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基于液相色谱-质谱联用技术的多囊卵巢综合征患者血清脂质组学分析 被引量:26

Serum Lipidomics Study of Polycystic Ovarian Syndrome Based on Liquid Chromatography-Mass Spectrometry
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摘要 多囊卵巢综合征是育龄期妇女最为常见的内分泌以及代谢紊乱疾病,是女性不孕的主要原因之一。多囊卵巢综合征患者临床存在很大的异质性,其致病机理尚未完全阐明。本研究基于反相液相色谱与串联四级杆飞行时间质谱联用技术(RPLC/Q-TOF-MS),对74例多囊卵巢综合征患者和54例正常女性的血清进行了脂质组学分析,定性鉴别出15类314种脂质化合物。数据采用非参数检验寻找潜在的差异化合物,p<0.05为显著性差异。结果表明:多囊卵巢综合征患者与正常人相比,不饱和脂肪酸、部分鞘磷脂、甘油二酯、甘油三酯呈上调趋势;部分磷脂酰胆碱、溶血性磷脂酰胆碱、溶血性磷脂酰乙醇胺呈下调趋势。而肥胖多囊卵巢综合征患者与非肥胖多囊卵巢综合征患者相比,不饱和脂肪酸、鞘磷脂、甘油二酯、甘油三酯上调更为明显;溶血性磷脂酰胆碱、部分磷脂酰胆碱下调更为明显。本研究结果表明,脂质组学是研究疾病分型的有效手段。 Polycystic ovarian syndrome( PCOS) is one of the most common endocrine disorders in fertile women and contributes to female infertility. Polycystic ovarian syndrome is heterogeneous and its pathogenesis has not been clearly elucidated. In this study,sera from 74 PCOS patients and 54 healthy persons as control were analyzed based on reversed-phase high performance liquid chromatography coupled with quadrupole timeof-flight mass spectrometry( RPLC / Q-TOF-MS). A total of 314 lipid compounds of 15 classes were indentified. Non-parametric statistical test was performed on metabolomics data and p 0. 05 was considered as significant. Compared with normal women, the serum levels of unsaturated fatty acids, most sphingomyelins, diacylglycerol and triglyceridewere were elevated, while the levels of mostphosphatidylcholine, lyso-phosphatidylcholine, lyso-phosphatidylethanolamine were reduced in PCOS women. Furthermore,in obese PCOS women,the higher levels of unsaturated fatty acids,sphingomyelins,diacylglycerol and triglyceride,and the lower levels of most phosphatidylcholine and lyso-phosphatidylcholine were found. This study demonstrated that lipidomics is a powerful tool for disease classification.
出处 《分析化学》 SCIE EI CAS CSCD 北大核心 2015年第10期1445-1451,共7页 Chinese Journal of Analytical Chemistry
基金 国家公益性行业专项基金资助项目(No.201107005)
关键词 多囊卵巢综合征 液相色谱-质谱联用 脂质组学 脂质代谢异常 Polycystic ovarian syndrome Liquid chromatography-mass spectrometry Lipidomics Abnormal lipid metabolism
作者简介 E-mail:xufangl217507@163.com E-mail:xj_zhaol@126.com.
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共引文献19

同被引文献278

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