Exploring the Association Between Cognitive Decline and Triglyceride‐Glucose Index: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Cognitive decline and dementia are debilitating conditions that compromise the quality of life and charge the healthcare system with a substantial socioeconomic burden. In this context, emerging evidence supports an association between the triglyceride-glucose index (TyG), a surrogate insulin resistance marker, and cognitive decline and dementia. Hence, we systematically reviewed the studies assessing the TyG index in patients with cognitive decline and their controls. METHODS: Online international databases (PubMed, Scopus, Embase, and the Web of Science) were searched comprehensively for studies showing the TyG index in patients with cognitive decline/impairment. Random-effect meta-analyses were conducted to calculate the standardized mean difference (SMD), pooled odds ratio (OR), and pooled area under the curve (AUC), in addition to 95% confidence intervals (CIs) for the comparisons of groups. RESULTS: Seventeen studies were included in our analysis. Then, we conducted a meta-analysis, demonstrating that patients with cognitive decline had significantly higher levels of TyG index than those without (SMD 0.83, 95% CI 0.16 to 1.50, p = 0.015). Moreover, our data showed that a 1-unit increase in the TyG index was associated with higher odds of cognitive decline (adjusted OR [aOR] 2.86, 95% CI 1.49 to 5.50, p = 0.002). Further, we observed that patients in the fourth TyG quartile with higher values of the TyG index than the first quartile presented with more increased cognitive decline (aOR 1.62, 95%CI 1.11 to 2.38, p = 0.013). Finally, pooled AUC data for the diagnostic performance of the TyG index resulted in an overall AUC value of 0.73 (95% CI 0.66 to 0.79). Sensitivity and specificity were also calculated as 0.695 and 0.687, respectively. CONCLUSION: This study supports the clinical utility of the TyG index in patients with cognitive decline and solicits more focused studies to consolidate its usage in clinical settings and real-world practice.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".