Journal of Inflammation Research (Jan 2024)
Exploring the Occurrence Mechanism and Early-Warning Model of Phlebitis Induced by Aescinate Based on Metabolomics in Cerebral Infarction Patients
Abstract
Zhipeng Wang,1,* You Yin,2,* Yuhui Mu,1,3,* Lili Cui,1 Xinhua Song,1 Jianhua Zhuang,2 Shouhong Gao,1 Xia Tao,1 Wansheng Chen1 1Department of Pharmacy, Second Affiliated Hospital of Naval Medical University (Shanghai Changzheng Hospital), Shanghai, 200003, People’s Republic of China; 2Department of Neurology, Second Affiliated Hospital of Naval Medical University (Shanghai Changzheng Hospital), Shanghai, 200003, People’s Republic of China; 3College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming, Yunnan, 650500, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jianhua Zhuang, Department of Neurology, Second Affiliated Hospital of Naval Medical University (Shanghai Changzheng Hospital), No. 415, Fengyang Road, Shanghai, 200003, People’s Republic of China, Email [email protected] Xia Tao, Department of Pharmacy, Second Affiliated Hospital of Naval Medical University (Shanghai Changzheng Hospital), No. 415, Fengyang Road, Shanghai, 200003, People’s Republic of China, Tel/Fax +86– 21– 81886191, Email [email protected]: This study aims to explore the mechanism underlying the induction of phlebitis by aescinate and create an early-warning model of phlebitis based on metabolomics.Methods: Patients with cerebral infarction enrolled had been treated with aescinate. Plasma samples were collected either before administration of aescinate, upon the occurrence of phlebitis, or at the end of treatment. Non-targeted metabolomics and targeted amino acid metabolomics were carried out to analyze metabolic profiles and quantify the metabolites.Results: Untargeted metabolomics revealed six differential metabolites in baseline samples versus post-treatment samples and four differential metabolites in baseline samples from patients with or without phlebitis. Pathways of these differential metabolites were mainly enriched in amino acid metabolism. Ten differential amino acids with a VIP value of > 1 were identified in the baseline samples, enabling us to distinguish between patients with or without phlebitis. A logistic regression model was constructed (AUC 0.825) for early warning of phlebitis of grade 2 or higher.Conclusion: The occurrence of aescinate-induced phlebitis, which can be predicted early during onset, may be associated with perturbations of the endogenous metabolic profile, especially the metabolism of amino acids. Keywords: aescinate, diagnostic model, mechanism, metabolomics, phlebitis
