Role of Environmental Pollutants in Skeletal Muscle Insulin Resistance and Mitochondrial Dysfunction
Bibliographic record
Abstract
In the last decade, the incidence of diabetes in Canada has nearly doubled and is now estimated to affect one in three individuals. Type 2 diabetes (T2D) is a serious public health problem: governments and health care professional are working to control its propagation, offer better treatment alternatives and reduce its impact on patient quality of life. Insulin resistance is an early event in the development of T2D. Due to its mass and important role in the maintenance of glucose homeostasis, skeletal muscle is believed to play a central role in the development of insulin resistance. The development of this metabolic disorder is multifaceted with obesity, physical activity and diet receiving the most research interest. It is recognized that mitochondrial dysfunction, increased oxidative stress and inflammation are implicated in the development of insulin resistance in muscle. Recently, the environmental hypothesis has been advanced to explain the increased number of patients with T2D. Various persistent organic pollutants (POPs), such as polychlorinated biphenyls (PCBs), bisphenol A (BPA) and p, p-dichlorodiphenylchloroethane (DDT) are being investigated in their relation to T2D. However, despite the importance of skeletal muscle in the development of insulin resistance and T2D, very few studies have focused on the effect of POPs on skeletal muscle energy metabolism. This review will highlight the implication of POPs in the development of diabetes and present work being done to asses POPs’ involvement in observed metabolic disarrangements, specifically at the level of 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".