ArticleCommunications medicine2025
A metabolomic signature of maternal BMI is associated with pregnancy complications across two independent pregnancy cohorts.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundMaternal 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.
methodsData 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.
resultsIn 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.
conclusionsThese findings suggest that integrating metabolomic profiling into prenatal care could improve the prediction and management of adverse pregnancy outcomes.
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