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Record W2914662834 · doi:10.1097/mol.0000000000000589

From HDL-cholesterol to HDL-function: cholesterol efflux capacity determinants

2019· review· en· W2914662834 on OpenAlexaff
David Rhainds, Jean‐Claude Tardif

Bibliographic record

VenueCurrent Opinion in Lipidology · 2019
Typereview
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsCholesterolEffluxInternal medicineMedicineBiomarkerEndocrinologyReverse cholesterol transportBiologyLipoproteinBiochemistry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The validity of HDL-cholesterol (HDL-C) elevation as a therapeutic target has been questioned, in comparison to enhancing HDL functionality. Cholesterol efflux capacity (CEC) is an in-vitro assay that measures the ability of an individual's HDL to promote cholesterol efflux from cholesterol donor cells such as macrophages. CEC of HDL is a predictor of cardiovascular risk independent of HDL-C levels. However, molecular determinants of CEC and the effects of diseases and therapeutic interventions on CEC have not been completely defined. RECENT FINDINGS: We review here recent findings on elevated HDL-C and disease risk, as well as determinants of CEC, from genetics and proteomics to pathophysiology and therapeutic interventions that contribute to our understanding of CEC as a biomarker of HDL functionality. SUMMARY: Elevated HDL-C levels are not always protective against cardiovascular disease and mortality. CEC is a heritable trait, and genetic polymorphisms in genes involved in HDL and triglycerides metabolism are associated with CEC. Multiple HDL proteins correlate positively with CEC levels and inversely with noncalcified plaque burden. Differences in CEC assays that make comparisons between studies difficult are also emphasized. CEC should be measured in clinical trials of lipid-modifying and anti-inflammatory therapies to determine whether increases are cardioprotective.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.003

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 head0.200
GPT teacher head0.414
Teacher spread0.214 · 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 designOther design
Domainnot available
GenreReview

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

Citations45
Published2019
Admission routes1
Has abstractyes

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