‘Catalytic’ doses of fructose may benefit glycaemic control without harming cardiometabolic risk factors: a small meta-analysis of randomised controlled feeding trials
Bibliographic record
Abstract
Contrary to concerns that fructose may have adverse metabolic effects, there is evidence that small, 'catalytic' doses ( ≤ 10 g/meal) of fructose decrease the glycaemic response to high-glycaemic index meals in human subjects. To assess the longer-term effects of 'catalytic' doses of fructose, we undertook a meta-analysis of controlled feeding trials. We searched MEDLINE, EMBASE, CINAHL and the Cochrane Library. Analyses included all controlled feeding trials ≥ 7 d featuring 'catalytic' fructose doses ( ≤ 36 g/d) in isoenergetic exchange for other carbohydrates. Data were pooled by the generic inverse variance method using random-effects models and expressed as mean differences (MD) with 95 % CI. Heterogeneity was assessed by the Q statistic and quantified by I 2. The Heyland Methodological Quality Score assessed study quality. A total of six feeding trials (n 118) met the eligibility criteria. 'Catalytic' doses of fructose significantly reduced HbA1c (MD - 0·40, 95 % CI - 0·72, - 0·08) and fasting glucose (MD - 0·25, 95 % CI - 0·44, - 0·07). This benefit was seen in the absence of adverse effects on fasting insulin, body weight, TAG or uric acid. Subgroup and sensitivity analyses showed evidence of effect modification under certain conditions. The small number of trials and their relatively short duration limit the strength of the conclusions. In conclusion, this small meta-analysis shows that 'catalytic' fructose doses ( ≤ 36 g/d) may improve glycaemic control without adverse effects on body weight, TAG, insulin and uric acid. There is a need for larger, longer ( ≥ 6 months) trials using 'catalytic' fructose to confirm these results.
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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 | 0.027 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.044 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 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".