Decoding the mechanism of hypertension through multiomics profiling

Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management

Hypertension presents the largest modifiable public health challenge due to its high prevalence, its intimate relationship to cardiovascular diseases, and its complex pathogenesis and pathophysiology. Low awareness of blood pressure elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. Advances in artificial intelligence in hypertension have permitted the integrative analysis of large data sets including omics, clinical (with novel sensor and wearable technologies), health-related, social, behavioral, and environmental sources, and hold transformative potential in achieving large-scale, data-driven approaches toward personalized diagnosis, treatment, and long-term management. However, although the emerging artificial intelligence science may advance the concept of precision hypertension in discovery, drug targeting and development, patient care, and management, its clinical adoption at scale today is lacking. Recognizing that clinical implementation of artificial intelligence-based solutions need evidence generation, this opinion statement examines a clinician-centric perspective of the state-of-art in using artificial intelligence in the management of hypertension and puts forward recommendations toward equitable precision hypertension care.

Epigenomics and transcriptomics association study of blood pressure and incident diagnosis of hypertension in twins

Hypertension is the most frequent health-related condition worldwide and is a primary risk factor for renal and cardiovascular diseases. However, the underlying molecular mechanisms are still poorly understood. To uncover these mechanisms, multi-omics studies have significant potential, but such studies are challenged by genetic and environmental confounding - an issue that can be effectively reduced by studying intra-pair differences in twins. Here, we coupled data on hypertension diagnoses from the nationwide Danish Patient Registry to a study population of 740 twins for whom genome-wide DNA methylation and gene expression data were available together with measurements of systolic and diastolic blood pressure. We investigated five phenotypes: incident hypertension cases, systolic blood pressure, diastolic blood pressure, hypertension (140/90 mmHg), and hypertension (130/80 mmHg). Statistical analyses were performed using Cox (incident cases) or linear (remaining) regression analyses at both the individual-level and twin pair-level. Significant genes (p < 0.05) at both levels and in both types of biological data were investigated by bioinformatic analyses, including gene set enrichment analysis and interaction network analysis. Overall, most of the identified pathways related to the immune system, particularly inflammation, and biology of vascular smooth muscle cell. Of specific genes, lysine methyltransferase 2 A (KMT2A) was found to be central for incident hypertension, ataxia-telangiectasia mutated (ATM) for systolic blood pressure, and beta-actin (ACTB) for diastolic blood pressure. Noteworthy, lysine methyltransferase 2A (KMT2A) was also identified in the systolic and diastolic blood pressure analyses. Here, we present novel biomarkers for hypertension. This study design is surprisingly rare in the field of hypertension. We identified biological pathways related to vascular smooth muscle cells and the immune system, particular inflammation, to be associated with hypertension and blood pressure. Of specific genes, we identified KMT2A (lysine methyltransferase 2A) to be central for blood pressure and hypertension development. ACTB beta-actin, ATM ataxiatelangiectasia mutated, BP blood pressure, EWAS epigenome-wide association studies, KMT2A lysine methyltransferase 2A, LMER linear mixed effect regression, LR linear regression, TWAS transcriptome-wide association studies.

Mechanistic Investigation of Calcium Channel Regulation-Associated Genes in Pulmonary Arterial Hypertension and Signatures for Diagnosis

Pulmonary arterial hypertension (PAH) is a severe cardiopulmonary disorder with complex causes. Calcium channel blockers have long been used in its treatment. Our study aimed to validate experimental results showing increased calcium ion concentration in PAH patients. We investigated the impact of genes related to calcium channel regulation on PAH development and developed an accurate diagnostic model. Clinical trial data from serum of 18 healthy individuals and 18 patients with PAH were retrospectively analyzed. Concentrations of calcium and potassium ions were determined and compared. Datasets were retrieved, selecting genes associated with calcium ion release. R packages processed the datasets, filtering 174 common genes, and conducting Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Six hub genes were identified, and nomogram and logistic regression prediction models were constructed. Random forest filtered cross genes, and a diagnostic model was developed and validated using an artificial neural network. The 174 intersection genes related to calcium ions showed significant correlations with biological processes, cellular components, and molecular functions. Six key genes were obtained by constructing a protein-protein interaction network. A diagnostic model with high accuracy (> 90%) and diagnostic capability (AUC = 0.98) was established using a neural network algorithm. This study validated the experimental results, identified key genes associated with calcium ions, and developed a highly accurate diagnostic model using a neural network algorithm. These findings provide insights into the role of calcium release genes in PAH and demonstrate the potential of the diagnostic model for clinical application. However, due to limitations in sample size and a lack of prognosis data, the regulatory mechanisms of calcium ions in PAH patients and their impact on the clinical prognosis of PAH patients still need further exploration in the future.

