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BMJ Open. 2019 Aug 10;9(8):e031238. doi: 10.1136/bmjopen-2019-031238.

Examining the predictive accuracy of metabolomics for small-for-gestational-age babies: a systematic review.

Author information

1
Department of Tocogynecology, Campinas' State University, Campinas, Brazil.
2
Department of Maternal and Child Health, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil.
3
Irish Centre for Fetal and Neonatal Translational Research (INFANT), University College Cork National University of Ireland, Cork, Ireland.
4
Clinics Hospital, Universidade Federal de Pernambuco, Recife, Brazil.
5
Obstetrics and Gynecology, Universidade Estadual de Campinas, Campinas, Brazil.
6
Department of Gynaecology and Obstetrics, St Thomas Hospital, Cork, UK.
7
Department of Epidemiology and Public Health, University College Cork, Cork, Ireland.
8
College of Medicine, University of Leicester, Leicester, UK.
9
Department of Women's and Children's Health, University of Liverpool School of Life Sciences, Liverpool, UK.
10
Obstetrics and Gynecology, University of Campinas, Campinas, Sao Paolo, Brazil cecatti@unicamp.br.

Abstract

INTRODUCTION:

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.

OBJECTIVE:

To determine the accuracy of metabolomics in predicting SGA babies and elucidate which metabolites are predictive of this condition.

DATA SOURCES:

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.

STUDY ELIGIBILITY CRITERIA:

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.

STUDY APPRAISAL AND SYNTHESIS METHODS:

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.

RESULTS:

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.

CONCLUSIONS AND IMPLICATIONS:

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.

PROSPERO REGISTRATION NUMBER:

CRD42018089985.

KEYWORDS:

fatty acids; fetal growth restriction; gas-chromatography; homocysteine; lipids; mass spectrometry; metabolomics; prediction; small for gestational age; vitamin d

PMID:
31401613
DOI:
10.1136/bmjopen-2019-031238
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Conflict of interest statement

Competing interests: None declared.

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