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Effects of various natural sweeteners on insulin resistance, inflammation and liver steatosis in a rat model of diet‐induced obesity

2016· article· en· W2963252052 on OpenAlexaffabout
Marion Valle, Philippe St‐Pierre, Geneviève Pilon, Fernando F. Anhê, Thibault Varin, André Marette

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCorn syrupInsulin resistanceFructoseHigh-fructose corn syrupSucroseGlycemic indexContext (archaeology)Internal medicineInsulinGlucose tolerance testEndocrinologyMedicineSteatosisObesityFood scienceChemistryGlycemicBiology

Abstract

fetched live from OpenAlex

Background The detrimental effect of refined sugars on health has been the subject of many investigations. However, very few studies have looked at the long‐term beneficial impact of natural sweeteners in a context of obesity. Objective The objective of this study was to compare the metabolic responses following the chronic ingestion of refined simple sugars (sucrose and fructose) to that of various natural sweeteners in a diet‐induced obese insulin‐resistant rat model. Methods Rats were fed a high‐fat high sucrose diet (HFHS) for 8 weeks and received daily gavage with a solution of equivalent amount of total carbohydrates from either sucrose, fructose, maple syrup, molasses, brown rice syrup, agave syrup, corn syrup, or honey. A group fed a standard chow diet receiving daily gavage with the sucrose solution was used as a control. Weekly body weight and food intake were assessed. At week 7, glucose tolerance was evaluated by an oral glucose tolerance test (OGTT), while insulin sensitivity was determined using the HOMA‐IR index at 8 weeks. Nonalcoholic fatty liver disease (NAFLD) was assessed by hepatic triglyceride (TG) content and liver inflammation. Results Daily gavage with the different natural sweeteners did not lead to differences in food intake or body weight gain. During the OGTT, similar glycemic responses were observed across the different treatment groups. However, the insulin excursions were lower in the maple syrup treated animals suggesting improved insulin sensitivity. Assessment of insulin resistance by calculation of the HOMA‐IR index further revealed that all natural sweeteners except corn syrup significantly improved insulin sensitivity, when compared to sucrose treatments. Furthermore, whereas hepatic TG content was reduced in maple syrup, molasses and agave syrup treated rats, there was an increased TG accumulation in the brown rice and corn syrup treated rats. Hepatic inflammation, as revealed by increased IL‐1B levels in obese rat liver, was also reduced to various extents by gavage of molasses, agave syrup, maple syrup and corn syrup. Conclusion Several natural sweeteners used in this study were found to improve insulin sensitivity and to reduce NAFLD as compared to an equivalent amount of sucrose in diet‐induced obese rats. Among the natural sweeteners, maple syrup, molasses and agave syrup appear to exert the most consistent beneficial effects on multiple metabolic health endpoints, suggesting that consumption of those natural sweeteners is a healthier alternative to simple refined sugar. Support or Funding Information This project was supported by Agriculture and Agri‐Food Canada from the Agricultural Innovation program, a federal‐provincial‐ territorial initiative in Canada, and with the participation of the Federation of Quebec Maple Syrup Producers.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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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Citations2
Published2016
Admission routes2
Has abstractyes

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