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Record W4381046757 · doi:10.1093/ehjcvp/pvad044

Triglycerides revisited: is hypertriglyceridaemia a necessary therapeutic target in cardiovascular disease?

2023· review· en· W4381046757 on OpenAlexaff
Heinz Drexel, Juan Tamargo, Juan Carlos Kaski, Basil S. Lewis, Christoph H. Saely, Peter Fraunberger, Dobromir Dobrev, Maki Komiyama, THOMAS PLATTNER, Stefan Agewall, Koji Hasegawa

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Pharmacotherapy · 2023
Typereview
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsResidual riskMedicineAtherosclerotic cardiovascular diseaseInternal medicineEicosapentaenoic acidTriglycerideDiseaseLipoproteinCholesterolEpidemiologyPrimary preventionEndocrinologyFatty acidBiologyBiochemistry

Abstract

fetched live from OpenAlex

Despite the atherosclerotic cardiovascular disease (ASCVD) risk reduction achieved by low-density lipoprotein cholesterol (LDL-C) lowering therapy, residual ASCVD risk still exists. Previous epidemiological studies have suggested high plasma triglyceride (TG) levels as a risk factor or risk marker for ASCVD independent of LDL-C levels. In this review, we highlighted the underlying pathophysiology of hypertriglyceridaemia, the mechanistic action of therapeutic agents, the interpretation of conflicting results on recent clinical trials, and the present options for primary and secondary prevention. The benefits of fibrates-induced reduction in TG and increase in high-density lipoprotein cholesterol might outweigh the disadvantages of increasing LDL-C levels in primary prevention. In secondary CVD prevention, using eicosapentaenoic acid without docosahexaenoic acid, in addition to statins, will be beneficial. This comprehensive review may prove useful for the development of novel approaches that target hypertriglyceridaemia in future.

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)0.013
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.932
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0120.041
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0010.002

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 head0.101
GPT teacher head0.357
Teacher spread0.256 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations26
Published2023
Admission routes1
Has abstractyes

Explore more

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