Diagnostic Strategies in Pulmonary Embolism.

Key to the diagnosis of pulmonary embolism (PE) is a careful bedside evaluation. After this, there are three further diagnostic steps. In all patients, estimation of the clinical probability of PE is performed. The other two steps are measurement of D-dimer when indicated and chest imaging when indicated. The clinical probability of PE is estimated at low, moderate, or high. The prevalence of PE is less than 15% among patients with low clinical probability, 15 to 40% with moderate clinical probability, and >40% in patients with high clinical probability. Clinical gestalt has been found to be very useful in estimating probability of PE. However, clinical prediction rules, such as Wells criteria, the modified Geneva score, and the PE rule out criteria have been advocated as adjuncts. In patients with high clinical probability, the high prevalence of PE can lower the D-dimer negative predictive value, which could increase the risk of diagnostic failure. Consequently, patients with high probability for PE need to proceed directly to chest imaging, without prior measurement of D-dimer level. Key studies in determining which low to moderate probability patients require chest imaging are the Age-adjusted D-dimer cutoff levels to rule out pulmonary embolism (ADJUST-PE), the Simplified diagnostic management of suspected pulmonary embolism (YEARS), and the Pulmonary Embolism Graduated D-Dimer trials. In patients with low clinical probability, PE can be excluded without imaging studies if D-dimer is less than 1,000 ng/mL. In patients in whom there is not a low likelihood for PE, this can be excluded without imaging studies if the D-dimer is below the age-adjusted threshold.

Toward mechanistic medical digital twins: some use cases in immunology.

A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific-and practical-medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.

Forum on immune digital twins: a meeting report.

Medical digital twins are computational models of human biology relevant to a given medical condition, which are tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. In February 2023, an international group of experts convened for two days to discuss these challenges related to immune digital twins. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.

Modeling the roles of cohesotaxis, cell-intercalation, and tissue geometry in collective cell migration of Xenopus mesendoderm.

Collectively migrating Xenopus mesendoderm cells are arranged into leader and follower rows with distinct adhesive properties and protrusive behaviors. In vivo, leading row mesendoderm cells extend polarized protrusions and migrate along a fibronectin matrix assembled by blastocoel roof cells. Traction stresses generated at the leading row result in the pulling forward of attached follower row cells. Mesendoderm explants removed from embryos provide an experimentally tractable system for characterizing collective cell movements and behaviors, yet the cellular mechanisms responsible for this mode of migration remain elusive. We introduce an agent-based computational model of migrating mesendoderm in the Cellular-Potts computational framework to investigate the relative contributions of multiple parameters specific to the behaviors of leader and follower row cells. Sensitivity analyses identify cohesotaxis, tissue geometry, and cell intercalation as key parameters affecting the migration velocity of collectively migrating cells. The model predicts that cohesotaxis and tissue geometry in combination promote cooperative migration of leader cells resulting in increased migration velocity of the collective. Radial intercalation of cells towards the substrate is an additional mechanism to increase migratory speed of the tissue. Summary Statement: We present a novel Cellular-Potts model of collective cell migration to investigate the relative roles of cohesotaxis, tissue geometry, and cell intercalation on migration velocity of Xenopus mesendoderm.

Tissue Forge: Interactive biological and biophysics simulation environment.

Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular, using a simple, consistent interface. While Tissue Forge is designed to simplify solving problems in complex subcellular, cellular and tissue biophysics, it supports applications ranging from classic molecular dynamics to agent-based multicellular systems with dynamic populations. Tissue Forge users can build and interact with models and simulations in real-time and change simulation details during execution, or execute simulations off-screen and/or remotely in high-performance computing environments. Tissue Forge provides a growing library of built-in model components along with support for user-specified models during the development and application of custom, agent-based models. Tissue Forge includes an extensive Python API for model and simulation specification via Python scripts, an IPython console and a Jupyter Notebook, as well as C and C++ APIs for integrated applications with other software tools. Tissue Forge supports installations on 64-bit Windows, Linux and MacOS systems and is available for local installation via conda.

