Predicting Pregnancy Complications: Is BMI Enough, or Are Blood Metabolic Profiles Better?

A study published in Communications Medicine analyzed blood samples from two independent pregnancy cohorts to explore how metabolites linked to maternal BMI relate to adverse outcomes. Using machine learning, researchers identified a 46-metabolite profile that correlated with BMI but showed stronger associations with pregnancy complications than BMI alone. A focused subset of 16 metabolites statistically mediated the relationship between obesity and gestational diabetes, suggesting that targeted blood profiling could refine prenatal risk stratification.

Rising Obesity and Pregnancy Risk

The global increase in obesity—especially in Western countries—has been accompanied by a rise in high-risk pregnancies. Maternal obesity is a well-recognized risk factor for GDM and preeclampsia. In routine practice, clinicians rely on pre-pregnancy BMI to estimate these risks. However, BMI reflects only height and weight and does not capture metabolic health. As a result, individuals with normal BMI may still carry significant metabolic risk, while some with higher BMI may be metabolically healthy.

A Biological Perspective: Metabolomics

Metabolomics examines small molecules circulating in the blood that reflect real-time metabolic activity. This approach provides a more nuanced biological snapshot of metabolic health and may better capture pregnancy-related metabolic stress than anthropometric measures alone.

Cohorts, Sampling, and Machine Learning

The analysis drew on two independent cohorts: one in Denmark and one in the United States. Plasma samples were assessed using untargeted liquid chromatography–tandem mass spectrometry (LC-MS/MS), enabling detection of hundreds of metabolites. A machine-learning method based on sparse partial least squares regression identified metabolite patterns associated with BMI and adverse pregnancy outcomes.

The Danish cohort (684 women, sampled at 24 weeks’ gestation) served as the discovery population. The U.S. cohort (775 women, sampled in early and late pregnancy) was used for validation.

Metabolic Profiles and Pregnancy Complications

Across both cohorts, 640 metabolites were linked to maternal BMI and pregnancy complications. These were refined to a robust 46-metabolite signature strongly associated with GDM and preeclampsia. Key contributors included sphingolipids involved in cell signaling and metabolites related to vitamin A metabolism.

In the discovery cohort, higher BMI was associated with GDM (odds ratio [OR] 1.90). The metabolite-based score was a stronger predictor (OR 2.47). Notably, BMI alone did not significantly predict preeclampsia, whereas the metabolite score did.

Timing, Validation, and Mediation

Validation confirmed the metabolomic signature’s robustness across populations. Timing was critical: metabolite scores measured in late pregnancy strongly predicted GDM and preeclampsia, while early-pregnancy scores were far less informative.

Mediation analyses identified 16 metabolites that partially explained the link between obesity and GDM. Plant-derived metabolites (e.g., carotene diol) were associated with lower diabetes risk, whereas lipid-related metabolites (ceramides and sphingomyelins) were linked to higher risk. A predictive model using only these 16 metabolites outperformed a BMI-only model for GDM prediction.

Implications for Prenatal Risk Assessment

These findings highlight the limitations of BMI as a standalone predictor and underscore the potential of metabolomic profiling as a more biologically meaningful tool. Combining BMI with metabolite-based risk scores may substantially improve prediction of GDM and preeclampsia.

While observational and conducted in high-resource settings, the evidence supports further exploration of blood-based metabolomic screening in prenatal care. With additional validation and comparison to existing tools, this approach could enable earlier identification of high-risk pregnancies and support more personalized monitoring and intervention strategies.

Source

NewsMedical Life Sciences – acess December 2025