Examining the benefits of cold exposure as a therapeutic strategy for obesity and type 2 diabetes
Bibliographic record
Abstract
The pathogenesis of metabolic diseases such as obesity and type 2 diabetes are characterized by a progressive dysregulation in energy partitioning, often leading to end-organ complications. One emerging approach proposed to target this metabolic dysregulation is the application of mild cold exposure. In healthy individuals, cold exposure can increase energy expenditure and whole body glucose and fatty acid utilization. Repeated exposures can lower fasting glucose and insulin levels and improve dietary fatty acid handling, even in healthy individuals. Despite its apparent therapeutic potential, little is known regarding the effects of cold exposure in populations for which this stimulation could benefit the most. The few studies available have shown that both acute and repeated exposures to the cold can improve insulin sensitivity and reduce fasting glycemia in individuals with type 2 diabetes. However, critical gaps remain in understanding the prolonged effects of repeated cold exposures on glucose regulation and whole body insulin sensitivity in individuals with metabolic syndrome. Much of the metabolic benefits appear to be attributable to the recruitment of shivering skeletal muscles. However, further work is required to determine whether the broader recruitment of skeletal muscles observed during cold exposure can confer metabolic benefits that surpass what has been historically observed from endurance exercise. In addition, although cold exposure offers unique cardiovascular responses for a physiological stimulus that increases energy expenditure, further work is required to determine how acute and repeated cold exposure can impact cardiovascular responses and myocardial function across a broader scope of individuals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".