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Serum magnesium and calcium levels in relation to ischemic stroke: Mendelian randomization study.
OBJECTIVE: To determine whether serum magnesium and calcium concentrations are causally associated with ischemic stroke or any of its subtypes using the mendelian randomization approach. METHODS: Analyses were conducted using summary statistics data for 13 single-nucleotide polymorphisms robustly associated with serum magnesium (n = 6) or serum calcium (n = 7) concentrations. The corresponding data for ischemic stroke were obtained from the MEGASTROKE consortium (34,217 cases and 404,630 noncases). RESULTS: In standard mendelian randomization analysis, the odds ratios for each 0.1 mmol/L (about 1 SD) increase in genetically predicted serum magnesium concentrations were 0.78 (95% confidence interval [CI] 0.69-0.89; p = 1.3 × 10-4) for all ischemic stroke, 0.63 (95% CI 0.50-0.80; p = 1.6 × 10-4) for cardioembolic stroke, and 0.60 (95% CI 0.44-0.82; p = 0.001) for large artery stroke; there was no association with small vessel stroke (odds ratio 0.90, 95% CI 0.67-1.20; p = 0.46). Only the association with cardioembolic stroke was robust in sensitivity analyses. There was no association of genetically predicted serum calcium concentrations with all ischemic stroke (per 0.5 mg/dL [about 1 SD] increase in serum calcium: odds ratio 1.03, 95% CI 0.88-1.21) or with any subtype. CONCLUSIONS: This study found that genetically higher serum magnesium concentrations are associated with a reduced risk of cardioembolic stroke but found no significant association of genetically higher serum calcium concentrations with any ischemic stroke subtype.
Genome-wide association meta-analysis of functional outcome after ischemic stroke.
OBJECTIVE: To discover common genetic variants associated with poststroke outcomes using a genome-wide association (GWA) study. METHODS: The study comprised 6,165 patients with ischemic stroke from 12 studies in Europe, the United States, and Australia included in the GISCOME (Genetics of Ischaemic Stroke Functional Outcome) network. The primary outcome was modified Rankin Scale score after 60 to 190 days, evaluated as 2 dichotomous variables (0-2 vs 3-6 and 0-1 vs 2-6) and subsequently as an ordinal variable. GWA analyses were performed in each study independently and results were meta-analyzed. Analyses were adjusted for age, sex, stroke severity (baseline NIH Stroke Scale score), and ancestry. The significance level was p < 5 × 10-8. RESULTS: We identified one genetic variant associated with functional outcome with genome-wide significance (modified Rankin Scale scores 0-2 vs 3-6, p = 5.3 × 10-9). This intronic variant (rs1842681) in the LOC105372028 gene is a previously reported trans-expression quantitative trait locus for PPP1R21, which encodes a regulatory subunit of protein phosphatase 1. This ubiquitous phosphatase is implicated in brain functions such as brain plasticity. Several variants detected in this study demonstrated suggestive association with outcome (p < 10-5), some of which are within or near genes with experimental evidence of influence on ischemic stroke volume and/or brain recovery (e.g., NTN4, TEK, and PTCH1). CONCLUSIONS: In this large GWA study on functional outcome after ischemic stroke, we report one significant variant and several variants with suggestive association to outcome 3 months after stroke onset with plausible mechanistic links to poststroke recovery. Future replication studies and exploration of potential functional mechanisms for identified genetic variants are warranted.
Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.
OBJECTIVE: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. METHODS: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. RESULTS: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p[BI] = 9.38 × 10-25; p[SSBI] = 5.23 × 10-14 for hypertension), smoking (p[BI] = 4.4 × 10-10; p[SSBI] = 1.2 × 10-4), diabetes (p[BI] = 1.7 × 10-8; p[SSBI] = 2.8 × 10-3), previous cardiovascular disease (p[BI] = 1.0 × 10-18; p[SSBI] = 2.3 × 10-7), stroke (p[BI] = 3.9 × 10-69; p[SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p[BI] = 1.43 × 10-157; p[SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. CONCLUSION: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.
Cerebral microbleeds and stroke risk after ischaemic stroke or transient ischaemic attack: a pooled analysis of individual patient data from cohort studies.
