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Record W4321616767 · doi:10.1016/j.ajcnut.2023.01.023

Important food sources of fructose-containing sugars and adiposity: A systematic review and meta-analysis of controlled feeding trials

2023· review· en· W4321616767 on OpenAlexafffund
Laura Chiavaroli, Annette Cheung, Sabrina Ayoub‐Charette, Amna Ahmed, Danielle Lee, Fei Au‐Yeung, Xinye Qi, Songhee Back, Néma McGlynn, Vanessa Ha, Ethan Lai, Tauseef Khan, Sonia Blanco Mejía, Andreea Zurbau, Vivian L. Choo, Russell J. de Souza, Thomas M.S. Wolever, Lawrence A. Leiter, Cyril W.C. Kendall, David J.A. Jenkins, John L. Sievenpiper

Bibliographic record

VenueAmerican Journal of Clinical Nutrition · 2023
Typereview
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsImpactHamilton Health SciencesUniversity of TorontoGlycemic Index LaboratoriesMcMaster UniversityQueen's UniversityUniversity of SaskatchewanPopulation Health Research InstituteSt. Michael's Hospital
FundersInstitute for the Advancement of Food and Nutrition SciencesInternational Nut and Dried Fruit CouncilUniversity of TorontoCanada Foundation for InnovationCanadian Society of Endocrinology and MetabolismOntario Ministry of Research, Innovation and ScienceCanadian Institutes of Health ResearchNational Honey BoardASN Foundation for Kidney ResearchGovernment of CanadaBanting and Best Diabetes Centre, University of TorontoU.S. Department of AgricultureInternational Life Sciences InstitutePeanut InstituteSt. Michael’s Hospital FoundationUnileverDiabetes CanadaOntario Research FoundationUnited Soybean BoardSoy Nutrition InstituteDairy Farmers of CanadaIan's Friends FoundationFlax Council of CanadaCanadian Diabetes Association
KeywordsMeta-analysisMedicineCochrane LibraryHigh-fructose corn syrupRefined grainsFood scienceFructoseRandomized controlled trialSugarInternal medicineChemistryWhole grains

Abstract

fetched live from OpenAlex

BACKGROUND: Sugar-sweetened beverages (SSBs) providing excess energy increase adiposity. The effect of other food sources of sugars at different energy control levels is unclear. OBJECTIVES: To determine the effect of food sources of fructose-containing sugars by energy control on adiposity. METHODS: In this systematic review and meta-analysis, MEDLINE, Embase, and Cochrane Library were searched through April 2022 for controlled trials ≥2 wk. We prespecified 4 trial designs by energy control: substitution (energy-matched replacement of sugars), addition (energy from sugars added), subtraction (energy from sugars subtracted), and ad libitum (energy from sugars freely replaced). Independent authors extracted data. The primary outcome was body weight. Secondary outcomes included other adiposity measures. Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used to assess the certainty of evidence. RESULTS: = 0.022) in subtraction trials with no effect in substitution or ad libitum trials. There was interaction/influence by food sources on body weight: substitution trials [fruits decreased; added nutritive sweeteners and mixed sources (with SSBs) increased]; addition trials [dried fruits, honey, fruits (≤10%E), and 100% fruit juice (≤10%E) decreased; SSBs, fruit drink, and mixed sources (with SSBs) increased]; subtraction trials [removal of mixed sources (with SSBs) decreased]; and ad libitum trials [mixed sources (with/without SSBs) increased]. GRADE scores were generally moderate. Results were similar across secondary outcomes. CONCLUSIONS: Energy control and food sources mediate the effect of fructose-containing sugars on adiposity. The evidence provides a good indication that excess energy from sugars (particularly SSBs at high doses ≥20%E or 100 g/d) increase adiposity, whereas their removal decrease adiposity. Most other food sources had no effect, with some showing decreases (particularly fruits at lower doses ≤10%E or 50 g/d). This trial was registered at clinicaltrials.gov as NCT02558920 (https://clinicaltrials.gov/ct2/show/NCT02558920).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.044
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0190.031
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.277
GPT teacher head0.488
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations31
Published2023
Admission routes2
Has abstractyes

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