The relationship between major food sources of fructose and cardiovascular disease, cancer, and all-cause mortality: a systematic review and dose-response meta-analysis of cohort studies
Bibliographic record
Abstract
We aimed to summarize the associations between food sources of fructose and cardiovascular diseases (CVD), cancer, and all-cause mortality risk using a systematic review and meta-analysis. We searched PubMed, Scopus, and Web of Science up to November 2020. We included cohort studies that investigated the relationship between mortality risk (all-cause, CVD, specific CVD, and total and site-specific cancers) and intake of ≥1 food source of fructose (fruit, fruit juice, breakfast cereals, sugar-sweetened beverages (SSBs), sweets, and yogurt) in general adult population. Summary hazard ratios and 95% CIs were estimated using a random-effects model for linear and nonlinear relationships. Findings indicated that each 100 g/d increase in fruit intake was associated with 8-13% lower risk of CVDs, stroke, gastrointestinal, and lung cancer mortality. For all-cause mortality, there was a beneficial relationship up to 200 g/d fruit, and then plateaued. For ischemic heart disease and cancer mortality, there was a beneficial relationship up to 300 g/d followed by a slight increase. Ingestion of breakfast cereals and sweets was also associated with lower risk of all-cause mortality. For yogurt, a non-linear marginal decrease in all-cause mortality was found. Ingestion of each 200 g/d yogurt was associated with a 14% lower risk of CVD mortality. Every 60 g/d increase in sweet intake was linked to a 5% lower risk of all-cause mortality. Contrariwise, every 250 g/d increase in SSBs intake was associated with 7–10% higher risk of all-cause and CVD mortality. In conclusion, beneficial associations were found between fruit, breakfast cereals, sweets, and yogurt with all-cause and/or CVD mortality risk. Fruit intake had also an inverse link with cancer mortality. Conversely, SSBs had a harmful relationship with all-cause and CVD mortality.Registry number: CRD42019144956
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".