Novel first-trimester serum biomarkers for early prediction of gestational diabetes mellitus
Abstract Background Gestational diabetes mellitus (GDM) is a common obstetric complication worldwide that seriously threatens maternal and fetal health. As the number of women conceiving through in vitro fertilization (IVF) continues to rise, this population is recognized as being at an elevated ris...
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025-04-01
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| Series: | Nutrition & Diabetes |
| Online Access: | https://doi.org/10.1038/s41387-025-00372-z |
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| _version_ | 1849744676682203136 |
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| author | Siqi Tian Mingxi Liu Shuwen Han Haiqi Wu Rencai Qin Kongyang Ma Lianlian Liu Hongjin Zhao Yan Li |
| author_facet | Siqi Tian Mingxi Liu Shuwen Han Haiqi Wu Rencai Qin Kongyang Ma Lianlian Liu Hongjin Zhao Yan Li |
| author_sort | Siqi Tian |
| collection | DOAJ |
| description | Abstract Background Gestational diabetes mellitus (GDM) is a common obstetric complication worldwide that seriously threatens maternal and fetal health. As the number of women conceiving through in vitro fertilization (IVF) continues to rise, this population is recognized as being at an elevated risk for GDM. However, there is still no consensus on the early prediction of GDM in IVF patients due to the lack of reliable biomarkers. Methods We compared the first-trimester serum cytokine and antibody profiles in 38 GDM women and 38 matched controls undergoing IVF treatment, based on the extensive human biobank of our large‑scale assisted reproductive cohort platform. The 76 samples were divided into a training set (n = 53) and a testing set (n = 23) using a 7:3 ratio, and five diverse machine-learning models for predicting GDM were constructed. Results By combining the top five differentially expressed first‑trimester serum biomarkers [including total immunoglobulin (Ig)G, total IgM, interleukin (IL)-7, anti‑phosphatidylserine (aPS)-IgG immune complexes (IC), and IL-15], a novel early prediction model was constructed, which achieved superior predictive value [area under the curve (AUC) and 95% confidence interval (CI) 0.906 (0.840-0.971), with a sensitivity of 75% and a specificity of 94.7%] for GDM development. The eXtreme Gradient Boosting (XGBoost) model achieved an AUC of 0.995 (95% CI: 0.995-1.000, P < 0.001) for the training set and 0.867 (95% CI: 0.789-0.952, P < 0.001) for the test set in predicting GDM. Conclusions We identified a set of novel first‑trimester serum cytokines and immune-related biomarkers and constructed an efficient first‑trimester prediction model for GDM in IVF population. These findings are expected to aid in the development of early predictive strategies for GDM and offer immunological insights for further mechanistic studies of GDM. |
| format | Article |
| id | doaj-art-55d5c5a5b46a44d99d3471f803ec5a5b |
| institution | DOAJ |
| issn | 2044-4052 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Nutrition & Diabetes |
| spelling | doaj-art-55d5c5a5b46a44d99d3471f803ec5a5b2025-08-20T03:10:13ZengNature Publishing GroupNutrition & Diabetes2044-40522025-04-0115111110.1038/s41387-025-00372-zNovel first-trimester serum biomarkers for early prediction of gestational diabetes mellitusSiqi Tian0Mingxi Liu1Shuwen Han2Haiqi Wu3Rencai Qin4Kongyang Ma5Lianlian Liu6Hongjin Zhao7Yan Li8State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Reproductive Medicine, Institute of Women, Children and Reproductive Health, Shandong UniversityState Key Laboratory of Reproductive Medicine and Offspring Health, Center for Reproductive Medicine, Institute of Women, Children and Reproductive Health, Shandong UniversityCheeloo College of Medicine, Shandong UniversityCentre for Infection and Immunity Studies, School of Medicine, The Sun Yat-sen UniversityCentre for Infection and Immunity Studies, School of Medicine, The Sun Yat-sen UniversityCentre for Infection and Immunity Studies, School of Medicine, The Sun Yat-sen UniversityModel Animal Research Center, Shandong UniversityDepartment of Cardiology, Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityState Key Laboratory of Reproductive Medicine and Offspring Health, Center for Reproductive Medicine, Institute of Women, Children and Reproductive Health, Shandong UniversityAbstract Background Gestational diabetes mellitus (GDM) is a common obstetric complication worldwide that seriously threatens maternal and fetal health. As the number of women conceiving through in vitro fertilization (IVF) continues to rise, this population is recognized as being at an elevated risk for GDM. However, there is still no consensus on the early prediction of GDM in IVF patients due to the lack of reliable biomarkers. Methods We compared the first-trimester serum cytokine and antibody profiles in 38 GDM women and 38 matched controls undergoing IVF treatment, based on the extensive human biobank of our large‑scale assisted reproductive cohort platform. The 76 samples were divided into a training set (n = 53) and a testing set (n = 23) using a 7:3 ratio, and five diverse machine-learning models for predicting GDM were constructed. Results By combining the top five differentially expressed first‑trimester serum biomarkers [including total immunoglobulin (Ig)G, total IgM, interleukin (IL)-7, anti‑phosphatidylserine (aPS)-IgG immune complexes (IC), and IL-15], a novel early prediction model was constructed, which achieved superior predictive value [area under the curve (AUC) and 95% confidence interval (CI) 0.906 (0.840-0.971), with a sensitivity of 75% and a specificity of 94.7%] for GDM development. The eXtreme Gradient Boosting (XGBoost) model achieved an AUC of 0.995 (95% CI: 0.995-1.000, P < 0.001) for the training set and 0.867 (95% CI: 0.789-0.952, P < 0.001) for the test set in predicting GDM. Conclusions We identified a set of novel first‑trimester serum cytokines and immune-related biomarkers and constructed an efficient first‑trimester prediction model for GDM in IVF population. These findings are expected to aid in the development of early predictive strategies for GDM and offer immunological insights for further mechanistic studies of GDM.https://doi.org/10.1038/s41387-025-00372-z |
| spellingShingle | Siqi Tian Mingxi Liu Shuwen Han Haiqi Wu Rencai Qin Kongyang Ma Lianlian Liu Hongjin Zhao Yan Li Novel first-trimester serum biomarkers for early prediction of gestational diabetes mellitus Nutrition & Diabetes |
| title | Novel first-trimester serum biomarkers for early prediction of gestational diabetes mellitus |
| title_full | Novel first-trimester serum biomarkers for early prediction of gestational diabetes mellitus |
| title_fullStr | Novel first-trimester serum biomarkers for early prediction of gestational diabetes mellitus |
| title_full_unstemmed | Novel first-trimester serum biomarkers for early prediction of gestational diabetes mellitus |
| title_short | Novel first-trimester serum biomarkers for early prediction of gestational diabetes mellitus |
| title_sort | novel first trimester serum biomarkers for early prediction of gestational diabetes mellitus |
| url | https://doi.org/10.1038/s41387-025-00372-z |
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