Comparison of the Associations of Apolipoprotein B and Low-Density Lipoprotein Cholesterol With Other Cardiovascular Risk Factors in the Insulin Resistance Atherosclerosis Study (IRAS)
Bibliographic record
Abstract
BACKGROUND: Risk factors for vascular disease include obesity, dyslipidemia, hypertension, dysglycemia, insulin resistance, inflammation, thrombosis, and subclinical atherosclerosis. This study compares the associations of apolipoprotein B (apoB) and LDL cholesterol (LDLC) with a wide array of measures of these risk factors. METHODS AND RESULTS: In 1522 individuals in the Insulin Resistance Atherosclerosis Study, anthropometric measures and measures of lipids, apoB, C-reactive protein, fibrinogen, plasminogen activator inhibitor-1 (PAI-1), fasting and postglucose load glucose and insulin concentrations, and carotid artery intima-media thickness (IMT) were taken and insulin sensitivity was determined by frequently sampled intravenous glucose tolerance test. There were significant differences in measures of abdominal obesity, dyslipidemia, hyperinsulinemia, and thrombosis between subjects with elevated apoB but normal LDLC versus those with elevated LDLC but normal apoB. In each statistically significant comparison, the elevated-apoB group had higher associated risk than the elevated-LDLC group. Moreover, apoB is highly significantly (P<0.0001) correlated with each measure in the direction of higher risk, whereas LDLC was significantly correlated (P<0.05) only with blood pressure, triglyceride, fibrinogen, and C-reactive protein. After further adjustment for LDLC, apoB correlations remained significant, whereas several LDLC correlations adjusted for apoB became significant in the direction of lower risk. CONCLUSIONS: Elevated apoB is more strongly associated than LDLC with other risk factors, including measures in the National Cholesterol Education Program guidelines for lipid treatment and other more recently established risk factors. This may provide new insight into why apoB is a better predictor of vascular risk than LDLC.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".