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Record W2041177977 · doi:10.1159/000207358

Beneficial Effects on Glucose Metabolism of Chronic Feeding of Isomaltulose versus Sucrose in Rats

2009· article· en· W2041177977 on OpenAlexaff
Doreen Häberer, Louise Thibault, Wolfgang Langhans, Nori Geary

Bibliographic record

VenueAnnals of Nutrition and Metabolism · 2009
Typearticle
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsMcGill University
Fundersnot available
KeywordsSucroseMetabolismCarbohydrate metabolismChemistryEndocrinologyInternal medicineFood scienceBiologyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: Isomaltulose (alpha-D-glucosylpyranosyl-1,6-D-fructofuranose) is a natural disaccharide used in human nutrition. It is structurally related to sucrose, but more slowly hydrolyzed and absorbed. Because this sugar's metabolic effects are poorly characterized, we compared the effects of chronic ad libitum access to high-isomaltulose and high-sucrose diets on glucose metabolism in rats. METHODS: Adult male rats were offered 62% isomaltulose, sucrose or starch diets ad libitum for 26 (trial 1) or 56 (trial 2) days. After 2- to 3-week adaptation, plasma glucose, fructose and insulin were measured after test meals of the adaptation diet. RESULTS: The main finding was that both plasma glucose and plasma insulin concentrations were transiently but markedly increased after sucrose test meals compared to isomaltulose or starch meals. These differences were not associated with consistent differences in food intake, body weight gain or adiposity. CONCLUSIONS: Chronic isomaltulose feeding has beneficial effects on postprandial glucose metabolism in comparison to sucrose feeding in rats, although the effects are modest. Further work is warranted to determine whether substitution of isomaltulose for sucrose or other sweet carbohydrates might be therapeutically useful in patients with, or at risk for, insulin resistance or type 2 diabetes mellitus.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.327
Teacher spread0.294 · 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 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".

Quick stats

Citations18
Published2009
Admission routes1
Has abstractyes

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