Leite Debora Farias Batista, Morillon Aude-Claire, Melo Júnior Elias F, Souza Renato T, McCarthy Fergus P, Khashan Ali, Baker Philip, Kenny Louise C, Cecatti Jose Guilherme
Department of Tocogynecology, Campinas' State University, Campinas, Brazil.
Department of Maternal and Child Health, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil.
BMJ Open. 2019 Aug 10;9(8):e031238. doi: 10.1136/bmjopen-2019-031238.
To date, there is no robust enough test to predict small-for-gestational-age (SGA) infants, who are at increased lifelong risk of morbidity and mortality.
To determine the accuracy of metabolomics in predicting SGA babies and elucidate which metabolites are predictive of this condition.
Two independent researchers explored 11 electronic databases and grey literature in February 2018 and November 2018, covering publications from 1998 to 2018. Both researchers performed data extraction and quality assessment independently. A third researcher resolved discrepancies.
Cohort or nested case-control studies were included which investigated pregnant women and performed metabolomics analysis to evaluate SGA infants. The primary outcome was birth weight <10th centile-as a surrogate for fetal growth restriction-by population-based or customised charts.
Two independent researchers extracted data on study design, obstetric variables and sampling, metabolomics technique, chemical class of metabolites, and prediction accuracy measures. Authors were contacted to provide additional data when necessary.
A total of 9181 references were retrieved. Of these, 273 were duplicate, 8760 were removed by title or abstract, and 133 were excluded by full-text content. Thus, 15 studies were included. Only two studies used the fifth centile as a cut-off, and most reports sampled second-trimester pregnant women. Liquid chromatography coupled to mass spectrometry was the most common metabolomics approach. Untargeted studies in the second trimester provided the largest number of predictive metabolites, using maternal blood or hair. Fatty acids, phosphosphingolipids and amino acids were the most prevalent predictive chemical subclasses.
Significant heterogeneity of participant characteristics and methods employed among studies precluded a meta-analysis. Compounds related to lipid metabolism should be validated up to the second trimester in different settings.
CRD42018089985.
迄今为止，尚无足够强大的检测方法来预测小于胎龄儿（SGA），这类婴儿一生的发病和死亡风险都会增加。
确定代谢组学在预测小于胎龄儿方面的准确性，并阐明哪些代谢物可预测这种情况。
2018年2月和2018年11月，两名独立研究人员检索了11个电子数据库和灰色文献，涵盖1998年至2018年的出版物。两名研究人员均独立进行数据提取和质量评估。第三名研究人员解决了分歧。
纳入队列研究或巢式病例对照研究，这些研究对孕妇进行了调查，并进行了代谢组学分析以评估小于胎龄儿。主要结局是根据基于人群或定制的图表，出生体重<第10百分位数，作为胎儿生长受限的替代指标。
两名独立研究人员提取了关于研究设计、产科变量和采样、代谢组学技术、代谢物化学类别以及预测准确性指标的数据。必要时会联系作者以提供额外数据。
共检索到9181篇参考文献。其中，273篇为重复文献，8760篇通过标题或摘要被剔除，133篇因全文内容被排除。因此，纳入了15项研究。只有两项研究使用第5百分位数作为临界值，且大多数报告对孕中期妇女进行了采样。液相色谱-质谱联用是最常见的代谢组学方法。孕中期的非靶向研究使用母体血液或头发提供了最多的预测性代谢物。脂肪酸、磷酸鞘脂和氨基酸是最常见的预测性化学亚类。
研究中参与者特征和所用方法存在显著异质性，无法进行荟萃分析。与脂质代谢相关的化合物应在不同环境下直至孕中期进行验证。
PROSPERO注册号：CRD42018089985。
