Dataset Information

The prediction of early preeclampsia: Results from a longitudinal proteomics study.

ABSTRACT:

Objectives

To identify maternal plasma protein markers for early preeclampsia (delivery <34 weeks of gestation) and to determine whether the prediction performance is affected by disease severity and presence of placental lesions consistent with maternal vascular malperfusion (MVM) among cases.

Study design

This longitudinal case-control study included 90 patients with a normal pregnancy and 33 patients with early preeclampsia. Two to six maternal plasma samples were collected throughout gestation from each woman. The abundance of 1,125 proteins was measured using high-affinity aptamer-based proteomic assays, and data were modeled using linear mixed-effects models. After data transformation into multiples of the mean values for gestational age, parsimonious linear discriminant analysis risk models were fit for each gestational-age interval (8-16, 16.1-22, 22.1-28, 28.1-32 weeks). Proteomic profiles of early preeclampsia cases were also compared to those of a combined set of controls and late preeclampsia cases (n = 76) reported previously. Prediction performance was estimated via bootstrap.

Results

We found that 1) multi-protein models at 16.1-22 weeks of gestation predicted early preeclampsia with a sensitivity of 71% at a false-positive rate (FPR) of 10%. High abundance of matrix metalloproteinase-7 and glycoprotein IIbIIIa complex were the most reliable predictors at this gestational age; 2) at 22.1-28 weeks of gestation, lower abundance of placental growth factor (PlGF) and vascular endothelial growth factor A, isoform 121 (VEGF-121), as well as elevated sialic acid binding immunoglobulin-like lectin 6 (siglec-6) and activin-A, were the best predictors of the subsequent development of early preeclampsia (81% sensitivity, FPR = 10%); 3) at 28.1-32 weeks of gestation, the sensitivity of multi-protein models was 85% (FPR = 10%) with the best predictors being activated leukocyte cell adhesion molecule, siglec-6, and VEGF-121; 4) the increase in siglec-6, activin-A, and VEGF-121 at 22.1-28 weeks of gestation differentiated women who subsequently developed early preeclampsia from those who had a normal pregnancy or developed late preeclampsia (sensitivity 77%, FPR = 10%); 5) the sensitivity of risk models was higher for early preeclampsia with placental MVM lesions than for the entire early preeclampsia group (90% versus 71% at 16.1-22 weeks; 87% versus 81% at 22.1-28 weeks; and 90% versus 85% at 28.1-32 weeks, all FPR = 10%); and 6) the sensitivity of prediction models was higher for severe early preeclampsia than for the entire early preeclampsia group (84% versus 71% at 16.1-22 weeks).

Conclusion

We have presented herein a catalogue of proteome changes in maternal plasma proteome that precede the diagnosis of preeclampsia and can distinguish among early and late phenotypes. The sensitivity of maternal plasma protein models for early preeclampsia is higher in women with underlying vascular placental disease and in those with a severe phenotype.

SUBMITTER: Tarca AL

PROVIDER: S-EPMC6548389 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

The prediction of early preeclampsia: Results from a longitudinal proteomics study.

Tarca Adi L AL Romero Roberto R Benshalom-Tirosh Neta N Than Nandor Gabor NG Gudicha Dereje W DW Done Bogdan B Pacora Percy P Chaiworapongsa Tinnakorn T Panaitescu Bogdan B Tirosh Dan D Gomez-Lopez Nardhy N Draghici Sorin S Hassan Sonia S SS Erez Offer O

PloS one 20190604 6

<h4>Objectives</h4>To identify maternal plasma protein markers for early preeclampsia (delivery <34 weeks of gestation) and to determine whether the prediction performance is affected by disease severity and presence of placental lesions consistent with maternal vascular malperfusion (MVM) among cases.<h4>Study design</h4>This longitudinal case-control study included 90 patients with a normal pregnancy and 33 patients with early preeclampsia. Two to six maternal plasma samples were collected thr ...[more]

PMID: 31163045

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Project description:BackgroundLate-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.MethodsA 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.Results1) 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.ConclusionsElevated 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.

