<i>ABCA1</i>gene promoter DNA methylation is associated with HDL particle profile and coronary artery disease in familial hypercholesterolemia
Bibliographic record
Abstract
High-density lipoproteins cholesterol (HDL-C) level, a strong coronary artery disease (CAD) clinical biomarker, shows significant interindividual variability. However, the molecular mechanisms involved remain mostly unknown. ATP-binding cassette A1 (ABCA1) catalyzes the cholesterol transfer from peripheral cells to nascent HDL particles. Recently, a differentially methylation region was identified in ABCA1 gene promoter locus, near the first exon. Therefore, we hypothesized that DNA methylation changes at ABCA1 gene locus is one of the molecular mechanisms involved in HDL-C interindividual variability. The study was conducted in familial hypercholesterolemia (FH), a monogenic disorder associated with a high risk of CAD . Ninety-seven FH patients (all p.W66G for the LDLR gene mutation and not under lipid-lowering treatment) were recruited and finely phenotyped for DNA methylation analyses at ABCA1 gene locus. ABCA1 DNA methylation levels were found negatively correlated with circulating HDL-C (r = -0.20; p = 0.05), HDL2-phospholipid levels (r = -0.43; p = 0.04), and with a trend for association with HDL peak particle size (r = -0.38; p = 0.08). ABCA1 DNA methylation levels were also found associated with prior history of CAD (CAD = 40.2% vs. without CAD = 34.3%; p = 0.003). These results suggest that epigenetic changes within the ABCA1 gene promoter contribute to the interindividual variability in plasma HDL-C concentrations and are associated with CAD expression. These findings could change our understanding of the molecular mechanisms involved in the pathophysiological processes leading to CAD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".