Physiological Bases for the Superiority of Apolipoprotein B Over Low‐Density Lipoprotein Cholesterol and Non–High‐Density Lipoprotein Cholesterol as a Marker of Cardiovascular Risk
Bibliographic record
Abstract
In 2019, the European Society of Cardiology/European Atherosclerosis Society stated that apolipoprotein B (apoB) was a more accurate marker of cardiovascular risk than low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol. Since then, the evidence has continued to mount in favor of apoB. This review explicates the physiological mechanisms responsible for the superiority of apoB as a marker of the cardiovascular risk attributable to the atherogenic apoB lipoprotein particles chylomicron remnants, very low-density lipoprotein, and low-density lipoprotein particles. First, the nature and relative numbers of these different apoB particles will be outlined. This will make clear why low-density lipoprotein particles are almost always the major determinants of cardiovascular risk and why the concentrations of triglycerides and LDL-C may obscure this relation. Next, the mechanisms that govern the number of very low-density lipoprotein and low-density lipoprotein particles will be outlined because, except for dysbetalipoproteinemia, the total number of apoB particles determines cardiovascular risk, Then, the mechanisms that govern the cholesterol mass within very low-density lipoprotein and low-density lipoprotein particles will be reviewed because these are responsible for the discordance between the mass of cholesterol within apoB particles, measured either as LDL-C or non-high-density lipoprotein cholesterol, and the number of apoB particles measured as apoB, which creates the superior predictive power of apoB over LDL-C and non-high-density lipoprotein cholesterol. Finally, the major apoB dyslipoproteinemias will be briefly outlined. Our objective is to provide a physiological framework for health care givers to understand why apoB is a more accurate marker of cardiovascular risk than LDL-C or non-high-density lipoprotein cholesterol.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".