Important Food Sources of Fructose-Containing Sugars and Non-Alcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis of Controlled Trials
Bibliographic record
Abstract
Background: Fructose providing excess calories in the form of sugar sweetened beverages (SSBs) increases markers of non-alcoholic fatty liver disease (NAFLD). Whether this effect holds for other important food sources of fructose-containing sugars is unclear. To investigate the role of food source and energy, we conducted a systematic review and meta-analysis of controlled trials of the effect of fructose-containing sugars by food source at different levels of energy control on non-alcoholic fatty liver disease (NAFLD) markers. Methods and Findings: MEDLINE, Embase, and the Cochrane Library were searched through 7 January 2022 for controlled trials ≥7-days. Four trial designs were prespecified: substitution (energy-matched substitution of sugars for other macronutrients); addition (excess energy from sugars added to diets); subtraction (excess energy from sugars subtracted from diets); and ad libitum (energy from sugars freely replaced by other macronutrients). The primary outcome was intrahepatocellular lipid (IHCL). Secondary outcomes were alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Independent reviewers extracted data and assessed risk of bias. The certainty of evidence was assessed using GRADE. We included 51 trials (75 trial comparisons, n = 2059) of 10 food sources (sugar-sweetened beverages (SSBs); sweetened dairy alternative; 100% fruit juice; fruit; dried fruit; mixed fruit sources; sweets and desserts; added nutritive sweetener; honey; and mixed sources (with SSBs)) in predominantly healthy mixed weight or overweight/obese younger adults. Total fructose-containing sugars increased IHCL (standardized mean difference = 1.72 [95% CI, 1.08 to 2.36], p < 0.001) in addition trials and decreased AST in subtraction trials with no effect on any outcome in substitution or ad libitum trials. There was evidence of influence by food source with SSBs increasing IHCL and ALT in addition trials and mixed sources (with SSBs) decreasing AST in subtraction trials. The certainty of evidence was high for the effect on IHCL and moderate for the effect on ALT for SSBs in addition trials, low for the effect on AST for the removal of energy from mixed sources (with SSBs) in subtraction trials, and generally low to moderate for all other comparisons. Conclusions: Energy control and food source appear to mediate the effect of fructose-containing sugars on NAFLD markers. The evidence provides a good indication that the addition of excess energy from SSBs leads to large increases in liver fat and small important increases in ALT while there is less of an indication that the removal of energy from mixed sources (with SSBs) leads to moderate reductions in AST. Varying uncertainty remains for the lack of effect of other important food sources of fructose-containing sugars at different levels of energy control.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.032 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".