Equivalent Glycemic Load and Insulinemic Responses Elicited by Low-Carbohydrate Foods: A Randomized Trial in Healthy Adults
Bibliographic record
Abstract
Background: The information on the Nutrition Facts Label may overestimate the available-carbohydrate (avCHO) content and glycemic impact of some low-carbohydrate foods containing novel carbohydrates. Objectives: The primary objective was to test the hypothesis that the glycemic impact of low-carbohydrate foods, quantified as equivalent-glycemic-load (EGL), measures their avCHO content accurately and precisely (within ±1g). The secondary objectives were to measure the glycemic and insulinemic responses elicited by 7 low-carbohydrate foods. Methods: = mean iAUC after WB20.8]; the mean of the resulting values (excluding outliers) was the test-food EGL. Results: The expected EGL of WB5.2 was 5.2 g and the measured value was 4.0 g (95% margin of error = 0.6 g). On the basis of the food-label, the test-products contained 3-12 g avCHO (total-carbohydrate minus dietary-fiber). However, because 5 of the test-products contained allulose, which is not included in dietary-fiber and not quantified on the food-label, their content of netCHO (avCHO minus allulose) ranged from 3 to 6 g; even so, their EGL values varied from just 0.6 to 2.4 g. The mean insulin responses elicited by the test-products were positively related to their protein content, but none differed significantly from that elicited by WB5.2. Conclusions: The results support the hypothesis that the EGL measure is accurate and precise to within ∼±1 g. The EGLs of the 7 test-products were 20%-90% less than expected from their food-labels. The test-products elicited small insulin responses that were positively related to their protein content.This trial was registered at clinicaltrials.gov as NCT05870891 (https://clinicaltrials.gov/study/NCT05870891).
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".