The association between triglyceride–glucose index and the recurrence of myocardial infarction in young patients with previous coronary heart disease
Bibliographic record
Abstract
The triglyceride-glucose (TyG) index is a reliable biomarker for assessing insulin resistance. While previous studies have demonstrated its strong predictive value for cardiovascular disease in the general population, its ability to predict adverse clinical outcomes in patients with coronary heart disease (CHD) remains uncertain. This study aims to explore the association between the TyG index and recurrent myocardial infarction (MI) in young patients with CHD. This retrospective cohort study included 1,013 patients aged 18-44 at the time of initial CHD diagnosis, recruited from the cardiology clinics at Beijing Anzhen Hospital between October 2022 and October 2023. Baseline data and information on recurrent MI were collected from electronic medical records and other medical documents. The TyG index was calculated using the formula: ln [TG (mg/dL) × glucose (mg/dL) / 2]. Multivariable Cox regression, multivariable logistic regression, and restricted cubic spline analyses were used to assess the correlation between the baseline or endpoint TyG index and the likelihood of recurrent MI. The mean baseline TyG index was 9.23 ± 0.71, which decreased to 9.04 ± 0.74 at the follow-up endpoint (P < 0.001). Over an average follow-up period of 2.2 years, 96 (9.5%) cases of recurrent MI were recorded. No significant association was found between the baseline TyG index and recurrent MI in both univariate (HR = 0.99, 95% CI: 0.76-1.32) and multivariate Cox regression analyses (HR = 1.10, 95% CI: 0.82-1.58). However, after adjusting for all influencing factors, the endpoint TyG index was associated with an increased risk of recurrent MI (OR = 1.36, 95% CI: 1.08-1.84). Individuals in the highest tertile of endpoint TyG index showed a higher risk of recurrent MI compared to those in the lowest tertile, with fully adjusted ORs (95% CIs) of 2.12 (1.16-3.86). The TyG index decreased significantly during the follow-up period. An elevated TyG index at the follow-up endpoint is more effective than the baseline measurement in predicting and preventing recurrent MI in young patients with CHD, highlighting its important clinical significance in this population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".