Association between the triglyceride-glucose index and arterial stiffness: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Studies have shown a strong association between the triglyceride-glucose (TyG) index, a simple marker of insulin resistance, and various metabolic diseases. We performed a systematic review of the interaction between the TyG index and arterial stiffness. METHODS: Relevant observational studies assessing the association between the TyG index and arterial stiffness were thoroughly searched in PubMed, Embase, and Scopus, and a manual search of the preprint server was conducted. A random-effects model was utilized to analyze the data. The risk of bias for the included studies was assessed using the Newcastle-Ottawa Scale. A pooled effect size estimate with a random-effects model was used for the meta-analysis. RESULTS: Thirteen observational studies comprising 48,332 subjects were included. Of these, 2 were prospective cohort studies, and the remaining 11 were cross-sectional studies. According to the results of the analysis, the risk of developing high arterial stiffness was 1.85 times greater for those in the highest TyG index subgroup versus the lowest group (risk ratio [RR]: 1.85, 95% confidence interval: 1.54-2.33, I2 = 70%, P < .001). Consistent results were observed when the index was analyzed as a continuous variable (RR: 1.46, 95% confidence interval: 1.32-1.61, I2 = 77%, P < .001). A sensitivity analysis excluding each of the studies one by one yielded similar results (RRs for categorical variables: 1.67-1.94, P all <.001; RRs for continuous variables: 1.37-1.48, P all <.001). A subgroup analysis showed that different characteristics of the study subjects, such as type of study design, age, population, disease status, (including hypertension and diabetes), and pulse wave velocity measurement methods had no substantial effect on the results (P for subgroup analysis, all >0.05). CONCLUSIONS: A relatively high TyG index might be linked to an increased incidence of arterial stiffness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".