Nobakht M Gh B Fatemeh
a Department of Basic Medical Sciences , Neyshabur University of Medical Sciences , Neyshabur , Iran.
Syst Biol Reprod Med. 2018 Oct;64(5):324-339. doi: 10.1080/19396368.2018.1482968. Epub 2018 Jul 2.
Preeclampsia is a multifactorial disorder defined by hypertension and increased urinary protein excretion during pregnancy. It is a significant cause of maternal and neonatal deaths worldwide. Despite various research efforts to clarify pathogenies of preeclampsia and predict this disease before beginning of symptoms, the pathogenesis of preeclampsia is unclear. Early prediction and diagnosis of women at risk of preeclampsia has not markedly improved. Therefore, the objective of this study was to perform a review on metabolomic articles assessing predictive and diagnostic biomarkers of preeclampsia. Four electronic databases including PubMed/Medline, Web of Science, Sciencedirect, and Scopus were searched to identify studies of preeclampsia in humans using metabolomics from inception to March 2018. Twenty-one articles in a variety of biological specimens and analytical platforms were included in the present review. Metabolite profiles may assist in the diagnosis of preeclampsia and discrimination of its subtypes. Lipids and their related metabolites were the most generally detected metabolites. Although metabolomic biomarkers of preeclampsia are not routinely used, this review suggests that metabolomics has the potential to be developed into a clinical tool for preeclampsia diagnosis and could contribute to an improved understanding of disease mechanisms.
PE: preeclampsia; sFlt-1: soluble FMS-like tyrosine kinase-1; PlGF: placental growth factor; GC-MS: gas chromatography-mass spectrometry; LC-MS: liquid chromatography-mass spectrometry; NMR: nuclear magnetic resonance spectroscopy; HMDB: human metabolome database; RCT: randomized control trial; e-PE: early-onset PE; l-PE: late-onset PE; PLS-DA: partial least-squares-discriminant analysis; CRL: crown-rump length; UtPI: uterine artery Doppler pulsatility index; BMI: body mass index; MAP: mean arterial pressure; OS: oxidative stress; PAPPA: plasma protein A; FTIR: Fourier transform infrared; BCAA: branched chain amino acids; Arg: arginine; NO: nitric oxide.
子痫前期是一种多因素疾病,其定义为孕期高血压和尿蛋白排泄增加。它是全球孕产妇和新生儿死亡的重要原因。尽管为阐明子痫前期的发病机制并在症状出现前预测该疾病进行了各种研究,但子痫前期的发病机制仍不清楚。子痫前期高危女性的早期预测和诊断并未得到显著改善。因此,本研究的目的是对评估子痫前期预测和诊断生物标志物的代谢组学文章进行综述。检索了包括PubMed/Medline、Web of Science、Sciencedirect和Scopus在内的四个电子数据库,以识别从开始到2018年3月使用代谢组学对人类子痫前期进行的研究。本综述纳入了21篇关于各种生物标本和分析平台的文章。代谢物谱可能有助于子痫前期的诊断及其亚型的鉴别。脂质及其相关代谢物是最常检测到的代谢物。尽管子痫前期的代谢组学生物标志物尚未常规使用,但本综述表明,代谢组学有潜力发展成为子痫前期诊断的临床工具,并有助于更好地理解疾病机制。
PE:子痫前期;sFlt-1:可溶性FMS样酪氨酸激酶-1;PlGF:胎盘生长因子;GC-MS:气相色谱-质谱联用仪;LC-MS:液相色谱-质谱联用仪;NMR:核磁共振波谱仪;HMDB:人类代谢组数据库;RCT:随机对照试验;e-PE:早发型子痫前期;l-PE:晚发型子痫前期;PLS-DA:偏最小二乘判别分析;CRL:顶臀长度;UtPI:子宫动脉多普勒搏动指数;BMI:体重指数;MAP:平均动脉压;OS:氧化应激;PAPPA:血浆蛋白A;FTIR:傅里叶变换红外光谱;BCAA:支链氨基酸;Arg:精氨酸;NO:一氧化氮。