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Record W1969025184 · doi:10.1210/en.2003-1543

Muscle-Specific Overexpression of CD36 Reverses the Insulin Resistance and Diabetes of MKR Mice

2004· article· en· W1969025184 on OpenAlexaff
Lisa Héron‐Milhavet, Martin Haluzı́k, Shoshana Yakar, Oksana Gavrilova, Stephanie Pack, William Jou, Azeddine Ibrahimi, Hyunsook Kim, Desmond Hunt, Daphne Yau, Jamie W. Joseph, Michael B. Wheeler, Nada A. Abumrad, Derek LeRoith

Bibliographic record

VenueEndocrinology · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInternal medicineEndocrinologyInsulin resistanceType 2 diabetesCD36HyperinsulinemiaInsulinLipotoxicitySkeletal muscleBiologyDiabetes mellitusMedicineReceptor

Abstract

fetched live from OpenAlex

Insulin resistance is one of the primary characteristics of type 2 diabetes. Mice overexpressing a dominant-negative IGF-I receptor specifically in muscle (MKR mice) demonstrate severe insulin resistance with high levels of serum and tissue lipids and eventually develop type 2 diabetes at 5-6 wk of age. To determine whether lipotoxicity plays a role in the progression of the disease, we crossed MKR mice with mice overexpressing a fatty acid translocase, CD36, in skeletal muscle. The double-transgenic MKR/CD36 mice showed normalization of the hyperglycemia and the hyperinsulinemia as well as a marked improvement in liver insulin sensitivity. The MKR/CD36 mice also exhibited normal rates of fatty acid oxidation in skeletal muscle when compared with the decreased rate of fatty acid oxidation in MKR. With the reduction in insulin resistance, beta-cell function returned to normal. These and other results suggest that the insulin resistance in the MKR mice is associated with increased muscle triglycerides levels and that whole-body insulin resistance can be, at least partially, reversed in association with a reduction in muscle triglycerides levels, although the mechanisms are yet to be determined.

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 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 score0.317
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations62
Published2004
Admission routes1
Has abstractyes

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