Acute Response of Equicaloric Test Meals Varying in Glycemic Index and Glycemic Load on Postprandial Glycemia, Arterial Stiffness and Blood Pressure in Healthy Adults
Bibliographic record
Abstract
Objectives The relative importance of the type of carbohydrate (Glycemic Index, GI) and/or the quantity (Glycemic Load, GL) in the context of a mixed meal on glycemia is still debated; whereas the effects on vascular health are unknown. This study therefore investigated the acute effect all 4 permutations of high and low GI and GL meals on postprandial glycemia and vascular function. Methods A total of 15 healthy subjects were recruited; preliminary data on the first eight are presented (3M, 5F; age 33 ± 16 y; BMI 21.6 ± 2.7 kg/m 2 ). On 4 separate occasions, subjects received 4 equicaloric meals where the GI and GL levels were manipulated in a block design. Capillary blood samples were collected at fasting and 15, 30, 45, 60, 90, 120, 180 and 240 minutes post‐consumption. Aortic augmentation index (AIx) and central BP were measured hourly using applanation tonometry by radial pulse wave analysis and peripheral BP was evaluated oscillometrically. Results The iAUC blood glucose response after the HiGI‐HiGL and LoGI‐HiGL meals were significantly higher than the LoGI‐LoGL and HiGI‐LoGL meals (p<0.0001), while AIx was significantly reduced at 60min after the HiGI‐LoGL and LowGI‐HiGL meals compared to HiGI‐HiGL (p<0.05). Conclusions Preliminary data analysis demonstrates that manipulating the GI and GL of a meal result in significantly different blood glucose responses and may acutely modulate vascular function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".