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.
