4.1 Article

Early prediction and longitudinal modeling of preeclampsia from multiomics

Journal

PATTERNS
Volume 3, Issue 12, Pages -

Publisher

CELL PRESS
DOI: 10.1016/j.patter.2022.100655

Keywords

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Funding

  1. March of Dimes Prematurity Research Center at Stanford University School of Medicine, Stanford Maternal & Child Health Research Institute
  2. Christopher Hess Research Fund
  3. National Institutes of Health [5RM1HG00773507]
  4. Burroughs Wellcome Fund
  5. Alfred E. Mann Foundation
  6. Bill & Melinda Gates Foundation [OPP1112382, OPP1113682, INV037517]
  7. Thomas C. and Joan M. Merigan Endowment at Stanford University
  8. Chan Zuckerberg Biohub Microbiome Initiative
  9. Bill and Melinda Gates Foundation [OPP1112382] Funding Source: Bill and Melinda Gates Foundation

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We developed machine-learning models to predict preeclampsia during early pregnancy using omics datasets. Our model based on urine metabolites achieved high accuracy and was validated on an independent cohort. Integration of multiomics data and immune cytometry data revealed novel associations and improved prediction accuracy. These findings provide a basis for a simple, early diagnostic test for preeclampsia.
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 predictionmodel 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.

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