Important Food Sources of Fructose-Containing Sugars and Adiposity: A Systematic Review and Meta-Analysis of Controlled Feeding Trials
Bibliographic record
Abstract
Sugar-sweetened beverages (SSBs) have been linked to weight gain. It is unclear if other food sources of fructose-containing sugars behave similarly. We conducted a systematic review and meta-analysis of controlled feeding trials to assess the effect of different food sources of fructose-containing sugars on body weight and markers of adiposity. MEDLINE, Embase, and the Cochrane Library were searched through January 2020 for controlled feeding trials ≥2 weeks on the effect of fructose-containing sugars. Trial designs were prespecified by energy control: substitution (energy matched replacement of sugars in diets); addition (excess energy from sugars added to diets); subtraction (energy from sugars subtracted from diets); and ad libitum (energy from sugars freely replaced in diets). The primary outcome was body weight. Secondary outcomes were body mass index, body fat and waist circumference. Independent reviewers extracted data and assessed risk of bias. Certainty of evidence was assessed using GRADE. (NCT02558920) We identified 119 controlled trials (368 trial comparisons, N = 5263) assessing the effect of 10 food sources (SSBs, sweetened dairy alternative (soy), fruit juice, fruit drink, fruit, dried fruit, sweetened cereal grains/bars, sweets, added sweeteners and mixed sources). Total fructose-containing sugars increased body weight (mean difference, 0.29 kg [95% confidence interval, 0.05 to 0.53 kg], P = 0.017) and body fat in addition trials with no effect in other analyses or outcomes. There was evidence of interaction by food source in substitution trials with fruit reducing and mixed sources increasing some outcomes and in addition trials with 100% fruit juice reducing and SSBs and mixed sources increasing some outcomes. The overall certainty of evidence was moderate for the decreasing effect of fruit and fruit juice and the increasing effect of SSBs and mixed sources and high-to-very low for other comparisons. Energy control and food source may mediate the effect of fructose-containing sugars on adiposity. The evidence provides good indication that fruit and 100% fruit juice decrease and SSBs and mixed sources increase markers of adiposity. More high-quality randomized trials of different foods are needed to improve our estimates. American Society for Nutrition, Diabetes Canada, CIHR, Mitacs.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".