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迟发型子痫前期的预测:一项纵向蛋白质组学研究的结果

The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study.

作者信息

Erez Offer, Romero Roberto, Maymon Eli, Chaemsaithong Piya, Done Bogdan, Pacora Percy, Panaitescu Bogdan, Chaiworapongsa Tinnakorn, Hassan Sonia S, Tarca Adi L

机构信息

Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, and Detroit, Michigan, United States of America.

Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, United States of America.

出版信息

PLoS One. 2017 Jul 24;12(7):e0181468. doi: 10.1371/journal.pone.0181468. eCollection 2017.

Abstract

BACKGROUND

Late-onset preeclampsia is the most prevalent phenotype of this syndrome; nevertheless, only a few biomarkers for its early diagnosis have been reported. We sought to correct this deficiency using a high through-put proteomic platform.

METHODS

A case-control longitudinal study was conducted, including 90 patients with normal pregnancies and 76 patients with late-onset preeclampsia (diagnosed at ≥34 weeks of gestation). Maternal plasma samples were collected throughout gestation (normal pregnancy: 2-6 samples per patient, median of 2; late-onset preeclampsia: 2-6, median of 5). The abundance of 1,125 proteins was measured using an aptamers-based proteomics technique. Protein abundance in normal pregnancies was modeled using linear mixed-effects models to estimate mean abundance as a function of gestational age. Data was then expressed as multiples of-the-mean (MoM) values in normal pregnancies. Multi-marker prediction models were built using data from one of five gestational age intervals (8-16, 16.1-22, 22.1-28, 28.1-32, 32.1-36 weeks of gestation). The predictive performance of the best combination of proteins was compared to placental growth factor (PIGF) using bootstrap.

RESULTS

  1. At 8-16 weeks of gestation, the best prediction model included only one protein, matrix metalloproteinase 7 (MMP-7), that had a sensitivity of 69% at a false positive rate (FPR) of 20% (AUC = 0.76); 2) at 16.1-22 weeks of gestation, MMP-7 was the single best predictor of late-onset preeclampsia with a sensitivity of 70% at a FPR of 20% (AUC = 0.82); 3) after 22 weeks of gestation, PlGF was the best predictor of late-onset preeclampsia, identifying 1/3 to 1/2 of the patients destined to develop this syndrome (FPR = 20%); 4) 36 proteins were associated with late-onset preeclampsia in at least one interval of gestation (after adjustment for covariates); 5) several biological processes, such as positive regulation of vascular endothelial growth factor receptor signaling pathway, were perturbed; and 6) from 22.1 weeks of gestation onward, the set of proteins most predictive of severe preeclampsia was different from the set most predictive of the mild form of this syndrome.

CONCLUSIONS

Elevated MMP-7 early in gestation (8-22 weeks) and low PlGF later in gestation (after 22 weeks) are the strongest predictors for the subsequent development of late-onset preeclampsia, suggesting that the optimal identification of patients at risk may involve a two-step diagnostic process.

摘要

背景

晚发型子痫前期是该综合征最常见的表型;然而,仅有少数可用于其早期诊断的生物标志物被报道。我们试图利用高通量蛋白质组学平台来弥补这一不足。

方法

进行了一项病例对照纵向研究,包括90例正常妊娠患者和76例晚发型子痫前期患者(诊断孕周≥34周)。在整个孕期收集孕妇血浆样本(正常妊娠:每位患者2 - 6份样本,中位数为2份;晚发型子痫前期:2 - 6份,中位数为5份)。使用基于适配体的蛋白质组学技术测量1125种蛋白质的丰度。采用线性混合效应模型对正常妊娠中的蛋白质丰度进行建模,以估计作为孕周函数的平均丰度。然后将数据表示为正常妊娠中均值倍数(MoM)值。使用来自五个孕周区间之一(妊娠8 - 16周、16.1 - 22周、22.1 - 28周、28.1 - 32周、32.1 - 36周)的数据建立多标志物预测模型。使用自助法将最佳蛋白质组合的预测性能与胎盘生长因子(PIGF)进行比较。

结果

1)在妊娠8 - 16周时,最佳预测模型仅包含一种蛋白质,即基质金属蛋白酶7(MMP - 7),在假阳性率(FPR)为20%时,其灵敏度为69%(AUC = 0.76);2)在妊娠16.1 - 22周时,MMP - 7是晚发型子痫前期的最佳单一预测指标,在FPR为20%时,灵敏度为70%(AUC = 0.82);3)妊娠22周后,PIGF是晚发型子痫前期的最佳预测指标,可识别出1/3至1/2注定会发生该综合征的患者(FPR = 20%);4)在至少一个孕周区间内,36种蛋白质与晚发型子痫前期相关(在对协变量进行调整后);5)若干生物学过程,如血管内皮生长因子受体信号通路的正调控受到干扰;6)从妊娠22.1周起,最能预测重度子痫前期的蛋白质组与最能预测该综合征轻度形式的蛋白质组不同。

结论

妊娠早期(8 - 22周)MMP - 7升高和妊娠后期(22周后)PIGF降低是晚发型子痫前期后续发生的最强预测指标,这表明对高危患者的最佳识别可能涉及两步诊断过程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4842/5524331/3b9d606f1dcc/pone.0181468.g001.jpg

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