General, open-source vertex modeling in biological applications using Tissue Forge.

Vertex models are a widespread approach for describing the biophysics and behaviors of multicellular systems, especially of epithelial tissues. Vertex models describe a wide variety of developmental scenarios and behaviors like cell rearrangement and tissue folding. Often, these models are implemented as single-use or closed-source software, which inhibits reproducibility and decreases accessibility for researchers with limited proficiency in software development and numerical methods. We developed a physics-based vertex model methodology in Tissue Forge, an open-source, particle-based modeling and simulation environment. Our methodology describes the properties and processes of vertex model objects on the basis of vertices, which allows integration of vertex modeling with the particle-based formalism of Tissue Forge, enabling an environment for developing mixed-method models of multicellular systems. Our methodology in Tissue Forge inherits all features provided by Tissue Forge, delivering open-source, extensible vertex modeling with interactive simulation, real-time simulation visualization and model sharing in the C, C++ and Python programming languages and a Jupyter Notebook. Demonstrations show a vertex model of cell sorting and a mixed-method model of cell migration combining vertex- and particle-based models. Our methodology provides accessible vertex modeling for a broad range of scientific disciplines, and we welcome community-developed contributions to our open-source software implementation.

Using deep LSD to build operators in GANs latent space with meaning in real space.

Generative models rely on the idea that data can be represented in terms of latent variables which are uncorrelated by definition. Lack of correlation among the latent variable support is important because it suggests that the latent-space manifold is simpler to understand and manipulate than the real-space representation. Many types of generative model are used in deep learning, e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs). Based on the idea that the latent space behaves like a vector space Radford et al. (2015), we ask whether we can expand the latent space representation of our data elements in terms of an orthonormal basis set. Here we propose a method to build a set of linearly independent vectors in the latent space of a trained GAN, which we call quasi-eigenvectors. These quasi-eigenvectors have two key properties: i) They span the latent space, ii) A set of these quasi-eigenvectors map to each of the labeled features one-to-one. We show that in the case of the MNIST image data set, while the number of dimensions in latent space is large by design, 98% of the data in real space map to a sub-domain of latent space of dimensionality equal to the number of labels. We then show how the quasi-eigenvectors can be used for Latent Spectral Decomposition (LSD). We apply LSD to denoise MNIST images. Finally, using the quasi-eigenvectors, we construct rotation matrices in latent space which map to feature transformations in real space. Overall, from quasi-eigenvectors we gain insight regarding the latent space topology.

Sodium-Glucose Cotransporter 2 Inhibitors: A Scoping Review of the Positive Implications on Cardiovascular and Renal Health and Dynamics for Clinical Practice.

Cardiorenal benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2is) have been demonstrated in patients with type 2 diabetes in multiple trials. We aim to provide a comprehensive review of the role of SGLT2i in cardiovascular disease. Reducing blood glucose to provide more effective vascular function, lowering the circulating volume, reducing cardiac stress, and preventing pathological cardiac re-modeling and function are the mechanisms implicated in the beneficial cardiovascular effects of SGLT2 inhibitors. Treatment with SGLT2i was associated with a decrease in cardiovascular and all-cause mortality, acute heart failure exacerbation hospitalization, and composite adverse renal outcomes. Improved symptoms, better functional status, and quality of life were also seen in heart failure with reduced ejection fraction (HFrEF), heart failure and mildly reduced ejection fraction (HFmrEF), and heart failure with preserved ejection fraction (HFpEF) patients. Recent trials have shown a notable therapeutic benefit of SGLT2is in acute heart failure and also suggest that SGLT2is have the potential to strengthen recovery after acute myocardial infarction (AMI) in percutaneous coronary Intervention (PCI) patients. The mechanism behind the cardio-metabolic and renal-protective effects of SGLT2i is multifactorial. Adverse events may occur with their usage including increased risk of genital infections, diabetic ketoacidosis, and perhaps limited amputations; however, all of them are preventable. Overall, SGLT2i clearly has many beneficial effects, and the benefits of using SGLT2i by far outweigh the risks.

