Impact of Beta-Blocker Treatment and the Nutritional Status on Glycemic Response During Exercise in Type 2 Diabetic Patients
Bibliographic record
Abstract
Background/Objective: More than 60% of type 2 diabetic individuals present with hypertension and have higher risk of cardiac complications. In addition to behavioural modifications, such as healthy food choices and regular physical activity, beta-blocker (BB) treatment may be considered in order to reduce morbidity and mortality especially following a cardiovascular event. However, this medication is generally associated with a deleterious impact on glucose metabolism. To assess the impact of a BB treatment on glucose response in type 2 diabetic patients exempt of cardiovascular complications. Methods: Six sedentary men, treated with diet and/or a hypoglycemic agent performed four exercise sessions at 60% of their VO2 peak, in the fasted state or 2 hours after a standardized breakfast, with and without BB (Atenolol 100 mg id for five consecutive days). Blood samples were assayed during the resting period, at 15-minutes intervals during the exercise session and the recovery period. Results: A reduction of blood glucose levels was observed following the exercise session performed in the postabsorptive state (41% and 37% reduction with and without BB treatment respectively; P < 0.01). One hour of exercise performed in the fasted state had minimal impact on glucose and insulin levels, with or without BB. BB treatment was not associated with increased baseline blood glucose or insulin levels in the fasted or the postabsorptive situation. Conclusion: These results suggest that the nutritional status has a more important impact on plasma glucose and insulin modulation than short-term use of BB per se.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".