mTOR Dysregulation, Insulin Resistance, and Hypertension

Worldwide, diabetes mellitus (DM) and cardiovascular diseases (CVDs) represent serious health problems associated with unhealthy diet and sedentarism. Metabolic syndrome (MetS) is characterized by obesity, dyslipidemia, hyperglycemia, insulin resistance (IR) and hypertension. The mammalian target of rapamycin (mTOR) is a serine/threonine kinase with key roles in glucose and lipid metabolism, cell growth, survival and proliferation. mTOR hyperactivation disturbs glucose metabolism, leading to hyperglycemia and further to IR, with a higher incidence in the Western population. Metformin is one of the most used hypoglycemic drugs, with anti-inflammatory, antioxidant and antitumoral properties, having also the capacity to inhibit mTOR. mTOR inhibitors such as rapamycin and its analogs everolimus and temsirolimus block mTOR activity, decrease the levels of glucose and triglycerides, and reduce body weight. The link between mTOR dysregulation, IR, hypertension and mTOR inhibitors has not been fully described. Therefore, the main aim of this narrative review is to present the mechanism by which nutrients, proinflammatory cytokines, increased salt intake and renin-angiotensin-aldosterone system (RAAS) dysregulation induce mTOR overactivation, associated further with IR and hypertension development, and also mTOR inhibitors with higher potential to block the activity of this protein kinase.

Epigenetic Alteration of Smooth Muscle Cells Regulates Endothelin-Dependent Blood Pressure and Hypertensive Arterial Remodeling

Long-standing hypertension (HTN) affects multiple organ systems and leads to pathologic arterial remodeling, which is driven largely by smooth muscle cell (SMC) plasticity. Although genome wide association studies (GWAS) have identified numerous variants associated with changes in blood pressure in humans, only a small percentage of these variants actually cause HTN. In order to identify relevant genes important in SMC function in HTN, we screened three separate human GWAS and Mendelian randomization studies to identify SNPs located within non-coding gene regions, focusing on genes encoding epigenetic enzymes, as these have been recently identified to control SMC fate in cardiovascular disease. We identified SNPs rs62059712 and rs74480102 in the promoter of the human JMJD3 gene and show that the minor C allele increases JMJD3 transcription in SMCs via increased SP1 binding to the JMJD3 promoter. Using our novel SMC-specific Jmjd3-deficient murine model ( Jmjd3 flox/flox Myh11 CreERT ), we show that loss of Jmjd3 in SMCs results in HTN, mechanistically, due to decreased EDNRB expression and a compensatory increase in EDNRA expression. As a translational corollary, through single cell RNA-sequencing (scRNA-seq) of human arteries, we found strong correlation between JMJD3 and EDNRB expression in SMCs. Further, we identified that JMJD3 is required for SMC-specific gene expression, and loss of JMJD3 in SMCs in the setting of HTN results in increased arterial remodeling by promoting the SMC synthetic phenotype. Our findings link a HTN-associated human DNA variant with regulation of SMC plasticity, revealing therapeutic targets that may be used in the screening and/or personalized treatment of HTN.

A perfectly imperfect engine: Utilizing the digital twin paradigm in pulmonary hypertension

Pulmonary hypertension (PH) is a severe medical condition with a number of treatment options, the majority of which are introduced without consideration of the underlying mechanisms driving it within an individual and thus a lack of tailored approach to treatment. The one exception is a patient presenting with apparent pulmonary arterial hypertension and shown to have vaso-responsive disease, whose clinical course and prognosis is significantly improved by high dose calcium channel blockers. PH is however characterized by a relative abundance of available data from patient cohorts, ranging from molecular data characterizing gene and protein expression in different tissues to physiological data at the organ level and clinical information. Integrating available data with mechanistic information at the different scales into computational models suggests an approach to a more personalized treatment of the disease using model-based optimization of interventions for individual patients. That is, constructing digital twins of the disease, customized to a patient, promises to be a key technology for personalized medicine, with the aim of optimizing use of existing treatments and developing novel interventions, such as new drugs. This article presents a perspective on this approach in the context of a review of existing computational models for different aspects of the disease, and it lays out a roadmap for a path to realizing it.

Microbial influence on blood pressure: unraveling the complex relationship for health insights

Hypertension, a critical global health concern, is characterized by persistent high blood pressure and is a major cause of cardiovascular events. This perspective explores the multifaceted implications of hypertension, its association with cardiovascular diseases, and the emerging role of the gut microbiota. The gut microbiota, a dynamic community in the gastrointestinal tract, plays a pivotal role in hypertension by influencing blood pressure through the generation of antioxidant, anti-inflammatory, and short-chain fatty acids metabolites, and the conversion of nitrates into nitric oxide. Antihypertensive medications interact with the gut microbiota, impacting drug pharmacokinetics and efficacy. Prebiotics and probiotics present promising avenues for hypertension management, with prebiotics modulating blood pressure through lipid and cholesterol modulation, and probiotics exhibiting a general beneficial effect. Personalized choices based on individual factors are crucial for optimizing prebiotic and probiotic interventions. In conclusion, the gut microbiota's intricate influence on blood pressure regulation offers innovative perspectives in hypertension therapeutics, with targeted strategies proving valuable for holistic blood pressure management and health promotion.

