期刊
HYPERTENSION
卷 61, 期 6, 页码 1281-+出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1161/HYPERTENSIONAHA.113.01168
关键词
mass spectrometry; preeclampsia; screening; selective reaction monitoring; sensitivity; specificity
资金
- New Enterprise Research Fund, Foundation for Research Science and Technology, New Zealand
- Health Research Council [04/198]
- Evelyn Bond Fund, Auckland District Health Board Charitable Trust
- Australia: Premier's Science and Research Fund, South Australian Government
- London: Guy's and St Thomas' Charity, United Kingdom
- Manchester: UK Biotechnology and Biological Sciences Research Council [GT084]
- UK National Health Services NEAT Grant [FSD025]
- University of Manchester Proof of Concept Funding
- Tommy's the Baby Charity
- NIHR
- Leeds: Cerebra, UK
- Cork, Ireland: Health Research Board, Ireland [CSA/2007/2]
- Chief Scientist Office
- Action Medical Research Endowment Fund
- NIHR Manchester Biomedical Research Center
- Pronota
- National Institutes of Health Research (NIHR) [FSD025] Funding Source: National Institutes of Health Research (NIHR)
Preeclampsia, a hypertensive pregnancy complication, is largely unpredictable in healthy nulliparous pregnant women. Accurate preeclampsia prediction in this population would transform antenatal care. To identify novel protein markers relevant to the prediction of preeclampsia, a 3-step mass spectrometric work flow was applied. On selection of candidate biomarkers, mostly from an unbiased discovery experiment (19 women), targeted quantitation was used to verify and validate candidate biomarkers in 2 independent cohorts from the SCOPE (SCreening fOr Pregnancy Endpoints) study. Candidate proteins were measured in plasma specimens collected at 19 to 21 weeks' gestation from 100 women who later developed preeclampsia and 200 women without preeclampsia recruited from Australia and New Zealand. Protein levels (n=25), age, and blood pressure were then analyzed using logistic regression to identify multimarker models (maximum 6 markers) that met predefined criteria: sensitivity >= 50% at 20% positive predictive value. These 44 algorithms were then tested in an independent European cohort (n=300) yielding 8 validated models. These 8 models detected 50% to 56% of preeclampsia cases in the training and validation sets; the detection rate for preterm preeclampsia cases was 80%. Validated models combine insulin-like growth factor acid labile subunit and soluble endoglin, supplemented with maximally 4 markers of placental growth factor, serine peptidase inhibitor Kunitz type 1, melanoma cell adhesion molecule, selenoprotein P, and blood pressure. Predictive performances were maintained when exchanging mass spectrometry measurements with ELISA measurements for insulin-like growth factor acid labile subunit. In conclusion, we demonstrated that biomarker combinations centered on insulin-like growth factor acid labile subunit have the potential to predict preeclampsia in healthy nulliparous women.
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