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已发表论文
基于液相色谱 - 质谱的血清代谢组学分析可预测重症患者替加环素所致凝血功能障碍的风险
Authors Yang N, Zheng X, Ji X, Yao H, Xu K, Zhang T, Jin L, Zhu H, Wang M
Received 28 May 2025
Accepted for publication 5 September 2025
Published 13 September 2025 Volume 2025:19 Pages 8237—8250
DOI https://doi.org/10.2147/DDDT.S539874
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Tuo Deng
Na Yang,1,2,* Xinxin Zheng,1,3,* Xinyue Ji,1,* Hui Yao,1 Ke Xu,1 Tianqi Zhang,2 Lu Jin,2 Huaijun Zhu,1,2 Min Wang1,2
1Department of Pharmacy, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; 2Department of Pharmacy, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, People’s Republic of China; 3Department of Pharmacy, Hospital of Zhejiang People’s Armed Police (PAP), Hangzhou, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Min Wang, Department of Pharmacy, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China, Email wangmin19881013@126.com Huaijun Zhu, Department of Pharmacy, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China, Email huaijun.zhu@gmail.com
Purpose: Tigecycline is widely used to treat multidrug-resistant infections. However, the high incidence of coagulopathy poses a significant clinical challenge. This observational study aimed to characterize the metabolomic profiles of critically ill patients receiving tigecycline and to identify potential metabolic traits to predict tigecycline-induced coagulopathy (TIC).
Patients and Methods: A total of 53 patients were enrolled and classified into TIC and non-TIC groups. Serum samples were collected at trough (Cmin), mid-dose (C1/2), and peak (Cmax) tigecycline concentrations. LC-MS–based untargeted metabolomics was applied to characterize metabolic profiles across these timepoints and to identify metabolites potentially predictive of TIC.
Results: By sequentially applying univariate analysis and multivariate LASSO-penalized Cox proportional hazards regression analysis, we identified 10, 10, and 9 metabolites at the Cmin, C1/2, and Cmax timepoints, respectively, as predictive markers of TIC. Importantly, patients with lower levels of lysophosphatidylcholines (LysoPCs) and lysophosphatidylethanolamines (LysoPEs) are more susceptible to coagulopathy following tigecycline therapy. In particular, receiver operating characteristic curve analysis of LysoPC (18:0), LysoPC (18:3), LysoPE (18:0), and LysoPE (18:4) measured at Cmin demonstrated an area under the curve close to 0.8, providing strong evidence for their potential as robust biomarkers for predicting TIC.
Conclusion: Our study indicated that metabolomics could be a valuable tool for predicting the risk of TIC and suggested that LysoPCs and LysoPEs might serve as hypothesis-generating candidates for future studies exploring potential therapeutic interventions.
Keywords: critically ill patients, metabolomics, tigecycline, coagulopathy, prediction
1Department of Pharmacy, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; 2Department of Pharmacy, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, People’s Republic of China; 3Department of Pharmacy, Hospital of Zhejiang People’s Armed Police (PAP), Hangzhou, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Min Wang, Department of Pharmacy, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China, Email wangmin19881013@126.com Huaijun Zhu, Department of Pharmacy, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China, Email huaijun.zhu@gmail.com
Purpose: Tigecycline is widely used to treat multidrug-resistant infections. However, the high incidence of coagulopathy poses a significant clinical challenge. This observational study aimed to characterize the metabolomic profiles of critically ill patients receiving tigecycline and to identify potential metabolic traits to predict tigecycline-induced coagulopathy (TIC).
Patients and Methods: A total of 53 patients were enrolled and classified into TIC and non-TIC groups. Serum samples were collected at trough (Cmin), mid-dose (C1/2), and peak (Cmax) tigecycline concentrations. LC-MS–based untargeted metabolomics was applied to characterize metabolic profiles across these timepoints and to identify metabolites potentially predictive of TIC.
Results: By sequentially applying univariate analysis and multivariate LASSO-penalized Cox proportional hazards regression analysis, we identified 10, 10, and 9 metabolites at the Cmin, C1/2, and Cmax timepoints, respectively, as predictive markers of TIC. Importantly, patients with lower levels of lysophosphatidylcholines (LysoPCs) and lysophosphatidylethanolamines (LysoPEs) are more susceptible to coagulopathy following tigecycline therapy. In particular, receiver operating characteristic curve analysis of LysoPC (18:0), LysoPC (18:3), LysoPE (18:0), and LysoPE (18:4) measured at Cmin demonstrated an area under the curve close to 0.8, providing strong evidence for their potential as robust biomarkers for predicting TIC.
Conclusion: Our study indicated that metabolomics could be a valuable tool for predicting the risk of TIC and suggested that LysoPCs and LysoPEs might serve as hypothesis-generating candidates for future studies exploring potential therapeutic interventions.
Keywords: critically ill patients, metabolomics, tigecycline, coagulopathy, prediction