Lipid metabolism, exercise and insulin action
Bibliographic record
Abstract
Skeletal muscle constitutes 40% of body mass and takes up 80% of a glucose load. Therefore, impaired glucose removal from the circulation, such as that which occurs in obesity and type 2 diabetes, is attributable in large part to the insulin resistance in muscle. Recent research has shown that fatty acids, derived from adipose tissue, can interfere with insulin signalling in muscle. Hence, insulin-stimulated GLUT4 translocation to the cell surface is impaired, and therefore, the rate of glucose removal from the circulation into muscle is delayed. The mechanisms provoking lipid-mediated insulin resistance are not completely understood. In sedentary individuals, excess intramyocellular accumulation of triacylglycerols is only modestly associated with insulin resistance. In contrast, endurance athletes, despite accumulating large amounts of intramyocellular triacylglycerols, are highly insulin sensitive. Thus it appears that lipid metabolites, other than triacylglycerols, interfere with insulin signalling. These metabolites, however, are not expected to accumulate in athletic muscles, as endurance training increases the capacity for fatty acid oxidation by muscle. These observations, and others in severely obese individuals and type 2 diabetes patients, suggest that impaired rates of fatty acid oxidation are associated with insulin resistance. In addition, in obesity and type 2 diabetes, the rates of fatty acid transport into muscle are also increased. Thus, excess intracellular lipid metabolite accumulation, which interferes with insulin signalling, can occur as a result of impaired rates of fatty acid oxidation and/or increased rates of fatty acid transport into muscle. Accumulation of excess intramyocellular lipid can be avoided by exercise, which improves the capacity for fatty acid oxidation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".