Key result
Endothelial dysfunction underlies hypertension and dyslipidemia, and AI-integrated omics may improve risk stratification.
Why the study?
To characterize the endothelium's role in the development of hypertension and dyslipidemia and to highlight promising therapeutic directions.
Endothelial dysfunction is the common pathophysiological mechanism linking hypertension and dyslipidemia, and integrating omics data with artificial intelligence offers a promising approach for personalized cardiovascular risk management.
May inform unified endothelial approaches in hypertension with dyslipidemia; leaves open validation of omics-AI risk stratification.
PURPOSE OF REVIEW: The goal of this article is to characterize the endothelium's role in the development of hypertension and dyslipidemia and to point out promising therapeutic directions. RECENT FINDINGS: Dyslipidemia may facilitate the development of hypertension, whereas the collaboration of these two silent killers potentiates the risk of atherosclerosis. The common pathophysiological denominator for hypertension and dyslipidemia is endothelial cell dysfunction, which manifests as dysregulation of homeostasis, redox balance, vascular tone, inflammation, and thrombosis. Treatment focused on mediators acting in these processes might be groundbreaking. Metabolomic research on hypertension and dyslipidemia has revealed new therapeutic targets. State-of-the-art solutions integrating interview, clinical examination, innovative imaging, and omics profiles along with artificial intelligence have been already shown to improve patients' risk stratification and treatment. Pathomechanisms underlying hypertension and dyslipidemia take place in the endothelium. Novel approaches involving endothelial biomarkers and bioinformatics advances could open new perspectives in patient management.
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Dąbrowska et al. (2023) conducted a review in Hypertension and Dyslipidemia. Endothelial dysfunction serves as the common pathophysiological denominator for hypertension and dyslipidemia, and integrating omics profiles with artificial intelligence may improve risk stratification.
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