Relation of Total Sugars, Sucrose, Fructose, and Added Sugars With the Risk of Cardiovascular Disease
Bibliographic record
Abstract
OBJECTIVE: To determine the association of total and added fructose-containing sugars on cardiovascular (CVD) incidence and mortality. METHODS: MEDLINE, EMBASE and Cochrane Library were searched from January 1, 1980, to July 31, 2018. Prospective cohort studies assessing the association of reported intakes of total, sucrose, fructose and added sugars with CVD incidence and mortality in individuals free from disease at baseline were included. Risk estimates were pooled using the inverse variance method, and dose-response analysis was modeled. RESULTS: Eligibility criteria were met by 24 prospective cohort comparisons (624,128 unique individuals; 11,856 CVD incidence cases and 12,224 CVD mortality cases). Total sugars, sucrose, and fructose were not associated with CVD incidence. Total sugars (risk ratio, 1.09 [95% confidence interval, 1.02 to 1.17]) and fructose (1.08 [1.01 to 1.15]) showed a harmful association for CVD mortality, there was no association for added sugars and a beneficial association for sucrose (0.94 [0.89 to 0.99]). Dose-response analyses showed a beneficial linear dose-response gradient for sucrose and nonlinear dose-response thresholds for harm for total sugars (133 grams, 26% energy), fructose (58 grams, 11% energy) and added sugars (65 grams, 13% energy) in relation to CVD mortality (P<.05). The certainty of the evidence using GRADE was very low for CVD incidence and low for CVD mortality for all sugar types. CONCLUSION: Current evidence supports a threshold of harm for intakes of total sugars, added sugars, and fructose at higher exposures and lack of harm for sucrose independent of food form for CVD mortality. Further research of different food sources of sugars is needed to define better the relationship between sugars and CVD. REGISTRATION: clinicaltrials.gov, NCT01608620.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".