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Record W2008411035 · doi:10.1177/14746514030030041501

Metformin inhibits monocyte adhesion to endothelial cells and foam cell formation

2003· article· en· W2008411035 on OpenAlexaff
Jean‐Claude Mamputu, Nicolas Wiernsperger, Geneviève Renier

Bibliographic record

VenueThe British Journal of Diabetes · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Glycation End Products research
Canadian institutionsUniversité de MontréalHôpital Notre-Dame
Fundersnot available
KeywordsMetforminMonocyteScavenger receptorMedicineFoam cellAdhesionCell adhesionGlycationEndothelial stem cellInternal medicineCell adhesion moleculeReceptorEndocrinologyEndotheliumDiabetes mellitusLipoproteinImmunologyIn vitroChemistryCholesterolBiochemistry

Abstract

fetched live from OpenAlex

The United Kingdom Prospective Diabetes Study (UKPDS) found that metformin reduces macrovascular complications in type 2 diabetic patients. To investigate the mechanisms involved we examined the effect of metformin on monocyte adhesion to human endothelial cells (ECs) induced by advanced glycation end-products (AGE), and on monocyte differentiation into macrophages and foam cell formation. Treatment of human ECs with AGEs (100 µg/ml) for up to 12 hours significantly increased human monocyte adhesion. Pre-treatment of the cells with metformin (0.1—2.5 µg/ml) inhibited AGE-induced monocyte adhesion and expression of endothelial cell adhesion molecules. In culture, human monocytes spontaneously differentiated into macrophages, as indicated by phenotypic changes, and increased expression of lectin-like oxidised low-density lipoprotein (LDL) receptor and scavenger receptor type A. Incubation of these cells in the presence of metformin decreased expression of all of these parameters. Metformin also inhibited foam cell formation induced by minimally modified LDL. Overall, these results suggest new mechanisms by which metformin may reduce the risk of vascular complications in patients with type 2 diabetes.

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)0.000
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 score0.037
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.247
Teacher spread0.237 · 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

Citations21
Published2003
Admission routes1
Has abstractyes

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