Center for Reproduction and Genetic, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
Horm Metab Res. 2021 Feb;53(2):112-123. doi: 10.1055/a-1300-2294. Epub 2020 Nov 27.
The changes of metabolite profiles in preterm birth have been demonstrated using newborn screening data. However, little is known about the holistic metabolic model in preterm neonates. The aim was to investigate the holistic metabolic model in preterm neonates. All metabolite values were obtained from a cohort data of routine newborn screening. A total of 261 758 newborns were recruited and randomly divided into a training subset and a testing subset. Using the training subset, 949 variates were considered to establish a logistic regression model for identifying preterm birth (<37 weeks) from term birth (≥37 weeks). Sventy-two variates (age at collection, TSH, 17α-OHP, proline, tyrosine, C16:1-OH, C18:2, and 65 ratios) entered into the final metabolic model for identifying preterm birth from term birth. Among the variates entering into the final model of PTB [Leucine+Isoleucine+Proline-OH)/Valine (OR=38.36], (C3DC+C4-OH)/C12 (OR=15.58), Valine/C5 (OR=6.32), [Leucine+isoleucine+Proline-OH)/Ornithine (OR=2.509)], and Proline/C18:1 (OR=2.465) have the top five OR values, and [Leucine+Isoleucine+Proline-OH)/C5 (OR=0.05)], [Leucine+Isoleucine+Proline-OH)/Phenylalanine (OR=0.214)], proline/valine (OR=0.230), C16/C18 (OR=0.259), and Alanine/free carnitine (OR=0.279) have the five lowest OR values. The final metabolic model had a capacity of identifying preterm infants with >80% accuracy in both the training and testing subsets. When identifying neonates ≤32 weeks from those >32 weeks, it had a robust performance with nearly 95% accuracy in both subsets. In summary, we have established an excellent metabolic model in preterm neonates. These findings could provide new insights for more efficient nutrient supplements and etiology of preterm birth.
使用新生儿筛查数据已经证明了早产儿代谢产物谱的变化。然而,对于早产儿的整体代谢模型知之甚少。本研究旨在探讨早产儿的整体代谢模型。所有代谢物值均来自常规新生儿筛查的队列数据。共纳入 261758 名新生儿,随机分为训练子集和测试子集。使用训练子集,考虑了 949 个变量,以建立一个逻辑回归模型,用于从足月产(≥37 周)中识别早产(<37 周)。72 个变量(采集时的年龄、TSH、17α-OHP、脯氨酸、酪氨酸、C16:1-OH、C18:2 和 65 个比值)进入最终的代谢模型,用于从足月产中识别早产。在进入 PTB 最终模型的变量中[亮氨酸+异亮氨酸+脯氨酸-OH)/缬氨酸(OR=38.36)]、(C3DC+C4-OH)/C12(OR=15.58)、缬氨酸/C5(OR=6.32)、[亮氨酸+异亮氨酸+脯氨酸-OH)/鸟氨酸(OR=2.509)]和脯氨酸/C18:1(OR=2.465)],有五个最高的 OR 值,而[亮氨酸+异亮氨酸+脯氨酸-OH)/C5(OR=0.05)]、[亮氨酸+异亮氨酸+脯氨酸-OH)/苯丙氨酸(OR=0.214)]、脯氨酸/缬氨酸(OR=0.230)、C16/C18(OR=0.259)和丙氨酸/游离肉碱(OR=0.279)的 OR 值最低。该最终代谢模型在训练集和测试集均具有>80%的准确率来识别早产儿。当从>32 周的早产儿中识别≤32 周的早产儿时,在两个子集中均具有近 95%的准确率,具有稳健的性能。总之,我们在早产儿中建立了一个优秀的代谢模型。这些发现为更有效的营养补充和早产病因提供了新的见解。