Sexual Dimorphism in Substrate Metabolism During Exercise
Bibliographic record
Abstract
During aerobic exercise, women oxidize significantly more lipids and less carbohydrates than men. This sexual dimorphism in substrate metabolism has been attributed, in part, to the observed differences in epinephrine and glucagon levels between men and women during exercise. To identify the underpinning candidate physiological mechanisms for these sex differences, we developed a sex-specific multi-scale mathematical model that relates cellular metabolism in the organs to whole-body responses during exercise. We conducted simulations to test the hypothesis that sex differences in the exercise-induced changes to epinephrine and glucagon would result in the sexual dimorphism of hepatic metabolic flux rates via the glucagon-to-insulin ratio (GIR). Indeed, model simulations indicate that the shift towards lipid metabolism in the female model is primarily driven by the liver. The female model liver exhibits resistance to GIR-mediated glycogenolysis, which helps maintain hepatic glycogen levels. This decreases arterial glucose levels and promotes the oxidation of free fatty acids. Furthermore, in the female model, skeletal muscle relies on plasma free fatty acids as the primary fuel source, rather than intramyocellular lipids, whereas the opposite holds true for the male model. This work was supported by the Canadian Institutes of Health Research (CIHR) and the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery award. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".