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The current clinical test of blood glucose levels late in the second trimester is inadequate for early detection of GDM. Here we show the utility of Raman spectroscopy (RS) for rapid and highly sensitive maternal metabolome screening for GDM in the first trimester. Key metabolites, including phospholipids, carbohydrates, and major amino acids, were identified with RS and validated with mass spectrometry, enabling insights into associated metabolic pathway enrichment. Using classical machine learning (ML) approaches, we showed the performance of the RS metabolic model (cross\u2010validation AUC 0.97) surpassed that achieved with patients' clinical data alone (cross\u2010validation AUC 0.59) or prior studies with single biomarkers. Further, we analyzed novel proteins and identified fetuin\u2010A as a promising candidate for early GDM prediction. A correlation analysis showed a moderate to strong correlation between multiple metabolites and proteins, suggesting a combined protein\u2010metabolic analysis integrated with ML would enable a powerful screening platform for first trimester diagnosis. Our study underscores RS metabolic profiling as a cost\u2010effective tool that can be integrated into the current clinical workflow for accurate risk stratification of GDM and to improve both maternal and neonatal outcomes.<\/jats:p>","DOI":"10.1002\/btm2.10691","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T01:15:21Z","timestamp":1721178921000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Diagnosis of pregnancy disorder in the first\u2010trimester patient plasma with Raman spectroscopy and protein analysis"],"prefix":"10.1002","volume":"9","author":[{"given":"Ansuja P.","family":"Mathew","sequence":"first","affiliation":[{"name":"Department of Chemical and Biological Engineering Iowa State University  Ames Iowa USA"},{"name":"Nanovaccine Institute Iowa State University  Ames Iowa 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