BACKGROUND: Cerebral microbleeds are a neuroimaging biomarker of stroke risk. A crucial clinical question is whether cerebral microbleeds indicate patients with recent ischaemic stroke or transient ischaemic attack in whom the rate of future intracranial haemorrhage is likely to exceed that of recurrent ischaemic stroke when treated with antithrombotic drugs. We therefore aimed to establish whether a large burden of cerebral microbleeds or particular anatomical patterns of cerebral microbleeds can identify ischaemic stroke or transient ischaemic attack patients at higher absolute risk of intracranial haemorrhage than ischaemic stroke. METHODS: We did a pooled analysis of individual patient data from cohort studies in adults with recent ischaemic stroke or transient ischaemic attack. Cohorts were eligible for inclusion if they prospectively recruited adult participants with ischaemic stroke or transient ischaemic attack; included at least 50 participants; collected data on stroke events over at least 3 months follow-up; used an appropriate MRI sequence that is sensitive to magnetic susceptibility; and documented the number and anatomical distribution of cerebral microbleeds reliably using consensus criteria and validated scales. Our prespecified primary outcomes were a composite of any symptomatic intracranial haemorrhage or ischaemic stroke, symptomatic intracranial haemorrhage, and symptomatic ischaemic stroke. We registered this study with the PROSPERO international prospective register of systematic reviews, number CRD42016036602. FINDINGS: Between Jan 1, 1996, and Dec 1, 2018, we identified 344 studies. After exclusions for ineligibility or declined requests for inclusion, 20 322 patients from 38 cohorts (over 35 225 patient-years of follow-up; median 1·34 years [IQR 0·19-2·44]) were included in our analyses. The adjusted hazard ratio [aHR] comparing patients with cerebral microbleeds to those without was 1·35 (95% CI 1·20-1·50) for the composite outcome of intracranial haemorrhage and ischaemic stroke; 2·45 (1·82-3·29) for intracranial haemorrhage and 1·23 (1·08-1·40) for ischaemic stroke. The aHR increased with increasing cerebral microbleed burden for intracranial haemorrhage but this effect was less marked for ischaemic stroke (for five or more cerebral microbleeds, aHR 4·55 [95% CI 3·08-6·72] for intracranial haemorrhage vs 1·47 [1·19-1·80] for ischaemic stroke; for ten or more cerebral microbleeds, aHR 5·52 [3·36-9·05] vs 1·43 [1·07-1·91]; and for ≥20 cerebral microbleeds, aHR 8·61 [4·69-15·81] vs 1·86 [1·23-1·82]). However, irrespective of cerebral microbleed anatomical distribution or burden, the rate of ischaemic stroke exceeded that of intracranial haemorrhage (for ten or more cerebral microbleeds, 64 ischaemic strokes [95% CI 48-84] per 1000 patient-years vs 27 intracranial haemorrhages [17-41] per 1000 patient-years; and for ≥20 cerebral microbleeds, 73 ischaemic strokes [46-108] per 1000 patient-years vs 39 intracranial haemorrhages [21-67] per 1000 patient-years). INTERPRETATION: In patients with recent ischaemic stroke or transient ischaemic attack, cerebral microbleeds are associated with a greater relative hazard (aHR) for subsequent intracranial haemorrhage than for ischaemic stroke, but the absolute risk of ischaemic stroke is higher than that of intracranial haemorrhage, regardless of cerebral microbleed presence, antomical distribution, or burden. FUNDING: British Heart Foundation and UK Stroke Association.
Prevalence of CADASIL and Fabry Disease in a Cohort of MRI Defined Younger Onset Lacunar Stroke.
