New biomarkers for the detection of fetal death derived from large-scale proteomic analysis of maternal plasma.

backgroundNormal pregnancy involves the modulation of thousands of maternal plasma proteins, and protein values not within the normal range may indicate the development of adverse pregnancy outcomes. A decrease in placental growth factor and an increase in soluble fms-like tyrosine kinase 1 in maternal plasma were shown to be associated with fetal death at the time of diagnosis and to predict this devastating pregnancy outcome at 24 to 28 weeks of gestation. However, these proteomic dysregulations are also present in other obstetrical syndromes, and more specific and sensitive biomarkers are needed to implement preventive strategies.

objectiveThis study aimed to identify candidate protein biomarkers that can improve the prediction of fetal death relative to placental growth factor and soluble fms-like tyrosine kinase 1. STUDY

designThis retrospective case-control study included 38 patients who experienced fetal death (cases) and 23 patients with uncomplicated pregnancies (controls). Plasma samples were collected at the time of diagnosis (20-41 weeks of gestation) from cases and during routine care from gestational age-matched controls. An aptamer-based multiplex assay was used to measure the abundance of >7000 protein analytes. Differential protein abundance was assessed using linear models with adjustment for gestational age at sample collection. Significance was inferred using a moderated t test adjusted P value of <.1 and a fold change of >1.25. Hypergeometric tests were performed to identify gene ontology biological processes enriched among proteins with significant changes in abundance. Random forest models were trained and evaluated via cross-validation to distinguish between fetal death cases and controls and to pinpoint the most salient predictors.

resultsAmong the 7146 protein assays tested, 97 assays (1.4%) corresponding to 87 unique proteins differed significantly in abundance between fetal death cases and controls: 63 of 87 proteins (72%) were less abundant in fetal death cases, and 24 of 87 proteins (26%) were more abundant in fetal death cases. Dysregulated proteins were involved in pregnancy-related processes, such as angiogenesis and lactation. Random forest models effectively differentiated fetal death cases from controls, achieving an area under the receiver operating characteristic curve of 72% for the combination of placental growth factor and soluble fms-like tyrosine kinase 1, which increased to 86% when up to 50 additional proteins were included in the models (Delong test: P=.004). In addition, the point estimate of sensitivity increased from 53% to 74% (false-positive rate of approximately 10% for both). Glycoprotein hormones alpha chain (CGA), DnaJ homolog subfamily B member 9 (DNAJB9), and DNA-directed RNA polymerase III subunit RPC10 (POLR3K) emerged as the top 3 candidates to improve discrimination relative to placental growth factor and soluble fms-like tyrosine kinase 1. The significant proteomic changes in a subset of fetal death cases diagnosed first with preeclampsia relative to controls were highly correlated (r=0.78; P<.001) with those reported in late preeclampsia cases leading to live births. On average, for each 2-fold change in protein abundance in late preeclampsia leading to live birth, there was an 8.6-fold change in preeclampsia leading to fetal death. Despite this overall correlation, transcobalamin 2, glucose-6-phosphate 1-dehydrogenase, and hepcidin, among others, demonstrated dysregulation only in preeclampsia leading to fetal death, suggesting both shared and distinct pathways perturbed in the 2 syndromes.

conclusionOur findings suggest that new maternal plasma proteins improve the discrimination of fetal death from controls relative to known biomarkers and that, although the signatures of fetal death and of preeclampsia are correlated, fetal death not only represents a much heightened disease state but also involves distinct perturbed pathways. Future studies are needed to determine whether the biomarkers can predict fetal death.