Triglycerides/High-Density Lipoprotein Ratio and Coronary Artery Disease: Results from a Large Single-Center Study
Bibliographic record
Abstract
Background. Despite the achievement of therapeutic goals regarding low-density lipoprotein cholesterol (LDL-C) levels with statins, high residual risk of events was reported in patients with coronary artery disease (CAD). Widespread attention has recently been focused on low plasmatic levels of high-density lipoproteins (HDLs) and high levels of triglycerides as risk factors for cardiovascular disease and as potential pharmacological targets, with particular attention paid to their ratio. Therefore, the aim of the current study was to investigate the association between triglycerides and HDLs and the TG/HDL ratio and their association with the prevalence and extent of CAD. Methods. We included patients undergoing non-urgent coronary angiography at Azienda Ospedaliera-Universitaria “Maggiore della Carità”, Novara, Italy, from 2007 to 2018. Patients chronically treated with triglyceride-lowering therapies (PUFA and Fibrates) were excluded from this analysis. Fasting samples were collected at the moment of angiography. CAD was defined as at least one vessel stenosis >50%. Results. Our study population of 5997 patients was divided according to TG/HDL ratio quartiles. The TG/HDL ratio was significantly associated with age, gender, smoking status, hypercholesterolemia, diabetes, and the chronic use of ACE inhibitors, statins, beta-blockers, aspirin, ADP antagonists, and diuretics. The TG/HDL ratio was additionally associated with several laboratory parameters. In multiple logistic regression analysis, HDLs but not the TG/HDL ratio were independently associated with the prevalence and extent of CAD. Conclusions. Our study showed that HDLs but not the TG/HDL ratio are independently associated with the extent and prevalence of CAD. Therefore, this ratio does not provide additional prognostic information to HDLs in the prediction of the prevalence and extent of this disease.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".