Abstract 166: Small Dense LDL Cholesterol Is Associated with Risk for Coronary Heart Disease: The Atherosclerosis Risk in Communities (ARIC) Study
Bibliographic record
Abstract
Background— Evidence from in vitro studies indicates that small dense LDL (sd-LDL) is more atherogenic than large buoyant LDL. Previously, sd-LDL has been associated with risk for vascular disease. However, the lack of a standardized sd-LDL assay has hampered its clinical application. Objectives— We tested the hypothesis that elevated plasma sd-LDL-cholesterol (sd-LDL-C) level is associated with risk for incident coronary heart disease (CHD) and stroke in the ARIC cohort. Methods— Plasma sd-LDL-C was measured in 11,419 men and women of the biracial ARIC study using a newly developed automated homogeneous assay. A proportional hazards model was used to examine the relationship between sd-LDL-C, vascular risk factors, and risk for CHD events and stroke over a period of ≈10 years. Results— Mean plasma sd-LDL-C was higher in Caucasians than in African Americans (45.2 vs. 37.4 mg/dL, p<0.0001). Plasma sd-LDL-C levels were strongly correlated with an atherogenic lipid profile and were higher in diabetics vs. non-diabetics (49.6 vs. 42.3 mg/dL, p<0.0001, respectively). sd-LDL-C was associated with incident CHD in a basic model as well as a model that included traditional risk factors and hs-CRP with hazard ratios (HRs) of 1.99 (95%CI: 1.68-2.36) and 1.56 (95%CI: 1.26-1.93) for the highest vs. the lowest quartile, respectively (Table). We did not find a significant association of sd-LDL-C with risk for stroke (Table). Conclusions— sd-LDL-C is associated with incident CHD but does not predict risk for stroke in ARIC participants. Further studies will need to determine whether sd-LDL-C will add value beyond traditional risk factors to cardiovascular risk assessment in clinical practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".