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Record W4385834101 · doi:10.1371/journal.pone.0264802

Fructose-containing food sources and blood pressure: A systematic review and meta-analysis of controlled feeding trials

2023· review· en· W4385834101 on OpenAlexafffund
Qi Liu, Laura Chiavaroli, Sabrina Ayoub‐Charette, Amna Ahmed, Tauseef Khan, Fei Au‐Yeung, Danielle Lee, Annette Cheung, Andreea Zurbau, Vivian L. Choo, Sonia Blanco Mejía, Russell J. de Souza, Thomas M.S. Wolever, Lawrence A. Leiter, Cyril W.C. Kendall, David J.A. Jenkins, John L. Sievenpiper

Bibliographic record

VenuePLoS ONE · 2023
Typereview
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsMcMaster UniversityImpactHamilton Health SciencesUniversity of TorontoGlycemic Index LaboratoriesUniversity of SaskatchewanPopulation Health Research InstituteSt. Michael's Hospital
FundersAgriculture and Agri-Food CanadaCanadian Institutes of Health ResearchSoy Nutrition InstitutePeanut InstituteLoblaw Companies LimitedAlberta Pulse Growers CommissionKellogg'sAlmond Board of CaliforniaCanadian Nutrition SocietyDiabetes CanadaCoca-Cola Foundation
KeywordsMeta-analysisFructoseBlood pressureFood scienceMedicineBiologyInternal medicine

Abstract

fetched live from OpenAlex

Whether food source or energy mediates the effect of fructose-containing sugars on blood pressure (BP) is unclear. We conducted a systematic review and meta-analysis of the effect of different food sources of fructose-containing sugars at different levels of energy control on BP. We searched MEDLINE, Embase and the Cochrane Library through June 2021 for controlled trials ≥7-days. We prespecified 4 trial designs: substitution (energy matched substitution of sugars); addition (excess energy from sugars added); subtraction (excess energy from sugars subtracted); and ad libitum (energy from sugars freely replaced). Outcomes were systolic and diastolic BP. Independent reviewers extracted data. GRADE assessed the certainty of evidence. We included 93 reports (147 trial comparisons, N = 5,213) assessing 12 different food sources across 4 energy control levels in adults with and without hypertension or at risk for hypertension. Total fructose-containing sugars had no effect in substitution, subtraction, or ad libitum trials but decreased systolic and diastolic BP in addition trials (P<0.05). There was evidence of interaction/influence by food source: fruit and 100% fruit juice decreased and mixed sources (with sugar-sweetened beverages [SSBs]) increased BP in addition trials and the removal of SSBs (linear dose response gradient) and mixed sources (with SSBs) decreased BP in subtraction trials. The certainty of evidence was generally moderate. Food source and energy control appear to mediate the effect of fructose-containing sugars on BP. The evidence provides a good indication that fruit and 100% fruit juice at low doses (up to or less than the public health threshold of ~10% E) lead to small, but important reductions in BP, while the addition of excess energy of mixed sources (with SSBs) at high doses (up to 23%) leads to moderate increases and their removal or the removal of SSBs alone (up to ~20% E) leads to small, but important decreases in BP in adults with and without hypertension or at risk for hypertension. Trial registration: Clinicaltrials.gov: NCT02716870.

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)0.016
metaresearch head score (Gemma)0.047
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 score0.020
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.047
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0200.026
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.001
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 head0.343
GPT teacher head0.376
Teacher spread0.033 · 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

Citations9
Published2023
Admission routes2
Has abstractyes

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