Insights into the development of insulin resistance: Unraveling the interaction of physical inactivity, lipid metabolism and mitochondrial biology
Bibliographic record
Abstract
While impairments in peripheral tissue insulin signalling have a well-characterized role in the development of insulin resistance and type 2 diabetes (T2D), the specific mechanisms that contribute to these impairments remain debatable. Nonetheless, a prominent hypothesis implicates the presence of a high-lipid environment, resulting in both reactive lipid accumulation and increased mitochondrial reactive oxygen species (ROS) production in the induction of peripheral tissue insulin resistance. While the etiology of insulin resistance in a high lipid environment is rapid and well documented, physical inactivity promotes insulin resistance in the absence of redox stress/lipid-mediated mechanisms, suggesting alternative mechanisms-of-action. One possible mechanism is a reduction in protein synthesis and the resultant decrease in key metabolic proteins, including canonical insulin signaling and mitochondrial proteins. While reductions in mitochondrial content associated with physical inactivity are not required for the induction of insulin resistance, this could predispose individuals to the detrimental effects of a high-lipid environment. Conversely, exercise-training induced mitochondrial biogenesis has been implicated in the protective effects of exercise. Given mitochondrial biology may represent a point of convergence linking impaired insulin sensitivity in both scenarios of chronic overfeeding and physical inactivity, this review aims to describe the interaction between mitochondrial biology, physical (in)activity and lipid metabolism within the context of insulin signalling.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".