First Trimester Prediction of Preterm Birth in Patient Plasma with Machine-Learning-Guided Raman Spectroscopy and Metabolomics.
First Trimester Prediction of Preterm Birth in Patient Plasma with Machine-Learning-Guided Raman Spectroscopy and Metabolomics.
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DOI：
10.1021/acsami.3c04260
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发表时间：
2023-08
影响因子：
9.5
通讯作者：
Lilly Synan;S. Ghazvini;Saji Uthaman;Gabriel Cutshaw;Che-Yu Lee;Joshua R. Waite;Xiaona Wen;S. Sarkar;Eugene C Lin;M. Santillan;D. Santillan;Rizia Bardhan
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文献类型：
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作者：
Lilly Synan;S. Ghazvini;Saji Uthaman;Gabriel Cutshaw;Che-Yu Lee;Joshua R. Waite;Xiaona Wen;S. Sarkar;Eugene C Lin;M. Santillan;D. Santillan;Rizia Bardhan
早产(PTB)是全球婴儿死亡的主要原因。目前的临床措施往往无法识别可能早产的女性。因此，准确的筛查工具对肺结核的早期预测势在必行。在这里，我们表明拉曼光谱是一种很有前途的研究生物界面的工具，我们检查了肺结核患者和足月(健康)孕妇血浆母体代谢组的差异。我们确定了15种具有统计学意义的代谢物，这些代谢物可以预测肺结核的发生。质谱学代谢组学验证了拉曼结果，确定了富含肺结核的关键代谢途径。我们还表明，单靠患者临床信息和标准炎性细胞因子的蛋白定量都不能识别肺结核患者。我们首次表明，在机器学习的指导下，拉曼和临床数据的协同集成导致了前所未有的85.1%的PTB风险分层准确性，这在目前临床上是不可能的。代谢物和临床特征之间的相关性突出表明，体重指数和产妇年龄是代谢重排的贡献者。我们的发现表明，拉曼光谱筛查可以补充目前的产前保健，以早期预测肺结核，我们的方法可以转换到其他患者特定的生物学接口。
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.