High fructose consumption induces cardiac dysfunction and vascular abnormalities
Bibliographic record
Abstract
Fructose is a simple sugar or monosaccharide, which is abundant in nature and commonly used in food industry as a strong natural sweetener. The growing epidemic of high fructose consumption has been linked to increased prevalence of obesity, type 2 diabetes, metabolic dysfunction-associated steatotic liver disease, and cardiovascular disease. Increasing evidence indicates that high dietary fructose can exert direct effects on the heart as well as the vasculature. Several underlying mechanisms involving metabolic disturbances, oxidative stress, and inflammation have been demonstrated to induce cardiac dysfunction and vascular abnormalities. Accordingly, the intent of this review is to discuss the metabolic consequences of increased fructose consumption and to provide an overview of the mechanisms associated with fructose-enriched diet-induced cardiovascular abnormalities. A description of some novel interventions that attenuate both cardiac and vascular dysfunction subsequent to fructose over consumption is also provided. The high intakes of fructose present a public health hazard, and thus, there is a pressing need to increase public awareness on the harmful health effects, including cardiovascular health, of high intake of foods and beverages enriched with fructose. Furthermore, the supplementation of processed foods and beverages with fructose additives needs regulation.
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 | 0.000 |
| 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) | 0.002 | 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".