ECEESPE2025 Rapid Communications Rapid Communications 15: Metabolism, Nutrition and Obesity (6 abstracts)
A metabolomic signature of maternal BMI is associated with pregnancy complications: insights from the COPSAC2010 and VDAART mother-child cohorts
David Horner 1 , Rebecca Kofod Vinding 1 , Tingting Wang 1 , Mina Ali 1 , Mario Lovric 1 , Nicole Prince 2 , Jessica Lasky-Su 2 , Klaus Bønnelykke 1 , Jakob Stokholm 1 , Bo Chawes 1 & Morten Rasmussen 1
1COPSAC clinical research unit, Copenhagen, Denmark; 2Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States
JOINT3302
Introduction: Pregnancy complications such as gestational diabetes, preeclampsia, and caesarean section are more common among individuals living with higher BMI, likely due to metabolic disturbances. Blood metabolomics may offer mechanistic insights into these disturbances.
Methods: This study utilised data from the COPSAC2010 (n=684) and VDAART (n=881) mother-child cohorts, leveraging untargeted blood metabolomics and machine learning (sparse partial least square modelling) to investigate the association between pre-pregnancy BMI and pregnancy complications. A BMI-metabolite score was trained in the COPSAC2010 cohort and externally validated in VDAART.
Results: In the COPSAC2010 cohort, individuals with higher pre-pregnancy BMI (per 1 SD increase) had increased odds of gestational diabetes (OR 1.90, P<0.001), caesarean section (OR 1.23, P=0.023), and birth induction (OR 1.42, P<0.001). A BMI-metabolite score predicted preeclampsia (OR 1.54, P=0.030) and other pregnancy complications more effectively. Validation in VDAART confirmed the metabolite scores predictive value for gestational diabetes (OR 2.10, P<0.001) and preeclampsia (OR 2.12, P=0.002). Mediation analysis identified 16 metabolites mediating BMI’s link to gestational diabetes. These mediators showed stronger predictive value for gestational diabetes in VDAART during early (OR 1.81, P<0.001) and late gestation (OR 2.26, P<0.001) than the full-metabolite score. Pathway enrichment analysis revealed that sphingomyelins and metabolites associated with Vitamin A metabolism were significantly enriched with higher pre-pregnancy BMI (FDR <0.05)
Conclusion: Metabolomic profiling enhances understanding of how higher BMI during pregnancy may impact complications, offering opportunities for personalised risk assessment. These findings underscore the value of integrating metabolomics into prenatal care to optimise maternal and child health outcomes.
Volume 110
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