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Background <p>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.</p> Methods <p>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 (<i>n</i> = 53) and a testing set (<i>n</i> = 23) using a 7:3 ratio, and five diverse machine-learning models for predicting GDM were constructed.</p> Results <p>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, <i>P</i> &lt; 0.001) for the training set and 0.867 (95% CI: 0.789-0.952, <i>P</i> &lt; 0.001) for the test set in predicting GDM.</p> Conclusions <p>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.</p>

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Novel first-trimester serum biomarkers for early prediction of gestational diabetes mellitus

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月12日

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  • Siqi Tian,
  • Mingxi Liu,
  • Shuwen Han,
  • Haiqi Wu,
  • Rencai Qin,
  • Kongyang Ma,
  • Lianlian Liu,
  • Hongjin Zhao,
  • Yan Li

摘要

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.