Lp(a) (Lipoprotein(a)) Levels Predict Progression of Carotid Atherosclerosis in Subjects With Atherosclerotic Cardiovascular Disease on Intensive Lipid Therapy
Bibliographic record
Abstract
Objective— To assess whether Lp(a) (lipoprotein(a)) levels and other lipid levels were predictive of progression of atherosclerosis burden as assessed by carotid magnetic resonance imaging in subjects who have been treated with LDL-C (low-density lipoprotein cholesterol)–lowering therapy and participated in the AIM-HIGH trial (Atherothrombosis Intervention in Metabolic Syndrome With Low HDL/High Triglycerides: Impact on Global Health Outcomes). Approach and Results— AIM-HIGH was a randomized, double-blind study of subjects with established vascular disease, elevated triglycerides, and low HDL-C (high-density lipoprotein cholesterol). One hundred fifty-two AIM-HIGH subjects underwent both baseline and 2-year follow-up carotid artery magnetic resonance imaging. Plaque burden was measured by the percent wall volume (%WV) of the carotid artery. Associations between annualized change in %WV with baseline and on-study (1 year) lipid variables were evaluated using multivariate linear regression and the Bonferroni correction to account for multiple comparisons. Average %WV at baseline was 41.6±6.8% and annualized change in %WV over 2 years ranged from −3.2% to 3.7% per year (mean: 0.2±1.1% per year; P =0.032). Increases in %WV were significantly associated with higher baseline Lp(a) (β=0.34 per 1-SD increase of Lp(a); 95% confidence interval, 0.15–0.52; P <0.001) after adjusting for clinical risk factors and other lipid levels. On-study Lp(a) had a similar positive association with %WV progression (β=0.33; 95% confidence interval, 0.15–0.52; P <0.001). Conclusions— Despite intensive lipid therapy, aimed at aggressively lowering LDL-C to <70 mg/dL, carotid atherosclerosis continued to progress as assessed by carotid magnetic resonance imaging and that elevated Lp(a) levels were independent predictors of increases in atherosclerosis burden.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".