Important Food Sources of Fructose-Containing Sugars and Fasting Serum Uric Acid Levels: A Systematic Review and Meta-Analysis of Controlled Feeding Trials
Bibliographic record
Abstract
OBJECTIVES: Fructose as a source of excess calories increases uric acid. Whether this effect is mediated by the food matrix at different levels of energy is unknown. We aim to conduct a systematic review and meta-analysis of controlled feeding trials on the effect of food sources of fructose-containing sugars at different energy levels on uric acid (NCT02716870). METHODS: MEDLINE, Embase and the Cochrane Library were searched through January 27, 2020 for controlled trials ≥7-days assessing the effect of food sources of fructose-containing sugars on uric acid. Trial designs were prespecified based on energy control: substitution (energy matched replacement of sugars by other macronutrients); addition (excess energy from sugars added to diets); subtraction (energy from sugars subtracted from diets); and ad libitum (energy from sugars freely replaced by other macronutrients) trials. Independent reviewers extracted data and assessed risk of bias. Certainty of evidence was assessed using the GRADE approach. RESULTS: Eligibility was met by 41 trials (72 trial comparisons, N = 2109) assessing the effect of 9 food sources (sugar-sweetened beverages [SSBs], sweetened dairy, fruit drink [lemonade], 100% fruit juice, fruit, dried fruit [raisins], baked goods desserts and sweets, added nutritive [caloric] sweetener and mixed sources) across the 4 energy levels. Total fructose-containing sugars increased uric acid in substitution trials (mean difference, 0.15 mg/dL [95% confidence interval, 0.03 to 0.27 mg/dL], P = 0.012) with no effect in addition, subtraction or ad libitum trials. There was evidence of interaction by food source with SSBs and baked goods, desserts and sweets increasing uric acid in substitution and SSBs increasing and 100% fruit juice decreasing uric acid in addition trials. The overall certainty of evidence was moderate for the increasing effect of SSBs in substitution and addition trials and low to very low for all other comparisons. CONCLUSIONS: Food source more than energy control mediate the effect of fructose-containing sugars on uric acid. SSBs and baked goods, desserts and sweets appear to increase, and 100% fruit juice appear to decrease uric acid. More high-quality trials of different food sources of fructose-containing sugars are needed to improve our estimates. FUNDING SOURCES: Diabetes Canada.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| 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 | 0.002 | 0.002 |
| 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".