通过羊水代谢组学筛查和预测唐氏综合征的生物标记物

IF 2.7 2区 医学 Q2 GENETICS & HEREDITY
Prenatal Diagnosis Pub Date : 2025-01-01 Epub Date: 2024-10-31 DOI:10.1002/pd.6693
Li-Chao Zhang, Xiang-Chun Yang, Yong-Hong Jiang, Zhen Yang, Lu-Lu Yan, Yu-Xin Zhang, Qiong Li, Li-Yun Tian, Juan Cao, Ying Zhou, Shan-Shan Wu, Dan-Yan Zhuang, Chang-Shui Chen, Hai-Bo Li
{"title":"通过羊水代谢组学筛查和预测唐氏综合征的生物标记物","authors":"Li-Chao Zhang, Xiang-Chun Yang, Yong-Hong Jiang, Zhen Yang, Lu-Lu Yan, Yu-Xin Zhang, Qiong Li, Li-Yun Tian, Juan Cao, Ying Zhou, Shan-Shan Wu, Dan-Yan Zhuang, Chang-Shui Chen, Hai-Bo Li","doi":"10.1002/pd.6693","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Down syndrome (DS) is a congenital disorder caused by the presence of an extra copy of all or part of chromosome 21. It is characterized by significant intellectual disability, distinct facial features, and growth and developmental challenges. The utilization of metabolomics to analyze specific metabolic markers in maternal amniotic fluid may provide innovative tools and screening methods for investigating the early pathophysiology of trisomy 21 at the functional level.</p><p><strong>Methods: </strong>Amniotic fluid samples were obtained via amniocentesis from 57 pregnancies with DS and 55 control pregnancies between 17<sup>3/7</sup> and 24<sup>0/7</sup> weeks of gestation. The targeted metabolomics focused on 34 organic acids, 17 amino acids, and 5 acylcarnitine metabolites. The untargeted metabolomics analysis concentrated on lipid profiles and included 602 metabolites that met quality control standards. Principal Component Analysis, Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and false discovery rate (FDR) adjustments were applied. MetaboAnalystR 5.0 was used to perform the metabolic pathway analysis on the identified differential metabolites.</p><p><strong>Results: </strong>Fifty differential metabolites, including L-glutamine, eight organic acids, and 41 lipids, were significantly altered in DS based on three criteria: VIP > 1 in the OPLS-DA model, FDR-adjusted p-value < 0.05, and |log<sub>2</sub>FC| > log<sub>2</sub>(1.5) from a volcano plot of all detected metabolites. An analysis of 212 differential metabolites, selected from both targeted and untargeted approaches (VIP > 1 in the OPLS-DA model and FDR-adjusted p-value < 0.05), revealed significant changes in nine metabolic pathways. Fourteen key metabolites were identified to establish a screening model for DS, achieving an area under the curve of 1.00.</p><p><strong>Conclusions: </strong>Our results underscore the potential of metabolomics approaches in identifying concise and reliable biomarker combinations that demonstrate promising screening performance in DS.</p>","PeriodicalId":20387,"journal":{"name":"Prenatal Diagnosis","volume":" ","pages":"57-69"},"PeriodicalIF":2.7000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Screening and Predictive Biomarkers for Down Syndrome Through Amniotic Fluid Metabolomics.\",\"authors\":\"Li-Chao Zhang, Xiang-Chun Yang, Yong-Hong Jiang, Zhen Yang, Lu-Lu Yan, Yu-Xin Zhang, Qiong Li, Li-Yun Tian, Juan Cao, Ying Zhou, Shan-Shan Wu, Dan-Yan Zhuang, Chang-Shui Chen, Hai-Bo Li\",\"doi\":\"10.1002/pd.6693\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Down syndrome (DS) is a congenital disorder caused by the presence of an extra copy of all or part of chromosome 21. It is characterized by significant intellectual disability, distinct facial features, and growth and developmental challenges. The utilization of metabolomics to analyze specific metabolic markers in maternal amniotic fluid may provide innovative tools and screening methods for investigating the early pathophysiology of trisomy 21 at the functional level.</p><p><strong>Methods: </strong>Amniotic fluid samples were obtained via amniocentesis from 57 pregnancies with DS and 55 control pregnancies between 17<sup>3/7</sup> and 24<sup>0/7</sup> weeks of gestation. The targeted metabolomics focused on 34 organic acids, 17 amino acids, and 5 acylcarnitine metabolites. The untargeted metabolomics analysis concentrated on lipid profiles and included 602 metabolites that met quality control standards. Principal Component Analysis, Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and false discovery rate (FDR) adjustments were applied. MetaboAnalystR 5.0 was used to perform the metabolic pathway analysis on the identified differential metabolites.</p><p><strong>Results: </strong>Fifty differential metabolites, including L-glutamine, eight organic acids, and 41 lipids, were significantly altered in DS based on three criteria: VIP > 1 in the OPLS-DA model, FDR-adjusted p-value < 0.05, and |log<sub>2</sub>FC| > log<sub>2</sub>(1.5) from a volcano plot of all detected metabolites. An analysis of 212 differential metabolites, selected from both targeted and untargeted approaches (VIP > 1 in the OPLS-DA model and FDR-adjusted p-value < 0.05), revealed significant changes in nine metabolic pathways. Fourteen key metabolites were identified to establish a screening model for DS, achieving an area under the curve of 1.00.</p><p><strong>Conclusions: </strong>Our results underscore the potential of metabolomics approaches in identifying concise and reliable biomarker combinations that demonstrate promising screening performance in DS.</p>\",\"PeriodicalId\":20387,\"journal\":{\"name\":\"Prenatal Diagnosis\",\"volume\":\" \",\"pages\":\"57-69\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Prenatal Diagnosis\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/pd.6693\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/31 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Prenatal Diagnosis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/pd.6693","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/31 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0

