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

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Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries
Science Advances ( IF 13.6 ) Pub Date : 2023-05-24 , DOI: 10.1126/sciadv.ade7692
Camilo A Espinosa _{1,

2,

3} , Waqasuddin Khan ₄ , Rasheda Khanam ₅ , Sayan Das ₆ , Javairia Khalid ₄ , Jesmin Pervin ₇ , Margaret P Kasaro _{8,

9} , Kévin Contrepois ₁₀ , Alan L Chang _{1,

2,

3} , Thanaphong Phongpreecha _{1,

3,

11} , Basil Michael ₁₀ , Mathew Ellenberger ₁₀ , Usma Mehmood ₄ , Aneeta Hotwani ₄ , Ambreen Nizar ₄ , Furqan Kabir ₄ , Ronald J Wong ₂ , Martin Becker _{1,

2,

3} , Eloise Berson _{1,

3,

11} , Anthony Culos _{1,

2,

3,

12} , Davide De Francesco _{1,

2,

3} , Samson Mataraso _{1,

2,

3} , Neal Ravindra _{1,

2,

3} , Melan Thuraiappah _{1,

2,

3} , Maria Xenochristou _{1,

2,

3} , Ina A Stelzer ₁ , Ivana Marić ₂ , Arup Dutta ₆ , Rubhana Raqib ₁₃ , Salahuddin Ahmed ₁₄ , Sayedur Rahman ₁₄ , A S M Tarik Hasan ₁₄ , Said M Ali ₁₅ , Mohamed H Juma ₁₅ , Monjur Rahman ₇ , Shaki Aktar ₇ , Saikat Deb _{6,

15} , Joan T Price _{9,

16} , Paul H Wise ₂ , Virginia D Winn ₁₇ , Maurice L Druzin ₁₇ , Ronald S Gibbs ₁₇ , Gary L Darmstadt ₂ , Jeffrey C Murray ₁₈ , Jeffrey S A Stringer ₉ , Brice Gaudilliere _{1,

2} , Michael P Snyder ₁₀ , Martin S Angst ₁ , Anisur Rahman ₇ , Abdullah H Baqui ₅ , Fyezah Jehan ₄ , Muhammad Imran Nisar ₄ , Bellington Vwalika _{9,

16} , Sunil Sazawal _{6,

19} , Gary M Shaw ₂ , David K Stevenson ₂ , Nima Aghaeepour _{1,

2,

3}

Affiliation

Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan.
Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Centre for Public Health Kinetics, New Delhi, Delhi, India.
Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
University of North Carolina Global Projects Zambia, Lusaka, Zambia.
Department of Obstetrics and Gynecology, University of North Carolina School of Medicine, Chapel Hill, NC, USA.
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Department of Computer Science, Columbia University, New York, NY, USA.
International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
Projahnmo Research Foundation, Dhaka, Bangladesh.
Public Health Laboratory-Ivo de Carneri, Pemba, Zanzibar, Tanzania.
Department of Obstetrics and Gynaecology, University of Zambia School of Medicine, Lusaka, Zambia.
Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.
Department of Pediatrics, University of Iowa, Iowa City, IA, USA.
Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

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.

中文翻译：

与导致低收入和中等收入国家早产的孕产妇流行病学因素相关的多组学信号

早产 (PTB) 是五岁以下儿童死亡的主要原因，但其多种复杂病因阻碍了全面研究。先前已经描述了 PTB 与母体特征之间的流行病学关联。这项工作使用多组学分析和多变量建模来研究这些特征的生物学特征。在怀孕期间从五个地点的 13,841 名孕妇中收集了母体协变量。对来自 231 名参与者的血浆样本进行了分析，以生成蛋白质组学、代谢组学和脂质组学数据集。机器学习模型在预测 PTB (AUROC = 0.70)、交付时间 (r= 0.65), 产妇年龄 (r= 0.59), 重力 (r= 0.56）和体重指数（r= 0.81）。分娩时间生物学相关因素包括胎儿相关蛋白（例如，ALPP、AFP 和 PGF）和免疫蛋白（例如，PD-L1、CCL28 和 LIFR）。母亲年龄与胶原蛋白 COL9A1、妊娠与内皮 NOS 和炎症趋化因子 CXCL13 以及 BMI 与瘦素和结构蛋白 FABP4 呈负相关。这些结果提供了与 PTB 相关的流行病学因素的综合观点，并确定了影响该疾病的临床协变量的生物学特征。

更新日期：2023-05-24

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