Impact of high‐fat /low‐carbohydrate, high‐, low‐glycaemic index or low‐caloric meals on glucose regulation during aerobic exercise in Type 2 diabetes
Bibliographic record
Abstract
AIMS: A decrement in blood glucose (BG) may be observed in patients with Type 2 diabetes (T2DM) when exercise is performed after a meal, in contrast to fasting. We determined the impact of different pre-exercise meal macronutrient compositions with modulation of the glycaemic index (GI) on glucose regulation during exercise in patients with T2DM. METHODS: Using a randomized, single-blind crossover design, 10 sedentary men performed five exercise sessions, once after an overnight fast, and also after each of four test meals, consisting of a high-fat/low-carbohydrate meal, a high-GI meal, a low-GI meal, and a low-calorie meal. RESULTS: Pre-exercise BG and insulin levels were comparable for all four meals. Exercise decreased BG and insulin levels during all meal conditions (all P < 0.001) compared with the fasting state in which BG levels did not change. The magnitude of BG and insulin decrements was similar after consuming the low-calorie, the high-GI and the high-fat/low-carbohydrate meals, whereas the low-GI meal induced the lowest BG fall. Adrenaline response was higher after consumption of the high-, the low-GI and the low-caloric meals compared with the high-fat/low-carbohydrate meal and with the fasting state (P < 0.05). CONCLUSIONS: This study underlines the beneficial effect of low-GI foods and the differential impact of pre-exercise meal macronutrient composition on BG decrease. This may protect against exercise-induced hypoglycaemia, and reiterates the safety of exercising while fasting in T2DM patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".