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Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth.
Tarca, Adi L; Pataki, Bálint Ármin; Romero, Roberto; Sirota, Marina; Guan, Yuanfang; Kutum, Rintu; Gomez-Lopez, Nardhy; Done, Bogdan; Bhatti, Gaurav; Yu, Thomas; Andreoletti, Gaia; Chaiworapongsa, Tinnakorn; Hassan, Sonia S; Hsu, Chaur-Dong; Aghaeepour, Nima; Stolovitzky, Gustavo; Csabai, Istvan; Costello, James C.
Afiliação
  • Tarca AL; Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI 48201, USA.
  • Pataki BÁ; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI 48201 USA.
  • Romero R; Department of Computer Science, Wayne State University College of Engineering, Detroit, MI 48202, USA.
  • Sirota M; Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary.
  • Guan Y; Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI 48201, USA.
  • Kutum R; Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Gomez-Lopez N; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.
  • Done B; Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA.
  • Bhatti G; Detroit Medical Center, Detroit, MI 48201, USA.
  • Yu T; Department of Obstetrics and Gynecology, Florida International University, Miami, FL 33199, USA.
  • Andreoletti G; Department of Pediatrics, University of California, San Francisco, San Francisco, CA 94143, USA.
  • Chaiworapongsa T; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
  • Hassan SS; Informatics and Big Data Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India.
  • Hsu CD; Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI 48201, USA.
  • Aghaeepour N; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI 48201 USA.
  • Stolovitzky G; Department of Biochemistry, Microbiology, and Immunology, Wayne State University School of Medicine, Detroit, MI 48201 USA.
  • Csabai I; Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI 48201, USA.
  • Costello JC; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI 48201 USA.
Cell Rep Med ; 2(6): 100323, 2021 06 15.
Article em En | MEDLINE | ID: mdl-34195686
Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27-33 weeks of gestation).
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pré-Eclâmpsia / Proteínas Sanguíneas / Idade Gestacional / Nascimento Prematuro / Transcriptoma / Ácidos Nucleicos Livres Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Female / Humans / Newborn / Pregnancy Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pré-Eclâmpsia / Proteínas Sanguíneas / Idade Gestacional / Nascimento Prematuro / Transcriptoma / Ácidos Nucleicos Livres Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Female / Humans / Newborn / Pregnancy Idioma: En Ano de publicação: 2021 Tipo de documento: Article