General, Open-Source Vertex Modeling in Biological Applications Using Tissue Forge.

Vertex models are a widespread approach for describing the biophysics and behaviors of multicellular systems, especially of epithelial tissues. Vertex models describe a wide variety of developmental scenarios and behaviors like cell rearrangement and tissue folding. Often, these models are implemented as single-use or closed-source software, which inhibits reproducibility and decreases accessibility for researchers with limited proficiency in software development and numerical methods. We developed a physics-based vertex model methodology in Tissue Forge, an open-source, particle-based modeling and simulation environment. Our methodology describes the properties and processes of vertex model objects on the basis of vertices, which allows integration of vertex modeling with the particle-based formalism of Tissue Forge, enabling an environment for developing mixed-method models of multicellular systems. Our methodology in Tissue Forge inherits all features provided by Tissue Forge, delivering opensource, extensible vertex modeling with interactive simulation, real-time simulation visualization and model sharing in the C,C++ and Python programming languages and a Jupyter Notebook. Demonstrations show a vertex model of cell sorting and a mixed-method model of cell migration combining vertex- and particle-based models. Our methodology provides accessible vertex modeling for a broad range of scientific disciplines, and we welcome community-developed contributions to our open-source software implementation.

Multiscale Modeling of Spheroid Tumors: Effect of Nutrient Availability on Tumor Evolution.

Recent years have revealed a large number of complex mechanisms and interactions that drive the development of malignant tumors. Tumor evolution is a framework that explains tumor development as a process driven by survival of the fittest, with tumor cells of different properties competing for limited available resources. To predict the evolutionary trajectory of a tumor, knowledge of how cellular properties influence the fitness of a subpopulation in the context of the microenvironment is required and is often inaccessible. Computational multiscale-modeling of tissues enables the observation of the full trajectory of each cell within the tumor environment. Here, we model a 3D spheroid tumor with subcellular resolution. The fitness of individual cells and the evolutionary behavior of the tumor are quantified and linked to cellular and environmental parameters. The fitness of cells is solely influenced by their position in the tumor, which in turn is influenced by the two variable parameters of our model: cell-cell adhesion and cell motility. We observe the influence of nutrient independence and static and dynamically changing nutrient availability on the evolutionary trajectories of heterogeneous tumors in a high-resolution computational model. Regardless of nutrient availability, we find a fitness advantage of low-adhesion cells, which are favorable for tumor invasion. We find that the introduction of nutrient-dependent cell division and death accelerates the evolutionary speed. The evolutionary speed can be increased by fluctuations in nutrients. We identify a distinct frequency domain in which the evolutionary speed increases significantly over a tumor with constant nutrient supply. The findings suggest that an unstable supply of nutrients can accelerate tumor evolution and, thus, the transition to malignancy.

Sequential changes in cellular properties accompanying amniote somite formation.

Somites are transient structures derived from the pre-somitic mesoderm (PSM), involving mesenchyme-to-epithelial transition (MET) where the cells change their shape and polarize. Using Scanning electron microscopy (SEM), immunocytochemistry and confocal microscopy, we study the progression of these events along the tail-to-head axis of the embryo, which mirrors the progression of somitogenesis (younger cells located more caudally). SEM revealed that PSM epithelialization is a gradual process, which begins much earlier than previously thought, starting with the dorsalmost cells, then the medial ones, and then, simultaneously, the ventral and lateral cells, before a somite fully separates from the PSM. The core (internal) cells of the PSM and somites never epithelialize, which suggests that the core cells could be 'trapped' within the somitocoele after cells at the surfaces of the PSM undergo MET. Three-dimensional imaging of the distribution of the cell polarity markers PKCζ, PAR3, ZO1, the Golgi marker GM130 and the apical marker N-cadherin reveal that the pattern of polarization is distinctive for each marker and for each surface of the PSM, but the order of these events is not the same as the progression of cell elongation. These observations challenge some assumptions underlying existing models of somite formation.

