Role of Lipoprotein(a) in Atherosclerotic Cardiovascular Disease in South Asian Individuals
Bibliographic record
Abstract
South Asian individuals (SA), representing approximately one quarter of the global population, experience a disproportionately high burden of cardiovascular disease. Some of this increased susceptibility is accounted for by traditional risk factors such as diabetes, hypertension, carbohydrate-rich diets, and rising rates of obesity and metabolic syndrome. However, other previously underappreciated risk factors may also play a crucial role. These include environmental pollution, genetic factors, and Lp(a) (lipoprotein(a)). Various epidemiological and genetic studies support the role of Lp(a) as a causal and independent risk factor for atherosclerotic cardiovascular disease. SA have a higher prevalence of elevated Lp(a) levels (25% have levels >50 mg/dL) compared with Western populations, and this may be one factor that accounts for the earlier age of onset of coronary artery disease, its more aggressive course, and higher morbidity and mortality in this group. SA experience myocardial infarction nearly 10 years earlier than individuals of European descent and have higher rates of premature and multivessel coronary artery disease. Additionally, socioeconomic shifts, cultural practices, and disparities in health care access may further exacerbate these risks, creating a complex interplay of factors that heighten cardiovascular vulnerability in SA. In this article, we review the data on the role of Lp(a) in mediating atherosclerotic cardiovascular disease, its epidemiology in SA, current screening guidelines, and drugs in the pipeline that will potentially be able to reduce high Lp(a) levels and associated cardiovascular risk. Ultimately, outcome trials with such drugs will be needed in this large population to examine their efficacy and safety.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".