Drug design, development and therapy · 2025
LC-MS-Based Serum Metabolomic Analysis Predicts the Risk of Tigecycline-Induced Coagulopathy in Critically Ill Patients.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Yang Na, Zheng Xinxin, Ji Xinyue, Yao Hui, Xu Ke, Zhang Tianqi, Jin Lu, Zhu Huaijun, Wang Min
Аннотация
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.
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.