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Record W4392611435 · doi:10.1097/hco.0000000000001140

Predictive utility of remnant cholesterol in atherosclerotic cardiovascular disease

2024· article· en· W4392611435 on OpenAlexaff
Spencer D. Proctor, Maggie Wang, Donna F. Vine, Paolo Raggi

Bibliographic record

VenueCurrent Opinion in Cardiology · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCardiologyCholesterolInternal medicineMedicineLdl cholesterolDiseaseAtherosclerotic cardiovascular disease

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Remnant cholesterol (RC) is the cholesterol carried in lipoproteins derived from the catabolism of chylomicrons and very low-density lipoproteins. Evidence supporting the causal relationship of RC with atherosclerotic cardiovascular disease (ASVD) is accumulating rapidly. The number of impactful contributions to this field are increasing and provide a pathophysiological insight into the current residual cardiovascular risk beyond low-density cholesterol (LDL)-cholesterol (LDL-C). They also raise the question of whether RC should be used in prediction models and become the target of new therapeutic interventions. The intent of this review is to highlight the recent advances on the role of RC in atherogenesis and the validation of RC as a predictor of ASVD. RECENT FINDINGS: Numerous prospective and retrospective cohorts helped validate a significant causal relationship of RC with various forms of ASVD, independent of LDL-C. A recent large Mendelian randomization study reinforced the existence of this relationship and showed that the risk of atherosclerotic events was driven nearly entirely by a direct effect of RC. SUMMARY: Both available and accumulating evidence suggest that a lifelong reduction in RC could translate into a substantial reduction in ASVD risk. The data support a revision of current guidelines to incorporate RC as an independent risk factor for ASVD. We propose that early screening of RC should be implemented and that RC lowering should become the target of future drug developments.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.321
Teacher spread0.263 · 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 designObservational
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

Citations7
Published2024
Admission routes1
Has abstractyes

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