Relationships between Exercise, Mitochondrial Biogenesis and Type 2 Diabetes
Bibliographic record
Abstract
Skeletal muscle is the major site of insulin-stimulated glucose utilization in the body. Central to this process is oxidative metabolism, which is controlled by mitochondria. Therefore, defects in the biogenesis of this organelle can impact the ability of muscle to oxidize substrates and can have grave consequences on the action of insulin on glucose uptake. In healthy muscle, glucose and free fatty acids (FFAs) are efficiently metabolized preventing the accumulation of harmful lipid by-products. In contrast, in pre-diabetic conditions, reduced oxidative capacity, high levels of reactive oxygen species, and chronic elevations in FFAs culminate in greater intramyocellular lipids and lipid metabolites that interfere with insulin signaling pathways, and contribute to lower insulin sensitivity. While the extent of the involvement of mitochondria in insulin resistance and type 2 diabetes (T2D) is still an ongoing debate, there is compelling evidence to suggest that dysfunction in mitochondria, mediated by changes in gene expression, morphology, and mitochondrial turnover, contributes to the dysregulation of insulin signaling pathways observed with this metabolic syndrome. In the present review, we discuss our current understanding of mitochondrial biogenesis and highlight how dysfunction in key mitochondrial biogenesis pathways may play an important role in the pathogenesis of T2D. Moreover, we provide evidence supporting the therapeutic value of exercise in the prevention and treatment of metabolic syndromes such as T2D.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".