The Role of Fructose, Sucrose, and High-fructose Corn Syrup in Diabetes
Bibliographic record
Abstract
Concerns are growing regarding the role of dietary sugars in the development of obesity and cardiometabolic diseases, including diabetes. High-fructose corn syrup (HFCS) and sucrose are the most important dietary sweeteners. Both HFCS and sucrose have overlapping metabolic actions with adverse effects attributed to their fructose moiety. Ecological studies have linked the rise in fructose availability with the increases in obesity and diabetes worldwide. This link has been largely underpinned by animal models and select human trials of fructose overfeeding at high levels of exposure. Although prospective cohort studies have shown significant associations comparing the highest with the lowest levels of intake sugar-sweetened beverages, these associations are small, do not hold at moderate levels of intake and are subject to collinearity effects from related dietary and lifestyle factors. Most systematic reviews and meta-analyses from controlled feeding trials have shown that fructose-containing sugars in isocaloric exchange for other carbohydrates do not show evidence of harm and, in the case of fructose, may even have advantages for glycaemic control, especially at small doses. Nevertheless, trials in which fructose-containing sugars supplement diets with excess energy have shown adverse effects, effects that appear more attributable to the excess energy than the sugar. There is no unequivocal evidence that fructose intake at moderate doses is directly related with adverse metabolic effects, although there is potentially cause for concern where fructose is provided at high doses or contributes excess energy to diets. Further investigation is warranted due to the significant knowledge gaps and weaknesses in existing research.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".