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Record W4416426708 · doi:10.1016/j.ijcha.2025.101836

Diagnostic and prognostic utility of heart-type fatty acid binding proteins in cardiovascular diseases and risk factors − an updated review of the literature

2025· article· en· W4416426708 on OpenAlexaff
Riaz Jiffry, A. Gupta, Jeisun Poornaselvan, Valerie Mok, Arkadeep Dhali, Aditi Gupta, Tong Liu, Gary Tse, Helen Huang

Bibliographic record

VenueIJC Heart & Vasculature · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDiseaseFatty acid-binding proteinRisk stratificationBiomarkerTroponinRisk assessmentAcute coronary syndromeReview article

Abstract

fetched live from OpenAlex

Fatty acid-binding proteins (FABPs) are intracellular lipid-binding proteins that significantly contribute to the transport and metabolism of long-chain fatty acids and other hydrophobic ligands. In this review, we focus on the role of heart-type FABP (H-FABPs) as diagnostic and prognostic biomarkers in several cardiovascular diseases. Despite its advantages over troponins and other cardiac biomarkers, H-FABP remains underutilized in clinical practice. The aim of this review is to reassess the role of H-FABPs across various cardiovascular pathologies and promote their adoption into standard clinical practice. Elevated H-FABP levels have been associated with worse outcomes in CAD and serve as sensitive markers for myocardial injury during the early stages of MI and reperfusion. Furthermore, we discuss the potential of H-FABPs in risk stratification for stable CAD and their utility in predicting long-term outcomes post-MI. The prognostic value of H-FABP in cardiac events such as heart failure, pulmonary embolism, and arrhythmias, alongside its application in peripheral arterial disease and non-ischemic dilated cardiomyopathy, highlights its importance in cardiovascular medicine. Given the global burden of cardiovascular diseases, understanding and utilising H-FABPs could enhance patient management through better risk assessment and early diagnosis.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.245
Teacher spread0.239 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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