Sex Hormone Androgen Elevates Blood Pressure Through Gut Microbiota-TMAO Pathway.

Background: Hypertension is a leading risk factor for all-cause mortality worldwide, affecting ≈1.3 billion people. Imbalanced gut microbiota contributes to blood pressure elevation.

Epigenetic Histone β-Hydroxybutyrylation Contributes to Renoprotection by β-Hydroxybutyrate in the Dahl Rat.

Background: Previously, we demonstrated that the ketone body, β-hydroxybutyrate, is a potent antihypertensive and reno-protective metabolite in Dahl Salt-Sensitive rats. However, the mechanism by which β-hydroxybutyrate confers these beneficial effects is understudied. Here we focused on determining whether the reno-protective effect of β-hydroxybutyrate is due to its known ability to epigenetically remodel chromatin via histone β-hydroxybutyrylation.

Ketone body β-hydroxybutyrate-mediated histone β-hydroxybutyrylation upregulates lipolysis and attenuates metabolic syndrome.

Metabolic syndrome (MetS) is on the rise globally. Features of MetS include obesity, hypertension, and abnormal glucose tolerance. Exercise, keto diets, and intermittent fasting are lifestyle modifications recommended to lower MetS, all of which increase the production of the endogenous ketone body β-hydroxybutyrate.

Ketone body mediated histone β-hydroxybutyrylation is reno-protective.

Starvation, intermittent fasting and exercise, all of which are recommended lifestyle modifiers share a common metabolic signature, ketogenesis to generate the ketone bodies, predominantly β-hydroxybutyrate. β-hydroxybutyrate exerts beneficial effects across various contexts, preventing or mitigating disease. We hypothesized that these dynamic health benefits of β-hydroxybutyrate might stem from its ability to regulate genome architecture through chromatin remodeling via histone β-hydroxybutyrylation, thereby influencing the transcriptome.

Salt-Responsive Gut Microbiota Induces Sex-Specific Blood Pressure Changes.

Background: Tryptophan metabolism is important in blood pressure regulation. The tryptophan-indole pathway is exclusively mediated by the gut microbiota. ACE2 (angiotensin-converting enzyme 2) participates in tryptophan absorption, and a lack of ACE2 leads to changes in the gut microbiota.

Genetically engineered Lactobacillus paracasei rescues colonic angiotensin converting enzyme 2 (ACE2) and attenuates hypertension in female Ace2 knock out rats.

Engineered gut microbiota represents a new frontier in medicine, in part serving as a vehicle for the delivery of therapeutic biologics to treat a range of host conditions. The gut microbiota plays a significant role in blood pressure regulation; thus, manipulation of gut microbiota is a promising avenue for hypertension treatment. In this study, we tested the potential of Lactobacillus paracasei, genetically engineered to produce and deliver human angiotensin converting enzyme 2 (Lacto-hACE2), to regulate blood pressure in a rat model of hypertension with genetic ablation of endogenous Ace2 (Ace2 and Ace2).

Survey and Evaluation of Hypertension Machine Learning Research.

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly.

Repurposing a Drug Targeting Inflammatory Bowel Disease for Lowering Hypertension.

Background The gut and gut microbiota, which were previously neglected in blood pressure regulation, are becoming increasingly recognized as factors contributing to hypertension. Diseases affecting the gut such as inflammatory bowel disease (IBD) present with aberrant energy metabolism of colonic epithelium and gut dysbiosis, both of which are also mechanisms contributing to hypertension. We reasoned that current measures to remedy deficits in colonic energy metabolism and dysbiosis in IBD could also ameliorate hypertension.

Application of Artificial Intelligence in Cardiovascular Medicine.

The advent of advances in machine learning (ML)-based techniques has popularized wide applications of artificial intelligence (AI) in various fields ranging from robotics to medicine. In recent years, there has been a surge in the application of AI to research in cardiovascular medicine, which is largely driven by the availability of large-scale clinical and multi-omics datasets. Such applications are providing a new perspective for a better understanding of cardiovascular disease (CVD), which could be used to develop novel diagnostic and therapeutic strategies.

Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases.

Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is a clinical need to further improve the diagnosis of IBD. As gut dysbiosis is reported in patients with IBD, we hypothesized that supervised machine learning (ML) could be used to analyze gut microbiome data for predictive diagnostics of IBD. To test our hypothesis, fecal 16S metagenomic data of 729 subjects with IBD and 700 subjects without IBD from the American Gut Project were analyzed using five different ML algorithms.

Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease.

Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not clearly established, the reported associations between alterations in gut microbiota and CVD are prominent.

Machine learning-based classification and diagnosis of clinical cardiomyopathies.

Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common types of cardiomyopathies leading to heart failure. Accurate diagnostic classification of different types of cardiomyopathies is critical for precision medicine in clinical practice. In this study, we hypothesized that machine learning (ML) can be used as a novel diagnostic approach to analyze cardiac transcriptomic data for classifying clinical cardiomyopathies.

Identification of Upstream Transcriptional Regulators of Ischemic Cardiomyopathy Using Cardiac RNA-Seq Meta-Analysis.

Ischemic cardiomyopathy (ICM), characterized by pre-existing myocardial infarction or severe coronary artery disease, is the major cause of heart failure (HF). Identification of novel transcriptional regulators in ischemic HF can provide important biomarkers for developing new diagnostic and therapeutic strategies. In this study, we used four RNA-seq datasets from four different studies, including 41 ICM and 42 non-failing control (NF) samples of human left ventricle tissues, to perform the first RNA-seq meta-analysis in the field of clinical ICM, in order to identify important transcriptional regulators and their targeted genes involved in ICM.