• Wei Chen, Meng Jia, Rui Yin, Chengwei Zhang, Jinchen He, Hong Yang, and Qi Wu.
• Department of Cardiology, The Second Affiliated Hospital of Chengdu Medical College, Nuclear Industry 416 Hospital, Chengdu, Sichuan, China.
• Brit J Hosp Med. 2025 Jan 24; 86 (1): 1191-19.

AbstractAims/Background Hypertension (HT) is a prevalent medical condition showing an increasing incidence rate in various populations over recent years. Long-term hypertension increases the risk of the occurrence of hypertensive nephropathy (HTN), which is also a health-threatening disorder. Given that very little is known about the pathogenesis of HTN, this study was designed to identify disease biomarkers, which enable early diagnosis of the disease, through the utilization of high-throughput untargeted metabolomics strategies. Methods The participants of this study were patients admitted to The Second Affiliated Hospital of Chengdu Medical College, Nuclear Industry 416 Hospital, who were randomly divided into three groups: Normal group (n = 11), HT group (n = 10), and HTN group (n = 12). Urine exosomes were extracted, purified, and subjected to untargeted metabolomics analysis. Differential metabolites and their significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified. The least absolute shrinkage and selection operator (LASSO) regression analysis was then employed to establish a diagnostic model for early-stage HTN. Finally, logistic regression and receiver operating characteristic (ROC) curve analysis were performed to identify biomarkers related to early HTN. Results Orthogonal partial least squares-discriminant analysis (OPLS-DA) revealed significant differences in the metabolic profiles of the three patient groups. Compared to subjects of the Normal group, the HT and HTN groups exhibited significantly upregulated and downregulated profiles of differential metabolites, respectively. LASSO regression analysis results indicated that 4-hydroxyphenylacetic acid, bilirubin, uracil, and iminodiacetic acid are potential biomarkers for HTN or HT. Conclusion With untargeted metabolomics analysis, we successfully identified differential metabolites in HTN. A further LASSO regression analysis revealed that four key metabolites, namely 4-hydroxyphenylacetic acid, bilirubin, uracil, and iminodiacetic acid, hold promise for the diagnosis of early-stage HTN.

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