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“Catalytic” Doses of Fructose and its Epimers on Glycemic Control: A Systematic Review and Meta‐Analysis of Controlled Feeding Trials

2017· review· en· W4389018756 on OpenAlexaffabout
Jarvis C. Noronha, Catherine R. Braunstein, Sonia Blanco Mejía, Adrian I. Cozma, Tauseef Khan, Andrea J. Glenn, Rebecca Noseworthy, Cyril W.C. Kendall, Thomas M.S. Wolever, Lawrence A. Leiter, John L. Sievenpiper

Bibliographic record

VenueThe FASEB Journal · 2017
Typereview
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsSt. Francis Xavier UniversityUniversity of TorontoUniversity of SaskatchewanSt. Michael's Hospital
Fundersnot available
KeywordsGlycemicMedicineMeta-analysisFructoseRandomized controlled trialInternal medicineAdverse effectGastroenterologyEndocrinologyDiabetes mellitusChemistryBiochemistry

Abstract

fetched live from OpenAlex

Objective Contrary to the concerns that fructose may have adverse metabolic effects, an emerging literature has shown that small, ‘catalytic’ doses (≤ 50‐g/day) of fructose and its epimers (allulose, tagatose and sorbose) decrease the glycemic response to high glycemic index meals in humans. This effect appears to be mediated by upregulation of glucokinase, leading to increased hepatic glycogen synthesis. Whether this acute ‘catalytic’ mechanism of fructose and its epimers will manifest as an improvement in long term glycemic control is unclear. To address this question, we synthesized evidence from controlled feeding trials assessing the effect of small, ‘catalytic’ doses of fructose and its epimers on HbA1c. Methods We searched MEDLINE, EMBASE, and Cochrane Library through Feb 12, 2016. We included controlled feeding trials of ≥ 2 weeks investigating the effect of small, ‘catalytic’ doses (≤ 50‐g/day) of fructose and its epimers in comparison to control diets. Two independent reviewers extracted relevant data. Risk of bias was assessed using the Cochrane Risk of Bias Tool. Data were pooled using the generic inverse variance method and expressed as mean differences (MD) with 95% confidence intervals (CIs). Heterogeneity was assessed using the Cochran Q statistic and quantified using the I 2 statistic. The overall quality of the evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. Results 7 trials (n = 467) over a median follow‐up of 3 months met the eligibility criteria. ‘Catalytic’ doses of fructose significantly reduced HbA 1c (MD = −0.40% [95% CI −0.72 to − 0.08]), but not allulose and tagatose for which there was only one trial comparison for each of the sugars. The overall quality of the evidence was graded as “moderate” quality for a decreasing effect of fructose on HbA 1c due to a downgrade for serious imprecision. The evidence for allulose and tagatose was not graded. Conclusion Pooled analyses indicated that small, ‘catalytic’ doses of fructose improve glycemic control over the shorter term. The evidence for allulose and tagatose remains inconclusive due to the limited number of trials. There is a need for large high quality, long‐term randomized trials for all 3 sugars to improve our confidence in the effect estimates. Protocol registration: clinicaltrials.gov identifier, NCT02776722 Support or Funding Information Funding: The Tate and Lyle Nutritional Research Fund at the University of Toronto, Canadian Diabetes Association (CDA), Banting & Best Diabetes Centre, and PSI foundation.

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)0.049
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 score0.024
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.049
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0240.026
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
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 head0.204
GPT teacher head0.425
Teacher spread0.221 · 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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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