Evidence map›Paper›PMID 34552554›Full record

SynthesisFrontiers in endocrinology2021

The Potential of Metabolomic Analyses as Predictive Biomarkers of Preterm Delivery: A Systematic Review.

Emma Ronde, Irwin K M Reiss, Thomas Hankemeier, Tim G De Meij, Nina Frerichs, Sam Schoenmakers

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Emma RondeDivision of Obstetrics and Prenatal Diagnosis, Erasmus University Medical Centre, Rotterdam, Netherlands.
Irwin K M ReissDepartment of Pediatrics, Division of Neonatology, Erasmus University Medical Centre, Rotterdam, Netherlands.
Thomas HankemeierDivision of Analytical Biosciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands.
Tim G De MeijDepartment of Pediatric Gastroenterology, Amsterdam University Medical Centre, Amsterdam, Netherlands.
Nina FrerichsDepartment of Pediatric Gastroenterology, Amsterdam University Medical Centre, Amsterdam, Netherlands.
Sam SchoenmakersDivision of Obstetrics and Prenatal Diagnosis, Erasmus University Medical Centre, Rotterdam, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Scope: as the leading cause of perinatal mortality and morbidity worldwide, the impact of premature delivery is undisputable. Thus far, non-invasive, cost-efficient and accurate biochemical markers to predict preterm delivery are scarce. The aim of this systematic review is to investigate the potential of non-invasive metabolomic biomarkers for the prediction of preterm delivery. Methods and Results: Databases were systematically searched from March 2019 up to May 2020 resulting in 4062 articles, of which 45 were retrieved for full-text assessment. The resulting metabolites used for further analyses, such as ferritin, prostaglandin and different vitamins were obtained from different human anatomical compartments or sources (vaginal fluid, serum, urine and umbilical cord) and compared between groups of women with preterm and term delivery. None of the reported metabolites showed uniform results, however, a combination of metabolomics biomarkers may have potential to predict preterm delivery and need to be evaluated in future studies.

Indexed as

MetabolomeBiomarkersFemaleHumansInfant, NewbornPregnancyPremature BirthBiomarkersbiomarkersmetabolitesmetabolomicsmicrobiotapreterm deliveryVOC

Identifiers

PMID34552554
PMCPMC8451156

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.