Elevated lipoprotein (a) levels are associated with the acute myocardial infarction in patients with normal low-density lipoprotein cholesterol levels
Bibliographic record
Abstract
Abstract Elevated lipoprotein (a) [Lp(a)] and coronary artery disease (CAD) risk has been renewed interested in recent years. However, the association between Lp(a) and acute myocardial infarction (AMI) risk in patients with normal low-density lipoprotein cholesterol (LDL-C) levels has yet to been established. A hospital-based observational study including 558 AMI patients and 1959 controls was conducted. Lp(a) level was significantly higher in AMI patients with normal LDL-C levels than that in non-CAD group (median: 134.5 mg/l vs 108 mg/l, P<0.001). According to Lp(a) quartiles (Q1, <51 mg/l; Q2, 51–108 mg/l; Q3, 108–215 mg/l; Q4, ≥215 mg/l), the incidence of AMI increased with the elevated Lp(a) quartiles (P<0.001 and P for trend<0.001). Logistic regression analysis suggested that patients with Q3 and Q4 of Lp(a) values had 1.666 (95%CI = 1.230–2.257, P<0.001) and 1.769 (95%CI = 1.305–2.398, P< 0.001) folds of AMI risk compared with patients with Q1, after adjusting for traditional confounders. Subgroup analyses stratified by gender and age showed that the association only existed in male and late-onset subgroups. In addition, we analyzed the association of Lp(a) with AMI risk in different cut-off values (cut-off 1 = 170 mg/l, cut-off 2 = 300 mg/l). A total of 873 (34.68%) and 432 (17.16%) participants were measured to have higher Lp(a) levels according to cut-off 1 and cut-off 2, respectively. Participants with high Lp(a) levels had 1.418- (cut-off1, 95%CI = 1.150–1.748, P<0.001) and 1.521- (cut-off 2, 95%CI = 1.179–1.963, P< 0.001) folds of AMI risk compared with patients with low Lp(a) levels. The present large-scale study revealed that elevated Lp(a) levels were associated with increased AMI risk in Chinese population with normal LDL-C levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".