Glycemic and insulinemic response to four different sweeteners in healthy individuals: A double blind, randomized controlled trial.
Bibliographic record
Abstract
Background The non‐nutritive sweetener, erythritol, does not change postprandial glucose levels, whether this holds true for forms which are modulated to be more intensely sweet is not known. Objectives Comparison of the postprandial glucose and insulin responses of two erythritol products differing in sweetness intensity. Methods 15 healthy volunteers (6 M, 9F; 38±12 yr; 26.9±4.4kg/m 2 ) consumed 4 test meals consisting of 24g sucrose, 24g erythritol (Swerve ® Granular), 2.4g erythritol (Swerve ® 10x) and 3g sucralose (Splenda ® ) mixed with 200ml of water. All meals were matched for sweetness level. Capillary blood samples were taken fasting and at 15, 30, 45, 60, 90 and 120 min after the start of the meal. Results Postprandial glucose and insulin levels were significantly lower after the non‐nutritive sweeteners compared to sucrose at 15, 30 and 45 min. Glucose levels were also lower after both erythritol products compared to sucralose at 15 min. At 90 and 120min, glucose levels were significantly higher after the non‐nutritive sweeteners compared to sucrose (p<0.001). Incremental glucose and insulin areas were significantly lower after the three non‐nutritive sweeteners compared to sucrose (p<0.0001). Conclusions This study confirms that irrespective of sweetness level, consumption of erythritol does not raise postprandial blood glucose or insulin levels significantly.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".