Related Experiment Video
Updated: Nov 25, 2025
Transcutaneous Microcirculatory Imaging in Preterm Neonates
Published on: December 31, 2015
Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries
Fyezah Jehan1, Sunil Sazawal2, Abdullah H Baqui3
1Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Insights
Combining multiple biological data sets improves prediction of preterm birth (PTB). This approach may lead to new tests for early detection and prevention of PTB, a major global health concern.
Area of Science:
- Reproductive biology
- Genomics and proteomics
- Biomarker discovery
Background:
- Preterm birth (PTB) is a leading cause of neonatal mortality worldwide.
- Identifying early biological markers for PTB is crucial for intervention.
Purpose of the Study:
- To investigate transcriptomics, proteomics, and metabolomics for early PTB detection.
- To develop a generalizable biological model for PTB prediction.
Main Methods:
- Analysis of plasma and urine samples from pregnant women in 5 LMIC cohorts.
- Utilized transcriptomics, proteomics, and metabolomics profiling.
- Applied machine learning for integrative data analysis.
Main Results:
- An integrated model combining omics data achieved an AUROC of 0.83 for PTB prediction.
- Key predictors included inflammatory markers and specific metabolic pathways.
- Combined omics data significantly improved predictive accuracy over individual modalities.
Conclusions:
- Biological adaptations in pregnancy follow a generalizable model across diverse populations.
- Integrating multi-omics data with machine learning enhances PTB prediction accuracy.
- This approach is promising for developing predictive tests and interventions for PTB.
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.
