Triglyceride-glucose index as a novel biomarker for mild cognitive impairment in patients with coronary artery disease
Bibliographic record
Abstract
Patients with coronary artery disease (CAD) are at an increased risk of cognitive impairment, yet reliable noninvasive biomarkers for identifying high-risk individuals in this population remain scarce. This study aimed to investigate the association between the triglyceride-glucose (TyG) index and cognitive function in patients with CAD. This study included 1163 patients with CAD who underwent cognitive function assessment. Multiple regression analyses were conducted to evaluate the relationship between the TyG index and cognitive function. Furthermore, receiver operating characteristic (ROC) curve analysis was conducted to determine the discriminatory ability of the TyG index in identifying patients with mild cognitive impairment (MCI). The TyG index was negatively correlated with Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) scores (r = -0.16, p < 0.001 and r = -0.33, p < 0.001, respectively). Specifically, when the TyG index was ≥8.72, higher values were associated with increased risk of MCI (OR: 3.69, 95% CI: 2.12, 6.58). ROC analysis revealed that the optimal cutoff value for the TyG index was 8.906 (specificity: 73.8%, sensitivity: 60.2%). The TyG index is independently associated with cognitive function in patients with CAD, with elevated values associated with an increased risk of MCI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".