Early prediction and longitudinal modeling of preeclampsia from multiomics. Patterns (New York, N.Y.) Maric, I., Contrepois, K., Moufarrej, M. N., Stelzer, I. A., Feyaerts, D., Han, X., Tang, A., Stanley, N., Wong, R. J., Traber, G. M., Ellenberger, M., Chang, A. L., Fallahzadeh, R., Nassar, H., Becker, M., Xenochristou, M., Espinosa, C., De Francesco, D., Ghaemi, M. S., Costello, E. K., Culos, A., Ling, X. B., Sylvester, K. G., Darmstadt, G. L., Winn, V. D., Shaw, G. M., Relman, D. A., Quake, S. R., Angst, M. S., Snyder, M. P., Stevenson, D. K., Gaudilliere, B., Aghaeepour, N. 2022; 3 (12): 100655


Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16weeks 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.

View details for DOI 10.1016/j.patter.2022.100655

View details for PubMedID 36569558