40 research outputs found
Cerebrovascular Disorders
No full textAbstract Cerebrovascular disorders pose a global health concern. Advances in basic and clinical research, including induced pluripotent stem cell models and multi-omic approaches, have improved our understanding and management of these disorders. However, gaps in our knowledge remain. BMC Cardiovascular Disorders invites authors to submit articles investigating what drives and affects Cerebrovascular disorders to improve patient care
Does illicit drug use increase stroke risk? A systematic review, meta-analyses, and Mendelian randomization analysis.
No full textBACKGROUND: Epidemiological evidence suggests associations between substance use disorders and risk of stroke, but whether these are due to confounding or are true causal relationships remains uncertain.
AIMS: To meta-analyze the observational evidence on illicit substance use and stroke risk and apply Mendelian randomization (MR) to evaluate potential causal effects of substance dependence on stroke subtypes.
METHODS: We conducted a systematic review and meta-analysis of studies reporting associations between illicit drug use and stroke (PROSPERO registration-CRD420251053702). The meta-analysis included 32 studies comprising more than 100 million total participants across administrative, hospital-based, and population-based datasets. Pooled odds ratios (ORs) were estimated using multivariate random-effects models for ischemic and hemorrhagic subtypes. We then performed two-sample MR using genome-wide association study summary statistics to examine associations between seven drug exposures and all stroke, ischemic and hemorrhagic stroke, and ischemic stroke subtypes.
RESULTS: Meta-analysis demonstrated significant associations of cannabis (OR = 1.37, 95% confidence interval (95% CI) = 1.14-1.65), cocaine (OR = 1.96; 95% CI = 1.27-3.01), and amphetamines (OR = 2.22, 95% CI = 1.40-3.53) with increased stroke risk, while no significant association was observed for opioids. Findings for cannabis showed some heterogeneity and small-study effects. MR analyses revealed that cannabis use disorder was associated with any stroke (OR = 1.11 [1.01-1.51]) and large artery stroke (OR = 1.35, 95% CI = 1.01-1.80), and cocaine dependence was associated with cardioembolic stroke (OR = 1.08, 95% CI = 1.02-1.14) and intracerebral hemorrhage (OR = 1.38, 95% CI = 1.15-1.65). Genetically predicted substance use disorder overall was associated with any stroke (OR = 1.33, 95% CI = 1.02-1.72) and intracerebral hemorrhage (OR = 7.79, 95% CI = 3.46-17.54). Problematic and dependent alcohol use was linked to large artery and cardioembolic stroke, whereas nicotine dependence showed no significant associations.
CONCLUSION: Our findings provide consistent observational and genetic evidence that several forms of substance misuse increase stroke risk, particularly cocaine, amphetamines, and cannabis. These findings suggest important public health implications for prevention strategies targeting substance use disorders to mitigate stroke risk
Contribution of Conventional Cardiovascular Risk Factors to Brain White Matter Hyperintensities
No full textBackground White matter hyperintensities (WMHs) are a major risk factor for stroke and dementia, but their pathogenesis is incompletely understood. It has been debated how much risk is accounted for by conventional cardiovascular risk factors (CVRFs), and this has major implications as to how effective a preventative strategy targeting these risk factors will be. Methods and Results We included 41 626 UK Biobank participants (47.2% men), with a mean age of 55 years (SD, 7.5 years), who underwent brain magnetic resonance imaging at the first imaging assessment beginning in 2014. The relationships among CVRFs, cardiovascular conditions, and WMH volume as a percentage of total brain volume were examined using correlations and structural equation models. Only 32% of the variance in WMH volume was explained by measures of CVRFs, sex, and age, of which age accounted for 16%. CVRFs combined accounted for ≈15% of the variance. However, a large portion of the variance (well over 60%) remains unexplained. Of the individual CVRFs, blood pressure parameters together accounted for ≈10.5% of the total variance (diagnosis of hypertension, 4.4%; systolic blood pressure, 4.4%; and diastolic blood pressure, 1.7%). The variance explained by most individual CVRFs declined with age. Conclusions Our findings suggest the presence of other vascular and nonvascular factors underlying the development of WMHs. Although they emphasize the importance of modification of conventional CVRFs, particularly hypertension, they highlight the need to better understand risk factors underlying the considerable unexplained variance in WMHs if we are to develop better preventative approaches
Water Purification in South Africa: Reflections on Curriculum Development Tools and Best Practices for Implementing Student-Led Sustainable Development Projects in Rural Communities
No full textThis paper presents a sustainable development project in which University of Virginia students collaborated with University of Venda faculty, Global Sustainability Club students, and local community members to address water problems in a village in the Venda region of the Limpopo Province, South Africa. The cohort’s goal was to implement a sustainable and contextually appropriate water purification and distribution system. The authors present the design and constructed process for a slow sand filtration system intended to provide clean drinking water to most households in the community. They present and analyze the successes, failures, and ethical dilemmas encountered throughout project execution. Also, the authors assess the project based on three evaluation criteria for service learning projects and explore possibilities for follow-up through the collaboration between the University of Virginia and the University of Venda. The paper ends with a reflection examining aspects of engineering community engagement projects including site assessments prior to project implementation, project timeframes, and crosscultural institutional collaborations
Artificial intelligence for dementia genetics and omics
No full textGenetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high‐dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia‐related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. Highlights: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research
Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia
No full textWith the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice
Artificial intelligence for dementia—applied models and digital health
No full textIntroduction:
The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as “deep phenotyping” cohorts with multi-omics health data become available.
