Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries

Abstract

Importance: Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies.

Objective: To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB.

Design, setting, and participants: This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019.

Exposures: Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites.

Main outcomes and measures: The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation.

Results: Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways.

Conclusions and relevance: This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

Figure 1.. Study Overview

A, The 3 data sets (plasma cell-free ribonucleic acid [cfRNA] or transcriptomics, metabolomics, and proteomics) produced a number of different features and had a range of correlations among the measured features. The internal correlation between features from each data set was quantified using the number of principal components (PCs) needed to capture 90% variance (eg, the cf-RNA data set had the most features but was highly correlated internally; therefore, fewer PCs were needed). B, A 2-dimensional representation of all measurements demonstrates the correlation between subsets of urine metabolites and cfRNA detected in plasma as well as a limited number of plasma proteins.

Figure 2.. Prediction of Gestational Age (GA) at the Time of Sample Collection

A, A cross-validation strategy was used to simultaneously optimize the integrated model and test the performance of the model on previously unseen patients. Models built on all 3 modalities (transcriptomics, metabolomics, and proteomics) and the integrated model were statistically significantly correlated with GA at the time of sample collection (Bonferroni-adjusted Spearman correlation P < .05). B, The correlation between GA at the time of sample collection and the estimated values on the blinded samples are shown. The shaded area represents the 95% CI. C, The features correlated with the progression of pregnancy (Spearman correlation P < .05) are color-coded according to biological modality. FGF indicates fibroblast growth factor; IGSF3, immunoglobulin superfamily member 3; PAPP-A, pregnancy-associated plasma protein A; PGF, placental growth factor; and SIGLEC6, sialic acid binding Ig-like lectin 6.

Figure 3.. Predictive Modeling of Preterm Birth (PTB)

A, This receiver operating characteristic (ROC) curve analysis used each biological modality and the integrated approach. The mean area under the ROC curve and 95% CI for each modality were as follows: transcriptomics (AUROC, 0.73; 95% CI, 0.61-0.83), metabolomics (AUROC, 0.59; 95% CI, 0.47-0.72), proteomics (AUROC, 0.75; 95% CI, 0.64-0.85), and integrated (AUROC, 0.83; 95% CI, 0.72-0.91). B, Circle size is proportional to −log₁₀ (Wilcoxon) P value for discrimination between term pregnancies and PTBs. Top features included an inflammatory module (which included interleukin 6 [IL-6]; IL-1 receptor antagonist [IL-1RA], a regulatory member of the IL-1 family whose expression is induced IL-1β under inflammatory conditions; granulocyte colony-stimulating factor [G-CSF]; retinoic acid receptor responder protein 2 [RARRES2]; chemokine ligand 3 [CCL3]; angiopoietin-like 4 [ANGPTL4]; protein-arginine deiminase type II [PADI2]; and transferrin receptor [TfR]) and a metabolomic module (which was enriched for glutamine and glutamate metabolism [Fisher test for pathway enrichment analysis P < 4.4 × 10⁻⁹] and valine, leucine, and isoleucine biosynthesis pathways [P < 7.3 × 10⁻⁶]).