Variance in the composition and number of VLDL and LDL particles with increasing triglyceride or increasing ApoB concentrations
Bibliographic record
Abstract
OBJECTIVE: The importance of any enhanced atherogenicity of triglyceride (TG)-rich lipoproteins (TRLs) will depend on the relative abundance of these particles compared with low-density lipoprotein (LDL) or total apolipoprotein (apo)B. Accordingly, we determined the contribution that TRLs make to total apoB as TG or apoB concentrations increase. We also describe compositional changes in TRLs as TG or apoB increases to assess whether very low-density lipoprotein (VLDL-[C]) is a valid proxy for VLDL-apoB. METHODS: We used sequential ultracentrifugation to separate lipoprotein fractions in plasma samples from 1940 dyslipidemic patients not on lipid-lowering medication, and measured apoB, cholesterol and TG in the plasma and in each subfraction. We analyzed these data in quartiles of TG or apoB. RESULTS: There was wide variance in all parameters in all quartiles of both TG and apoB. Although VLDL-apoB accounted for almost all the increase in total apoB across TG quartiles, LDL-apoB still accounted for 80% of the total in TG quartile 4. In contrast, LDL-apoB accounted for 90% of the increase in apoB across apoB quartiles. As TG increases, the increase in VLDL-C is explained more by increased VLDL-C/apoB when TG is moderately elevated, and more by increased VLDL-apoB when TG is very high. CONCLUSIONS: In conclusion, VLDL-apoB only becomes a substantial component of total apoB with extreme hypertriglyceridemia and VLDL-C is not an appropriate proxy for VLDL-apoB.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".