Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
Sci Rep. 2022 May 16;12(1):8033. doi: 10.1038/s41598-022-11866-6.
Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC-MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.
评估妊娠期（GA）是为孕妇提供最佳护理的关键。然而，在获得产科超声检查受限的中低收入国家，其准确确定仍然具有挑战性。因此，迫切需要开发能够准确且廉价地估计 GA 的临床方法。我们使用广谱液相色谱-质谱联用（LC-MS）平台，在一个由健康和病理性妊娠组成的多地点队列中（n=99），研究了尿液代谢物在收集时预测 GA 的能力。我们的方法检测到了许多甾体激素及其衍生物，包括雌激素、孕激素、皮质类固醇和雄激素，它们与妊娠进展有关。我们开发了一种受限模型，该模型使用三种代谢物（rho=0.87，RMSE=1.58 周）进行了高度准确的 GA 预测，并在独立队列（n=20）中进行了验证。与早产妊娠相比，足月妊娠的预测结果更可靠。总体而言，我们证明了在大规模多地点研究中实施尿液代谢组学分析的可行性，并报告了具有潜在临床价值的 GA 预测模型。
