Metabolomic Approaches to Preeclampsia Prediction and Management

Preeclampsia remains a leading cause of maternal and perinatal morbidity worldwide, characterised by hypertension, proteinuria and multi-organ dysfunction arising in the second half of pregnancy. Metabolomic strategies harness high-throughput analytical platforms to capture comprehensive small-molecule signatures in blood, urine or placental tissue, with the dual aims of identifying early biomarkers for risk stratification and elucidating perturbed biochemical pathways for targeted intervention. Both untargeted and targeted workflows have revealed distinct alterations in lipid metabolism, amino acid turnover, oxidative stress mediators and energy-generation pathways, reflecting placental ischaemia, endothelial dysfunction and inflammatory processes. Integration of metabolite panels with clinical parameters, such as mean arterial pressure and maternal history, enhances predictive accuracy, while longitudinal profiling offers insights into disease progression and response to prophylactic measures. Ultimately, these approaches promise to inform personalised surveillance schedules, guide prophylactic therapies and improve maternal–fetal outcomes by enabling timely management of at-risk pregnancies.

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Metabolomic Approaches to Preeclampsia Prediction and Management publication trend

The graph below shows the total number of articles in metabolomic approaches to preeclampsia prediction and management across all publications each year (not limited to Nature Index journals).

Technical terms

Metabolomics: comprehensive measurement of small-molecule metabolites in biological samples. Lipidomics: subset of metabolomics focused on profiling lipid species. Biomarker: measurable indicator of physiological or pathological processes. Raman spectroscopy: optical technique that provides molecular fingerprints via inelastic light scattering. Volatile organic compounds: low-molecular-weight metabolites detectable by gas chromatographic methods. Machine learning: computational frameworks that uncover patterns and predictive features in large datasets. Metabolic clock: model relating metabolite dynamics to gestational age and time to delivery.

References

1. Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy. Bioengineering & Translational Medicine (2023).
2. Untargeted Urinary Volatilomics Reveals Hexadecanal as a Potential Biomarker for Preeclampsia. International Journal of Molecular Sciences (2024).
3. Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women. Cell (2020).