A comprehensive narrative review on precision medicine approach to hypertension: exploring the role of genetics, epigenetics, microbiome, and artificial intelligence

Abstract

Background Hypertension (HTN) impacts approximately 1.28 billion individuals globally and poses a great burden of disease. The objectives of this study are to explore the role of genetics, epigenetics, microbiome, and artificial intelligence (AI) in the management of HTN. A thorough literature search was conducted across various databases including PubMed, Google Scholar, Web of Science (WoS), and Medline to retrieve articles related to the role of genetics, epigenetics, microbiome, and AI in the precision medicine of HTN. Genes—including ACE, NOS3, ADD1, CYP11B2, NPPA, and NPPB—have a profound impact on blood pressure (BP) regulation in our body and polymorphism in these key genes can lead to HTN. Up or down-regulation of genes by epigenetic factors such as miRNA-155, miRNA-210, and miRNA-122 can significantly contribute to the development of HTN. These genetic and epigenetic factors can also be used as specific targets for gene editing and gene therapy for long-term management of HTN. However, the implementation of these techniques has not been possible in clinical settings due to lack of human studies and safety concerns related to unpredictable DNA alterations, nucleotide deletions, and loss of allelespecific chromosomes. Modulation of gut microbiome through oral supplements, fecal microbiota transplant (FMT), and dietary interventions has emerged as one the most effective and safe techniques for managing HTN in human models. AI-based cutting-edge models have helped curate personalized diet plans based on an individual’s unique microbiome, genomic information, and physiological conditions leading to a reduction in BMI, fat, BP, and heart rate while improving overall cardiac health and gut microbial diversity. Despite the significant advantages offered by AI-based medicine, ethical concerns—related to data privacy, bias, and discrimination—and ineffective models have led to limited integration of AI in precision medicine of HTN. The integration of genetics, epigenetics, microbiome, and AI-based models can play a key role in improving the current landscape of precision medicine of HTN. These cutting-edge techniques can lead to a shift from the current one-size-fits all approach to more personalized treatment plan however further research in human models is needed to determine the safety and true efficacy of these techniques. Additionally, new AI-models need to be developed that address ethical concerns and are effective in real-world clinical settings.