Editorial: Exercise intervention for prevention and management of type 2 diabetes
Bibliographic record
Abstract
Editorial on the Research Topic Exercise intervention for prevention and management of type 2 diabetes More than half a billion people are living with diabetes worldwide, and almost all global cases (96%) are type 2 diabetes (T2D).With that number projected to more than double to 1.3 billion people in the next 30 years, the need for effective pharmacological and nonpharmacological prevention and treatment strategies is unmet and dire.Obesity and physical inactivity (i.e., sedentary lifestyle) are two significant risk factors for the development and progression of T2D.The molecular mechanisms driving the disease onset and progression in distinct peripheral tissues, as well as their cross-talk, remain unclear.In addition to pharmacological treatments such as glucose-lowering medications (e.g., metformin, glucagon-like peptide 1 (GLP1) agonists, sodium-glucose cotransporter-2 (SGLT2) inhibitors, etc.), physical activity and structured exercise (exercise) are well-known therapeutic strategies for both prevention and management of T2D.Comparatively, adherence is high for medication use, while exercise adherence is often low for a variety of reasons such as lack of time, pain, costs, lack of expected results, thus making it difficult to prescribe physical activity alone or in combination with pharmacotherapy to patients with T2D.In recent years, the exercise physiology field has shifted its focus toward finding the type of exercise training that could improve adherence and answer the question of "how little can I do?"For example, replacing longer moderate intensity aerobic exercise bouts with shorter and more intense exercises such as those practiced in high intensity exercise training (HIIT) has yielded promising results.This area of research requires further investigations in larger and more diverse cohorts to reach the goal of prescribing the optimal program for individuals with T2D that will not only manage their symptoms but also promote lifetime adherence.In this Research Topic, we aimed to shed more light on how decreasing sedentary time and/or increasing structured exercise training-either alone or in combination with diet or pharmacotherapy-aids in the management of T2D at the molecular, cellular, and physiological levels with the ultimate goal of finding new avenues to help individuals with T2D manage their disease.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.033 | 0.024 |
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".