Sex differences in lipid metabolism and metabolic disease risk
Bibliographic record
Abstract
The ability of nutrients to regulate specific metabolic pathways is often overshadowed by their role in basic sustenance. Consequently, the mechanisms whereby these nutrients protect against or promote a variety of acquired metabolic syndromes remains poorly understood. Premenopausal women are generally protected from the adverse effects of obesity despite having a greater proportion of body fat than men. Menopause is often associated with a transformation in body fat morphology and a gradual increase in the susceptibility to metabolic complications, eventually reaching the point where women and men are at equal risk. These phenomena are not explained solely by changes in food preference or nutrient intake suggesting an important role for the sex hormones in regulating the metabolic fate of nutrients and protecting against metabolic disease pathophysiology. Here, we discuss how differences in the acquisition, trafficking, and subceullular metabolism of fats and other lipid soluble nutrients in major organ systems can create overt sex-specific phenotypes, modulate metabolic disease risk, and contribute to the rise in obesity in the modern sedentary climate. Identifying the molecular mechanisms underpinning sex differences in fat metabolism requires the unravelling of the interactions among sex chromosome effects, the hormonal milieu, and diet composition. Understanding the mechanisms that give rise to sex differences in metabolism will help to rationalize treatment strategies for the management of sex-specific metabolic disease risk factors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".