Differential effects of fructose on glycemic control: A systematic review and meta‐analysis of controlled feeding trials
Bibliographic record
Abstract
Objective Fructose has become a focus of concern. To assess the effect of fructose on glycemic control, we conducted a systematic review and meta‐analysis. Methods We searched MEDLINE, EMBASE, and Cochrane Library (up to Nov 5, 2012) for relevant controlled trials ≥7‐days. Two independent reviewers extracted data. Data were pooled using random effects models for glycated blood proteins (GBP) and fasting glucose (FG) and insulin (FI). Results Eligibility criteria were met by 47 isocaloric trials (n=863), in which fructose was exchanged isocalorically for other carbohydrate, and 12 hypercaloric trials (n=143), in which the diet was supplemented with excess energy from fructose. Fructose in isocaloric trials reduced GBP (standardized mean difference = − 0.29 [95% CI: −0.46, −0.11]), equivalent to a ~0.59% reduction in HbA1c, in people with and without diabetes. In contrast, fructose in hypercaloric trials increased FG (mean difference [MD] = 0.18 mmol/L [95% CI: 0.08, 0.29]) and FI (MD = 6.06 pmol/L [95% CI: 3.70, 8.31]). Limitations Most trials were small, short, and of poor quality. Conclusions Isocaloric exchange of fructose for other carbohydrate improves glycemic control in people with and without diabetes. Fructose providing excess energy, however, raises fasting glucose and insulin levels, an effect that may be more owing to excess energy than fructose. Funding : Canadian Institutes of Health Research. Grant Funding Source : CIHR
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".