<i>ABCA1</i> regulatory variants influence coronary artery disease independent of effects on plasma lipid levels
Bibliographic record
Abstract
The authors have previously shown that individuals heterozygous for ABCA1 mutations have decreased high density lipoprotein cholesterol, increased triglycerides and an increased frequency of coronary artery disease (CAD), and that single nucleotide polymorphisms (SNPs) in the coding region of the ABCA1 gene significantly impact plasma lipid levels and the severity of CAD in the general population. They have now identified several SNPs in non-coding regions of ABCA1 which may be important for the appropriate regulation of ABCA1 expression (i.e. in the promoter, intron 1 and the 5' untranslated region), and have examined the phenotypic effects of these SNPs in the REGRESS population. Out of 12 SNPs, four were associated with a clinical outcome. A threefold increase in coronary events with an increased family history of CAD was evident for the G-191C variant. Similarly, the C69T SNP was associated with a twofold increase in events. In contrast, the C-17G was associated with a decrease in coronary events and the InsG319 was associated with less atherosclerosis. For all these SNPs, the changes in atherosclerosis and CAD occurred without detectable changes in plasma lipid levels. These data suggest that common variation in non-coding regions of ABCA1 may significantly alter the severity of atherosclerosis, without necessarily influencing plasma lipid levels.
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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) | 0.003 | 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".