Methods:
This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors.
Results:
This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health.
Discussion:
Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry)
Modifiable Lifestyle Factors and Risk of Stroke: A Mendelian Randomization Analysis.
No full textBACKGROUND AND PURPOSE: Assessing whether modifiable risk factors are causally associated with stroke risk is important in planning public health measures, but determining causality can be difficult in epidemiological data. We evaluated whether modifiable lifestyle factors including educational attainment, smoking, and body mass index are causal risk factors for ischemic stroke and its subtypes and hemorrhagic stroke. METHODS: We performed 2-sample and multivariable Mendelian randomization to assess the causal effect of 12 lifestyle factors on risk of stroke and whether these effects are independent. RESULTS: Genetically predicted years of education was inversely associated with ischemic, large artery, and small vessel stroke, and intracerebral hemorrhage. Genetically predicted smoking, body mass index, and waist-hip ratio were associated with ischemic and large artery stroke. The effects of education, body mass index, and smoking on ischemic stroke were independent. CONCLUSIONS: Our findings support the hypothesis that reduced education and increased smoking and obesity increase risk of ischemic, large artery, and small vessel stroke, suggesting that lifestyle modifications addressing these risk factors will reduce stroke risk
Antithrombotic Treatment for Cervical Artery Dissection: A Systematic Review and Individual Patient Data Meta-Analysis.
No full textIMPORTANCE
Cervical artery dissection is the most common cause of stroke in younger adults. To date, there is no conclusive evidence on which antithrombotic therapy should be used to treat patients.
OBJECTIVE
To perform an individual patient data meta-analysis of randomized clinical trials comparing anticoagulants and antiplatelets in prevention of stroke after cervical artery dissection.
DATA SOURCES
PubMed.gov, Cochrane database, Embase, and ClinicalTrials.gov were searched from inception to August 1, 2023.
STUDY SELECTION
Randomized clinical trials that investigated the effectiveness and safety of antithrombotic treatment (antiplatelets vs anticoagulation) in patients with cervical artery dissection were included in the meta-analysis. The primary end point was required to include a composite of (1) any stroke, (2) death, or (3) major bleeding (extracranial or intracranial) at 90 days of follow-up.
DATA EXTRACTION/SYNTHESIS
Two independent investigators performed a systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and inconsistencies were resolved by a principal investigator.
MAIN OUTCOMES AND MEASURES
The primary outcome was a composite of (1) ischemic stroke, (2) death, or (3) major bleeding (extracranial or intracranial) at 90 days of follow-up. The components of the composite outcome were also secondary outcomes. Subgroup analyses based on baseline characteristics with a putative association with the outcome were performed. Logistic regression was performed using the maximum penalized likelihood method including interaction in the subgroup analyses.
RESULTS
Two randomized clinical trials, Cervical Artery Dissection in Stroke Study and Cervical Artery Dissection in Stroke Study and the Biomarkers and Antithrombotic Treatment in Cervical Artery Dissection, were identified, of which all participants were eligible. A total of 444 patients were included in the intention-to-treat population and 370 patients were included in the per-protocol population. Baseline characteristics were balanced. There were fewer primary end points in those randomized to anticoagulation vs antiplatelet therapy (3 of 218 [1.4%] vs 10 of 226 [4.4%]; odds ratio [OR], 0.33 [95% CI, 0.08-1.05]; P = .06), but the finding was not statistically significant. In comparison with aspirin, anticoagulation was associated with fewer strokes (1 of 218 [0.5%] vs 10 of 226 [4.0%]; OR, 0.14 [95% CI, 0.02-0.61]; P = .01) and more bleeding events (2 vs 0).
CONCLUSIONS AND RELEVANCE
This individual patient data meta-analysis of 2 currently available randomized clinical trial data found no significant difference between anticoagulants and antiplatelets in preventing early recurrent events
Central obesity is selectively associated with cerebral gray matter atrophy in 15,634 subjects in the UK Biobank.
No full textBACKGROUND: Obesity is a risk factor for both cardiovascular disease and dementia, but the mechanisms underlying this association are not fully understood. We examined associations between obesity, including estimates of central obesity using different modalities, with brain gray matter (GM) volume in the UK Biobank, a large population-based cohort study. METHODS: To determine relationships between obesity and the brain we used brain MRI, abdominal MRI, dual-energy X-ray absorptiometry (DXA), and bioelectric whole-body impedance. We determined whether obesity was associated with any change in brain gray matter (GM) and white matter (WM) volumes, and brain network efficiency derived from the structural connectome (wiring of the brain) as determined from diffusion-tensor MRI tractography. Using Waist-Hip Ratio (WHR), abdominal MRI and DXA we determined whether any associations were primarily with central rather than peripheral obesity, and whether associations were mediated by known cardiovascular risk factors. We analyzed brain MRI data from 15,634. RESULTS: We found that central obesity, was associated with decreased GM volume (anthropometric data: p = 6.7 × 10-16, DXA: p = 8.3 × 10-81, abdominal MRI: p = 0.0006). Regional associations were found between central obesity and with specific GM subcortical nuclei (thalamus, caudate, pallidum, nucleus accumbens). In contrast, no associations were found with WM volume or structure, or brain network efficiency. The effects of central obesity on GM volume were not mediated by C-reactive protein or blood pressure, glucose, lipids. CONCLUSIONS: Central body-fat distribution rather than the overall body-fat percentage is associated with gray matter changes in people with obesity. Further work is required to identify the factors that mediate the association between central obesity and GM atrophy
