Urine metabolomic biomarkers for prediction of isolated fetal congenital heart defect.

Autor:	Friedman P; Division of Maternal Fetal Medicine, William Beaumont Health, Royal Oak, MI, USA., Yilmaz A; Division of Maternal Fetal Medicine, William Beaumont Health, Royal Oak, MI, USA., Ugur Z; Division of Maternal Fetal Medicine, William Beaumont Health, Royal Oak, MI, USA., Jafar F; Division of Maternal Fetal Medicine, William Beaumont Health, Royal Oak, MI, USA., Whitten A; Division of Maternal Fetal Medicine, William Beaumont Health, Royal Oak, MI, USA., Ustun I; Center for Data Science,DePaul University School of Computing, Chicago, IL, USA., Turkoglu O; Division of Maternal Fetal Medicine, William Beaumont Health, Royal Oak, MI, USA., Graham S; Division of Maternal Fetal Medicine, William Beaumont Health, Royal Oak, MI, USA., Bahado Singh R; Division of Maternal Fetal Medicine, William Beaumont Health, Royal Oak, MI, USA.
Jazyk:	angličtina
Zdroj:	The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians [J Matern Fetal Neonatal Med] 2022 Dec; Vol. 35 (25), pp. 6380-6387. Date of Electronic Publication: 2021 May 04.
DOI:	10.1080/14767058.2021.1914572
Abstrakt:	Objective: To identify maternal second and third trimester urine metabolomic biomarkers for the detection of fetal congenital heart defects (CHDs). Study Design: This was a prospective study. Metabolomic analysis of randomly collected maternal urine was performed, comparing pregnancies with isolated, non-syndromic CHDs versus unaffected controls. Mass spectrometry (liquid chromatography and direct injection and tandem mass spectrometry, LC-MS-MS) as well as nuclear magnetic resonance spectrometry, 1 H NMR, were used to perform the analyses between 14 0/7 and 37 0/7 weeks gestation. A total of 36 CHD cases and 41 controls were compared. Predictive algorithms using urine markers alone or combined with, clinical and ultrasound (US) (four-chamber view) predictors were developed and compared. Results: A total of 222 metabolites were identified, of which 16 were overlapping between the two platforms. Twenty-three metabolite concentrations were found in significantly altered in CHD gestations on univariate analysis. The concentration of methionine was most significantly altered. A predictive algorithm combining metabolites (histamine, choline, glucose, formate, methionine, and carnitine) plus US four-chamber view achieved an AUC = 0.894; 95% CI, 0814-0.973 with a sensitivity of 83.8% and specificity of 87.8%. Enrichment pathway analysis identified several lipid related pathways that are dysregulated in CHD, including phospholipid biosynthesis, phosphatidylcholine biosynthesis, phosphatidylethanolamine biosynthesis, and fatty acid metabolism. This could be consistent with the increased risk of CHD in diabetic pregnancies. Conclusions: We report a novel, noninvasive approach, based on the analysis of maternal urine for isolated CHD detection. Further, the dysregulation of lipid- and folate metabolism appears to support prior data on the mechanism of CHD.
Databáze:	MEDLINE
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