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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".