Research Progress on the Value of Inflammatory Markers Combined with the TyG Index in the Early Prediction of the Severity of Acute Pancreatitis

Introduction: Acute pancreatitis (AP) is a common acute gastrointestinal emergency with an increasing global incidence. Some patients may rapidly progress to severe acute pancreatitis (SAP), complicated by pancreatic necrosis, multiple organ dysfunction syndrome (MODS), and other conditions, leading to a significant increase in mortality. Early and accurate assessment of AP severity is crucial for formulating individualized treatment plans, reducing complications, and improving prognosis. However, traditional prediction methods have inherent limitations: the Ranson score requires 48-hour dynamic monitoring, which restricts early assessment; the APACHE II score involves complex indicators; the BISAP score has the risk of missed diagnosis of local severe lesions; and CTSI/MCTSI rely on imaging examinations with delayed imaging changes. None of these can meet the needs of early and rapid assessment. Based on the pathological basis of the “inflammation-metabolism” interaction in the pathogenesis of AP (inflammation exacerbates insulin resistance, while metabolic disorders further promote inflammation), the combined application of inflammatory markers and the triglyceride-glucose (TyG) index provides a new approach for predicting AP severity. Methods: This study conducted a literature review by searching Chinese and English literatures published from 2018 to 2025 in databases such as China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform, and PubMed. A total of 19 valid studies were included, and the relevant research progress was systematically summarized and analyzed. Results: Inflammatory markers can effectively reflect the body’s inflammatory state. For example, the area under the curve (AUC) of C-reactive protein (CRP) at 24 hours after admission for predicting severe acute pancreatitis (SAP) is 0.787; the systemic immune-inflammation index (SII) has an AUC of 0.809 - 0.920 for predicting SAP, which is superior to single indicators such as neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR). As a marker of insulin resistance, the TyG index is significantly higher in SAP patients than in mild AP patients (mean difference = 0.61, 95% confidence interval: 0.46 - 0.76) and is an independent predictor of SAP (odds ratio [OR] = 1.835 - 7.14). Importantly, the combined models integrating inflammatory markers and the TyG index perform better. For instance, the AUC of the CRP + NLR + TyG model for predicting SAP is 0.882 (sensitivity = 77.2%, specificity = 88.5%); in hyperlipidemic AP (HLAP), the model combining CT findings with blood biomarkers (such as calcium, CRP) has an AUC of 0.957 in the training group and 0.930 in the validation group, which is superior to traditional methods like the Ranson score. These combined models use routine clinical indicators, featuring low cost and fast turnaround, and have good potential for clinical translation, such as integration into electronic medical records for emergency triage. Discussion: The combined application of inflammatory markers and the TyG index has important value in the early prediction of AP severity, making up for the shortcomings of traditional methods. However, most existing studies are single-center retrospective studies with limited sample sizes, and there is a lack of data on special populations (such as pregnant women and elderly patients). Future research should focus on multicenter prospective studies to verify external validity, optimize models for specific subtypes (such as HLAP) and populations, and explore their long-term prognostic value to accelerate clinical application.