The Pulmonary Embolism Response Team: Rationale, Operation, and Outcomes.

The pulmonary embolism response team (PERT) is an institutionally based multidisciplinary team that is able to rapidly assess and provide treatment for patients with acute pulmonary embolism (PE). Intrinsic to the team's structure is a formal mechanism to execute a full range of medical, endovascular, and surgical therapies. In addition, the PERT provides appropriate multidisciplinary follow-up of patients. In the 10 years since the PERT was first introduced, it has gained acceptance in many centers in the United States and around the world. These PERTs have joined together to form an international association, called the PERT Consortium. The mission of this consortium is to advance the diagnosis, treatment, and outcomes of patients with PE. There is considerable evidence that the PERT model improves delivery and standardization of care of PE patients, particularly those patients with massive and submassive PE. However, it is not yet clear whether PERTs improve clinical outcomes. A large prospective database is currently being compiled by the PERT Consortium. Analysis of this database will likely further delineate the role of PERTs in the management of intermediate-to-high risk PE patients and, importantly, help determine in which PE patients PERT may improve clinical outcomes.

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology.

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.

Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2.

We extend our established agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of remdesivir. We model remdesivir treatment for COVID-19; however, our methods are general to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, treatment initiation delay, antiviral half-life, and variability in cellular uptake and metabolism of remdesivir and its active metabolite on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models which treat all cells of a given class as identical can clarify how treatment dosage and timing influence treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, including cell-to-cell variation in drug uptake, permeability and metabolism increase the likelihood of uncontrolled infection as the cells with the lowest internal levels of antiviral act as super-spreaders within the tissue. The model predicts substantial variability in infection outcomes between similar tissue patches for different treatment options. In models with cellular metabolic variability, antiviral doses have to be increased significantly (>50% depending on simulation parameters) to achieve the same treatment results as with the homogeneous cellular metabolism.

Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection.

To understand the difference between benign and severe outcomes after Coronavirus infection, we urgently need ways to clarify and quantify the time course of tissue and immune responses. Here we re-analyze 72-hour time-series microarrays generated in 2013 by Sims and collaborators for SARS-CoV-1 in vitro infection of a human lung epithelial cell line. Transcriptograms, a Bioinformatics tool to analyze genome-wide gene expression data, allow us to define an appropriate context-dependent threshold for mechanistic relevance of gene differential expression. Without knowing in advance which genes are relevant, classical analyses detect every gene with statistically-significant differential expression, leaving us with too many genes and hypotheses to be useful. Using a Transcriptogram-based top-down approach, we identified three major, differentially-expressed gene sets comprising 219 mainly immune-response-related genes. We identified timescales for alterations in mitochondrial activity, signaling and transcription regulation of the innate and adaptive immune systems and their relationship to viral titer. The methods can be applied to RNA data sets for SARS-CoV-2 to investigate the origin of differential responses in different tissue types, or due to immune or preexisting conditions or to compare cell culture, organoid culture, animal models and human-derived samples.

