High intramuscular triglyceride turnover rates and the link to insulin sensitivity: influence of obesity, type 2 diabetes and physical activity
Bibliographic record
Abstract
Large intramuscular triglyceride (IMTG) stores in sedentary, obese individuals have been linked to insulin resistance, yet well-trained athletes exhibit high IMTG levels whilst maintaining insulin sensitivity. Contrary to previous assumptions, it is now known that IMTG content per se does not result in insulin resistance. Rather, insulin resistance is caused, at least in part, by the presence of high concentrations of harmful lipid metabolites, such as diacylglycerols and ceramides in muscle. Several mechanistic differences between obese sedentary individuals and their highly trained counterparts have been identified, which determine the differential capacity for IMTG synthesis and breakdown in these populations. In this review, we first describe the most up-to-date mechanisms by which a low IMTG turnover rate (both breakdown and synthesis) leads to the accumulation of lipid metabolites and results in skeletal muscle insulin resistance. We then explore current and potential exercise and nutritional strategies that target IMTG turnover in sedentary obese individuals, to improve insulin sensitivity. Overall, improving IMTG turnover should be an important component of successful interventions that aim to prevent the development of insulin resistance in the ever-expanding sedentary, overweight and obese populations. Novelty: A description of the most up-to-date mechanisms regulating turnover of the IMTG pool. An exploration of current and potential exercise/nutritional strategies to target and enhance IMTG turnover in obese individuals. Overall, highlights the importance of improving IMTG turnover to prevent the development of insulin resistance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".