Objective

To examine recurrent preterm birth and early term birth in women's initial and immediately subsequent pregnancies.

Methods

This retrospective cohort study included 163,889 women who delivered their first and second liveborn singleton neonates between 20 and 44 weeks of gestation in California from 2005 through 2011. Data from hospital discharge records and birth certificates were used for analyses. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression models adjusted for risk factors.

Results

Shorter gestational duration in the first pregnancy increased the risk of subsequent preterm birth (both early, before 32 weeks of gestation, and later, from 32 to 36 weeks of gestation) as well as early term birth (37-38 weeks of gestation). Compared with women with a prior term birth, women with a prior early preterm birth (before 32 weeks of gestation) were at the highest risk for a subsequent early preterm birth (58/935 [6.2%] compared with 367/118,505 [0.3%], adjusted OR 23.3, 95% CI 17.2-31.7). Women with a prior early term birth had more than a twofold increased risk for subsequent preterm birth (before 32 weeks of gestation: 171/36,017 [0.5%], adjusted OR 2.0, 95% CI 1.6-2.3; from 32 to 36 weeks of gestation: 2,086/36,017 [6.8%], adjusted OR 3.0, 95% CI 2.9-3.2) or early term birth (13,582/36,017 [37.7%], adjusted OR 2.2, 95% CI 2.2-2.3).

Conclusion

Both preterm birth and early term birth are associated with these outcomes in a subsequent pregnancy. Increased clinical attention and research efforts may benefit from a focus on women with a prior early term birth as well as those with prior preterm birth.

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.

Motivation

Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.

Results

We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.

Availability and implementation

Datasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.

Supplementary information

Supplementary data are available at Bioinformatics online.