Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses

被引：3

作者：

Miranda, Jezid ^{[1
,2
,3
,4
]}

Paules, Cristina ^{[1
,2
,3
,5
]}

Noell, Guillaume ^{[6
]}

Youssef, Lina ^{[1
,2
,3
]}

Paternina-Caicedo, Angel ^{[7
]}

Crovetto, Francesca ^{[1
,2
,3
]}

Canellas, Nicolau ^{[8
]}

Garcia-Martin, Maria L. ^{[9
]}

Amigo, Nuria ^{[10
]}

Eixarch, Elisenda ^{[1
,2
,3
]}

Faner, Rosa ^{[6
]}

Figueras, Francesc ^{[1
,2
,3
]}

Simos, Rui, V ^{[1
,2
,3
,11
]}

Crispi, Fatima ^{[1
,2
,3
]}

Gratacos, Eduard ^{[1
,2
,3
]}

机构：

[1] Univ Barcelona, IDIBAPS, BCNatal Barcelona Ctr Maternal Fetal & Neonatal Me, Hosp Clin, Barcelona, Spain

[2] Univ Barcelona, Hosp St Joan Deu, IDIBAPS, Barcelona, Spain

[3] Ctr Biomed Res Rare Dis CIBER ER, Barcelona, Spain

[4] Univ Cartagena, Fac Med, Dept Obstet & Gynecol, Cartagena De Indias, Colombia

[5] Univ Lozano Blesa, Aragon Inst Hlth Res IIS Aragon, Obstet Dept, Hosp Clin, Zaragoza, Spain

[6] Univ Barcelona, Ctr Biomed Res Resp Dis CIBERES, Biomed Dept, IDIBAPS, Barcelona, Spain

[7] Univ Sinu, Fac Med, Cartagena De Indias, Colombia

[8] Univ Rovira I Virgili, Biomed Res Ctr Diabet & Associated Metab Disorders, Metabol Platform, IISPV,DEEiA, Tarragona, Spain

[9] Univ Malaga, Andalusian Ctr Nanomed & Biotechnol, BIONAND, Junta Andalucia, Malaga, Spain

[10] Biosfer Teslab, Reus, Spain

[11] Univ Porto, Inst Res & Innovat Hlth i3S, Porto, Portugal

来源：

ISCIENCE | 2023年 / 26卷 / 09期

关键词：

FETAL-GROWTH RESTRICTION; EARLY-ONSET; NATRIURETIC PEPTIDE; NMR-SPECTROSCOPY; TERM; MANAGEMENT; DOPPLER; PREECLAMPSIA; CONSEQUENCES; PREGNANCY;

D O I：

10.1016/j.isci.2023.107620

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multiomic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.

引用

页数：23

共 121 条

[1] ISUOG Practice Guidelines: diagnosis and management of small-for-gestational-age fetus and fetal growth restriction [J].