Postnatal gestational age estimation via newborn screening analysis: application and potential. (2nd September 2019)

• Record Type: Journal Article
• Authors: Wilson, Lindsay A.; Murphy, Malia SQ.; Ducharme, Robin; Denize, Kathryn; Jadavji, Nafisa M.; Potter, Beth; Little, Julian; Chakraborty, Pranesh; Hawken, Steven; Wilson, Kumanan
• Is Part Of: Expert review of proteomics. Volume 16:Number 9(2019)
• Page Start: 727
• Page End: 731
• Publication Date: 2019-09-02
• Subjects: Gestational age -- metabolomics -- newborn screening; Proteins -- Biotechnology -- Periodicals; Proteomics -- Periodicals; 572.6
• DOI: 10.1080/14789450.2019.1654863
• Languages: English
• ISSNs: 1478-9450

Abstract

ABSTRACT: Introduction : Preterm birth is a major global health concern, contributing to 35% of all neonatal deaths in 2016. Given the importance of accurately ascertaining estimates of preterm birth and in light of current limitations in postnatal gestational age (GA) estimation, novel methods of estimating GA postnatally in the absence of prenatal ultrasound are needed. Previous work has demonstrated the potential for metabolomics to estimate GA by analyzing data captured through routine newborn screening. Areas covered : Circulating analytes found in newborn blood samples vary by GA. Leveraging newborn screening and demographic data, our group developed an algorithm capable of estimating GA postnatally to within approximately 1 week of ultrasound-validated GA. Since then, we have built on the model by including additional analytes and validating the model's performance through internal and external validation studies, and through implementation of the model internationally. Expert opinion : Currently, using metabolomics to estimate GA postnatally holds considerable promise but is limited by issues of cost-effectiveness and resource access in low-income settings. Future work will focus on enhancing the precision of this approach while prioritizing point-of-care testing that is both accessible and acceptable to individuals in low-resource settings.