Yang Na, Zheng Xinxin, Ji Xinyue, Yao Hui, Xu Ke, Zhang Tianqi, Jin Lu, Zhu Huaijun, Wang Min
Department of Pharmacy, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College, Nanjing University of Chinese Medicine, Nanjing, People's Republic of China.
Department of Pharmacy, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, People's Republic of China.
Drug Des Devel Ther. 2025 Sep 13;19:8237-8250. doi: 10.2147/DDDT.S539874. eCollection 2025.
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).
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.
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.
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.
替加环素广泛用于治疗多重耐药感染。然而，凝血病的高发生率带来了重大的临床挑战。本观察性研究旨在描述接受替加环素治疗的重症患者的代谢组学特征，并确定预测替加环素诱导的凝血病（TIC）的潜在代谢特征。
共纳入53例患者，分为TIC组和非TIC组。在替加环素浓度谷值（Cmin）、中剂量（C1/2）和峰值（Cmax）时采集血清样本。应用基于液相色谱-质谱联用的非靶向代谢组学技术来描述这些时间点的代谢特征，并识别可能预测TIC的代谢物。
通过依次进行单变量分析和多变量LASSO惩罚Cox比例风险回归分析，我们分别在Cmin、C1/2和Cmax时间点鉴定出10种、10种和9种代谢物作为TIC的预测标志物。重要的是，溶血磷脂酰胆碱（LysoPCs）和溶血磷脂酰乙醇胺（LysoPEs）水平较低的患者在接受替加环素治疗后更容易发生凝血病。特别是，对Cmin时测量的LysoPC（18:0）、LysoPC（18:3）、LysoPE（18:0）和LysoPE（18:4）进行的受试者工作特征曲线分析显示曲线下面积接近0.8，为它们作为预测TIC的有力生物标志物的潜力提供了有力证据。
我们的研究表明，代谢组学可能是预测TIC风险的有价值工具，并提示LysoPCs和LysoPEs可能作为未来探索潜在治疗干预措施研究的假设生成候选物。
