Untargeted metabolomics for acute intra-abdominal infection diagnosis in serum and urine using UHPLC-TripleTOF MS - Zhenhua Dong; Shaopeng Zhang; Hongwei Zhang; Dingliang Zhao; Ziwen Pan; Daguang Wang (Frontiers in Molecular Biosciences)
IntroductionAcute intra-abdominal infection (IAI) is a prevalent and life-threatening condition in general surgery, with significant implications for patient mortality. However, the timely identification of IAI is often hindered by the limitations of current medical laboratory sciences and imaging diagnostics.MethodsTo address this critical issue, we employed metabolomics to identify early biomarkers for IAI. In this study, we enrolled a cohort of 30 IAI patients and 20 healthy volunteers. Following preliminary experimental processing, all serum and urinary samples were subjected to ultrahigh performance liquid chromatography-triple time-of-flight mass spectrometry analysis. Initial metabolite profiling was conducted using total ion current chromatography and principal component analysis. Differential metabolites were subsequently identified through Student's t-test, partial least squares discriminant analysis, and support vector machine. Hierarchical clustering analysis was then applied to assess the discriminatory power of the selected metabolites. Based on receiver operating characteristic curve analysis, we identified the most promising biomarkers, which were further subjected to enrichment analysis. Additionally, we stratified patients according to the severity and etiology of IAI to explore potential differences among these subgroups.ResultsOur findings revealed five serum and two urinary metabolites as potential biomarkers for IAI. The serum biomarkers were associated with the Fatty Acid Biosynthesis pathway, while the urinary biomarkers were linked to the Catecholamine Biosynthesis pathway. Notably, no significant differences were observed among the three types of IAI or the seven etiologies studied.DiscussionFor individuals at risk of IAI, regular screening of these biomarkers could facilitate the early and convenient identification of the condition, thereby improving patient outcomes.