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J Matern Fetal Neonatal Med. 2019 Oct;32(20):3435-3441. doi: 10.1080/14767058.2018.1465552. Epub 2018 Apr 30.

First-trimester metabolomic prediction of stillbirth.

Author information

1
a Department of Obstetrics and Gynecology , William Beaumont Health , Royal Oak , MI , USA.
2
b Department of Obstetrics and Gynecology , King's College Hospital , London , England.
3
c Departments of Biological Sciences , University of Alberta , Edmonton , Alberta , Canada.
4
d Department of Obstetrics and Gynecology , University of Missouri , Kansas City , MO , USA.
5
e Department of Computing Sciences , University of Alberta , Edmonton , Alberta , Canada.

Abstract

Background: Stillbirth remains a major problem in both developing and developed countries. Omics evaluation of stillbirth has been highlighted as a top research priority. Objective: To identify new putative first-trimester biomarkers in maternal serum for stillbirth prediction using metabolomics-based approach. Methods: Targeted, nuclear magnetic resonance (NMR) and mass spectrometry (MS), and untargeted liquid chromatography-MS (LC-MS) metabolomic analyses were performed on first-trimester maternal serum obtained from 60 cases that subsequently had a stillbirth and 120 matched controls. Metabolites by themselves or in combination with clinical factors were used to develop logistic regression models for stillbirth prediction. Prediction of stillbirths overall, early (<28 weeks and <32 weeks), those related to growth restriction/placental disorder, and unexplained stillbirths were evaluated. Results: Targeted metabolites including glycine, acetic acid, L-carnitine, creatine, lysoPCaC18:1, PCaeC34:3, and PCaeC44:4 predicted stillbirth overall with an area under the curve [AUC, 95% confidence interval (CI)] = 0.707 (0.628-0.785). When combined with clinical predictors the AUC value increased to 0.740 (0.667-0.812). First-trimester targeted metabolites also significantly predicted early, unexplained, and placental-related stillbirths. Untargeted LC-MS features combined with other clinical predictors achieved an AUC (95%CI) = 0.860 (0.793-0.927) for the prediction of stillbirths overall. We found novel preliminary evidence that, verruculotoxin, a toxin produced by common household molds, might be linked to stillbirth. Conclusions: We have identified novel biomarkers for stillbirth using metabolomics and demonstrated the feasibility of first-trimester prediction.

KEYWORDS:

Stillbirth prediction; mass spectrometry; metabolites; metabolomics; nuclear magnetic resonance

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