Modeling the cumulative benefits of regular physical activity on type 2 diabetes progression
Bibliographic record
Abstract
Despite the well-acknowledged benefits of physical activity for type 2 diabetes (T2D) prevention, the literature lacks validated models able to predict the long-term benefits of exercise on T2D progression and that could be used to support personalized risk prediction and prevention. To bridge this gap, we developed a novel mathematical model that formalizes the link between exercise and short- and long-termglucose-insulin dynamics to predict the benefits of regular exercise on T2D progression. Specifically, we combine a well-known T2D progression model that describes a fast dynamics of physical activity with a slow dynamics that accounts for the cumulative effects of regular physical activity on pancreatic beta-cell mass and on individual insulin sensitivity, mediated by the integral effect of interleukin-6 produced during exercise. The model was used to estimate the benefits of physical activity in four conditions: (i) regular exercise of varying intensity; (ii) regular exercise following the World Health Organization (WHO) recommendations for chronic disease prevention; (iii) discontinuation of a regular exercise program; and (iv) assessment of the inter-individual variability in a wide range of simulated scenarios. These results are encouraging and can set the basis for future development of decision support tools able to assist patients and clinicians in tailoring preventive lifestyle interventions. Results showed that the model quantitatively captured the dose-response relationship (larger benefits with increasing intensity and/or duration of exercise), it consistently reproduced the benefits of clinical guidelines for diabetes prevention, and it accurately predicted persistent benefits following interruption of physical activity, in line with real-world evidence from the literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".