Sex-based differences in hepatic and skeletal muscle triglyceride storage and metabolism
Bibliographic record
Abstract
Women and men store lipid differently within the body with men storing more fat in the android region and women storing more fat in the gynoid region. Fat is predominately stored in adipose tissue as triacylglycerides (TG); however, TG are also stored in other tissues including the liver and skeletal muscle. Excess hepatic TG storage, defined as a TG concentration >5% of liver weight and known as nonalcoholic fatty liver disease (NAFLD), is related to the metabolic syndrome. Similarly, elevated skeletal muscle TG, termed intramyocellular lipids (IMCL), are related to insulin resistance in obesity and type II diabetes. Men store more hepatic TG than women and, unsurprisingly, NAFLD is more prevalent in men than women. Women store more IMCL than men, yet type II diabetes risk is not greater, which is likely due to the manner in which women store TG within muscle. Sex-based differences in TG storage between men and women are underpinned by differences in messenger RNA expression, protein content, and enzyme activities of skeletal muscle and hepatic lipid metabolic pathways. Furthermore, women have a greater reliance on lipid during exercise because of upregulation of lipid oxidative pathways. The purpose of this review is to discuss the role of sex in mediating lipid storage and metabolism within skeletal muscle and the liver at rest and during exercise and its relationship with metabolic disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".