Hypertension最新文献

Recent breakthroughs in artificial intelligence (AI) have caught the attention of many fields, including health care. The vision for AI is that a computer model can process information and provide output that is indistinguishable from that of a human and, in specific repetitive tasks, outperform a human's capability. The 2 critical underlying technologies in AI are used for supervised and unsupervised machine learning. Machine learning uses neural networks and deep learning modeled after the human brain from structured or unstructured data sets to learn, make decisions, and continuously improve the model. Natural language processing, used for supervised learning, is understanding, interpreting, and generating information using human language in chatbots and generative and conversational AI. These breakthroughs result from increased computing power and access to large data sets, setting the stage for releasing large language models, such as ChatGPT and others, and new imaging models using computer vision. Hypertension management involves using blood pressure and other biometric data from connected devices and generative AI to communicate with patients and health care professionals. AI can potentially improve hypertension diagnosis and treatment through remote patient monitoring and digital therapeutics.

Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.

Podocytes are specialized cells within the glomerular filtration barrier, which are crucial for maintaining glomerular structural integrity and convective ultrafiltration. Podocytes exhibit a unique arborized morphology with foot processes interfacing by slit diaphragms, ladder-like, multimolecular sieves, which provide size and charge selectivity for ultrafiltration and transmembrane signaling. Podocyte dysfunction, resulting from oxidative stress, dysregulated prosurvival signaling, or structural damage, can drive the development of proteinuria and glomerulosclerosis in hypertensive nephropathy. Functionally, podocyte injury leads to actin cytoskeleton rearrangements, foot process effacement, dysregulated slit diaphragm protein expression, and impaired ultrafiltration. Notably, the renin-angiotensin system plays a pivotal role in podocyte function, with beneficial AT2R (angiotensin receptor 2)-mediated nitric oxide (NO) signaling to counteract AT1R (angiotensin receptor 1)-driven calcium (Ca²⁺) influx and oxidative stress. Disruption of this balance contributes significantly to podocyte dysfunction and drives albuminuria, a marker of kidney damage and overall disease progression. Oxidative stress can also lead to sustained ion channel-mediated Ca²⁺ influx and precipitate cytoskeletal disorganization. The complex interplay between GPCR (G-protein coupled receptor) signaling, ion channel activation, and redox injury pathways underscores the need for additional research aimed at identifying targeted therapies to protect podocytes and preserve glomerular function. Earlier detection of albuminuria and podocyte injury through routine noninvasive diagnostics will also be critical in populations at the highest risk for the development of hypertensive kidney disease. In this review, we highlight the established mechanisms of oxidative stress-mediated podocyte damage in proteinuric kidney diseases, with an emphasis on a hypertensive renal injury. We will also consider emerging therapies that have the potential to selectively protect podocytes from redox-related injury.

Background: Renal denervation lowers blood pressure (BP) in patients with uncontrolled hypertension. We conducted an unbiased genomic screen to identify genetic variants that may associate with BP response to renal denervation (RDN).

Methods: Patients (n=268) with uncontrolled resistant hypertension (baseline BP, 166±21/90±15 mm Hg) who underwent endovascular RDN using the Symplicity catheter (Medtronic, Inc, Santa Rosa, CA) were included. Reduction in 24-hour ambulatory systolic BP was assessed at 6 months and divided into 2 groups: above and below the median response of 6.0 mm Hg, taking preintervention 24-hour ambulatory BP and regression to the mean into account. Whole exome sequencing assessing 249 669 variants, was conducted using Illumina NovaSeq technology read on a NovaSeq S4 Flow Cell device.

Results: We did not identify individual gene variants associated with BP response following RDN. These findings were confirmed after adjustment for sex and in a sensitivity analysis looking at tertiles of BP response. We also explored specific variants in AGT, ADD1, ADRB1, ADRB2, and SCNN1A that have been proposed as potential candidate genes for response and found no association (all P>0.13). Gene ontology analysis of variants across the 2 responder groups highlighted differences in biologic processes such as cell adhesion and molecular function such as protein tyrosine kinase activity.

Conclusions: The response to RDN, in terms of 24-hour BP reduction, was not associated with the genetic profile of patients with resistant hypertension. These data do not support the use of a genetic score to identify potential responders to RDN.

Background: Primary aldosteronism is predominantly caused by excessive aldosterone production from the adrenal cortex, and the aldosterone-producing structures could take many forms, like adenomas, nodules, micronodules, and so on. Most studies of primary aldosteronism were limited to the hotspot driver genes responsible for autonomous aldosterone production; however, the panoramic genetic architecture and genomic alterations of aldosterone-producing structures and their adjacent hyperplasia glands remain unknown.

Methods: In this study, whole-exome sequencing and transcriptome sequencing (RNA-seq) analyses were performed using functional nodules and matched hyperplasia tissues, which were microdissected guided by aldosterone synthase immunohistochemistry. Phylogenetic trees were constructed based on the shared and unique mutations, gene mutation spectrums, and clonal characteristics.

Results: The rates of mutations represented higher means of functional nodules than hyperplasia samples, and the little mutational overlap was shown between the 2 groups on phylogenetic trees. The mutations of the aldosterone driver gene (KCNJ5 or CACNA1D) were only observed in functional nodules and indicated almost the largest values of cancer cell fraction. Moreover, the functional nodules also harbored some potential variants related to cell proliferation, which were not detected in hyperplasia tissues. Transcriptome analysis suggested that only 25.5% upregulated and 23.3% downregulated genes overlapped between functional nodules and hyperplasia tissues.

Conclusions: This study demonstrated a genetic and transcriptome landscape of aldosterone-producing structures and adjacent hyperplasia glands in primary aldosteronism. The results indicated independent clonal origins on functional nodules and hyperplasia tissues, and little mutual evolutionary relationship was found on their phylogenetic trees.

Background: Patient education is needed to perform home blood pressure measurement (HBPM) according to blood pressure (BP) guidelines. It is not known how BP is measured at home and what education is provided, which was the aim of the study.

Methods: Mixed-methods study among Australian adults who perform HBPM (June to December 2023). Participants completed a 30-item online survey on whether they followed guideline recommendations and the education they received for HBPM. Phone interviews were conducted among a purposive sample to further explore survey topics.

Results: Participants (n=350) were middle-aged (58±16 years; 54% women), and most (n=250, 71%) had hypertension. Guideline recommendations for HBPM were not always followed by survey participants. Most participants measured BP seated (n=316, 90%) with the cuff fitted to a bare arm (n=269, 77%). Only 15% measured BP in the morning and evening (n=54) and 26% averaged the BP readings over 7 days (n=90). Interview participants (n=34) described measuring BP at "different times of the day after doing different things." One-third of participants (n=112, 37%) received education for HBPM, which interview participants described as vague verbal instructions from health care practitioners. Participants who received education did not perform high-quality HBPM. Participants who did not receive education mimicked BP measurement methods of health care practitioners, "I do it the way I've seen them do it."

Conclusions: HBPM is not performed according to guideline recommendations, and adults who received ad hoc education did not perform high-quality HBPM. These findings highlight a need for effective education to support HBPM for clinical decision-making.