Sweeteners: erythritol, xylitol and cardiovascular risk—friend or foe?
Bibliographic record
Abstract
Hyperglycaemia harms vascular health and promotes platelet aggregation. Reducing glucose concentration is crucial, and sugar alcohols may aid this effort. Used for over 50 years in food, cosmetic, and pharmaceutical industries, erythritol and xylitol minimally affect plasma glucose and insulin levels while promoting the release of beneficial gastrointestinal hormones such as e.g. glucagon-like peptide-1. These properties make them particularly appealing for individuals with diabetes, obesity, and metabolic syndrome. Recent pilot trials suggest that xylitol and erythritol might temporarily alter platelet aggregation. Studies on critically ill patients receiving large intravenous doses and Mendelian randomisation trials do not link sugar alcohols to significant cardiovascular risks. Sugar alcohols are also endogenously produced in the body, and while their increased production under certain conditions is not fully understood, it requires further research. This review discusses the physiology and metabolism of erythritol and xylitol, and other sugar alcohols, their roles in metabolomic profiling, effects on platelet aggregation and cardiovascular risk, related genetic disorders, vascular impacts, and usage in critically ill patients.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".