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    Biomedical Imaging and Instrumentation

    (E-196) Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy

    Friday, October 13, 2023
    9:30 AM – 10:30 AM PDT
    Location: Exhibit Hall - Row E - Poster # 196
    Introduction:: Preeclampsia is a pregnancy disorder that affects 5-10 % of pregnancies worldwide and can cause fetal mortality and maternal morbidity. Currently, preeclampsia cannot be predicted in clinics before the symptoms manifest. Thus, there is a need for rapid, cheap, and effective predictive methods to be used in clinics. We show Raman Spectroscopy could be an effective tool to predict preeclampsia based on a patient’s metabolic profile even before preeclampsia fully develops and thus help patients navigate this dangerous disorder.

    Materials and Methods:: We processed maternal plasma samples from 3 trimesters in both healthy and preeclamptic cohorts with the help of Raman spectroscopy. A Renishaw InVia Raman confocal microscope with a 785 nm laser was used to take the scans. For each sample, we aliquoted and dried 3 µl of plasma on a CaF2 disk, and then ran 100 Raman scans and averaged them. A custom MATLAB code was used to apply cosmic ray removal, baseline correction, smoothing and normalization. Next, we used T-ditributed Stochastic Neighbor Embedding (t-SNE) to show that there is separation between the healthy and preeclamptic cohorts. Metaboanalyst online tool was used to generate metabolic pathway analysis.

    Results, Conclusions, and Discussions:: Based on our data, the area under the curve- receiver operating characteristics (AUC-ROC) plot, showed a 0.904 score for the 1st trimester, which increased to 0.917 and 0.979 in the 2nd and 3rd trimester. This shows that our method can predict preeclampsia in the 1st trimester, which is currently not being done at clinics. We also identified key metabolites such as carotenoids, tryptophan, DNA, citric acid and lipids and how they changed in both cohorts. With our metabolic pathway analysis we also showed which pathways are more activated in preeclampsia in comparison to healthy, and which pathways change as the pregnancy progresses.

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