摘要

背景:唐氏综合征(Down Syndrome,DS)是一种先天性疾病,由 21 号染色体的全部或部分额外拷贝引起。其特征是严重的智力障碍、明显的面部特征以及生长和发育障碍。利用代谢组学分析母体羊水中的特定代谢标记物可为从功能层面研究 21 三体综合征的早期病理生理学提供创新工具和筛查方法:方法:在妊娠 173/7 周至 240/7 周期间,通过羊膜穿刺术从 57 例 DS 孕妇和 55 例对照孕妇中获取羊水样本。靶向代谢组学主要研究 34 种有机酸、17 种氨基酸和 5 种酰基肉碱代谢物。非靶向代谢组学分析侧重于脂质图谱,包括 602 个符合质量控制标准的代谢物。应用了主成分分析、正交偏最小二乘法判别分析(OPLS-DA)和错误发现率(FDR)调整。使用 MetaboAnalystR 5.0 对鉴定出的差异代谢物进行代谢途径分析:根据三个标准,50 种差异代谢物(包括 L-谷氨酰胺、8 种有机酸和 41 种脂类)在 DS 中发生了显著变化:在 OPLS-DA 模型中 VIP > 1,经 FDR 调整的 p 值 < 0.05,以及所有检测到的代谢物的火山图中 |log2FC| > log2(1.5)。对从靶向和非靶向方法(OPLS-DA 模型中 VIP > 1 且 FDR 调整后 p 值 < 0.05)中筛选出的 212 个差异代谢物进行分析,发现九种代谢途径发生了显著变化。鉴定出的 14 种关键代谢物建立了 DS 筛选模型,曲线下面积达到 1.00:我们的研究结果凸显了代谢组学方法在确定简明可靠的生物标记物组合方面的潜力,这些生物标记物组合在DS筛查中表现出良好的筛查性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Screening and Predictive Biomarkers for Down Syndrome Through Amniotic Fluid Metabolomics.

Background: Down syndrome (DS) is a congenital disorder caused by the presence of an extra copy of all or part of chromosome 21. It is characterized by significant intellectual disability, distinct facial features, and growth and developmental challenges. The utilization of metabolomics to analyze specific metabolic markers in maternal amniotic fluid may provide innovative tools and screening methods for investigating the early pathophysiology of trisomy 21 at the functional level.

Methods: Amniotic fluid samples were obtained via amniocentesis from 57 pregnancies with DS and 55 control pregnancies between 173/7 and 240/7 weeks of gestation. The targeted metabolomics focused on 34 organic acids, 17 amino acids, and 5 acylcarnitine metabolites. The untargeted metabolomics analysis concentrated on lipid profiles and included 602 metabolites that met quality control standards. Principal Component Analysis, Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and false discovery rate (FDR) adjustments were applied. MetaboAnalystR 5.0 was used to perform the metabolic pathway analysis on the identified differential metabolites.

Results: Fifty differential metabolites, including L-glutamine, eight organic acids, and 41 lipids, were significantly altered in DS based on three criteria: VIP > 1 in the OPLS-DA model, FDR-adjusted p-value < 0.05, and |log2FC| > log2(1.5) from a volcano plot of all detected metabolites. An analysis of 212 differential metabolites, selected from both targeted and untargeted approaches (VIP > 1 in the OPLS-DA model and FDR-adjusted p-value < 0.05), revealed significant changes in nine metabolic pathways. Fourteen key metabolites were identified to establish a screening model for DS, achieving an area under the curve of 1.00.

Conclusions: Our results underscore the potential of metabolomics approaches in identifying concise and reliable biomarker combinations that demonstrate promising screening performance in DS.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Prenatal Diagnosis
Prenatal Diagnosis 医学-妇产科学
CiteScore
5.80
自引率
13.30%
发文量
204
审稿时长
2 months
期刊介绍: Prenatal Diagnosis welcomes submissions in all aspects of prenatal diagnosis with a particular focus on areas in which molecular biology and genetics interface with prenatal care and therapy, encompassing: all aspects of fetal imaging, including sonography and magnetic resonance imaging; prenatal cytogenetics, including molecular studies and array CGH; prenatal screening studies; fetal cells and cell-free nucleic acids in maternal blood and other fluids; preimplantation genetic diagnosis (PGD); prenatal diagnosis of single gene disorders, including metabolic disorders; fetal therapy; fetal and placental development and pathology; development and evaluation of laboratory services for prenatal diagnosis; psychosocial, legal, ethical and economic aspects of prenatal diagnosis; prenatal genetic counseling
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信