Common ABCA1 variants, HDL levels, and cellular cholesterol efflux in subjects with familial low HDL
Bibliographic record
Abstract
HDL promotes cholesterol efflux from peripheral cells via ABCA1 in the first step of reverse cholesterol transport (RCT). We investigated whether the early steps of RCT were disturbed in subjects with familial low HDL and an increased risk for early atherosclerosis. Cholesterol efflux from monocyte-derived macrophages to lipid-free apolipoprotein A-I (apoA-I; %) was measured in 22 patients with familial low HDL without Tangier disease mutations and in 21 healthy controls. In addition, we defined the different alleles of ABCA1 using single-nucleotide polymorphism haplotypes and measured ABCA1 and ABCG1 mRNA transcript levels in cholesterol-loaded macrophages. Similar ABCA1-mediated cholesterol efflux levels were observed for macrophages derived from control subjects and from low-HDL subjects. However, when efflux of cholesterol was estimated as cholesterol efflux to apoA-I (%)/relative ABCA1 mRNA expression level, cholesterol removal was significantly (P = 0.001) lower in the low-HDL group. Cholesterol-loaded macrophages from low-HDL subjects showed significantly increased levels of ABCA1 mRNA but not of ABCG1 mRNA and were more often carriers of the rare ABCA1 alleles L158 and R219K. These results suggest that defective ABCA1 function in cholesterol-loaded macrophages is one potential contributor to the impaired RCT process and the increased coronary heart disease risk in subjects with familial low HDL.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".