First Trimester Prediction of Preterm Birth in Patient Plasma with Machine-Learning-Guided Raman Spectroscopy and Metabolomics

Abstract

Preterm birth (PTB) is the leading cause of infant deaths globally. Current clinical measures often fail to identify women who may deliver preterm. Therefore, accurate screening tools are imperative for early prediction of PTB. Here, we show that Raman spectroscopy is a promising tool for studying biological interfaces, and we examine differences in the maternal metabolome of the first trimester plasma of PTB patients and those that delivered at term (healthy). We identified fifteen statistically significant metabolites that are predictive of the onset of PTB. Mass spectrometry metabolomics validates the Raman findings identifying key metabolic pathways that are enriched in PTB. We also show that patient clinical information alone and protein quantification of standard inflammatory cytokines both fail to identify PTB patients. We show for the first time that synergistic integration of Raman and clinical data guided with machine learning results in an unprecedented 85.1% accuracy of risk stratification of PTB in the first trimester that is currently not possible clinically. Correlations between metabolites and clinical features highlight the body mass index and maternal age as contributors of metabolic rewiring. Our findings show that Raman spectral screening may complement current prenatal care for early prediction of PTB, and our approach can be translated to other patient-specific biological interfaces.

Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy

“…In our previous work focused on Raman spectral analysis of pregnant patient plasma samples who had preterm birth, we also observed similar tends in AAs. 28 Metabolic rewiring in AAs also contributes to disrupted protein synthesis and corroborates with the statistically significant decrease we observe in amide III peak at 1243 cm −1 (Figure 3m ) that represents proteins and type I collagen. Indeed literature evidence shows that in preeclampsia multiple closely‐packed proteomic pathways within the protein networks and corresponding gene expression are simultaneously dysregulated.…”

“…We observed a similar trend in carotenoids in preterm labor pregnancies relative to healthy suggesting the important role of β‐carotene during pregnancy. 28 We also observed that sugar levels (848 cm −1 , Figure 3e ) are higher in preeclampsia cohort in all three trimesters relative to healthy. Indeed, there is a strong relationship between abnormal glucose metabolism and hypertension during pregnancy, and preeclamptic patients often have higher blood glucose levels that are exaggerated closer to term.…”

“… 22 , 23 , 24 , 25 , 26 , 27 This study also builds upon our recent work where we showed metabolite changes in sera measured with Raman when combined with clinical information of pregnant patients who delivered preterm enables an unprecedented 87.6% accuracy of risk stratification in the first trimester. 28 Indeed other groups have also leveraged Raman spectroscopy for in vivo measure of metabolic changes in pregnant patient cervix. 29 , 30 …”