Triglyceride-glucose index, low-density lipoprotein levels, and cardiovascular outcomes in chronic stable cardiovascular disease: results from the ONTARGET and TRANSCEND trials
Bibliographic record
Abstract
AIMS: The triglyceride-glucose index (TyG) has been proposed as an alternative to insulin resistance and as a predictor of cardiovascular outcomes. Little is known on its role in chronic stable cardiovascular disease and its predictive power at controlled low density lipoprotein (LDL) levels. METHODS AND RESULTS: Our study population consisted of 29 960 participants in the ONTARGET and TRANSCEND trials that enrolled patients with known atherosclerotic disease. Triglycerides and glucose were measured at baseline. TyG was calculated as the logarithmized product of fasting triglycerides and glucose divided by 2. The primary endpoint of both trials was a composite of cardiovascular death, myocardial infarction, stroke, or hospitalization for heart failure. The secondary endpoint was all-cause death and the components of the primary endpoint. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CI) with extensive covariate adjustment for demographic, medical history, and lifestyle factors. During a mean follow-up of 4.3 years, 4895 primary endpoints and 3571 all-cause deaths occurred. In fully adjusted models, individuals in the highest compared to the lowest quartile of the TyG index were at higher risk for the primary endpoint (HR 1.14; 95% CI 1.05-1.25) and for myocardial infarction (HR 1.30; 95% CI 1.11-1.53). A higher TyG index did not associate with the primary endpoint in individuals with LDL levels < 100 mg/dL. CONCLUSION: A higher TyG index is associated with a modestly increased cardiovascular risk in chronic stable cardiovascular disease. This association is largely attenuated when LDL levels are controlled. REGISTRATION: www.clinicaltrials.gov: NCT00153101.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".