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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".