Conceptualised psycho-medical footprint for health status outcomes and the potential impacts for early detection and prevention of chronic diseases in the context of 3P medicine

Background

The Suboptimal Health Status Questionnaire-25 (SHSQ-25) is a distinctive medical psychometric diagnostic tool designed for the early detection of chronic diseases. However, the synaptic connections between the 25 symptomatic items and their relevance in supporting the monitoring of suboptimal health outcomes, which are precursors for chronic diseases, have not been thoroughly evaluated within the framework of predictive, preventive, and personalised medicine (PPPM/3PM). This baseline study explores the internal structure of the SHSQ-25 and demonstrates its discriminatory power to predict optimal and suboptimal health status (SHS) and develop photogenic representations of their distinct relationship patterns.

Methods

The cross-sectional study involved healthy Ghanaian participants (n = 217; aged 30-80 years; ~ 61% female), who responded to the SHSQ-25. The median SHS score was used to categorise the population into optimal and SHS. Graphical LASSO model and multi-dimensional scaling configuration methods were employed to describe the network structures for the two populations.

Results

We observed differences in the structural, node placement and node distance of the synaptic networks for the optimal and suboptimal populations. A statistically significant variance in connectivity levels was noted between the optimal (58 non-zero edges) and suboptimal (43 non-zero edges) networks (p = 0.024). Fatigue emerged as a prominently central subclinical condition within the suboptimal population, whilst the cardiovascular system domain had the greatest relevance for the optimal population. The contrast in connectivity levels and the divergent prominence of specific subclinical conditions across domain networks shed light on potential health distinctions.

Conclusions

We have demonstrated the feasibility of creating dynamic visualizers of the evolutionary trends in the relationships between the domains of SHSQ-25 relative to health status outcomes. This will provide in-depth comprehension of the conceptual model to inform personalised strategies to circumvent SHS. Additionally, the findings have implications for both health care and disease prevention because at-risk individuals can be predicted and prioritised for monitoring, and targeted intervention can begin before their symptoms reach an irreversible stage.

Supplementary information

The online version contains supplementary material available at 10.1007/s13167-023-00344-2.

Hypertensive Heart Failure

Despite overwhelming epidemiological evidence, the contribution of hypertension (HTN) to heart failure (HF) development has been undermined in current clinical practice. This is because approximately half of HF patients have been labeled as suffering from HF with preserved left ventricular (LV) ejection fraction (EF) (HFpEF), with HTN, obesity, and diabetes mellitus (DM) being considered virtually equally responsible for its development. However, this suggestion is obviously inaccurate, since HTN is by far the most frequent and devastating morbidity present in HFpEF. Further, HF development in obesity or DM is rare in the absence of HTN or coronary artery disease (CAD), whereas HTN often causes HF per se. Finally, unlike HTN, for most major comorbidities present in HFpEF, including anemia, chronic kidney disease, pulmonary disease, DM, atrial fibrillation, sleep apnea, and depression, it is unknown whether they precede HF or result from it. The purpose of this paper is to provide a contemporary overview on hypertensive HF, with a special emphasis on its inflammatory nature and association with autonomic nervous system (ANS) imbalance, since both are of pathophysiologic and therapeutic interest.

Maternal Prenatal Factors and Child Adiposity in Associations with Cardiometabolic Risk Factors in Term-Born Chinese Children at the Age of 2 Years

Early growth has long-lasting associations with adult metabolic health. However, the association of adiposity with cardiometabolic risk factors in toddlers remains poorly understood. This study aimed to examine the association of maternal prenatal factors and child adiposity with child cardiometabolic risk factors among boys and girls aged 2 years. This was a birth cohort study of 549 term-born children in Shanghai, China, with follow-up data at the age of 2-years. Child anthropometric and adiposity measurements included weight, length, and skinfold thickness (triceps, subscapular, and abdominal). Child cardiometabolic risk factors included random morning plasma glucose, serum insulin, lipids, and systolic and diastolic blood pressure (SBP, DBP). At 2 years, overweight/obesity (weight-for-length z score, ZWFL > 2) was associated with 12.6 (95%CI 7.7, 17.4) mmHg higher SBP, and 7.9 (4.1, 11.8) mmHg higher DBP in boys, with similar results observed in girls. Maternal hypertensive disorders of pregnancy were associated with 3.0 (0.1, 5.8) higher SBP, 3.17 (0.90, 5.44) mmHg higher DBP, 0.24 (0.01,0.47) mmol/L higher plasma glucose, and 0.26 (0.01,0.51) mmol/L higher serum triglycerides after adjustment for child age, sex, and ZWFL. Maternal hypertensive disorders of pregnancy and child overweight/obesity were associated with higher SBP and DBP at the age of 2 years.