Iron Deposition in the Brain After Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND: After aneurysmal subarachnoid hemorrhage (SAH), thrombus forms over the cerebral cortex and releases hemoglobin. When extracellular, hemoglobin is toxic to neurones. High local hemoglobin concentration overwhelms the clearance capacity of macrophages expressing the hemoglobin-haptoglobin scavenger receptor CD163. We hypothesized that iron is deposited in the cortex after SAH and would associate with outcome. METHODS: Two complementary cross-sectional studies were conducted. Postmortem brain tissue from 39 SAH (mean postictal interval of 9 days) and 22 control cases was studied with Perls' staining for iron and immunolabeling for CD163, ADAM17 (a disintegrin and metallopeptidase domain 17), CD68, and Iba1 (ionized calcium binding adaptor molecule 1). In parallel, to study the persistence of cortical iron and its relationship to clinical outcome, we conducted a susceptibility-weighted imaging study of 21 SAH patients 6 months postictus and 10 control individuals. RESULTS: In brain tissue from patients dying soon after SAH, the distribution of iron deposition followed a gradient that diminished with distance from the brain surface. Iron was located intracellularly (mainly in macrophages, and occasionally in microglia, neurones, and glial cells) and extracellularly. Microglial activation and motility markers were increased after SAH, with a similar inward diminishing gradient. In controls, there was a positive correlation between CD163 and iron, which was lost after SAH. In SAH survivors, iron-sensitive imaging 6 months post-SAH confirmed persistence of cortical iron, related to the size and location of the blood clot immediately after SAH, and associated with cognitive outcome. CONCLUSIONS: After SAH, iron deposits in the cortical gray matter in a pattern that reflects proximity to the brain surface and thrombus and is related to cognitive outcome. These observations support therapeutic manoeuvres which prevent the permeation of hemoglobin into the cortex after SAH.

Exact solution for the Anisotropic Ornstein-Uhlenbeck process.

Active-Matter models commonly consider particles with overdamped dynamics subject to a force (speed) with constant modulus and random direction. Some models also include random noise in particle displacement (a Wiener process), resulting in diffusive motion at short time scales. On the other hand, Ornstein-Uhlenbeck processes apply Langevin dynamics to the particles' velocity and predict motion that is not diffusive at short time scales. Experiments show that migrating cells have gradually varying speeds at intermediate and long time scales, with short-time diffusive behavior. While Ornstein-Uhlenbeck processes can describe the moderate-and long-time speed variation, Active-Matter models for over-damped particles can explain the short-time diffusive behavior. Isotropic models cannot explain both regimes, because short-time diffusion renders instantaneous velocity ill-defined, and prevents the use of dynamical equations that require velocity time-derivatives. On the other hand, both models correctly describe some of the different temporal regimes seen in migrating biological cells and must, in the appropriate limit, yield the same observable predictions. Here we propose and solve analytically an Anisotropic Ornstein-Uhlenbeck process for polarized particles, with Langevin dynamics governing the particle's movement in the polarization direction and a Wiener process governing displacement in the orthogonal direction. Our characterization provides a theoretically robust way to compare movement in dimensionless simulations to movement in experiments in which measurements have meaningful space and time units. We also propose an approach to deal with inevitable finite-precision effects in experiments and simulations.

A multiscale multicellular spatiotemporal model of local influenza infection and immune response.

Respiratory viral infections pose a serious public health concern, from mild seasonal influenza to pandemics like those of SARS-CoV-2. Spatiotemporal dynamics of viral infection impact nearly all aspects of the progression of a viral infection, like the dependence of viral replication rates on the type of cell and pathogen, the strength of the immune response and localization of infection. Mathematical modeling is often used to describe respiratory viral infections and the immune response to them using ordinary differential equation (ODE) models. However, ODE models neglect spatially-resolved biophysical mechanisms like lesion shape and the details of viral transport, and so cannot model spatial effects of a viral infection and immune response. In this work, we develop a multiscale, multicellular spatiotemporal model of influenza infection and immune response by combining non-spatial ODE modeling and spatial, cell-based modeling. We employ cellularization, a recently developed method for generating spatial, cell-based, stochastic models from non-spatial ODE models, to generate much of our model from a calibrated ODE model that describes infection, death and recovery of susceptible cells and innate and adaptive responses during influenza infection, and develop models of cell migration and other mechanisms not explicitly described by the ODE model. We determine new model parameters to generate agreement between the spatial and original ODE models under certain conditions, where simulation replicas using our model serve as microconfigurations of the ODE model, and compare results between the models to investigate the nature of viral exposure and impact of heterogeneous infection on the time-evolution of the viral infection. We found that using spatially homogeneous initial exposure conditions consistently with those employed during calibration of the ODE model generates far less severe infection, and that local exposure to virus must be multiple orders of magnitude greater than a uniformly applied exposure to all available susceptible cells. This strongly suggests a prominent role of localization of exposure in influenza A infection. We propose that the particularities of the microenvironment to which a virus is introduced plays a dominant role in disease onset and progression, and that spatially resolved models like ours may be important to better understand and more reliably predict future health states based on susceptibility of potential lesion sites using spatially resolved patient data of the state of an infection. We can readily integrate the immune response components of our model into other modeling and simulation frameworks of viral infection dynamics that do detailed modeling of other mechanisms like viral internalization and intracellular viral replication dynamics, which are not explicitly represented in the ODE model. We can also combine our model with available experimental data and modeling of exposure scenarios and spatiotemporal aspects of mechanisms like mucociliary clearance that are only implicitly described by the ODE model, which would significantly improve the ability of our model to present spatially resolved predictions about the progression of influenza infection and immune response.

