Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries-Reference-Cited by-同舟云学术

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries

Published:2023-05-26 Issue:21 Volume:9 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Espinosa Camilo A.¹²³^ORCID,Khan Waqasuddin⁴^ORCID,Khanam Rasheda⁵^ORCID,Das Sayan⁶^ORCID,Khalid Javairia⁴,Pervin Jesmin⁷^ORCID,Kasaro Margaret P.⁸⁹^ORCID,Contrepois Kévin¹⁰^ORCID,Chang Alan L.¹²³^ORCID,Phongpreecha Thanaphong¹³¹¹^ORCID,Michael Basil¹⁰^ORCID,Ellenberger Mathew¹⁰^ORCID,Mehmood Usma⁴,Hotwani Aneeta⁴,Nizar Ambreen⁴,Kabir Furqan⁴,Wong Ronald J.²^ORCID,Becker Martin¹²³^ORCID,Berson Eloise¹³¹¹^ORCID,Culos Anthony¹²³¹²^ORCID,De Francesco Davide¹²³^ORCID,Mataraso Samson¹²³^ORCID,Ravindra Neal¹²³^ORCID,Thuraiappah Melan¹²³^ORCID,Xenochristou Maria¹²³,Stelzer Ina A.¹^ORCID,Marić Ivana²^ORCID,Dutta Arup⁶^ORCID,Raqib Rubhana¹³^ORCID,Ahmed Salahuddin¹⁴^ORCID,Rahman Sayedur¹⁴^ORCID,Hasan A. S. M. Tarik¹⁴,Ali Said M.¹⁵,Juma Mohamed H.¹⁵,Rahman Monjur⁷^ORCID,Aktar Shaki⁷^ORCID,Deb Saikat⁶¹⁵^ORCID,Price Joan T.⁹¹⁶^ORCID,Wise Paul H.²,Winn Virginia D.¹⁷^ORCID,Druzin Maurice L.¹⁷^ORCID,Gibbs Ronald S.¹⁷^ORCID,Darmstadt Gary L.²^ORCID,Murray Jeffrey C.¹⁸,Stringer Jeffrey S. A.⁹^ORCID,Gaudilliere Brice¹²^ORCID,Snyder Michael P.¹⁰^ORCID,Angst Martin S.¹^ORCID,Rahman Anisur⁷^ORCID,Baqui Abdullah H.⁵^ORCID,Jehan Fyezah⁴^ORCID,Nisar Muhammad Imran⁴^ORCID,Vwalika Bellington⁹¹⁶^ORCID,Sazawal Sunil⁶¹⁹,Shaw Gary M.²^ORCID,Stevenson David K.²,Aghaeepour Nima¹²³^ORCID

Affiliation:

1. Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.

2. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.

3. Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.

4. Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan.

5. Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

6. Centre for Public Health Kinetics, New Delhi, Delhi, India.

7. Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.

8. University of North Carolina Global Projects Zambia, Lusaka, Zambia.

9. Department of Obstetrics and Gynecology, University of North Carolina School of Medicine, Chapel Hill, NC, USA.

10. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.

11. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.

12. Department of Computer Science, Columbia University, New York, NY, USA.

13. International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.

14. Projahnmo Research Foundation, Dhaka, Bangladesh.

15. Public Health Laboratory—Ivo de Carneri, Pemba, Zanzibar, Tanzania.

16. Department of Obstetrics and Gynaecology, University of Zambia School of Medicine, Lusaka, Zambia.

17. Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.

18. Department of Pediatrics, University of Iowa, Iowa City, IA, USA.

19. Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

Abstract

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery ( r = 0.65), maternal age ( r = 0.59), gravidity ( r = 0.56), and BMI ( r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Link

https://www.science.org/doi/pdf/10.1126/sciadv.ade7692

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