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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".