ApoB versus non-HDL-cholesterol: Diagnosis and cardiovascular risk management
Bibliographic record
Abstract
The most recent guidelines released by the EAS/ESC and the Canadian Cardiovascular Society (CCS) retain low-density lipoprotein cholesterol (LDL-C) as the primary measure of the atherogenic risk of the apolipoprotein B (apoB) lipoproteins and the primary target of LDL-C lowering therapy. Both organizations endorse non-high-density lipoprotein cholesterol (non-HDL-C) and apoB as "alternate/secondary" targets, but neither group offers evidence supporting the continued preference of LDL-C as the primary target over non-HDL-C and apoB. Further, both suggest that non-HDL-C and apoB more or less measure the same thing and, therefore, are essentially interchangeable. But what is the evidence that LDL-C should remain the primary target, and are apoB and non-HDL-C mirror images of one another? Furthermore, are estimation of risk and establishment of treatment targets the only relevant issues, or is diagnosis also an essential objective? These are the questions this article will address. Our principal objectives are: (1) to clarify the differences between LDL-C, non-HDL-C, and apoB and to distinguish what they measure; (2) to summarize the evidence relating to LDL-C, non-HDL-C, and apoB as predictors of cardiovascular risk and as targets for treatment; and (3) to demonstrate that diagnosis of atherogenic dyslipoproteinemias should be a fundamental clinical priority.
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 | 0.021 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".