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Record W2127466067 · doi:10.1093/gerona/glq184

Resveratrol Improves Insulin Resistance Hyperglycemia and Hepatosteatosis But Not Hypertriglyceridemia, Inflammation, and Life Span in a Mouse Model for Werner Syndrome

2010· article· en· W2127466067 on OpenAlexafffund
Adam Labbé, Chantal Garand, Victoria C. Cogger, Éric R. Paquet, Myriam Desbiens, David G. Le Couteur, Michel Lebel

Bibliographic record

VenueThe Journals of Gerontology Series A · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversité LavalHôtel-Dieu de Québec
FundersCanadian Institutes of Health Research
KeywordsResveratrolInsulin resistanceHypertriglyceridemiaWerner syndromeEndocrinologyInternal medicineMutantMetabolic syndromeBiologyInflammationInsulinDiabetes mellitusMedicineHelicaseTriglycerideGeneticsGenePharmacologyCholesterol

Abstract

fetched live from OpenAlex

Werner syndrome is a premature aging disorder caused by mutations in a RecQ-like DNA helicase. Mice lacking the helicase domain of the WRN homologue exhibit many features of Werner syndrome, including a pro-oxidant status and a shorter mean life span. Here, we show that resveratrol supplementation improved the hyperglycemia and the insulin resistance phenotype in these Wrn mutant mice. In addition, resveratrol reversed liver steatosis, lipid peroxidaton, and the defenestration phenotypes observed in such mice. Resveratrol, however, did not improve the hypertriglyceridemia, inflammatory stress, nor extend the mean life span of these mutant mice. Microarray and biologic pathway enrichment analyses on liver tissues revealed that resveratrol mainly decreased lipidogenesis and increased genes involved in the insulin signaling pathway and the glutathione metabolism in Wrn mutant mice. Finally, resveratrol-treated mutant mice exhibited an increase in the frequency of lymphoma and of several solid tumors. These results indicate that resveratrol supplementation might exert at least metabolic benefits for Werner syndrome patients.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.018
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.252
Teacher spread0.235 · 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.

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".

Quick stats

Citations71
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueThe Journals of Gerontology Series ASame topicDNA Repair MechanismsFrench-language works237,207