Project description:Preeclampsia (PE) is a hypertensive complication affecting 8-10% of US pregnancies annually. While there is no cure for PE, aspirin may reduce complications for those at high risk for PE. Furthermore, PE disproportionately affects racial minorities, with a higher burden of morbidity and mortality. Previous studies have shown early prediction of PE would allow for prevention. We approached the prediction of PE using a new method based on a cost-sensitive deep neural network (CSDNN) by considering the severe imbalance and sparse nature of the data, as well as racial disparities. We validated our model using large extant rich data sources that represent a diverse cohort of minority populations in the US. These include Texas Public Use Data Files (PUDF), Oklahoma PUDF, and the Magee Obstetric Medical and Infant (MOMI) databases. We identified the most influential clinical and demographic features (predictor variables) relevant to PE for both general populations and smaller racial groups. We also investigated the effectiveness of multiple network architectures using three hyperparameter optimization algorithms: Bayesian optimization, Hyperband, and random search. Our proposed models equipped with focal loss function yield superior and reliable prediction performance compared with the state-of-the-art techniques with an average area under the curve (AUC) of 66.3% and 63.5% for the Texas and Oklahoma PUDF respectively, while the CSDNN model with weighted cross-entropy loss function outperforms with an AUC of 76.5% for the MOMI data. Furthermore, our CSDNN model equipped with focal loss function leads to an AUC of 66.7% for Texas African American and 57.1% for Native American. The best results are obtained with 62.3% AUC with CSDNN with weighted cross-entropy loss function for Oklahoma African American, 58% AUC with DNN and balanced batch for Oklahoma Native American, and 72.4% AUC using either CSDNN with weighted cross-entropy loss function or CSDNN with focal loss with balanced batch method for MOMI African American dataset. Our results provide the first evidence of the predictive power of clinical databases for PE prediction among minority populations.

Project description:IntroductionPreeclampsia is associated with maternal metabolic disturbances, but longitudinal studies with comprehensive metabolic profiling are lacking. We aimed to determine metabolic profiles across gestation in women who developed preeclampsia compared with women with healthy pregnancies. We also explored the respective effects of body mass index (BMI) and preeclampsia on various metabolic measures.Material and methodsWe measured 91 metabolites by high-throughput nuclear magnetic resonance spectroscopy at four time points (visits) during pregnancy (weeks 14-16, 22-24, 30-32 and 36-38). Samples were taken from a Norwegian pregnancy cohort. We fitted a linear regression model for each metabolic measure to compare women who developed preeclampsia (n = 38) and healthy controls (n = 70).ResultsAmong women who developed preeclampsia, 92% gave birth after 34 weeks of gestation. Compared to women with healthy pregnancies, women who developed preeclampsia had higher levels of several lipid-related metabolites at visit 1, whereas fewer differences were observed at visit 2. At visit 3, the pattern from visit 1 reappeared. At visit 4 the differences were larger in most subgroups of very-low-density lipoprotein particles, the smallest high-density lipoprotein, total lipids and triglycerides. Total fatty acids were also increased, of which monounsaturated fatty acids and saturated fatty acids showed more pronounced differences. Concentration of glycine tended to be lower in pregnancies with preeclampsia until visit 3, although this was not significant after correction for multiple testing. After adjustment for age, BMI, parity and gestational weight gain, all significant differences were attenuated at visits 1 and 2. The estimates were less affected by adjustment at visits 3 and 4.ConclusionsIn early pregnancy, the metabolic differences between preeclamptic and healthy pregnancies were primarily driven by maternal BMI, probably representing the women's pre-pregnancy metabolic status. In early third trimester, several weeks before clinical manifestation, the differences were less influenced by BMI, indicating preeclampsia-specific changes. Near term, women with preeclampsia developed an atherogenic metabolic profile, including elevated total lipids, very-low-density lipoprotein, triglycerides, and total fatty acids.

Dataset Information

The prediction of early preeclampsia: Results from a longitudinal proteomics study.

Objectives

Study design

Results

Conclusion

Publications

The prediction of early preeclampsia: Results from a longitudinal proteomics study.

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