Abstract 66: Small Dense LDL Cholesterol Predicts Incident Diabetes Mellitus: The Atherosclerosis Risk in Communities Study
Bibliographic record
Abstract
Background: Small dense low-density lipoprotein cholesterol (sd-LDL-C) is an independent predictor of vascular events even in individuals with lower levels of LDL-C. Diabetics in particular tend to have higher levels of sd-LDL-C compared to those without diabetes. It is not known if sd-LDL-C predicts incident diabetes mellitus (DM). Objectives: We tested the hypothesis that elevated levels of sd-LDL-C measured using a new automated assay predict incident DM in the biracial ARIC study. Methods: Plasma sd-LDL-C was measured in 9,451 men and women without prevalent DM using a newly developed automated homogeneous assay. A Cox proportional hazards model was used to examine the association of sd-LDL-C with risk for incident DM. Results: More individuals in the highest vs. lowest sd-LDL-C quartiles were men, Caucasians, had hypertension and higher mean body mass index (BMI) (P<0.001 for all comparisons). Similarly more individuals in the highest vs. lowest sd-LDL-C quartiles had parental history of DM (31.2 vs. 28.8%, P=0.012) and higher mean fasting blood glucose (116 vs. 102 mg/dL, P<0.001). Over a period of 10.4 years 911 individuals developed new onset DM at a rate of 9.27 per thousand person years. In a fully adjusted model, individuals in highest vs. lowest sd-LDL-C quartiles have a 44% increased risk for the development of incident DM even after adjusting for fasting blood glucose (Table). Conclusion: sd-LDL-C predicts incident DM in the biracial ARIC study.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".