Authors

Ina A. Stelzer, Stanford University School of Medicine
Mohammad Sajjad Ghaemi, Stanford University School of Medicine
Xiaoyuan Han, University of the PacificFollow
Kazuo Ando, Stanford University School of Medicine
Julien J. Hédou, Stanford University School of Medicine
Dorien Feyaerts, Stanford University School of Medicine
Laura S. Peterson, Stanford University School of Medicine
Kristen K. Rumer, Stanford University School of Medicine
Eileen S. Tsai, Stanford University School of Medicine
Edward A. Ganio, Stanford University School of Medicine
Dyani K. Gaudillière, Stanford University School of Medicine
Amy S. Tsai, Stanford University School of Medicine
Benjamin Choisy, Stanford University School of Medicine
Lee P. Gaigne, Stanford University School of Medicine
Franck Verdonk, Stanford University School of Medicine
Danielle Jacobsen, Stanford University School of Medicine
Sonia Gavasso, Stanford University School of Medicine
Gavin M. Traber, Stanford University School of Medicine
Mathew Ellenberger, Stanford University School of Medicine
Natalie Stanley, Stanford University School of Medicine
Martin Becker, Stanford University School of Medicine
Anthony Culos, Stanford University School of Medicine
Ramin Fallahzadeh, Stanford University School of Medicine
Ronald J. Wong, Stanford University School of Medicine
Gary L. Darmstadt, Stanford University School of Medicine
Maurice L. Druzin, Stanford University School of Medicine
Virginia D. Winn, Stanford University School of Medicine
Ronald S. Gibbs, Stanford University School of Medicine
Xuefeng B. Ling, Stanford University School of Medicine

Document Type

Article

Publication Title

Science Translational Medicine

ISSN

1946-6234

Volume

13

Issue

592

DOI

10.1126/scitranslmed.abd9898

Publication Date

5-5-2021

Abstract

Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 × 10−40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 × 10−7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.

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