Integrating Metabolomics with Family History for Preeclampsia Risk Prediction: Interpretability, Bias, and Real-World Performance, Implementation, and Equity Considerations

Nakawungu Catherine

Department of Pharmaceutical Microbiology and Biotechnology Kampala International University Uganda

Email: catherine.nakawungu@studwc.kiu.ac.ug

ABSTRACT

Preeclampsia is a leading cause of maternal and neonatal morbidity and mortality worldwide, and early risk prediction remains a major clinical challenge. This paper examines the integration of metabolomic signatures with family history information to improve preeclampsia risk prediction, focusing on interpretability, bias, real-world performance, implementation feasibility, and equity implications. Metabolomics provides high-dimensional biochemical insights that may reveal early pathophysiological changes, while family history captures heritable and shared environmental risk factors that are widely accessible in clinical settings. The proposed integrative framework explores how these complementary data sources can be combined through feature engineering, model construction, and validation strategies to enhance predictive accuracy without undermining usability. Particular attention is given to explainable modelling approaches, cohort representativeness, measurement and sampling bias, and fairness across populations. The analysis also addresses clinical workflow integration, decision-support thresholds, regulatory governance, data privacy, and cost considerations that influence real-world adoption. While integrating metabolomics may improve biological specificity, reliance on high-cost assays risks widening disparities unless accompanied by equitable implementation strategies and stakeholder engagement. The study concludes that combining metabolomic data with family history offers a promising pathway for more precise and clinically actionable preeclampsia risk assessment, provided that transparent modelling, rigorous validation, and accessibility-focused deployment remain central to implementation.

Keywords: Preeclampsia, Metabolomics, Family History, Risk Prediction Models, and Health Equity.

CITE AS: Nakawungu Catherine (2026). Integrating Metabolomics with Family History for Preeclampsia Risk Prediction: Interpretability, Bias, and Real-World Performance, Implementation, and Equity Considerations. RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES 6(1):35-43. https://doi.org/10.59298/RIJSES/2026/613543