Age and residual cholesterol efflux affect HDL cholesterol levels and coronary artery disease in ABCA1 heterozygotes
Bibliographic record
Abstract
We and others have recently identified mutations in the ABCA1 gene as the underlying cause of Tangier disease (TD) and of a dominantly inherited form of familial hypoalphalipoproteinemia (FHA) associated with reduced cholesterol efflux. We have now identified 13 ABCA1 mutations in 11 families (five TD, six FHA) and have examined the phenotypes of 77 individuals heterozygous for mutations in the ABCA1 gene. ABCA1 heterozygotes have decreased HDL cholesterol (HDL-C) and increased triglycerides. Age is an important modifier of the phenotype in heterozygotes, with a higher proportion of heterozygotes aged 30-70 years having HDL-C greater than the fifth percentile for age and sex compared with carriers less than 30 years of age. Levels of cholesterol efflux are highly correlated with HDL-C levels, accounting for 82% of its variation. Each 8% change in ABCA1-mediated efflux is predicted to be associated with a 0.1 mmol/l change in HDL-C. ABCA1 heterozygotes display a greater than threefold increase in the frequency of coronary artery disease (CAD), with earlier onset than unaffected family members. CAD is more frequent in those heterozygotes with lower cholesterol efflux values. These data provide direct evidence that impairment of cholesterol efflux and consequently reverse cholesterol transport is associated with reduced plasma HDL-C levels and increased risk of 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".