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This study aims to investigate the relationship between the first-trimester TyG index and the risk of adverse pregnancy outcomes in pregnant women.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The data for the retrospective cohort study were derived from the Maternal and Child Health Hospital of Longgang District, Shenzhen, China. To calculate the TyG index, health indicators were measured in the early pregnancy period (&lt;14 gestational weeks), including triglycerides and fasting blood glucose levels in pregnant women. Multivariable regression analysis and subgroup analysis were used to ascertain the independent association between the TyG index and the possibility of adverse pregnancy outcomes. Interaction analysis was performed to assess the potential heterogeneity of associations among subgroups. Nonlinear associations and the predictive value of the TyG index were explored using restricted cubic splines and receiver operating characteristic (ROC) curves. The discrimination and accuracy of the fully adjusted model were evaluated using calibration curves, Brier scores, and decision curve analysis (DCA). Mediation analysis was conducted to assess the impact of GDM (gestational diabetes mellitus) and PE (preeclampsia) as intermediaries on the risk of Preterm delivery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The study included a cohort of 11,942 pregnant women, with an average TyG index of 8.36\u00a0\u00b1\u00a00.41. Logistic regression analysis showed that after adjusting for covariates, for each 1-unit increase in the TyG index, the risk of GDM increased by 2.21-fold, and this result was significantly different across all quartiles. Compared to the lowest quartile group, the highest TyG index group had the highest risk of PE (OR: 2.89; 95% CI 1.39\u00a0~\u00a06.50), GH (gestational hypertension) (OR: 1.47; 95% CI 1.07\u00a0~\u00a02.02), and Preterm delivery (OR: 1.75; 95% CI 1.21\u00a0~\u00a02.56).The analysis of data stratification and interaction confirmed the validity of our study results. However, the analysis found no statistically significant association between the TyG index and low birth weight and macrosomia. GDM and PE were identified as partial mediating factors between TyG and the risk of preterm delivery, with variance contributions of 7.23% and 20.33%. The TyG index demonstrated the highest area under the curve (AUC) values in the ROC curves for GDM, PE, GH, and preterm delivery, with values of 0.61, 0.67, 0.58, and 0.56, respectively. The combination of the TyG index, maternal age, and pre-pregnancy body mass index predicted outcomes better than the TyG index alone (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01).After adjustment for confounders, the model showed good accuracy and net benefit in predicting adverse pregnancy outcomes, as supported by calibration curves, Brier scores, and decision curve analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An elevated first-trimester TyG index correlates with a heightened risk of GDM, PE, GH and Preterm delivery.The TyG index presents a promising tool for more effectively identifying populations at early risk for adverse pregnancy outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00404-025-07973-0","type":"journal-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T07:34:06Z","timestamp":1740555246000},"page":"915-927","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["An elevated triglyceride-glucose index in the first-trimester predicts adverse pregnancy outcomes: a retrospective cohort study"],"prefix":"10.1007","volume":"311","author":[{"given":"Songhong","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyang","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Man","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinxiu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"issue":"1","key":"7973_CR1","doi-asserted-by":"publisher","first-page":"S19","DOI":"10.2337\/dc23-S002","volume":"46","author":"NA ElSayed","year":"2023","unstructured":"ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D et al (2023) 2. 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