Relationship of the trajectory of the triglyceride-glucose index with heart failure: the Kailuan study
Bibliographic record
Abstract
BACKGROUND: A high triglyceride-glucose index (TyG) is associated with a higher risk of incident heart failure. However, the effects of longitudinal patterns of TyG index on the risk of heart failure remain to be characterized. Therefore, in the present study, we aimed to characterize the relationship between the trajectory of TyG index and the risk of heart failure. METHODS: We performed a prospective study of 56,149 participants in the Kailuan study who attended three consecutive surveys in 2006-2007, 2008-2009, and 2010-2011 and had no history of heart failure or cancer before the third wave survey (2010-2011). The TyG index was calculated as ln [fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2], and we used latent mixture modeling to characterize the trajectory of the TyG index over the period 2006-2010. Additionally, Cox proportional risk models were used to calculate the hazard ratio (HR) and 95% confidence interval (CI) for incident heart failure for the various TyG index trajectory groups. RESULTS: From 2006 to 2010, four different TyG trajectories were identified: low-stable (n = 13,554; range, 7.98-8.07), moderate low-stable (n = 29,435; range, 8.60-8.65), moderate high-stable (n = 11,262; range, 9.31-9.30), and elevated-stable (n = 1,898; range, 10.04-10.25). A total of 1,312 new heart failure events occurred during a median follow-up period of 10.04 years. After adjustment for potential confounders, the hazard ratios (HRs) and 95% confidence intervals (CIs) for incident heart failure for the elevated-stable, moderate high-stable, and moderate low-stable groups were 1.55 (1.15, 2.08), 1.32 (1.08, 1.60), and 1.17 (0.99, 1.37), respectively, compared to the low-stable group. CONCLUSIONS: Higher TyG index trajectories were associated with a higher risk of heart failure. This suggests that monitoring TyG index trajectory may help identify individuals at high risk for heart failure and highlights the importance of early control of blood glucose and lipids for the prevention of heart failure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".