Lipid metabolism for predicting the recurrence of hypertriglyceridemic acute pancreatitis

Tang, Lingling; Jia, Qing; Liu, Nian; Liu, Qianqian; Pan, Ke; Lei, Lixing; Xiao-hua, Huang

doi:10.1016/j.heliyon.2023.e17443

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…In addition to serving as vital sources of energy and key players in cellular metabolic activities, rigorous scientific investigations have revealed the complex dynamics interlinking lipid metabolism with obesity and AP. These studies have shed light on the profound and intricate connections underpinning these biological phenomena, offering new insights into their interdependencies [ 7 , 12 , 22 , 23 ]. In the pursuit of tailored diagnostic and therapeutic approaches for AP, it has become crucial to investigate the cumulative effect of BMI > 30, AP, and lipid metabolism from the standpoint of gene expression.…”

Section: Discussionmentioning

confidence: 99%

“…Gaining such insights is essential for enhancing our understanding of AP pathogenesis and could pave the way for innovative therapeutic approaches for individuals affected by this condition. Furthermore, lipid metabolism has been identified as a key element in the pathogenesis of various diseases, including obesity, AP, cancer, immune disorders, and neurodegenerative diseases [ 9 – 12 ]. The complex interplay between lipid metabolism and disease pathogenesis highlights its importance in understanding disease mechanisms and developing personalized treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

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

Ji,

Tang,

Jiang

et al. 2024

Lipids Health Dis

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%