BACKGROUND AND PURPOSE: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), caused by mutations in the NOTCH3 gene, is the most common monogenic disorder causing lacunar stroke and cerebral small vessel disease (SVD). Fabry disease (FD) due to mutations in the GLA gene has been suggested as an underdiagnosed cause of stroke, and one feature is SVD. Previous studies reported varying prevalence of CADASIL and FD in stroke, likely due to varying subtypes studied; no studies have looked at a large cohort of younger onset SVD. We determined the prevalence in a well-defined, MRI-verified cohort of apparently sporadic patients with lacunar infarct. METHODS: Caucasian patients with lacunar infarction, aged ≤70 years (mean age 56.7 (SD8.6)), were recruited from 72 specialist stroke centres throughout the UK as part of the Young Lacunar Stroke DNA Resource. Patients with a previously confirmed monogenic cause of stroke were excluded. All MRI's and clinical histories were reviewed centrally. Screening was performed for NOTCH3 and GLA mutations. RESULTS: Of 994 subjects five had pathogenic NOTCH3 mutations (R169C, R207C, R587C, C1222G and C323S) all resulting in loss or gain of a cysteine in the NOTCH3 protein. All five patients had confluent leukoaraiosis (Fazekas grade ≥2). CADASIL prevalence overall was 0.5% (95% CI 0.2%-1.1%) and among cases with confluent leukoaraiosis 1.5% (95% CI 0.6%-3.3%). No classic pathogenic FD mutations were found; one patient had a missense mutation (R118C), associated with late-onset FD. CONCLUSION: CADASIL cases are rare and only detected in SVD patients with confluent leukoaraiosis. No definite FD cases were detected.
Sensitivity of Administrative Coding in Identifying Inpatient Acute Strokes Complicating Procedures or Other Diseases in UK Hospitals.
Background Administrative hospital diagnostic coding data are increasingly used in "big data" research and to assess complication rates after surgery or acute medical conditions. Acute stroke is a common complication of several procedures/conditions, such as carotid interventions, but data are lacking on the sensitivity of administrative coding in identifying acute stroke during inpatient stay. Methods and Results Using all acute strokes ascertained in a population-based cohort (2002-2017) as the reference, we determined the sensitivity of hospital administrative diagnostic codes ( International Classification of Diseases, Tenth Revision; ICD-10) for identifying acute strokes that occurred during hospital admission for other reasons, stratified by coding strategies, study periods, and stroke severity (National Institutes of Health Stroke Score</≥5). Of 3011 acute strokes, 198 (6.6%) occurred during hospital admissions for procedures/other diseases, including 122 (61.6%) major strokes. Using stroke-specific codes ( ICD-10=I60-I61 and I63-I64) in the primary diagnostic position, 66 of the 198 cases were correctly identified (sensitivity for any stroke, 33.3%; 95% CI, 27.1-40.2; minor stroke, 30.3%; 95% CI, 21.0-41.5; major stroke, 35.2%; 95% CI, 27.2-44.2), with no improvement of sensitivity over time ( Ptrend=0.54). Sensitivity was lower during admissions for surgery/procedures than for other acute medical admissions (n/% 17/23.3% versus 49/39.2%; P=0.02). Sensitivity improved to 60.6% (53.6-67.2) for all and 61.6% (50.0-72.1) for surgery/procedures if other diagnostic positions were used, and to 65.2% (58.2-71.5) and 68.5% (56.9-78.1) respectively if combined with use of all possible nonspecific stroke-related codes (ie, adding ICD-10=I62 and I65-I68). Conclusions Low sensitivity of administrative coding in identifying acute strokes that occurred during admission does not support its use alone for audit of complication rates of procedures or hospitalization for other reasons.
Longitudinal Brain Atrophy Rates in Transient Ischemic Attack and Minor Ischemic Stroke Patients and Cognitive Profiles.
Introduction: Patients with transient ischemic attack (TIA) and minor stroke demonstrate cognitive impairment, and a four-fold risk of late-life dementia. Aim: To study the extent to which the rates of brain volume loss in TIA patients differ from healthy controls and how they are correlated with cognitive impairment. Methods: TIA or minor stroke patients were tested with a neuropsychological battery and underwent T1 weighted volumetric magnetic resonance imaging scans at fixed intervals over a 3 years period. Linear mixed effects regression models were used to compare brain atrophy rates between groups, and to determine the relationship between atrophy rates and cognitive function in TIA and minor stroke patients. Results: Whole brain atrophy rates were calculated for the TIA and minor stroke patients; n = 38 between 24 h and 18 months, and n = 68 participants between 18 and 36 months, and were compared to healthy controls. TIA and minor stroke patients demonstrated a significantly higher whole brain atrophy rate than healthy controls over a 3 years interval (p = 0.043). Diabetes (p = 0.012) independently predicted higher atrophy rate across groups. There was a relationship between higher rates of brain atrophy and processing speed (composite P = 0.047 and digit symbol coding P = 0.02), but there was no relationship with brain atrophy rates and memory or executive composite scores or individual cognitive tests for language (Boston naming, memory recall, verbal fluency or Trails A or B score). Conclusion: TIA and minor stroke patients experience a significantly higher rate of whole brain atrophy. In this cohort of TIA and minor stroke patients changes in brain volume over time precede cognitive decline.
