Lipid droplet size and location in human skeletal muscle fibers are associated with insulin sensitivity
Bibliographic record
Abstract
In skeletal muscle, an accumulation of lipid droplets (LDs) in the subsarcolemmal space is associated with insulin resistance, but the underlying mechanism is not clear. We aimed to investigate how the size, number, and location of LDs are associated with insulin sensitivity and muscle fiber types and are regulated by aerobic training and treatment with an erythropoiesis-stimulating agent (ESA) in healthy young untrained men. LD analyses were performed by quantitative transmission electron microscopy, and insulin sensitivity was assessed by a hyperinsulinemic-euglycemic clamp. At baseline, we found that only the diameter (and not the number) of individual subsarcolemmal LDs was negatively associated with insulin sensitivity ( R2 = 0.20, P = 0.03, n = 29). Despite 34% ( P = 0.004) fewer LDs, the diameter of individual subsarcolemmal LDs was 20% ( P = 0.0004) larger in type 2 fibers than in type 1 fibers. Furthermore, aerobic training decreased the size of subsarcolemmal LDs in the type 2 fibers, and ESA treatment lowered the number of both intermyofibrillar and subsarcolemmal LDs in the type 1 fibers. In conclusion, the size of individual subsarcolemmal LDs may be involved in the mechanism by which LDs are associated with insulin resistance in skeletal muscle.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".