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Record W3166616051 · doi:10.1093/cdn/nzab053_045

Effect of Important Food Sources of Fructose-Containing Sugars on Non-alcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis of Controlled Trials

2021· review· en· W3166616051 on OpenAlexaffabout
Danielle Lee, Laura Chiavaroli, Sabrina Ayoub‐Charette, Tauseef Khan, Andreea Zurbau, Xinye Qi, Fei Au‐Yeung, Annette Cheung, Qi Liu, Amna Mohamed Ahmed, Vivian L. Choo, Sonia Blanco Mejía, Russell J. de Souza, Thomas Wolever, Lawrence A. Leiter, Cyril W.C. Kendall, David J.A. Jenkins, John L. Sievenpiper

Bibliographic record

VenueCurrent Developments in Nutrition · 2021
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMeta-analysisFructoseMedicineCalorieCochrane LibraryHigh-fructose corn syrupFood scienceFatty liverSugarRandomized controlled trialDietary SucroseInternal medicineChemistryDisease

Abstract

fetched live from OpenAlex

Fructose as a source of excess calories increases non-alcoholic fatty liver disease (NAFLD) markers. Whether this effect is mediated by the food matrix is unknown. We thus conducted a systematic review and meta-analysis of controlled feeding trials assessing the effect of important food sources of fructose-containing sugars at different energy control levels on NAFLD markers. MEDLINE, Embase, and Cochrane Library were searched through January 18, 2021 for controlled trials ≥7-days. Four 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). 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. Certainty of evidence was assessed by GRADE. We included 44 trials (65 trial comparisons, n = 1941) assessing the effect of 8 food sources (sugar-sweetened beverages [SSBs]; sweetened dairy alternative [soy]; fruit juice; fruit; dried fruit; baked goods, desserts and sweets; added nutritive sweetener; and mixed sources) across 4 energy levels. Total fructose-containing sugars increased IHCL in addition trials (standardized mean difference = 1.69 [95% CI, 1.00–2.37], P < 0.001), but no effect in substitution, subtraction and ad libitum trials. There was evidence of interaction by food source in addition trials with SSBs increasing IHCL and ALT, and mixed sources increasing AST. The overall certainty of evidence was high for SSBs on IHCL and ALT in addition trials and high to very low for all other comparisons. Energy control and food source appear to mediate the effect of fructose-containing sugars on NAFLD markers. High certainty evidence suggests that SSBs providing excess energy increase NAFLD markers, while the evidence is less certain that mixed sources share the same effect and other food sources do not. More high-quality randomized trials of different food sources are needed to improve our estimates (ClinicalTrials.gov identifier, NCT02716870). Primary funding: Diabetes Canada.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.423
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0280.005
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.131
GPT teacher head0.414
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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