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Record W4410985056 · doi:10.1093/cvr/cvaf093

Ceramides in cardiovascular disease: emerging role as independent risk predictors and novel therapeutic targets

2025· review· en· W4410985056 on OpenAlexaff
Roland Klingenberg, Andreas Leiherer, Dobromir Dobrev, Juan Carlos Kaski, Bodo Levkau, Winfried März, Samuel Sossalla, Arnold von Eckardstein, Heinz Drexel

Bibliographic record

VenueCardiovascular Research · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSphingolipid Metabolism and Signaling
Canadian institutionsMontreal Heart Institute
FundersNational Heart, Lung, and Blood InstituteNational Institutes of HealthEuropean CommissionServierDeutsche ForschungsgemeinschaftDeutsches Zentrum für Herz-Kreislaufforschung
KeywordsDiseaseMedicineAtherosclerotic cardiovascular diseaseInternal medicineBioinformaticsCardiologyIntensive care medicineBiology

Abstract

fetched live from OpenAlex

Ceramides are bioactive lipid mediators involved in apoptosis, inflammation, and fibrosis. This narrative review provides a concise overview of the emerging role of ceramides in cardiovascular disease with an emphasis on atherosclerotic vascular disease and heart failure, suggesting the potential use of ceramides in risk stratification and as putative therapeutic targets. Recent developments based on observational evidence and genetic associations, including Mendelian randomization studies in humans, are summarized and put into context with experimental evidence for the role of ceramides in human and animal models of disease. Emerging scores composed of ceramides and phosphatidylcholines that are based on the length and desaturation of the N-acyl chains are discussed in the light of novel data demonstrating age- and sex-specific differences. Also reviewed is the structural heterogeneity of the sphingoid bases, including non-conventional sphingolipids that are increasingly recognized for their importance in health and disease. Lastly, novel targets and potential modalities for tissue-specific transfer of drugs are discussed.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.037
GPT teacher head0.341
Teacher spread0.304 · 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 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

Citations14
Published2025
Admission routes1
Has abstractyes

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