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Abstract：
BACKGROUND: Drug-induced liver injury (DILI) is one of the most common adverse events of medication use, and its incidence is increasing. However, early detection of DILI is a crucial challenge due to a lack of biomarkers and noninvasive tests.
OBJECTIVE: To identify salivary metabolic biomarkers of DILI for the future development of noninvasive diagnostic tools.
METHODS: Saliva samples from 31 DILI patients and 35 healthy controls (HCs) were subjected to untargeted metabolomics using ultrahigh-pressure liquid chromatography coupled with tandem mass spectrometry. Subsequent analyses, including partial least squares-discriminant analysis modeling, t tests and weighted metabolite coexpression network analysis (WMCNA), were conducted to identify key differentially expressed metabolites (DEMs) and metabolite sets. Furthermore, we utilized least absolute shrinkage and selection operato and random fores analyses for biomarker prediction. The use of each metabolite and metabolite set to detect DILI was evaluated with area under the receiver operating characteristic curves.
RESULTS: We found 247 differentially expressed salivary metabolites between the DILI group and the HC group. Using WMCNA, we identified a set of 8 DEMs closely related to liver injury for further prediction testing. Interestingly, the distinct separation of DILI patients and HCs was achieved with five metabolites, namely, 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, tetradecanedioic acid, hypoxanthine, and inosine (area under the curve: 0.733-1).
CONCLUSIONS: Salivary metabolomics revealed previously unreported metabolic alterations and diagnostic biomarkers in the saliva of DILI patients. Our study may provide a potentially feasible and noninvasive diagnostic method for DILI, but further validation is needed.
OBJECTIVE: To identify salivary metabolic biomarkers of DILI for the future development of noninvasive diagnostic tools.
METHODS: Saliva samples from 31 DILI patients and 35 healthy controls (HCs) were subjected to untargeted metabolomics using ultrahigh-pressure liquid chromatography coupled with tandem mass spectrometry. Subsequent analyses, including partial least squares-discriminant analysis modeling, t tests and weighted metabolite coexpression network analysis (WMCNA), were conducted to identify key differentially expressed metabolites (DEMs) and metabolite sets. Furthermore, we utilized least absolute shrinkage and selection operato and random fores analyses for biomarker prediction. The use of each metabolite and metabolite set to detect DILI was evaluated with area under the receiver operating characteristic curves.
RESULTS: We found 247 differentially expressed salivary metabolites between the DILI group and the HC group. Using WMCNA, we identified a set of 8 DEMs closely related to liver injury for further prediction testing. Interestingly, the distinct separation of DILI patients and HCs was achieved with five metabolites, namely, 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, tetradecanedioic acid, hypoxanthine, and inosine (area under the curve: 0.733-1).
CONCLUSIONS: Salivary metabolomics revealed previously unreported metabolic alterations and diagnostic biomarkers in the saliva of DILI patients. Our study may provide a potentially feasible and noninvasive diagnostic method for DILI, but further validation is needed.
摘要：
背景：药物性肝损伤（DILI）是药物使用中最常见的不良事件之一，其发病率正在增加。然而,由于缺乏生物标志物和非侵入性检测,DILI的早期检测是一项至关重要的挑战.
目的：确定DILI的唾液代谢生物标志物，为未来非侵入性诊断工具的开发提供依据。
方法：对来自31名DILI患者和35名健康对照（HC）的唾液样本进行使用超高压液相色谱和串联质谱的非靶向代谢组学。随后的分析，包括偏最小二乘-判别分析建模，t检验和加权代谢物共表达网络分析(WMCNA)，进行鉴定关键差异表达代谢物(DEM)和代谢物集。此外，我们利用最小绝对收缩和选择操作和随机预测分析进行生物标志物预测。用接收器工作特征曲线下的面积评估每种代谢物和代谢物组用于检测DILI的用途。
结果：我们在DILI组和HC组之间发现了247种差异表达的唾液代谢物。使用WMCNA，我们确定了一组8个与肝损伤密切相关的DEM，用于进一步的预测测试。有趣的是,DILI患者和HCs的不同分离是用五种代谢物实现的，即,12-羟基十二烷酸,3-羟基癸酸,十四烷二酸，次黄嘌呤,和肌苷（曲线下面积：0.733-1）。
结论：唾液代谢组学揭示了先前未报道的DILI患者唾液中的代谢改变和诊断性生物标志物。我们的研究可能为DILI提供一种潜在可行的非侵入性诊断方法，但需要进一步验证。
目的：确定DILI的唾液代谢生物标志物，为未来非侵入性诊断工具的开发提供依据。
方法：对来自31名DILI患者和35名健康对照（HC）的唾液样本进行使用超高压液相色谱和串联质谱的非靶向代谢组学。随后的分析，包括偏最小二乘-判别分析建模，t检验和加权代谢物共表达网络分析(WMCNA)，进行鉴定关键差异表达代谢物(DEM)和代谢物集。此外，我们利用最小绝对收缩和选择操作和随机预测分析进行生物标志物预测。用接收器工作特征曲线下的面积评估每种代谢物和代谢物组用于检测DILI的用途。
结果：我们在DILI组和HC组之间发现了247种差异表达的唾液代谢物。使用WMCNA，我们确定了一组8个与肝损伤密切相关的DEM，用于进一步的预测测试。有趣的是,DILI患者和HCs的不同分离是用五种代谢物实现的，即,12-羟基十二烷酸,3-羟基癸酸,十四烷二酸，次黄嘌呤,和肌苷（曲线下面积：0.733-1）。
结论：唾液代谢组学揭示了先前未报道的DILI患者唾液中的代谢改变和诊断性生物标志物。我们的研究可能为DILI提供一种潜在可行的非侵入性诊断方法，但需要进一步验证。
