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.