Authors

Ivana Maric, Stanford University School of MedicineFollow
Kevin Contrepois, Stanford University School of Medicine
Mira Moufarrej, Stanford University School of Medicine
Ina A. Stelzer, Stanford University School of Medicine
Dorien Feyaerts, Stanford University School of Medicine
Xiaoyuan Han, University of the PacificFollow
Andy Tang, Stanford University School of Medicine
Natalie Stanley, Stanford University School of Medicine
Ronald J. Wong, Stanford University School of Medicine
Gavin M. Traber, Stanford University School of Medicine
Mathew Ellenberger, Stanford University School of Medicine
Alan Chang, Stanford University School of Medicine
Ramin Fallahzadeh, Stanford University School of Medicine
Huda Nassar, Stanford University School of Medicine
Martin Becker, Stanford University School of Medicine
Maria Xenochristou, Stanford University School of Medicine
Camilo Espinosa, Stanford University School of Medicine
Davide De Francesco, Stanford University School of Medicine
Mohammad Sajjad Ghaemi, Stanford University School of Medicine
Elizabeth Costello, Stanford University School of Medicine
Anthony Culos, Stanford University School of Medicine
Xuefend B. Ling, Stanford University School of Medicine
Karl G. Sylvester, Stanford University School of Medicine
Gary L. Darmstadt, Stanford University School of Medicine
Virginia D. Winn, Stanford University School of Medicine
Gary M. Shaw, Stanford University School of Medicine
David A. Relman, Stanford University School of Medicine
Stephen R. Quake, Stanford University School of Medicine
Martin S. Angst, Stanford University School of Medicine
Michael P. Snyder, Stanford University School of Medicine
David K. Stevenson, Stanford University School of Medicine
Brice Gaudilliere, Stanford University
Nima Aghaeepour, Stanford University School of Medicine

Document Type

Article

Publication Date

1-13-2021

Abstract

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women's physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology, can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI):[0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC= 0.87, 95% CI:[0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC= 0.90, 95% CI:[0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia.

Comments

This is an unpublished pre-print that has not undergone peer review. It should not be considered conclusive, used to inform clinical practice, or referenced by the media as validated information.

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