Association of Triglyceride-Glucose (TyG) Index With Severe Acute Pancreatitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The triglyceride-glucose (TyG) index has emerged as a surrogate marker for insulin resistance and has been implicated in various metabolic and inflammatory diseases. Its potential role in predicting the severity of acute pancreatitis (AP), particularly severe acute pancreatitis (SAP), remains underexplored. This study aims to evaluate the association between the TyG index and SAP through a systematic review and meta-analysis. We systematically searched PubMed, Scopus, and Europe PMC databases from inception to March 26, 2025. Eligible studies included adult patients with AP stratified by severity (SAP vs. non-SAP) and reported TyG index values. Data extraction and quality assessment using the Newcastle-Ottawa Scale were conducted independently by two reviewers. A random-effects model was used to compute pooled mean differences (MD), with subgroup and sensitivity analyses to explore heterogeneity. Ten studies comprising 2262 patients were included. The TyG index was significantly higher in SAP patients compared to non-SAP patients (MD = 0.61; 95% CI: 0.46-0.76; p < 0.0001; I² = 79.1%). The index also predicted ICU admission (MD = 0.52) and mortality (MD = 0.71) in AP patients. Subgroup analyses showed consistent findings across study designs and sample sizes, though geographic variability was observed. The TyG index is significantly associated with the severity of AP and may serve as a reliable, accessible prognostic biomarker for identifying patients at risk of SAP.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".