Computational modelling of nephron progenitor cell movement and aggregation during kidney organogenesis.

During early kidney organogenesis, nephron progenitor (NP) cells move from the tip to the corner region of the ureteric bud (UB) branches in order to form the pretubular aggregate, the early structure giving rise to nephron formation. NP cells derive from metanephric mesenchymal cells and physically interact with them during the movement. Chemotaxis and cell-cell adhesion differences are believed to drive the cell patterning during this critical period of organogenesis. However, the effect of these forces to the cell patterns and their respective movements are known in limited details. We applied a Cellular Potts Model to explore how these forces and organizations contribute to directed cell movement and aggregation. Model parameters were estimated based on fitting to experimental data obtained in ex vivo kidney explant and dissociation-reaggregation organoid culture studies. Our simulations indicated that optimal enrichment and aggregation of NP cells in the UB corner niche requires chemoattractant secretion from both the UB epithelial cells and the NP cells themselves, as well as differences in cell-cell adhesion energies. Furthermore, NP cells were observed, both experimentally and by modelling, to move at higher speed in the UB corner as compared to the tip region where they originated. The existence of different cell speed domains along the UB was confirmed using self-organizing map analysis. In summary, we saw faster NP cell movements near aggregation. The applicability of Cellular Potts Model approach to simulate cell movement and patterning was found to be good during for this early nephrogenesis process. Further refinement of the model should allow us to recapitulate the effects of developmental changes of cell phenotypes and molecular crosstalk during further organ development.

Pathophysiology, Diagnosis, and Management of Hypertension in the Elderly.

There is a high prevalence of systemic arterial hypertension in the elderly; 70% of adults >65 years have this disease. A key mechanism in the development of hypertension in the elderly is increased arterial stiffness. This accounts for the increase in systolic blood pressure and pulse pressure and fall in diastolic blood pressure (isolated systolic hypertension) that are commonly seen in the elderly, compared with younger persons. The diagnosis of hypertension is made on the basis of in-office blood pressure measurements together with ambulatory and home blood pressure recordings. Lifestyle changes are the cornerstone of management of hypertension. Comprehensive guidelines regarding blood pressure threshold at which to start pharmacotherapy as well as target blood pressure levels have been issued by both European and American professional bodies. In recent years, there has been considerable interest in intensive lowering of blood pressure in older patients with hypertension. Several large, randomized controlled trials have suggested that a strategy of aiming for a target systolic blood pressure of <120 mm Hg (intensive treatment) rather than a target of <140 mm Hg (standard treatment) results in significant reduction in the incidence of adverse cardiovascular events and total mortality. A systolic blood pressure treatment of <130 mm Hg should be considered favorably in non-institutionalized, ambulatory, free living older patients. In contrast, in the older patient with a high burden of comorbidities and limited life expectancy, an individualized team-based approach, based on clinical judgment and patient preference should be adopted. An increasing body of evidence for older adults with hypertension suggests that intensive blood pressure lowering may prevent or at least partially prevent cognitive decline.