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This study aimed to identify metabolic heterogeneity across distinct classes of biomarkers in GDM women and link this metabolic heterogeneity with adverse pregnancy outcomes through a complementary analytic approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted a retrospective cohort study using medical records of GDM women from 2018 to 2023 in Central South China. We identified metabolic heterogeneity of GDM using unsupervised\n                      <jats:italic>k<\/jats:italic>\n                      \u2010means clustering based on oral glucose tolerance test (OGTT) 0, 1 and 2\u2009h, uric acid (UA), triglycerides (TG), high\u2010density lipoprotein (HDL) and low\u2010density lipoprotein (LDL). We then linked this metabolic heterogeneity to adverse pregnancy outcomes through a complementary analytic approach, including logistic regression for group traits and machine learning for single features.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The first approach categorised 2246 GDM women into four distinct metabolic subgroups: reference (36%, relatively normal and average biomarker levels), fasting hyperglycemia (23.3%), hyperuricemia and hypertriglyceridemia (34.9%) and combined metabolic dysregulation (5.8%). The complementary analytic approach yielded consistent results. The hyperuricemia and hypertriglyceridemia subgroup showed higher odds of preterm delivery (OR\u2009=\u20091.86, 95% CI\u2009=\u20091.15\u20133.01). The combined metabolic dysregulation subgroup had the highest risks of preterm delivery (OR\u2009=\u20093.45, 95% CI\u2009=\u20091.79\u20136.65), insulin treatment (OR\u2009=\u200914.67, 95% CI\u2009=\u20098.72\u201324.66) and hypertensive disorders of pregnancy (OR\u2009=\u20092.72, 95% CI\u2009=\u20091.48\u20135.01). SHAP analysis showed that 57%\u201371% of the subgroup\u2010defining biomarkers ranked among the top 15 predictive features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study confirms the metabolic heterogeneity in GDM women, supporting synergistic effects among metabolic factors and the need for integrated metabolic management. The identified metabolic subgroups may facilitate risk stratification in real\u2010world settings by integrating biomarkers from multiple metabolic pathways. Women in the hyperuricemia and hypertriglyceridemia subgroup, as well as those in the combined metabolic dysregulation subgroup, urgently require interventions to improve pregnancy outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.71246","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T02:41:44Z","timestamp":1787107304000},"page":"10292-10305","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Linking Metabolic Subgroups and Adverse Pregnancy Outcomes in\n                    <scp>GDM<\/scp>\n                    : A Complementary Analytic Approach of Logistic Regressions and Machine Learning"],"prefix":"10.1111","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3668-5063","authenticated-orcid":false,"given":"Yanjing","family":"Zeng","sequence":"first","affiliation":[{"name":"Xiangya School of Nursing Central South University  Changsha 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