{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T20:44:16Z","timestamp":1788986656316,"version":"build-2803163510"},"reference-count":52,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T00:00:00Z","timestamp":1738195200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["dom-pubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Obesity Metabolism"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>The utilization of targeted metabolomics technology promises to facilitate the identification of novel metabolic markers in women with gestational diabetes mellitus (GDM), which may in turn facilitate a more comprehensive investigation of the underlying mechanisms of gestational diabetes GDM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>In this study, we used targeted metabolomics to identify serum metabolites from women with or without GDM. The differential metabolites were categorized and analysed using pathway analyses, correlated with maternal glucose level, and assessed as predictors of GDM by receiver operating characteristics analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Notably, we detected 46 differential metabolites (24 upregulated and 22 downregulated) between GDM and normal pregnancy, which were catalogued into amino acids, peptides and analogues, and organic acids and derivatives, and others. Pathway analysis showed that amino acid metabolites were abnormally active. In addition, most of the metabolites were closely related to maternal glucose level. Of these, two metabolites were associated with fasting blood glucose, 22 correlated with 1\u2010h postprandial plasma glucose and 13 were related to 2\u2010h postprandial plasma glucose. Next, we identified metabolites that could better diagnose GDM with the area under the receiver operating characteristics above 0.75, including 2\u2010hydroxybutyric acid, itaconic acid,\n                      <jats:italic>O<\/jats:italic>\n                      \u2010acetylcarnitine, glutathione disulfide, P\u2010cresolsulfate, 2\u2010furoic acid,\n                      <jats:sc>l<\/jats:sc>\n                      \u2010asparagine,\n                      <jats:sc>d<\/jats:sc>\n                      \u2010biotin, choline and homovanillic acid.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We identified abnormal serum metabolites caused by GDM, which may contribute to our understanding of the pathomechanisms of GDM.<\/jats:p>\n                  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