Association between elevated lipoprotein(a) levels and cardiovascular risk
Bibliographic record
Abstract
Lipoprotein(a) [Lp(a)] is a genetically determined lipoprotein particle implicated in atherosclerotic cardiovascular disease (ASCVD). Elevated Lp(a) levels have been recognized as a residual cardiovascular risk factor independent of low-density lipoprotein cholesterol. However, prior studies have reported inconsistent results due to differences in measurement techniques, population diversity, and confounding factors. This meta-analysis evaluated the association between elevated Lp(a) levels and cardiovascular risk across observational and genetic studies and quantified the impact of Lp(a)-lowering interventions on clinical outcomes. A comprehensive literature search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library up to October 20, 2025, following PRISMA 2020 guidelines. Studies assessing relationships between Lp(a) and cardiovascular outcomes were included, and data were analyzed using random-effects models in R studio. Heterogeneity was measured using the I² statistic, and risk of bias was evaluated using the Newcastle-Ottawa scale (NOS) and ROBINS-I tool. Twenty-five studies encompassing 95,206 participants were included. Elevated Lp(a) levels were significantly associated with increased risk of major adverse cardiovascular events (MACE) (OR=0.81; 95% CI: 0.68-0.98; I²=54.9%), myocardial infarction (OR=0.86; 95% CI: 0.75-0.99), ischemic stroke (OR=0.87; 95% CI: 0.76-0.99), and cardiovascular mortality (OR=0.89; 95% CI: 0.87-0.91). PCSK9 inhibitors reduced Lp(a) by a pooled mean difference of -15.58 mg/dL, and anti-inflammatory therapies by -11.21 mg/dl. Elevated Lp(a) is independently associated with cardiovascular risk, underscoring its importance in prevention strategies.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.023 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".