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验证分析方法,以确定新的唾液脂质过氧化化合物作为潜在的神经退行性生物标志物。

Validated analytical method to determine new salivary lipid peroxidation compounds as potential neurodegenerative biomarkers.

机构信息

Neonatal Research Unit, Health Research Institute La Fe, Valencia, Spain.

Institut des Biomolécules Max Mousseron (IBMM), UMR 5247 - CNRS - University of Montpellier - ENSCM, Faculty of Pharmacy, Montpellier, France.

出版信息

J Pharm Biomed Anal. 2019 Feb 5;164:742-749. doi: 10.1016/j.jpba.2018.11.043. Epub 2018 Nov 19.

Abstract

Lipid peroxidation is closely related to neurodegenerative diseases since brain shows high lipid composition and oxygen consumption. The determination of lipid peroxidation compounds in non-invasive biological samples would help to monitor the patients' oxidative stress status. A new analytical method based on ultrasound-assisted liquid-liquid semi-microextraction (UA-LLsME) followed by Ultra Performance Liquid Chromatography coupled to tandem Mass Spectrometry was developed to determine 18 lipid peroxidation biomarkers in saliva samples. The variables affecting the UA-LLsME efficiency were systematically studied. Under the optimum conditions, the methodology was validated and showed high-throughput, high sensitivity (limits of detection 0.02-2 nmol L), and satisfactory precision (coefficients of variation 2-11% (intra-day) and 5-12% (inter-day)). The reliability of the described method was assessed analysing spiked saliva samples, and the recoveries were between 80% and 120% for most of the analytes. Then, the method suitability was demonstrated by analysing saliva samples (n = 30) from elderly people with neurodegenerative diseases. To conclude, the new developed analytical method is a useful tool to determine salivary lipid peroxidation compounds as potential biomarkers in further clinical studies in which oxidative stress plays an important role.

摘要

脂质过氧化与神经退行性疾病密切相关,因为大脑具有高脂质组成和耗氧量。在非侵入性生物样本中测定脂质过氧化化合物有助于监测患者的氧化应激状态。本研究建立了一种新的基于超声辅助液-液半微萃取(UA-LLsME)结合超高效液相色谱-串联质谱(UPLC-MS/MS)的分析方法,用于检测唾液样本中的 18 种脂质过氧化生物标志物。系统研究了影响 UA-LLsME 效率的变量。在最佳条件下,对该方法进行了验证,结果表明该方法具有高通量、高灵敏度(检出限为 0.02-2 nmol L)和令人满意的精密度(日内变异系数为 2-11%,日间变异系数为 5-12%)。通过分析添加唾液样本评估了所描述方法的可靠性,大多数分析物的回收率在 80%至 120%之间。然后,通过分析来自患有神经退行性疾病的老年人的唾液样本(n=30)证明了该方法的适用性。总之,新开发的分析方法是一种有用的工具,可用于确定唾液中的脂质过氧化化合物作为进一步研究氧化应激起重要作用的临床研究中的潜在生物标志物。

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