The Triglyceride/High-Density Lipoprotein Cholesterol Ratio, the Small Dense Low-Density Lipoprotein Phenotype, and Ischemic Heart Disease Risk
Bibliographic record
Abstract
This study investigated the relevance of using the plasma triglyceride to high-density lipoprotein cholesterol ratio (Log TG/HDL-C) for the prediction of the small dense lowdensity lipoprotein (LDL) phenotype and the risk of ischemic heart disease (IHD). Analyses were based on data from the Quebec Cardiovascular Study in a cohort of 2072 men free of IHD at baseline, among whom 262 had a first IHD event (coronary death, non fatal myocardial infarction and unstable angina) during a 13-year follow-up period. LDL particle size phenotype was characterized using 2-16% polyacrylamide gradient gel electrophoresis (PAGGE) of whole plasma. There were significant associations between the Log TG/HDL-C ratio and features of LDL size phenotype such as the proportion of LDL with a diameter <255A (r = 0.43, p < 0.001) and LDL peak particle size (r = -20.55, p < 0.001). However, the Log TG/HDL-C ratio brought no additional value (p â yen 0.1) in predicting the small dense LDL phenotype (area under the receiver operating curve (AUROC = 71.9%) compared to TG alone (AUROC = 71.2%) or to a combination of Log TG and HDL-C (AUROC = 72.4%) after multivariate adjustment for non lipid risk factors. Finally, elevations in the Log TG/HDL-C ratio did not improve the discrimination of incident IHD cases from non IHD cases compared to the use of plasma TG levels alone (p = 0.5) or a combination of the individual TG and HDL-C values (p = 0.5). The Log TG/HDL-C ratio does not improve our ability to identify individuals with the small dense LDL phenotype compared to plasma TG levels alone. The Log TG/HDLC is also not superior to plasma TG levels alone in predicting IHD risk in men of the QuA(c)bec Cardiovascular Study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".