Cumulative stress hyperglycemia ratio exposure and dynamic trajectories reveal prognostic determinants of acute hyperlipidemic pancreatitis: an 5-year cohort study

Chen et al. Lipids in Health and Disease (2026) 25:157 https://doi.org/10.1186/s12944-026-02970-x Lipids in Health and Disease Open Access RESEARCH Cumulative stress hyperglycemia ratio exposure and dynamic trajectories reveal prognostic determinants of acute hyperlipidemic pancreatitis: an 5-year cohort study Yiji Chen1†, Jianhua Wan1†, Wenqing Shu1, Xiaoyu Yang1, Huajing Ke1, Wenhua He1, Yin Zhu1, Nonghua Lu1 and Liang Xia1* Abstract Background The stress hyperglycemia ratio (SHR) and glycemic variability (GV) both reflect acute glycemic fluctuations, with established roles in cardiovascular and stroke diseases. However, their prognostic value in acute hyperlipidemic pancreatitis (HTG-AP) remains underexplored. Traditional assessments based on single measurements fail to capture the dynamic evolution. Therefore, this study aims to evaluate the early predictive value of Cumulative stress hyperglycemia ratio (CumSHR) and trajectory combined with GV in patients with hyperlipidemic pancreatitis. Methods This study collected data from 959 patients with HTG-AP. SHR and GV were calculated using standardized formulas; CumSHR was derived from the area under the curve (AUC) based on SHR data from the 7 days prior to admission. Multivariable analyses and RCS analysis assessed whether CumSHR predicted severe hyperlipidemic acute pancreatitis (HTG-SAP). Subgroup stratification was performed based on clinically distinct glucose metabolic states, while Latent class growth mixture model (LCGMM) identified dynamic SHR trajectory sub-phenotypes. Kaplan– Meier (K-M) curves compared survival rates across different risk trajectory groups. Finally, machine learning models predicted HTG-SAP risk, and SHapley Additive exPlanations (SHAP) identified key predictors. Results In the Jiangxi cohort, 162 cases (16.9%) of HTG-AP developed HTG-SAP. RCS analysis demonstrated a U-shaped association between CumSHR and HTG-SAP and persistent organ failure (POF) (P < 0.001). The LCGMM identified three dynamic trajectories: the sustained high-value group (SHG-T3) exhibited the highest risk of HTG-SAP (55.6%, compared with 10.6% in the low-value gradually decreasing group [LDG-T1]). Subsequently, stratified into normal glucose regulation (NGR), pre-diabetes Mellitus (Pre-DM), and diabetes mellitus (DM), high CumSHR + high GV showed increased risk compared to low CumSHR + low LGV (P < 0.05). Among machine learning models predicting † Yiji Chen and Jianhua Wan contributed equally to this work. *Correspondence: Liang Xia Full list of author information is available at the end of the article © The Author(s) 2026. 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To view a copy of this licence, visit http://creati vecommons.org/licenses/by-nc-nd/4.0/. Chen et al. Lipids in Health and Disease (2026) 25:157 Page 2 of 14 HTG-SAP risk, the Naive-Bayes model demonstrated the highest predictive accuracy. SHAP analysis identified CumSHR as one of the most important predictors. Conclusions This study demonstrates that CumSHR exhibits significant association with early assessment of severe conditions in patients with HTG-AP, with its trajectory effectively capturing dynamic changes to compensate for the limitations of static prediction. Keywords Acute hyperlipidemic pancreatitis, HTG-AP, CumSHR, GV, SHR trajectory, Persistent organ failure, Acute necrotic collection Introduction Acute pancreatitis (AP) is a common disorder of the digestive system, with a etiology primarily including biliary stones, alcohol consumption, and hypertriglyceridemia [1, 2]. In recent years, the prevalence of hypertriglyceridemic acute pancreatitis (HTG-AP) has risen sharply driven by the growing prevalence of unhealthy dietary habits. Severe hypertriglyceridemic acute pancreatitis (HTG-SAP) has also become a significant therapeutic challenge, characterized by its unpredictable early course and pronounced lipid metabolism disturbances [3, 4]. As the disease progresses dynamically, approximately 20–30% of HTG-AP patients may develop adverse outcomes such as persistent organ failure and severe stress states, complicating the timing and approach of early intervention [2, 5–7]. Current research relying solely on static clinical parameters and traditional scoring systems at admission may fail to predict the dynamic changes in patients' conditions and thus limits prognostic accuracy. Stress-induced hyperglycemia is a pathological phenomenon characterized by transient elevations in blood glucose resulting from inflammatory responses and neurohormonal dysregulation [8]. Its pathogenesis involves hormonal disturbances, neuromodulation, and immune responses, reflecting the metabolic alterations that occur during stress states. It is also associated with insulin resistance and increased hepatic glycogen storage [9–11]. While stress responses may provide transient energy support, chronic or sustained stress can lead to adverse clinical outcomes. This phenomenon is frequently observed in patients with acute myocardial infarction and stroke [12–14]. However, for HTG-AP patients, acute glycemic fluctuations may pose a greater risk than chronic hyperglycemia and are associated with a more complex clinical course. Neither admission blood glucose (ABG) nor glycated hemoglobin (HbA1c) adequately captures early stress-induced glucose fluctuations. However, the stress hyperglycemia ratio (SHR), derived from ABG and HbA1c, provides a quantitative metric for assessing acute glucose dysregulation [15, 16]. Furthermore, several recent studies have also linked glycemic variability (GV) to adverse clinical outcomes, underscoring its prognostic relevance [17–21]. For patients with HTG-AP, meticulous management of both blood glucose and lipid levels is critical, necessitating close clinical monitoring and timely intervention. However, most current studies on SHR rely predominantly on static measurements (e.g., based on peak or baseline values), thereby overlooking the cumulative impact of the disease's dynamic progression and fluctuations on patients. Based on prior research, we found tha (...truncated)