Lipoprotein(a) Interactions With Low‐Density Lipoprotein Cholesterol and Other Cardiovascular Risk Factors in Premature Acute Coronary Syndrome (ACS)
Bibliographic record
Abstract
BACKGROUND: Current recommendations for lipoprotein(a) (Lp[a]) focus on the control of other risk factors, including lowering low-density lipoprotein cholesterol (LDL-C), with little evidence to support this approach. Identifying interactions between Lp(a) and other risk factors could identify individuals at increased risk for Lp(a)-mediated disease. METHODS AND RESULTS: We used a case-only study design and included 939 participants (median age=49 years, interquartile range 46-53, women=33.1%) from the GENdEr and Sex determInantS of cardiovascular disease: from bench to beyond-Premature Acute Coronary Syndrome (GENESIS-PRAXY) study, a multicenter prospective cohort study of premature acute coronary syndrome. There was a higher prevalence of elevated Lp(a) levels (>50 mg/dL; 80th percentile) in PRAXY participants as compared to the general population (31% versus 20%; P<0.001). Lp(a) was strongly associated with LDL-C (adjusted β 0.17; P<0.001). Individuals with high Lp(a) were more likely to have LDL-C >2.5 mmol/L, indicating a synergistic interaction (adjusted odds ratio 1.51; 95% CI 1.08-2.09; P=0.015). The interaction with high Lp(a) was stronger at increasing LDL-C levels (LDL-C >3.5, adjusted odds ratio 1.87; LDL-C >4.5, adjusted odds ratio 2.72). In a polytomous logistic model comparing mutually exclusive LDL-C categories, the interaction with high Lp(a) became attenuated at LDL-C ≤3.5 mmol/L (odds ratio 1.16; 95% CI 0.80-1.68, P=0.447). Other risk factors were not associated with high Lp(a). CONCLUSIONS: In young acute coronary syndrome patients, high Lp(a) is more prevalent than in the general population and is strongly associated with high LDL-C, suggesting that Lp(a) confers greater risk for acute coronary syndrome when LDL-C is elevated. Individuals with high Lp(a) and LDL-C >3.5 mmol/L may warrant aggressive LDL-C lowering.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".