Pathophysiology, classification, and MRI parallels in microvascular disease of the heart and brain.
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ABSTRACT:
SUBMITTER: Thomas MA
PROVIDER: S-EPMC7680357 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Heart failure with preserved ejection fraction (HFpEF) currently accounts for approximately half of all new heart failure cases in the community. HFpEF is closely associated with chronic lifestyle-related diseases, such as obesity and type 2 diabetes, and clinical outcomes are worse in those with than without comorbidities. HFpEF is pathophysiologically distinct from heart failure with reduced ejection fraction, which may explain, in part, the disparity of treatment options available between the two heart failure phenotypes. The mechanisms underlying HFpEF are complex, with coronary microvascular dysfunction (MVD) being proposed as a potential key driver in its pathophysiology. In this review, the authors highlight the evidence implicating MVD in HFpEF pathophysiology, the diagnostic approaches for identifying MVD (both invasive and non-invasive) and the prevalence and prognostic significance of MVD.
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Project description:The development of novel, non-invasive techniques and standardization of protocols to assess microvascular dysfunction have elucidated the key role of microvascular changes in the evolution of cardiovascular (CV) damage, and their capacity to predict an increased risk of adverse events. These technical advances parallel with the development of novel biological assays that enabled the ex vivo identification of pathways promoting microvascular dysfunction, providing novel potential treatment targets for preventing cerebral-CV disease. In this article, we provide an update of diagnostic testing strategies to detect and characterize microvascular dysfunction and suggestions on how to standardize and maximize the information obtained from each microvascular assay. We examine emerging data highlighting the significance of microvascular dysfunction in the development CV disease manifestations. Finally, we summarize the pathophysiology of microvascular dysfunction emphasizing the role of oxidative stress and its regulation by epigenetic mechanisms, which might represent potential targets for novel interventions beyond conventional approaches, representing a new frontier in CV disease reduction.
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Project description:BackgroundCerebral small vessel disease (cSVD) is a late consequence of cerebral microvascular dysfunction (MVD). MVD is hard to measure in the brain due to its limited accessibility. Extracerebral MVD (eMVD) measures can give insights in the etiology of cerebral MVD, as MVD may be a systemic process. We aim to investigate whether a compound score consisting of several eMVD measures is associated with structural cSVD MRI markers.MethodsCross-sectional data of the population-based Maastricht Study was used (n = 1872, mean age 59 ± 8 years, 49% women). Measures of eMVD included flicker light-induced retinal arteriolar and venular dilation response (retina), albuminuria and glomerular filtration rate (kidney), heat-induced skin hyperemia (skin), and plasma biomarkers of endothelial dysfunction (sICAM-1, sVCAM-1, sE-selectin, and von Willebrand factor). These measures were standardized into z scores and summarized into a compound score. Linear and logistic regression analyses were used to investigate the associations between the compound score and white matter hyperintensity (WMH) volume, and the presence of lacunes and microbleeds, as measured by brain MRI.ResultsThe eMVD compound score was associated with WMH volume independent of age, sex, and cardiovascular risk factors (St β 0.057 [95% CI 0.010-0.081], p value 0.01), but not with the presence of lacunes (OR 1.011 [95% CI 0.803-1.273], p value 0.92) or microbleeds (OR 1.055 [95% CI 0.896-1.242], p value 0.52).ConclusionA compound score of eMVD is associated with WMH volume. Further research is needed to expand the knowledge about the role of systemic MVD in the pathophysiology of cSVD.
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Project description:Movement disorders are a prominent and common feature in many autoantibody-associated neurological diseases, a group of potentially treatable conditions that can mimic infectious, metabolic or neurodegenerative disease. Certain movement disorders are likely to associate with certain autoantibodies; for example, the characteristic dyskinesias, chorea and dystonia associated with NMDAR antibodies, stiff person spectrum disorders with GAD, glycine receptor, amphiphysin or DPPX antibodies, specific paroxysmal dystonias with LGI1 antibodies, and cerebellar ataxia with various anti-neuronal antibodies. There are also less-recognized movement disorder presentations of antibody-related disease, and a considerable overlap between the clinical phenotypes and the associated antibody spectra. In this review, we first describe the antibodies associated with each syndrome, highlight distinctive clinical or radiological 'red flags', and suggest a syndromic approach based on the predominant movement disorder presentation, age, and associated features. We then examine the underlying immunopathophysiology, which may guide treatment decisions in these neuroimmunological disorders, and highlight the exceptional interface between neuronal antibodies and neurodegeneration, such as the tauopathy associated with IgLON5 antibodies. Moreover, we elaborate the emerging pathophysiological parallels between genetic movement disorders and immunological conditions, with proteins being either affected by mutations or targeted by autoantibodies. Hereditary hyperekplexia, for example, is caused by mutations of the alpha subunit of the glycine receptor leading to an infantile-onset disorder with exaggerated startle and stiffness, whereas antibodies targeting glycine receptors can induce acquired hyperekplexia. The spectrum of such immunological and genetic analogies also includes cerebellar ataxias and some encephalopathies. Lastly, we discuss how these pathophysiological considerations could reflect on possible future directions regarding antigen-specific immunotherapies or targeting the pathophysiological cascades downstream of the antibody effects.
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Project description:BackgroundDetermining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict AD-specific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)].MethodsInvolving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs.ResultsOur deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images.ConclusionDeep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D.
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Project description:ObjectiveThe purpose of this study was to develop an algorithm incorporating MRI metrics to classify patients with mild traumatic brain injury (mTBI) and controls.MethodsThis was an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant prospective study. We recruited patients with mTBI and healthy controls through the emergency department and general population. We acquired data on a 3.0T Siemens Trio magnet including conventional brain imaging, resting-state fMRI, diffusion-weighted imaging, and magnetic field correlation (MFC), and performed multifeature analysis using the following MRI metrics: mean kurtosis (MK) of thalamus, MFC of thalamus and frontal white matter, thalamocortical resting-state networks, and 5 regional gray matter and white matter volumes including the anterior cingulum and left frontal and temporal poles. Feature selection was performed using minimal-redundancy maximal-relevance. We used classifiers including support vector machine, naive Bayesian, Bayesian network, radial basis network, and multilayer perceptron to test maximal accuracy.ResultsWe studied 24 patients with mTBI and 26 controls. Best single-feature classification uses thalamic MK yielding 74% accuracy. Multifeature analysis yields 80% accuracy using the full feature set, and up to 86% accuracy using minimal-redundancy maximal-relevance feature selection (MK thalamus, right anterior cingulate volume, thalamic thickness, thalamocortical resting-state network, thalamic microscopic MFC, and sex).ConclusionMultifeature analysis using diffusion-weighted imaging, MFC, fMRI, and volumetrics may aid in the classification of patients with mTBI compared with controls based on optimal feature selection and classification methods.Classification of evidenceThis study provides Class III evidence that classification algorithms using multiple MRI features accurately identifies patients with mTBI as defined by American Congress of Rehabilitation Medicine criteria compared with healthy controls.
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