Modeling sex-specific whole-body metabolic responses to feeding and fasting
Bibliographic record
Abstract
Men generally favor carbohydrate metabolism, while women lean towards lipid metabolism, resulting in significant sex-based differences in energy oxidation across various metabolic states such as fasting and feeding. These differences are influenced by body composition and inherent metabolic fluxes, including increased lipolysis rates in women. However, understanding how sex influences organ-specific metabolism and systemic manifestations remains incomplete. To address these gaps, we developed a sex-specific, whole-body metabolic model for feeding and fasting scenarios in healthy young adults. Our model integrates organ metabolism with whole-body responses to mixed meals, particularly high-carbohydrate and high-fat meals. Our predictions suggest that differences in liver and adipose tissue nutrient storage and oxidation patterns drive systemic metabolic disparities. We propose that sex differences in fasting hepatic glucose output may result from the different handling of free fatty acids, glycerol, and glycogen. We identified a metabolic pathway, possibly more prevalent in female livers, redirecting lipids towards carbohydrate metabolism to support hepatic glucose production. This mechanism is facilitated by the TG-FFA cycle between adipose tissue and the liver. Incorporating sex-specific data into multi-scale frameworks offers insights into how sex modulates human metabolism.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".