학술논문
Early prediction and longitudinal modeling of preeclampsia from multiomics.
Document Type
article
Author
Marić, Ivana; Contrepois, Kévin; Moufarrej, Mira; Stelzer, Ina; Feyaerts, Dorien; Han, Xiaoyuan; Tang, Andy; Stanley, Natalie; Wong, Ronald; Traber, Gavin; Ellenberger, Mathew; Chang, Alan; Fallahzadeh, Ramin; Nassar, Huda; Becker, Martin; Xenochristou, Maria; Espinosa, Camilo; De Francesco, Davide; Ghaemi, Mohammad; Costello, Elizabeth; Culos, Anthony; Ling, Xuefeng; Sylvester, Karl; Darmstadt, Gary; Winn, Virginia; Shaw, Gary; Relman, David; Quake, Stephen; Angst, Martin; Stevenson, David; Gaudilliere, Brice; Aghaeepour, Nima; Snyder, Michael
Source
Patterns. 3(12)
Subject
Language
Abstract
Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.