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. 2020 Dec 1;3(12):e2029655.
doi: 10.1001/jamanetworkopen.2020.29655.

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

Fyezah Jehan  1 Sunil Sazawal  2 Abdullah H Baqui  3 Muhammad Imran Nisar  1 Usha Dhingra  2 Rasheda Khanam  3 Muhammad Ilyas  1 Arup Dutta  2 Dipak K Mitra  3 Usma Mehmood  1 Saikat Deb  2   4 Arif Mahmud  3 Aneeta Hotwani  1 Said Mohammed Ali  4 Sayedur Rahman  3 Ambreen Nizar  1 Shaali Makame Ame  4 Mamun Ibne Moin  3 Sajid Muhammad  1 Aishwarya Chauhan  2 Nazma Begum  3 Waqasuddin Khan  1 Sayan Das  2 Salahuddin Ahmed  3 Tarik Hasan  3 Javairia Khalid  1 Syed Jafar Raza Rizvi  3 Mohammed Hamad Juma  4 Nabidul Haque Chowdhury  3 Furqan Kabir  1 Fahad Aftab  2 Abdul Quaiyum  3 Alexander Manu  5 Sachiyo Yoshida  5 Rajiv Bahl  5 Anisur Rahman  6 Jesmin Pervin  7 Jennifer Winston  8 Patrick Musonda  9 Jeffrey S A Stringer  8 James A Litch  10 Mohammad Sajjad Ghaemi  11   12 Mira N Moufarrej  13 Kévin Contrepois  14 Songjie Chen  14 Ina A Stelzer  11 Natalie Stanley  11 Alan L Chang  11 Ghaith Bany Hammad  11 Ronald J Wong  15 Candace Liu  11 Cecele C Quaintance  16 Anthony Culos  11 Camilo Espinosa  11 Maria Xenochristou  11 Martin Becker  11 Ramin Fallahzadeh  11 Edward Ganio  11 Amy S Tsai  11 Dyani Gaudilliere  11 Eileen S Tsai  11 Xiaoyuan Han  11 Kazuo Ando  11 Martha Tingle  11 Ivana Maric  11   15 Paul H Wise  15 Virginia D Winn  17 Maurice L Druzin  17 Ronald S Gibbs  17 Gary L Darmstadt  15 Jeffrey C Murray  18 Gary M Shaw  15 David K Stevenson  15 Michael P Snyder  14 Stephen R Quake  13 Martin S Angst  11 Brice Gaudilliere  11   15 Nima Aghaeepour  11   15   19 Alliance for Maternal and Newborn Health Improvement, the Global Alliance to Prevent Prematurity and Stillbirth, and the Prematurity Research Center at Stanford University
Affiliations

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

Fyezah Jehan et al. JAMA Netw Open. .

Erratum in

  • Errors in the eTable.
    [No authors listed] [No authors listed] JAMA Netw Open. 2021 Feb 1;4(2):e210399. doi: 10.1001/jamanetworkopen.2021.0399. JAMA Netw Open. 2021. PMID: 33576811 Free PMC article. No abstract available.

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.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Jehan reported receiving grants from the Bill & Melinda Gates Foundation (BMGF), World Health Organization (WHO), and PATH as well as grants and nonfinancial support from Emory University during the conduct of the study. Dr Baqui reported that his employer Johns Hopkins University received a biorepository grant from the BMGF under which specimens were collected for this research. Dr Nisar reported receiving grants from the BMGF during the conduct of the study and grants from the BMGF, Vital Pakistan Trust, and the WHO outside the submitted work. Dr Dutta reported receiving grants from the BMGF during the conduct of the study. Dr Deb reported receiving grants from the BMGF during the conduct of the study. Dr Ali reported receiving grants from the BMGF during the conduct of the study. Dr Chauhan reported receiving grants from the BMGF during the conduct of the study. Dr Aftab reported receiving grants from the BMGF during the conduct of the study. Dr Bahl reported receiving grants from the BMGF during the conduct of the study. Dr Stringer reported receiving grants from the BMGF and the National Institutes of Health (NIH) during the conduct of the study. Dr Litch reported receiving grants from the BMGF during the conduct of the study. Ms Moufarrej reported receiving personal fees from Nemours, Stanford University, and Stanford BioX Bowes Fellowship outside the submitted work. Dr Contrepois reported holding a pending patent to Prediction of Gestational Age Using Urine Metabolites. Dr Stelzer reported receiving grants from German Research Foundation during the conduct of the study. Dr Quaintance reported receiving grants from the BMGF and the March of Dimes Foundation during the conduct of the study. Dr Murray reported being a former employee of the BMGF during the conduct of the study. Dr Stevenson reported receiving grants from the BMGF during the conduct of the study. Dr Snyder reported receiving grants from the BMGF during the conduct of the study, receiving nonfinancial support from MirVie Shareholder outside the submitted work, and holding a patent based on this work that will be submitted. Dr Quake reported being a shareholder, consultant, and board member of MirVie and holding a patent to Cell Free RNA Analysis of Preterm Birth that is licensed to MirVie. Dr Angst reported receiving grants from the BMGF and the March of Dimes Foundation during the conduct of the study as well as holding a patent to Onset of Labor. Dr Aghaeepour reported receiving grants from the BMGF, NIH, Burroughs Wellcome Fund, Robertson Foundation, and March of Dimes Foundation during the conduct of the study. No other disclosures were reported.

Figures

Figure 1.
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.
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.
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]).

References

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