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Background <p>Maternal obesity is increasingly common and linked to pregnancy complications, likely driven by underlying metabolic perturbations. This study investigates the association between maternal pre-pregnancy body mass index (BMI) and pregnancy complications through blood metabolomics, aiming to identify specific metabolites mediating these associations.</p> Methods <p>Data from the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC2010) and Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohorts were used, with untargeted blood metabolomics performed on blood samples taken during early-, mid-, and late gestation. Associations were assessed using multivariable logistic regression and mediation analyses to explore metabolite pathways linking maternal BMI with pregnancy complications.</p> Results <p>In the COPSAC2010 cohort, maternal pre-pregnancy BMI is associated with gestational diabetes (OR 1.90 [1.29-2.74], p = 6.75×10⁻⁴), caesarean section (OR 1.23 [1.03-1.47], p = 0.023), and birth induction (OR 1.42 [1.21-1.67], p = 2.86×10⁻⁵). A BMI-associated metabolite score is even more strongly associated with these complications and is independently associated with preeclampsia (OR 1.54 [1.04-2.26], p = 0.030). Validation in the VDAART cohort confirms the predictive value of the metabolite score for gestational diabetes (OR 2.10 [1.48-3.03], p = 4.97×10⁻⁵) and preeclampsia (OR 2.12 [1.32-3.47], p = 0.002), particularly in late gestation. Mediation analysis in COPSAC2010 identifies 16 metabolites as mediating the effect of BMI on gestational diabetes. A model based on this subset of metabolites significantly outperforms the full maternal BMI model in predicting outcomes during both early (p = 0.009) and late gestation (p = 0.016) in the VDAART cohort.</p> Conclusions <p>These findings suggest that integrating metabolomic profiling into prenatal care could improve the prediction and management of adverse pregnancy outcomes.</p>

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A metabolomic signature of maternal BMI is associated with pregnancy complications across two independent pregnancy cohorts

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月17日

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  • David Horner,
  • Rebecca Vinding,
  • Tingting Wang,
  • Mina Ali,
  • Mario Lovric,
  • Nicole Prince,
  • Jessica Lasky-Su,
  • Klaus Bønnelykke,
  • Jakob Stokholm,
  • Bo Chawes,
  • Morten Arendt Rasmussen

摘要

Background

Maternal obesity is increasingly common and linked to pregnancy complications, likely driven by underlying metabolic perturbations. This study investigates the association between maternal pre-pregnancy body mass index (BMI) and pregnancy complications through blood metabolomics, aiming to identify specific metabolites mediating these associations.

Methods

Data from the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC2010) and Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohorts were used, with untargeted blood metabolomics performed on blood samples taken during early-, mid-, and late gestation. Associations were assessed using multivariable logistic regression and mediation analyses to explore metabolite pathways linking maternal BMI with pregnancy complications.

Results

In the COPSAC2010 cohort, maternal pre-pregnancy BMI is associated with gestational diabetes (OR 1.90 [1.29-2.74], p = 6.75×10⁻⁴), caesarean section (OR 1.23 [1.03-1.47], p = 0.023), and birth induction (OR 1.42 [1.21-1.67], p = 2.86×10⁻⁵). A BMI-associated metabolite score is even more strongly associated with these complications and is independently associated with preeclampsia (OR 1.54 [1.04-2.26], p = 0.030). Validation in the VDAART cohort confirms the predictive value of the metabolite score for gestational diabetes (OR 2.10 [1.48-3.03], p = 4.97×10⁻⁵) and preeclampsia (OR 2.12 [1.32-3.47], p = 0.002), particularly in late gestation. Mediation analysis in COPSAC2010 identifies 16 metabolites as mediating the effect of BMI on gestational diabetes. A model based on this subset of metabolites significantly outperforms the full maternal BMI model in predicting outcomes during both early (p = 0.009) and late gestation (p = 0.016) in the VDAART cohort.

Conclusions

These findings suggest that integrating metabolomic profiling into prenatal care could improve the prediction and management of adverse pregnancy outcomes.