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.

简介：早产是全球关注的主要健康问题，在2016年占所有新生儿死亡的35％。鉴于准确确定早产估计值的重要性，并鉴于目前对产后胎龄（GA）估计值的局限性，采用新的方法需要在没有产前超声检查的情况下估计出生后的GA。先前的工作已经证明了代谢组学通过分析常规新生儿筛查所收集的数据来估计GA的潜力。

覆盖区域：新生儿血液样本中发现的循环分析物因GA而异。利用新生儿筛查和人口统计学数据，我们小组开发了一种算法，能够在超声验证的GA大约1周内估算出出生后的GA。从那时起，我们通过包括其他分析物并通过内部和外部验证研究以及通过在国际上实施该模型来验证模型的性能来建立模型。

专家意见：目前，使用代谢组学估算产后遗传资源前景可观，但受到低收入环境下成本效益和资源获取问题的限制。未来的工作将着重于提高这种方法的精度，同时优先考虑资源稀缺环境中个人可以访问和接受的即时检验。