Exercise and insulin increase muscle fatty acid uptake by recruiting putative fatty acid transporters to the sarcolemma
Bibliographic record
Abstract
PURPOSE OF REVIEW: Skeletal muscle metabolic energy, needed to maintain contractile activity, is mainly obtained from glucose and long-chain fatty acids. Recent studies have revealed a remarkable parallel between the regulation of uptake of glucose and fatty acids by muscle, in that each is mediated by sarcolemmal transporters that are recruited from an intracellular storage site. The focus of this review is to describe newly obtained insights on the recruitment of fatty acid transporters and their malfunctioning in diabetes. RECENT FINDINGS: The major fatty acid transporter involved is fatty acid translocase (CD36). Translocation of this protein to the membrane is triggered by muscle contraction and by insulin, and presumably occurs from distinct intracellular pools. This resembles the well documented exercise and insulin-induced recruitment of glucose transporter-4. Whether another transporter, plasma membrane fatty acid-binding protein, is also subject to such recycling is not yet clear. In a rodent model of insulin-dependent (type 1) diabetes, the increased rate of muscle fatty acid uptake could be associated with an increased total amount of fatty acid translocase (CD36). In a model of non-insulin dependent (type 2) diabetes, this increased rate could be associated with a permanent relocalization of fatty acid translocase to the sarcolemma. SUMMARY: These findings indicate a pivotal role for the membrane transporter fatty acid translocase in the exercise and insulin-induced increases of muscle fatty acid uptake and utilization, and suggest that malfunctioning of the cellular recycling of fatty acid translocase is involved in the etiology of insulin resistance and type 2 diabetes.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".