Exercise-nutrient interactions for improved postprandial glycemic control and insulin sensitivity
Bibliographic record
Abstract
Type 2 diabetes (T2D) is a rapidly growing yet largely preventable chronic disease. Exaggerated increases in blood glucose concentration following meals is a primary contributor to many long-term complications of the disease that decrease quality of life and reduce lifespan. Adverse health consequences also manifest years prior to the development of T2D due to underlying insulin resistance and exaggerated postprandial concentrations of the glucose-lowering hormone insulin. Postprandial hyperglycemic and hyperinsulinemic excursions can be improved by exercise, which contributes to the well-established benefits of physical activity for the prevention and treatment of T2D. The aim of this review is to describe the postprandial dysmetabolism that occurs in individuals at risk for and with T2D, and highlight how acute and chronic exercise can lower postprandial glucose and insulin excursions. In addition to describing the effects of traditional moderate-intensity continuous exercise on glycemic control, we highlight other forms of activity including low-intensity walking, high-intensity interval exercise, and resistance training. In an effort to improve knowledge translation and implementation of exercise for maximal glycemic benefits, we also describe how timing of exercise around meals and post-exercise nutrition can modify acute and chronic effects of exercise on glycemic control and insulin sensitivity. Novelty: Exaggerated postprandial blood glucose and insulin excursions are associated with disease risk. Both a single session and repeated sessions of exercise improve postprandial glycemic control in individuals with and without T2D. The glycemic benefits of exercise can be enhanced by considering the timing and macronutrient composition of meals around exercise.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".