Lipid metabolism for predicting the recurrence of hypertriglyceridemic acute pancreatitis

A systematic review of the epidemiology and risk factors for severity and recurrence of hypertriglyceridemia-induced acute pancreatitis

This systematic review aims to comprehensively assess the epidemiology and identify risk factors associated with the severity and recurrence of hypertriglyceridemia-induced acute pancreatitis (HTG-AP). A search of PubMed, Web of Science, and Cochrane databases was conducted to identify all relevant randomized controlled trials (RCTs), prospective, or retrospective cohort studies on HTG-AP. Data related to epidemiology and risk factors for severity and recurrence of HTG-AP were extracted and analyzed. Seventy-seven studies met the inclusion criteria, comprising 1 RCT, 21 prospective studies, and 55 retrospective studies. A total of 56,617 acute pancreatitis (AP) patients were included, of which 19.99% were diagnosed with HTG-AP (n = 11,315). Compared to non-HTG-AP patients, HTG-AP patients were more likely to be male (68.7% vs. 57.3%) and younger (mean age 41.47 ± 4.32 vs. 50.25 ± 7.70 years). HTG-AP patients exhibited higher mortality rates (up to 20% vs. 15.2%), increased severity (8.3% to 100% vs. 3.8% to 47.2%), and higher recurrence rates (up to 64.8% vs. 23.3%). Analysis of temporal trends from 2002 to 2023 showed a range of HTG-AP prevalence in overall AP patients from 1.6% to 47.6%, with a slight upward trend that was not statistically significant (P = 0.1081). Regional analysis indicated relatively stable prevalence in North America (P = 0.5787), Europe (P = 0.0881), other regions (P = 0.738), while prevalence in China showed a significant increase (P = 0.0119). Thirteen studies investigated risk factors affecting HTG-AP severity, with elevated serum triglyceride (TG) levels associated with increased risk of complications such as pancreatic necrosis, systemic inflammatory response syndrome (SIRS), shock, and multi-organ failure. Additional factors including high neutrophil-to-lymphocyte ratio (NLR), elevated levels of amylase and C-reactive protein (CRP), hypocalcemia, and hypoalbuminemia were also implicated in HTG-AP severity. Smoking history, poor lipid control (TG > 3.1 mmol/L), or recurrent hypertriglyceridemia during follow-up were identified as potential predictors of HTG-AP recurrence. Our findings indicate a stable global prevalence of HTG-AP within AP patients, but a notable increase in China, possibly attributed to socio-economic and dietary factors.

Development and Validation of a Clinical Predictive Nomogram for Assessing the Risk of Recurrence of Acute Pancreatitis in Combined Hypertriglyceridemia

BACKGROUND

The objective of this study is to develop and validate a new nomogram-based scoring system for anticipating the recurrence of acute pancreatitis (AP) in combined hypertriglyceridemia (HTG).

METHODS

A total of 292 patients diagnosed with AP combined with HTG participated in this research. Among them, 201 patients meeting the inclusion criteria were randomly divided into training and validation sets at a ratio of 7:3. Clinical data were collected for all patients. In the training set, predictive indicators were chosen through backward stepwise multivariable logistic regression analysis. Subsequently, a nomogram was developed based on the selected indicators. Finally, the model's performance was validated in both the training and validation sets.

RESULTS

By employing backward stepwise multivariable logistic regression analysis, we identified diabetes, gallstones, alcohol consumption, and triglyceride levels as predictive indicators. Subsequently, a clinical nomogram that incorporates these four independent risk factors was constructed. Model validation demonstrated an AUC of 0.726 (95% CI 0.644-0.809) in the training set and an AUC of 0.712 (95% CI 0.583-0.842) in the validation set, indicating a good discriminative ability. The Hosmer-Lemeshow test yielded P-values of 0.882 and 0.536 in the training and validation sets, respectively, suggesting good calibration. Calibration curves further confirmed good agreement. Ultimately, decision curve analysis (DCA) emphasized the clinical utility of our model.

CONCLUSION

We have developed a nomogram for predicting the recurrence of AP combined with HTG in patients, and this nomogram demonstrates good discriminative ability, calibration, and clinical utility. This tool holds the potential to assist clinicians in offering more personalized treatment strategies for AP combined with HTG.

Investigating potential biomarkers of acute pancreatitis in patients with a BMI>30 using Mendelian randomization and transcriptomic analysis

BACKGROUND

Acute pancreatitis (AP) has become a significant global health concern, and a high body mass index (BMI) has been identified as a key risk factor exacerbating this condition. Within this context, lipid metabolism assumes a critical role. The complex relationship between elevated BMI and AP, mediated by lipid metabolism, markedly increases the risk of complications and mortality. This study aimed to accurately define the correlation between BMI and AP, incorporating a comprehensive analysis of the interactions between individuals with high BMI and AP.

METHODS

Mendelian randomization (MR) analysis was first applied to determine the causal relationship between BMI and the risk of AP. Subsequently, three microarray datasets were obtained from the GEO database. This was followed by an analysis of differentially expressed genes and the application of weighted gene coexpression network analysis (WGCNA) to identify key modular genes associated with AP and elevated BMI. Functional enrichment analysis was then performed to shed light on disease pathogenesis. To identify the most informative genes, machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were employed. Subsequent analysis focused on the colocalization of the Quantitative Trait Loci (eQTL) data associated with the selected genes and Genome-Wide Association Studies (GWAS) data related to the disease. Preliminary verification of gene expression trends was conducted using external GEO datasets. Ultimately, the diagnostic potential of these genes was further confirmed through the development of an AP model in mice with a high BMI.

RESULTS

A total of 21 intersecting genes related to BMI>30, AP, and lipid metabolism were identified from the datasets. These genes were primarily enriched in pathways related to cytosolic DNA sensing, cytokine‒cytokine receptor interactions, and various immune and inflammatory responses. Next, three machine learning techniques were utilized to identify HADH as the most prevalent diagnostic gene. Colocalization analysis revealed that HADH significantly influenced the risk factors associated with BMI and AP. Furthermore, the trend in HADH expression within the external validation dataset aligned with the trend in the experimental data, thus providing a preliminary validation of the experimental findings.The changes in its expression were further validated using external datasets and quantitative real-time polymerase chain reaction (qPCR).

CONCLUSION

This study systematically identified HADH as a potential lipid metabolism-grounded biomarker for AP in patients with a BMI>30.