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Record W2901759299 · doi:10.3390/nu10111805

The Effect of Small Doses of Fructose and Its Epimers on Glycemic Control: A Systematic Review and Meta-Analysis of Controlled Feeding Trials

2018· review· en· W2901759299 on OpenAlexafffund
Jarvis C. Noronha, Catherine R. Braunstein, Sonia Blanco Mejía, Tauseef Khan, Cyril W.C. Kendall, Thomas M.S. Wolever, Lawrence A. Leiter, John L. Sievenpiper

Bibliographic record

VenueNutrients · 2018
Typereview
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsUniversity of TorontoUniversity of SaskatchewanSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchEuropean Association for the Study of DiabetesDiabetes CanadaDairy Farmers of CanadaBanting and Best Diabetes Centre, University of TorontoCoca-Cola FoundationCanadian Cardiovascular SocietyPhysicians' Services Incorporated FoundationPepsiCoAbbott Laboratories
KeywordsGlycemicMedicineMeta-analysisDiabetes mellitusInternal medicineAdverse effectFructoseRandomized controlled trialGlycemic indexGastroenterologyEndocrinologyFood scienceChemistry

Abstract

fetched live from OpenAlex

Objective: Contrary to the concerns that fructose may have adverse metabolic effects, an emerging literature has shown that small doses (≤10 g/meal) of fructose and its low-caloric epimers (allulose, tagatose, and sorbose) decrease the glycemic response to high glycemic index meals. Whether these acute reductions manifest as sustainable improvements in glycemic control is unclear. Our objective was to synthesize the evidence from controlled feeding trials that assessed the effect of small doses of fructose and its low-caloric epimers on glycemic control. Methods: We searched MEDLINE, EMBASE, and the Cochrane Library through April 18, 2018. We included controlled feeding trials of ≥1 week that investigated the effect of small doses (≤50 g/day or ≤10% of total energy intake/day) of fructose and its low-caloric epimers on HbA1c, fasting glucose, and fasting insulin. Two independent reviewers extracted data and assessed risk of bias. Data were pooled using the generic inverse variance method and expressed as mean differences (MDs) with 95% confidence intervals (CIs). Heterogeneity was assessed using the Cochran Q statistic and quantified using the I2 statistic. Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessed the certainty of the evidence. Results: We identified 14 trial comparisons (N = 337) of the effect of fructose in individuals with and without diabetes, 3 trial comparisons (N = 138) of the effect of allulose in individuals without diabetes, 3 trial comparisons (N = 376) of the effect of tagatose mainly in individuals with type 2 diabetes, and 0 trial comparisons of the effect of sorbose. Small doses of fructose and tagatose significantly reduced HbA1c (MD = −0.38% (95% CI: −0.64%, −0.13%); MD = −0.20% (95% CI: −0.34%, −0.06%)) and fasting glucose (MD = −0.13 mmol/L (95% CI: −0.24 mmol/L, −0.03 mmol/L)); MD = −0.30 mmol/L (95% CI: −0.57 mmol/L, −0.04 mmol/L)) without affecting fasting insulin (p > 0.05). Small doses of allulose did not have a significant effect on HbA1c and fasting insulin (p > 0.05), while the reduction in fasting glucose was of borderline significance (p = 0.05). The certainty of the evidence of the effect of small doses of fructose and allulose on HbA1c, fasting glucose, and fasting insulin was graded as low. The certainty of the evidence of the effect of tagatose on HbA1c, fasting glucose, and fasting insulin was graded as moderate. Conclusions: Our results indicate that small doses of fructose and tagatose may improve glycemic control over the long term. There is a need for long-term randomized controlled trials for all four sugars to improve our certainty in the estimates.

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)0.022
metaresearch head score (Gemma)0.061
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 score0.027
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.061
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0270.030
Bibliometrics0.0070.009
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.003
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 head0.105
GPT teacher head0.391
Teacher spread0.286 · 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

Citations35
Published2018
Admission routes2
Has abstractyes

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