Effect of fructose on triglycerides: a meta‐analysis of controlled feeding trials
Bibliographic record
Abstract
Background Health agencies have expressed concern that fructose may contribute to hyper‐triglyceridemia (HTG) in people with and without diabetes (DM). Purpose To investigate the effect of fructose on triglycerides (TG), we conducted a meta‐analysis of controlled feeding trials. Methods We searched MEDLINE, EMBASE, CINAHL and the Cochrane Library for relevant trials of ≥7days. Data were aggregated by the generic inverse variance method using random effects models and expressed as mean differences (MD) with 95% confidence intervals (CI). Heterogeneity was assessed (Chi 2 ) and quantified (I 2 ). Study quality was assessed by the Heyland score. Results 46 isocaloric (n=549 non‐DM, n=174 DM) and 7 hypercaloric (n=127 non‐DM) trials met eligibility criteria. In isocaloric trials, fructose did not significantly effect TG (MD= 0.05 [95% CI: −0.02, 0.12]) with significant evidence of inter‐study heterogeneity. There was no effect modification by diabetes status. In hypercaloric feeding trials, fructose had a TG‐raising effect (MD=0.32 [95% CI: 0.08, 0.56]) with significant inter‐study heterogeneity. Limitations The majority of the trials were <12‐weeks and of poor study quality. Conclusions Isocaloric fructose does not have significant TG‐raising effects whereas hypercaloric trials did, however excess energy may be a cofounder. Funding: Canadian Institutes of Health Research (CIHR). Grant Funding Source : ASN
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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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.047 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".