Revisiting the contribution of mitochondrial biology to the pathophysiology of skeletal muscle insulin resistance
Bibliographic record
Abstract
While the etiology of type 2 diabetes is multifaceted, the induction of insulin resistance in skeletal muscle is a key phenomenon, and impairments in insulin signaling in this tissue directly contribute to hyperglycemia. Despite the lack of clarity regarding the specific mechanisms whereby insulin signaling is impaired, the key role of a high lipid environment within skeletal muscle has been recognized for decades. Many of the proposed mechanisms leading to the attenuation of insulin signaling - namely the accumulation of reactive lipids and the pathological production of reactive oxygen species (ROS), appear to rely on this high lipid environment. Mitochondrial biology is a central component to these processes, as these organelles are almost exclusively responsible for the oxidation and metabolism of lipids within skeletal muscle and are a primary source of ROS production. Classic studies have suggested that reductions in skeletal muscle mitochondrial content and/or function contribute to lipid-induced insulin resistance; however, in recent years the role of mitochondria in the pathophysiology of insulin resistance has been gradually re-evaluated to consider the biological effects of alterations in mitochondrial content. In this respect, while reductions in mitochondrial content are not required for the induction of insulin resistance, mechanisms that increase mitochondrial content are thought to enhance mitochondrial substrate sensitivity and submaximal adenosine diphosphate (ADP) kinetics. Thus, this review will describe the central role of a high lipid environment in the pathophysiology of insulin resistance, and present both classic and contemporary views of how mitochondrial biology contributes to 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".