Association of triglyceride glucose index with cardiovascular events: insights from the Isfahan Cohort Study (ICS)
Bibliographic record
Abstract
BACKGROUND: There is limited evidence regarding the evaluation of the association between the triglyceride glucose (TyG) index, an indicator of insulin resistance, and the incident risk of cardiovascular disease (CVD). Therefore, we aimed to examine the relationship between the TyG index and CVD incidence in a cohort of Iranian adults. METHODS: This study was performed in the framework of the Isfahan Cohort Study (ICS). The study population included 5,432 individuals aged ≥ 35 years. CVD events, including acute myocardial infarction (MI), stroke, and unstable angina (UA), were diagnosed by physicians. The TyG index was calculated as Ln [fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2]. The relationship between the TyG index and CVD events was investigated using Cox regression models. Receiver operating characteristics (ROC) curve analysis was used to determine the best cut-off for the TyG index for predicting CVD outcomes. RESULTS: During a median follow-up period of 11.2 years, a total number of 819 CVD, 164 MI, 172 stroke, and 384 UA were recorded. Following adjustment for multiple confounders, elevated TyG levels were associated with a higher risk of CVD (HR = 1.48; 95% CI 1.22-1.79; p < 0.001), MI (HR = 2.24; 95% CI 1.42-3.52; p < 0.001), stroke (HR = 1.45; 95% CI 0.96-2.19; p = 0.042), but not UA (HR = 1.28; 95% CI 0.96-1.69). The optimal TyG index cut-off was 8.91 for predicting CVD (sensitivity 58%; specificity 58%), 9.04 for predicting MI (sensitivity 57%; specificity 65%), 8.92 for predicting stroke (sensitivity 57%; specificity 57%), and 8.98 for predicting UA (sensitivity 53%; specificity 61%). CONCLUSION: We found a robust, direct association between the TyG index and the incidence of CVD events. This emphasizes the significance of observing the TyG index as an indicator of the occurrence of CVD events.
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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 | 0.037 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".