Lipoprotein Lipase ( <i>LPL</i> ) Gene Variation and Progression of Carotid Artery Plaque
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Coding single nucleotide polymorphisms (cSNPs) in the lipoprotein lipase (LPL) gene have been associated with lipoprotein phenotypes and vascular disease risk. We studied the association between LPL cSNPs and a novel noninvasive measure of disease, namely, cross-sectional carotid plaque area (CPA) on B-mode ultrasound. METHODS: Four hundred fifty-two patients from an atherosclerosis prevention clinic had determinations of baseline and total CPA. Traditional atherosclerosis risk factors were recorded, and the LPL D9N, N291S, and S447X cSNPs were genotyped. Multiple regression analysis was used to identify determinants of CPA. RESULTS: Minor allele frequencies for LPL D9N, N291S, and S447X were 2.8%, 0.9%, and 4.4%, respectively. There were no significant between-genotype differences in treated fasting lipids. The LPL D9N genotype was a significant predictor of both baseline CPA (P=0.008) and plaque progression from baseline to 1 year later (P=0.001). Heterozygotes for the N9 allele had higher mean baseline CPA and plaque progression than did LPL D9/D9 homozygotes. CONCLUSIONS: LPL D9N genotype may be a determinant of atherosclerosis as estimated by static baseline CPA and by progression of CPA.
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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) | 0.002 | 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".