Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries.
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Jehan Fyezah, Sazawal Sunil, Baqui Abdullah H, Nisar Muhammad Imran, Dhingra Usha, Khanam Rasheda, Ilyas Muhammad, Dutta Arup, Mitra Dipak K, Mehmood Usma, Deb Saikat, Mahmud Arif, Hotwani Aneeta, Ali Said Mohammed, Rahman Sayedur, Nizar Ambreen, Ame Shaali Makame, Moin Mamun Ibne, Muhammad Sajid, Chauhan Aishwarya, Begum Nazma, Khan Waqasuddin, Das Sayan, Ahmed Salahuddin, Hasan Tarik, Khalid Javairia, Rizvi Syed Jafar Raza, Juma Mohammed Hamad, Chowdhury Nabidul Haque, Kabir Furqan, Aftab Fahad, Quaiyum Abdul, Manu Alexander, Yoshida Sachiyo, Bahl Rajiv, Rahman Anisur, Pervin Jesmin, Winston Jennifer, Musonda Patrick, Stringer Jeffrey S A, Litch James A, Ghaemi Mohammad Sajjad, Moufarrej Mira N, Contrepois Kévin, Chen Songjie, Stelzer Ina A, Stanley Natalie, Chang Alan L, Hammad Ghaith Bany, Wong Ronald J, Liu Candace, Quaintance Cecele C, Culos Anthony, Espinosa Camilo, Xenochristou Maria, Becker Martin, Fallahzadeh Ramin, Ganio Edward, Tsai Amy S, Gaudilliere Dyani, Tsai Eileen S, Han Xiaoyuan, Ando Kazuo, Tingle Martha, Maric Ivana, Wise Paul H, Winn Virginia D, Druzin Maurice L, Gibbs Ronald S, Darmstadt Gary L, Murray Jeffrey C, Shaw Gary M, Stevenson David K, Snyder Michael P, Quake Stephen R, Angst Martin S, Gaudilliere Brice, Aghaeepour Nima, Alliance for Maternal and Newborn Health Improvement, the Global Alliance to Prevent Prematurity and Stillbirth, and the Prematurity Research Center at Stanford University
Аннотация
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.