{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:53:42Z","timestamp":1785423222650,"version":"3.56.0"},"reference-count":20,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication. Current diagnostic approaches are inherently retrospective, necessitating the development of effective early prediction models for timely intervention and improved outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and validate a prediction model for GDM risk by integrating first-trimester clinical and metabolomic indicators.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A retrospective cohort study was conducted involving 342 singleton pregnancies that received routine antenatal care and underwent mid-pregnancy oral glucose tolerance tests (OGTT) from January 2022 to December 2024. Participants were randomly allocated to a training set (n\u202f=\u202f239) and a validation set (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f103) in a 7:3 ratio. Core predictors were identified through a univariate analysis, LASSO regression, and subsequent multivariable logistic regression. Four machine learning models\u2014Random Forest, Support Vector Machine (SVM), Gradient Boosting Machine, and Logistic Regression\u2014were constructed and compared. Performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve analysis. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) values.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A multivariable analysis identified seven independent predictors: pre-pregnancy BMI, first-trimester fasting plasma glucose, triglycerides, C-reactive protein, and the branched-chain amino acid score (risk factors), as well as pregnancy-associated plasma protein-A and 1,5-anhydroglucitol (protective factors). In the validation set, the SVM model achieved optimal performance with an AUC of 0.861 (95% confidence interval (CI): 0.772\u20130.949). Calibration and decision curve analyses demonstrated good agreement between predicted and observed risks and affirmed clinical utility across a wide threshold probability range.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A prediction model integrating first-trimester clinical and metabolomic markers was successfully developed and validated. The model demonstrates favorable predictive accuracy and clinical applicability, offering potential as an auxiliary tool for early risk stratification and personalized GDM management. Future multi-center external validation is warranted to confirm generalizability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1806848","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T05:57:05Z","timestamp":1781675825000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Development and validation of a model integrating clinical and metabolomic markers for gestational diabetes mellitus prediction"],"prefix":"10.3389","volume":"13","author":[{"given":"Guixi","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Obstetrics, Jinan Central Hospital","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"Cd011507","DOI":"10.1002\/14651858.CD011507.pub3","article-title":"Antenatal dietary supplementation with myo-inositol for preventing gestational diabetes","volume":"2023","author":"Motuhifonua","year":"2023","journal-title":"Cochrane Database Syst Rev"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"612829","DOI":"10.3389\/fendo.2021.612829","article-title":"Hyperglycemia at 1h-OGTT in pregnancy: a reliable predictor of metabolic outcomes?","volume":"12","author":"Succurro","year":"2021","journal-title":"Front Endocrinol (Lausanne)"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"3782","DOI":"10.1080\/14767058.2021.1946779","article-title":"Do patients with a single abnormal OGTT value need a globally admitted definition such as \"borderline GDM\"? 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study","volume":"15","author":"Antoniou","year":"2024","journal-title":"Front Endocrinol (Lausanne)"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1186\/s12958-024-01320-9","article-title":"Pre-pregnancy LDL\/HDL and total cholesterol\/HDL ratios are strong predictors of gestational diabetes mellitus in women undergoing assisted reproductive technologies","volume":"22","author":"Liu","year":"2024","journal-title":"Reprod Biol Endocrinol"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"12257","DOI":"10.1038\/s41598-022-16539-y","article-title":"Branched-chain and aromatic amino acid levels response to an oral glucose load associated with gestational diabetes mellitus","volume":"12","author":"Gao","year":"2022","journal-title":"Sci Rep"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"10.3390\/ijerph192114142","DOI":"10.3390\/ijerph192114142","article-title":"Prepregnancy protein source and BCAA intake are associated with gestational 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