Endurance training in obese humans improves glucose tolerance and mitochondrial fatty acid oxidation and alters muscle lipid content
Bibliographic record
Abstract
Muscle fatty acid (FA) metabolism is impaired in obesity and insulin resistance, reflected by reduced rates of FA oxidation and accumulation of lipids. It has been suggested that interventions that increase FA oxidation may enhance insulin action by reducing these lipid pools. Here, we examined the effect of endurance training on rates of mitochondrial FA oxidation, the activity of carnitine palmitoyltransferase I (CPT I), and the lipid content in muscle of obese individuals and related these to measures of glucose tolerance. Nine obese subjects completed 8 wk of moderate-intensity endurance training, and muscle biopsies were obtained before and after training. Training significantly improved glucose tolerance, with a reduction in the area under the curve for glucose (P < 0.05) and insulin (P = 0.01) during an oral glucose tolerance test. CPT I activity increased 250% (P = 0.001) with training and became less sensitive to inhibition by malonyl-CoA. This was associated with an increase in mitochondrial FA oxidation (+120%, P < 0.001). Training had no effect on muscle triacylglycerol content; however, there was a trend for training to reduce both the total diacylglcyerol (DAG) content (-15%, P = 0.06) and the saturated DAG-FA species (-27%, P = 0.06). Training reduced both total ceramide content (-42%, P = 0.01) and the saturated ceramide species (-32%, P < 0.05). These findings suggest that the improved capacity for mitochondrial FA uptake and oxidation leads not only to a reduction in muscle lipid content but also a to change in the saturation status of lipids, which may, at least in part, provide a mechanism for the enhanced insulin action observed with endurance training in obese individuals.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".