Application and validation of a diagnostic algorithm for the atherogenic apoB dyslipoproteinemias
Bibliographic record
Abstract
BACKGROUND: We applied a diagnostic algorithm using apolipoprotein B (apoB) in combination with triglycerides (TG) and total cholesterol to determine the prevalence of dyslipoproteinemias in the general population. We also characterized the overall cardiovascular (CV) risk profiles, including arterial structure and function as measured with a panel of noninvasive parameters. DESIGN: Clinical and biochemical characteristics and noninvasive measurements of atherosclerosis (NIMA) were determined in 1517 individuals, aged 50-70 years. RESULTS: In general, all dyslipoproteinemias were characterized by a worse CV risk profile and deteriorated outcomes of NIMA compared to those with normal apolipoprotein B (< 1·2 g L(-1)) and TG (< 1·5 mM) levels. The prevalence of hyperapoB-hyperTG was 15·1%, and these individuals showed the most abnormal atheroma-related parameters: reduced ankle-brachial-index at rest (-3·5%) and after exercise (-9·8%), increased intima-media thickness (+5·5%) and more carotid plaques (+39·1%). The prevalence of normoapoB-hyperTG because of increased VLDL was 18·1% and 2·3% because of increased chylomicrons and VLDL, and in these groups, the parameters related to stiffness (e.g. pulse-wave-velocity +7·6% and +5·2%, respectively) were most abnormal. Adjustment for apolipoprotein B (apoB) reduced differences in NIMA in the hyperapoB-hyperTG group, whereas adjustment for TG reduced differences in NIMA in the normoapoB-hyperTG group. CONCLUSIONS: The overall prevalence of dyslipoproteinemias according to the algorithm was approximately 40% in the Dutch population. The different dyslipoproteinemias showed a less favourable CV risk profile and deteriorated NIMA parameters, reflecting increased subclinical atherosclerosis. Furthermore, different effects on different NIMA parameters were observed in the different dyslipoproteinemias.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".