Epipolymorphisms within lipoprotein genes contribute independently to plasma lipid levels in familial hypercholesterolemia
Bibliographic record
Abstract
Gene polymorphisms associated so far with plasma lipid concentrations explain only a fraction of their heritability, which can reach up to 60%. Recent studies suggest that epigenetic modifications (DNA methylation) could contribute to explain part of this missing heritability. We therefore assessed whether the DNA methylation of key lipoprotein metabolism genes is associated with high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglyceride levels in patients with familial hypercholesterolemia (FH). Untreated FH patients (61 men and 37 women) were recruited for the measurement of blood DNA methylation levels at the ABCG1, LIPC, PLTP and SCARB1 gene loci using bisulfite pyrosequencing. ABCG1, LIPC and PLTP DNA methylation was significantly associated with HDL-C, LDL-C and triglyceride levels in a sex-specific manner (all P<0.05). FH subjects with previous history of coronary artery disease (CAD) had higher LIPC DNA methylation levels compared with FH subjects without CAD (P = 0.02). Sex-specific multivariable linear regression models showed that new and previously reported epipolymorphisms (ABCG1-CpGC3, LIPC-CpGA2, mean PLTP-CpGC, LPL-CpGA3, CETP-CpGA2, and CETP-CpGB2) significantly contribute to variations in plasma lipid levels (all P<0.001 in men and P<0.02 in women), independently of traditional predictors such as age, waist circumference, blood pressure, fasting plasma lipids and glucose levels. These results suggest that epigenetic perturbations of key lipoprotein metabolism genes are associated with plasma lipid levels, contribute to the interindividual variability and might partially explain the missing heritability of plasma lipid levels, at least in FH.
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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".