Sugar-Free Dark Chocolate Consumption Results in Lower Blood Glucose in Adults With Diabetes
Bibliographic record
Abstract
Diabetes is characterized by an impaired ability to appropriately control blood glucose. Postprandial hyperglycemia, in particular, is associated with complications in people with type 1 diabetes (T1D) and type 2 diabetes (T2D). The objective of this study was to determine how sugar-free dark chocolate sweetened with stevia, erythritol, and inulin impacts postprandial blood glucose levels in individuals with diabetes compared to conventional dark chocolate. In a randomized crossover design, 13 participants consumed 1 bar (34 g) of sugar-free dark chocolate or 1 bar (34 g) of conventional dark chocolate with glucose levels measured before and throughout a 120-min postprandial period. The incremental area under the curve (iAUC) was lower after the consumption of sugar-free dark chocolate (−65%, P = .04) compared to conventional dark chocolate. No significant differences between chocolates were found for peak glucose value above baseline, the total area under the curve, or peak glucose values. Our results suggest that a sugar-free dark chocolate bar sweetened with stevia, erythritol and inulin led to a lower blood glucose iAUC compared to the conventional dark chocolate bar in people with diabetes, whilst longer-term effects on glucose control remain to be determined.
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 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) | 0.003 | 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".