Update on the detection and treatment of atherogenic low-density lipoproteins
Bibliographic record
Abstract
PURPOSE OF REVIEW: To explain why epidemiological studies have reached such diverse views as to whether apolipoprotein B (apoB) and/or low-density lipoprotein particle number (LDL-P) are more accurate markers of the risk of cardiovascular disease than LDL-C or non-high-density lipoprotein cholesterol (HDL-C) and to review the treatment options to lower LDL. RECENT FINDINGS: The Emerging Risk Factor Collaboration, a large prospective participant level analysis, a meta-analysis of statin clinical trials, and the Heart Protection Study have each reported that apoB does not add significantly to the cholesterol markers as indices of cardiovascular risk. By contrast, a meta-analysis of published prospective studies demonstrated that non-HDL-C was superior to LDL-C, and apoB was superior to non-HDL-C. As well, three studies using discordance analysis each demonstrated that apoB and LDL-P were superior to the cholesterol markers. Two approaches to resolve these differences are brought to bear in this article: first, which results are credible and second, how does taking the known differences in LDL composition into account, help resolve them. The best identification of individuals at risk of coronary artery disease or with coronary artery disease allows the most efficacious treatment of elevated LDL-P and will permit a more extensive use of some of the more novel LDL-lowering agents. SUMMARY: Much of the controversy vanishes once the physiologically driven differences in the composition of the apoB lipoprotein particles are taken into account, illustrating that epidemiology, not directed by physiology, is like shooting without aiming.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 | 0.000 |
| 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".