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.