Effect of fructose on markers of non-alcoholic fatty liver disease (NAFLD): a systematic review and meta-analysis of controlled feeding trials
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: In the absence of consistent clinical evidence, there are concerns that fructose contributes to non-alcoholic fatty liver disease (NAFLD). To determine the effect of fructose on markers of NAFLD, we conducted a systematic review and meta-analysis of controlled feeding trials. SUBJECTS/METHODS: We searched MEDLINE, EMBASE, CINAHL and the Cochrane Library (through 3 September 2013). We included relevant trials that involved a follow-up of ≥ 7 days. Two reviewers independently extracted relevant data. Data were pooled by the generic inverse variance method using random effects models and expressed as standardized mean difference (SMD) for intrahepatocellular lipids (IHCL) and mean difference (MD) for alanine aminotransferase (ALT). Inter-study heterogeneity was assessed (Cochran Q statistic) and quantified (I(2) statistic). RESULTS: Eligibility criteria were met by eight reports containing 13 trials in 260 healthy participants: seven isocaloric trials, in which fructose was exchanged isocalorically for other carbohydrates, and six hypercaloric trials, in which the diet was supplemented with excess energy (+21-35% energy) from high-dose fructose (+104-220 g/day). Although there was no effect of fructose in isocaloric trials, fructose in hypercaloric trials increased both IHCL (SMD=0.45 (95% confidence interval (CI): 0.18, 0.72)) and ALT (MD=4.94 U/l (95% CI: 0.03, 9.85)). LIMITATIONS: Few trials were available for inclusion, most of which were small, short (≤ 4 weeks), and of poor quality. CONCLUSIONS: Isocaloric exchange of fructose for other carbohydrates does not induce NAFLD changes in healthy participants. Fructose providing excess energy at extreme doses, however, does raise IHCL and ALT, an effect that may be more attributable to excess energy than fructose. Larger, longer and higher-quality trials of the effect of fructose on histopathological NAFLD changes are required.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.021 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".