10.1152/physiolgenomics.00169.2022
Bibliographic record
Abstract
Sex differences in energy metabolism during acute, sub-maximal exercise are well documented. Whether these sex differences influence metabolic and physiologic responses to sustained, physically demanding activities is not well characterized. This study aimed to identify sex differences within changes in the serum metabolome in relation to changes in body composition, physical performance, and endocrine status during a 17-day military training exercise. Blood was collected, and body composition and lower-body power were measured before and after the training on 72 cadets (18 women). Total daily energy expenditure (TDEE) was assessed in a subset throughout. TDEE was greater in men (4,085±482kcal/d) than women (2,982±472kcal/d, p<0.001), but not after adjustment for dry lean mass (DLM). Men tended to lose more DLM than women (-0.2[-0.3,-0.1] vs. -0.0[-0.0,0.0] kg, p=0.063, Cohen's d=0.50) and have greater reductions in lower body power (-244[-314,-174] vs. -130[-209,-51] Watts, p=0.085, d=0.49). Reductions in DLM and lower body power were correlated (r=0.325, p=0.006). Women demonstrated greater fat oxidation than men (Δfat mass/DLM: -0.20[-0.24,-0.17] vs. -0.15[-0.17,-0.13] kg, p=0.012, d=0.64). Metabolites within pathways of fatty acid, endocannabinoid, lysophospholipid, phosphatidylcholine, phosphatidylethanolamine, and plasmalogen metabolism increased in women relative to men. Independent of sex, changes in metabolites related to lipid metabolism were inversely associated with changes in body mass and positively associated with changes in endocrine and metabolic status. These data suggest that during sustained military training, women preferentially mobilize fat stores compared with men, which may be beneficial for mitigating loss of lean mass and lower body power.
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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) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.589 | 0.529 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".