ICA-based denoising for ASL perfusion imaging.
Arterial Spin Labelling (ASL) imaging derives a perfusion image by tracing the accumulation of magnetically labeled blood water in the brain. As the image generated has an intrinsically low signal to noise ratio (SNR), multiple measurements are routinely acquired and averaged, at a penalty of increased scan duration and opportunity for motion artefact. However, this strategy alone might be ineffective in clinical settings where the time available for acquisition is limited and patient motion are increased. This study investigates the use of an Independent Component Analysis (ICA) approach for denoising ASL data, and its potential for automation. 72 ASL datasets (pseudo-continuous ASL; 5 different post-labeling delays: 400, 800, 1200, 1600, 2000 m s; total volumes = 60) were collected from thirty consecutive acute stroke patients. The effects of ICA-based denoising (manual and automated) where compared to two different denoising approaches, aCompCor, a Principal Component-based method, and Enhancement of Automated Blood Flow Estimates (ENABLE), an algorithm based on the removal of corrupted volumes. Multiple metrics were used to assess the changes in the quality of the data following denoising, including changes in cerebral blood flow (CBF) and arterial transit time (ATT), SNR, and repeatability. Additionally, the relationship between SNR and number of repetitions acquired was estimated before and after denoising the data. The use of an ICA-based denoising approach resulted in significantly higher mean CBF and ATT values (p < 0.001), lower CBF and ATT variance (p < 0.001), increased SNR (p < 0.001), and improved repeatability (p < 0.05) when compared to the raw data. The performance of manual and automated ICA-based denoising was comparable. These results went beyond the effects of aCompCor or ENABLE. Following ICA-based denoising, the SNR was higher using only 50% of the ASL-dataset collected than when using the whole raw data. The results show that ICA can be used to separate signal from noise in ASL data, improving the quality of the data collected. In fact, this study suggests that the acquisition time could be reduced by 50% without penalty to data quality, something that merits further study. Independent component classification and regression can be carried out either manually, following simple criteria, or automatically.
Characterising neural plasticity at the single patient level using connectivity fingerprints.
The occurrence of wide-scale neuroplasticity in the injured human brain raises hopes for biomarkers to guide personalised treatment. At the individual level, functional reorganisation has proven challenging to quantify using current techniques that are optimised for population-based analyses. In this cross-sectional study, we acquired functional MRI scans in 44 patients (22 men, 22 women, mean age: 39.4 ± 14 years) with a language-dominant hemisphere brain tumour prior to surgery and 23 healthy volunteers (11 men, 12 women, mean age: 36.3 ± 10.9 years) during performance of a verbal fluency task. We applied a recently developed approach to characterise the normal range of functional connectivity patterns during task performance in healthy controls. Next, we statistically quantified differences from the normal in individual patients and evaluated factors driving these differences. We show that the functional connectivity of brain regions involved in language fluency identifies "fingerprints" of brain plasticity in individual patients, not detected using standard task-evoked analyses. In contrast to healthy controls, patients with a tumour in their language dominant hemisphere showed highly variable fingerprints that uniquely distinguished individuals. Atypical fingerprints were influenced by tumour grade and tumour location relative to the typical fluency-activated network. Our findings show how alterations in brain networks can be visualised and statistically quantified from connectivity fingerprints in individual brains. We propose that connectivity fingerprints offer a statistical metric of individually-specific network organisation through which behaviourally-relevant adaptations could be formally quantified and monitored across individuals, treatments and time.