Citations

Abstract 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.

The burden of cirrhosis hospitalizations is increasing. The admission Model for End‐Stage Liver Disease–lactate (MELD‐lactate) was recently demonstrated to be a superior predictor of in‐hospital mortality compared with MELD in limited cohorts. We identified specific classes of hospitalizations where MELD‐lactate may be especially useful and evaluated the predictive role of lactate clearance. This was a retrospective cohort study of 1036 cirrhosis hospitalizations for gastrointestinal bleeding, infection, or other portal hypertension–related indications in the Veterans Health Administration where MELD‐lactate was measured on admission. Performance characteristics for in‐hospital mortality were compared between MELD‐lactate and MELD/MELD‐sodium (MELD‐Na), with stratified analyses of MELD categories (≤15, >15 to <25, ≥25) and reason for admission. We also incorporated day 3 lactate levels into modeling and tested for an interaction between day 1 MELD‐lactate and day 3 lactate clearance. MELD‐lactate had superior discrimination for in‐hospital mortality compared with MELD or MELD‐Na (area under the curve [AUC] 0.789 versus 0.776 versus 0.760, respectively; P < 0.001) and superior calibration. MELD‐lactate had higher discrimination among hospitalizations with MELD ≤15 (AUC 0.763 versus 0.608 for MELD, global P = 0.01) and hospitalizations for infection (AUC 0.791 versus 0.674 for MELD, global P < 0.001). We found a significant interaction between day 1 MELD‐lactate and day 3 lactate clearance; heat maps were created as clinical tools to risk‐stratify patients based on these clinical data. MELD‐lactate had significantly superior performance in predicting in‐hospital mortality among patients hospitalized for infection and/or with MELD ≤15 when compared with MELD or MELD‐Na. Incorporating day 3 lactate clearance may further improve prognostication.

Abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.

AbstractIntroductionThe objective of this study was to evaluate the relationship between self‐reported exercise levels and Alzheimer's disease (AD) biomarkers, in a cohort of autosomal dominant AD mutation carriers.MethodsIn 139 presymptomatic mutation carriers from the Dominantly Inherited Alzheimer Network, the relationship between self‐reported exercise levels and brain amyloid load, cerebrospinal fluid (CSF) Aβ42, and CSF tau levels was evaluated using linear regression.ResultsNo differences in brain amyloid load, CSF Aβ42, or CSF tau were observed between low and high exercise groups. Nevertheless, when examining only those already accumulating AD pathology (i.e., amyloid positive), low exercisers had higher mean levels of brain amyloid than high exercisers. Furthermore, the interaction between exercise and estimated years from expected symptom onset was a significant predictor of brain amyloid levels.DiscussionOur findings indicate a relationship exists between self‐reported exercise levels and brain amyloid in autosomal dominant AD mutation carriers.