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A Molecular Diagnostic Approach for Hypertension Through Establishment of a Metabolite Risk Score Using a Multi-Metabolite Panel Identified via UHPLC-MS/MS.

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چکیده اصلی

Hypertension (HTN) is often asymptomatic and difficult to detect using blood pressure (BP) measurements unless BP is substantially elevated. Given its association with metabolic alterations and complications, this study aimed to establish a metabolite risk score (MRS) as a molecular tool to complement BP-based diagnosis. Plasma samples and clinical data from healthy individuals and HTN patients were obtained through the Korea Biobank Network, and non-targeted metabolomics was performed. Eight HTN-associated key metabolites were selected by least absolute shrinkage and selection operator (LASSO) regression. An MRS was calculated as their weighted sum in the discovery set and subsequently validated in the replication set. The MRS showed strong discriminative performance for HTN status in the replication set [area under the curve (AUC) = 0.926, 95% confidence interval (CI): 0.876-0.976] and remained significantly associated with prevalent HTN after adjustment for age and BMI [odds ratio (OR) = 1.747, 95% CI: 1.317-2.318, p < 0.001]. At the MRS cut-off, classification accuracy in the replication set was approximately 84%, with 83.7% sensitivity and 84% specificity. The MRS also showed weak-to-moderate positive correlations with systolic BP in both the discovery set (r = 0.320, p = 0.001) and the replication set (r = 0.335, p < 0.001); however, these correlations were no longer statistically significant after adjustment for age and BMI. These findings support further evaluation of the MRS as a complementary molecular approach for HTN discrimination. Integration with other omics platforms may facilitate the development of more comprehensive molecular approaches for HTN. Further validation in larger prospective longitudinal cohorts is required before its potential clinical application.

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کلیدواژه‌ها

discriminative performancehypertensionmetabolite risk scoremetabolomicsmolecular diagnosis
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