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Record W4409307839 · doi:10.1002/mnfr.70014

Effects and Mechanisms of Steviol Glycosides on Glucose Metabolism: Evidence From Preclinical Studies

2025· review· en· W4409307839 on OpenAlexaff
Changfa Zhang, Ruoting Wang, Kangjun Li, Xuerui Bai, N.D. Qi, Hongying Qu, Guowei Li

Bibliographic record

VenueMolecular Nutrition & Food Research · 2025
Typereview
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsSt. Joseph’s Healthcare Hamilton
FundersNational Natural Science Foundation of China
KeywordsCarbohydrate metabolismSteviolInsulin resistanceMedicineSugarPopulationDiabetes mellitusSweetnessType 2 Diabetes MellitusInsulinBiologyPharmacologyBiochemistryEndocrinologyStevioside

Abstract

fetched live from OpenAlex

The natural sweeteners of steviol glycosides (SGs) have been widely used as a substitute for sugar due to their high sweetness, low-calorie properties, and potential health benefits. Some studies reported that SGs could regulate glucose metabolism and prevent Type 2 diabetes mellitus (T2DM); however, the detailed mechanisms remained further elucidated. Therefore, in this review, we aimed to systematically summarize the effects and mechanisms of SGs on glucose metabolism based on evidence from preclinical studies. We searched PubMed and Web of Science (up to March 31, 2024), and included a total of 40 animal and 5 cell studies for review. Results showed that SGs could improve glucose metabolism by enhancing insulin secretion, simulating insulin effects, improving insulin resistance, advancing key enzyme activities, or regulating gut microbiota. To conclude, if further validated in clinical trials and population studies, the sugar substitute of SGs may serve as a potential nutritional strategy for effective prevention and treatment of T2DM.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.823
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.208
GPT teacher head0.489
Teacher spread0.281 · 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 designOther design
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

Citations3
Published2025
Admission routes1
Has abstractyes

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