changes that favor leanness.
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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) | 0.002 | 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".