Association of the triglyceride glucose index with all-cause and cardiovascular mortality in a general population of Iranian adults
Bibliographic record
Abstract
BACKGROUND: The triglyceride glucose (TyG) index is a new and low-cost marker to determine insulin resistant which may be a predictor of cardiovascular disease (CVD). Although available evidence showed that its association with CVD mortality (CVM) and all-cause mortality (ACM) may differ in different populations, scarce data are available in this regard specially in low and middle-income countries. PURPOSE: To examine the association between TyG index and risk of CVM and ACM in Iranians. METHODS: This prospective cohort study included 5432 adults (age ≥ 35 years) with no history of CVD events. Fasting glucose and triglyceride were measured at baseline in all participants and TyG index was calculated. Cox frailty model was used to calculate hazard ratios (HRs) for CVM and ACM across the tertiles of TyG index. RESULTS: After a median follow-up of 11.25 years, a total number of 191 cardiovascular deaths, and 487 all-cause mortality was recorded. The risk of both CVM and ACM increased across the tertiles of TyG index. In the adjusted model for lifestyle and metabolic variables, the risks of ACM and CVM increased by 41% (95% CI 1.11, 1.81; P for trend = 0.005) and 64% (95% CI 1.07, 2.50; P for trend = 0.024), respectively. However, adjustment for diabetes mellitus disappeared the significance for both ACM and CVM. These associations may vary by sex. TyG was not related to the risk of non-CVD mortality. CONCLUSION: The predicting value of TyG index for ACM and CVM might be mediated by diabetes status. Further studies are required to confirm these findings.
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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 | 0.001 |
| 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".