The Clinomics Approach to Precision Health

Electronic health record (EHR) data, such as clinical assays, anthropometric measures, and vital signs, provide support for clinical decision-making; however, data volume and heterogeneity can limit comprehensive interpretation.¹⁻⁸ Predictive models using various techniques have been developed to address this challenge and aid in clinical decision-making.⁹⁻²⁰              This study evaluated the utility of orthogonal partial least squares discriminant analysis (OPLS-DA) for identifying demographic-based differences in clinical biomarkers using National Health and Nutrition Examination Survey (NHANES) data. Two NHANES waves (Cycles: 1999-2000, 2001-2002) were analyzed using 53 clinical measures to differentiate participant data by age, sex, and ethnicity. Age-stratified analysis was conducted to account for confounding. Model performance was assessed with R²Y, R²X, Q², and variable importance to projection (VIP).              Models demonstrated consistent performance across NHANES waves, with replicate models having similar VIP scores. Age-stratification improved performances while retaining most important variables. These findings provide evidence for the utility of OPLS-DA for revealing reproducible, biologically associated patterns in clinical data.