Targeted urine metabolomics in preterm neonates with intraventricular hemorrhage

Sarafidis, Kosmas; Begou, Olga; Deda, Olga; Gika, Helen; Agakidis, Charalampos; Efstathiou, Nikolaos; Theodoridis, Georgios

doi:10.1016/j.jchromb.2018.11.024

Cited by 11 publications

(13 citation statements)

References 38 publications

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“…Long chain fatty acids are needed for the development of brain, retina, and nervous system of fetal during the third trimester of pregnancy. Stearic acid (a long chain saturated fatty acid) level was found lower in the preterm birth infants than in the term birth infants (22). Actually, there are very few studies indicating the direct relationship between i-Propyl 12-methyltetradecanoate, Eicosane and preterm birth.…”

Section: Discussionmentioning

confidence: 99%

Stress and Metabolomics for Prediction of Spontaneous Preterm Birth: A Prospective Nested Case-Control Study in a Tertiary Hospital

Huang

Liu

et al. 2021

Front. Pediatr.

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Spontaneous preterm birth (sPTB) is the leading cause of infant morbidity and mortality worldwide. Deficiency of effective predict methods is an urgent problem that needs to be solved. Numbers of researchers spare no efforts to investigate differential indicators. To evaluate the value of the differential indicators, a prospective nested case-control study was carried out. Among an overall cohort of 1,050 pregnancies, 20 sPTB pregnancies, and 20 full-term pregnancies were enrolled in this study. Participants were followed-up until labor. The psychological profile was evaluated utilizing the Zung Self-Rating Depression Scale at 11–14 weeks. Stress-related biomarker-cortisol and metabolites were detected by Electrochemiluminescence Immunoassay (ECLIA) and Gas Chromatography-Mass Spectrometry (GC-MS) in serum samples during pregnancy, respectively. The expression level of cortisol was up-regulated in serum and the score of the Zung Self-Rating Depression Scale was significantly higher in the sPTB group when compared to the control group. Note that, 29 metabolomics were differentially expressed between the sPTB group and the control group. The scores of the Zung Self-Rating Depression Scale, the level of cortisol, Eicosane, methyltetradecanoate, and stearic acid in serum were selected to establish the model with lasso logistic regression. Validation of the model yielded an optimum corrected AUC value of 89.5%, 95% CI: 0.8006–0.9889 with a sensitivity of 100.0%, and specificity of 78.9%. In conclusion, this study establishes a prediction model of sPTB with five variables, which may predict sPTB more accurately and sensitively in the second trimester.

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Section: Discussionmentioning

confidence: 99%

Stress and Metabolomics for Prediction of Spontaneous Preterm Birth: A Prospective Nested Case-Control Study in a Tertiary Hospital

Huang

Liu

et al. 2021

Front. Pediatr.

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“…A targeted metabolomics approach study using liquid chromatography-mass spectrometry in urine samples of preterm neonates showed clear differences between preterms presenting either with or without GMH-IVH-IPH. A total of 20 metabolites were differentially expressed between groups and appeared as strong predictors of GMH-IVH-IPH onset, especially at day 1 [84]. Omics approaches in preterm hemorrhage are still rare but seem to be promising and could circumvent the pitfalls of reductionist classical approaches.…”

Section: A New Era: Omics-based Approachesmentioning

confidence: 99%

Intraventricular Hemorrhage in Very Preterm Infants: A Comprehensive Review

Gilard

Tebani

Bekri

et al. 2020

JCM

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Germinal matrix-intraventricular-intraparenchymal hemorrhage (GMH-IVH-IPH) is a major complication of very preterm births before 32 weeks of gestation (WG). Despite progress in clinical management, its incidence remains high before 27 WG. In addition, severe complications may occur such as post-hemorrhagic hydrocephalus and/or periventricular intraparenchymal hemorrhage. IVH is strongly associated with subsequent neurodevelopmental disabilities. For this review, an automated literature search and a clustering approach were applied to allow efficient filtering as well as topic clusters identification. We used a programmatic literature search for research articles related to intraventricular hemorrhage in preterms that were published between January 1990 and February 2020. Two queries ((Intraventricular hemorrhage) AND (preterm)) were used in PubMed. This search resulted in 1093 articles. The data manual curation left 368 documents that formed 12 clusters. The presentation and discussion of the clusters provide a comprehensive overview of existing data on the pathogenesis, complications, neuroprotection and biomarkers of GMH-IVH-IPH in very preterm infants. Clinicians should consider that the GMH-IVH-IPH pathogenesis is mainly due to developmental immaturity of the germinal matrix and cerebral autoregulation impairment. New multiomics investigations of intraventricular hemorrhage could foster the development of predictive biomarkers for the benefit of very preterm newborns.

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“…Metabolomics can also provide valuable information for outcome prediction. Metabolites offer a unique signature potentially usable to predict neonatal diseases and evaluate disease progression and treatments' effect (156)(157)(158).…”

Section: (Epi)genetics and Omics-futurementioning

confidence: 99%

Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury

et al. 2021

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Despite advances in neonatal care to prevent neonatal brain injury and neurodevelopmental impairment, predicting long-term outcome in neonates at risk for brain injury remains difficult. Early prognosis is currently based on cranial ultrasound (CUS), MRI, EEG, NIRS, and/or general movements assessed at specific ages, and predicting outcome in an individual (precision medicine) is not yet possible. New algorithms based on large databases and machine learning applied to clinical, neuromonitoring, and neuroimaging data and genetic analysis and assays measuring multiple biomarkers (omics) can fulfill the needs of modern neonatology. A synergy of all these techniques and the use of automatic quantitative analysis might give clinicians the possibility to provide patient-targeted decision-making for individualized diagnosis, therapy, and outcome prediction. This review will first focus on common neonatal neurological diseases, associated risk factors, and most common treatments. After that, we will discuss how precision medicine and machine learning (ML) approaches could change the future of prediction and prognosis in this field.

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Targeted urine metabolomics in preterm neonates with intraventricular hemorrhage

Cited by 11 publications

References 38 publications

Stress and Metabolomics for Prediction of Spontaneous Preterm Birth: A Prospective Nested Case-Control Study in a Tertiary Hospital

Stress and Metabolomics for Prediction of Spontaneous Preterm Birth: A Prospective Nested Case-Control Study in a Tertiary Hospital

Intraventricular Hemorrhage in Very Preterm Infants: A Comprehensive Review

Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury

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