Genetic Imbalance Is Associated With Functional Outcome After Ischemic Stroke

Genomics of stroke recovery and outcome

The understanding of genomics has improved tremendously during the last decades. The concept of recovery and outcome after stroke has also progressed during this time. However, the connection between genomics and stroke recovery has only begun to emerge in a more structural and comprehensive way. Different types of outcomes and recovery occur after stroke. This depends on domain of neurological deficit, severity, resilience, receptivity for rehabilitation measures, and concomitant morbidity. Methods for assessing stroke patients' prognosis depend on these factors. Different genetic approaches are possible and there is an increasing need for linking genetic findings to other omics as well as to clinically meaningful results. This review addresses recent advances and views in clinical genomics of stroke recovery and outcome in humans with focus on current and previous studies, concepts, and future perspectives.

Advances in chromosomal microarray analysis: Transforming neurology and neurosurgery

Over the past two decades, genomics has transformed our understanding of various clinical conditions, with Chromosomal Microarray Analysis (CMA) standing out as a key technique. Offering unparalleled sensitivity, CMA detects submicroscopic chromosomal imbalances, enabling the examination of DNA for copy number variations, deletions, duplications, and other structural differences. In neurology, CMA has revolutionised diagnoses, personalised treatment plans, and patient outcomes. By identifying genetic anomalies linked to neurological conditions, CMA allows clinicians to tailor treatments based on individual genetic profiles, enhancing precision medicine. CMA's clinical utility spans numerous neurological conditions, providing crucial insights into neurodevelopmental disorders, CNS tumours, neurodegenerative diseases, cerebrovascular diseases, and epilepsy. In neurodevelopmental disorders, CMA aids in diagnosing autism and intellectual disabilities, facilitating early interventions that improve long-term outcomes. In epilepsy, CMA helps identify genetic causes of drug-resistant seizures, enabling more targeted therapies and reducing adverse reactions. CMA also aids in stratifying risk for cerebrovascular diseases, enabling preventive interventions that improve patient prognosis. Despite its potential, challenges remain, such as interpreting variants of uncertain significance (VOUS), the lack of standardised testing guidelines, and issues of cost and accessibility. Addressing these challenges will optimise CMA's impact, advancing personalised medicine and reshaping neurology. This review discusses CMA's pivotal role in bridging the gap between genomics and clinical practice, underscoring its potential to transform neurogenetics and ultimately improve patient care.

Systematic Druggable Genome-Wide Mendelian Randomization Identifies Therapeutic Targets for Functional Outcome After Ischemic Stroke

BACKGROUND

Stroke is a leading cause of death worldwide, with a lack of effective treatments for improving the prognosis. The aim of the present study was to identify novel therapeutic targets for functional outcome after ischemic stroke .

METHODS AND RESULTS

Cis-expression quantitative trait loci data for druggable genes were used as instrumental variables. The primary outcome was the modified Rankin Scale score at 3 months after ischemic stroke, evaluated as a dichotomous variable (3-6 versus 0-2) and also as an ordinal variable. Drug target Mendelian randomization, Steiger filtering analysis, and colocalization analysis were performed. Additionally, phenome-wide Mendelian randomization analysis was performed to identify the safety of the drug target genes at the genetic level. Among >2600 druggable genes, genetically predicted expression of 16 genes (ABCC2, ATRAID, BLK, CD93, CHST13, NR1H3, NRBP1, PI3, RIPK4, SEMG1, SLC22A4, SLC22A5, SLCO3A1, TEK, TLR4, and WNT10B) demonstrated the causal associations with ordinal modified Rankin Scale (P<1.892×10-5) or poor functional outcome (modified Rankin Scale 3-6 versus 0-2, P<1.893×10-5). Steiger filtering analysis suggested potential directional stability (P<0.05). Colocalization analysis provided further support for the associations between genetically predicted expression of ABCC2, NRBP1, PI3, and SEMG1 with functional outcome after ischemic stroke. Furthermore, phenome-wide Mendelian randomization revealed additional beneficial indications and few potential safety concerns of therapeutics targeting ABCC2, NRBP1, PI3, and SEMG1, but the robustness of these results was limited by low power.

CONCLUSIONS

The present study revealed 4 candidate therapeutic targets for improving functional outcome after ischemic stroke, while the underlying mechanisms need further investigation.

GWAS-based polygenic risk scoring for predicting cerebral artery dissection in the Chinese population

OBJECTIVE

Cerebral artery dissection (CeAD) is a rare but serious disease. Genetic risk assessment for CeAD is lacking in Chinese population. We performed genome-wide association study (GWAS) and computed polygenic risk score (PRS) to explore genetic susceptibility factors and prediction model of CeAD based on patients in Huashan Hospital.

METHODS

A total of 210 CeAD patients and 280 controls were enrolled from June 2017 to September 2022 in Department of Neurology, Huashan Hospital, Fudan University. We performed GWAS to identify genetic variants associated with CeAD in 140 CeAD patients and 210 control individuals according to a case and control 1:1.5 design rule in the training dataset, while the other 70 patients with CeAD and 70 controls were used as validation. Then Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were utilized to identify the significant pathways. We constructed a PRS by capturing all independent GWAS SNPs in the analysis and explored the predictivity of PRS, age, and sex for CeAD.

RESULTS

Through GWAS analysis of the 140 cases and 210 controls in the training dataset, we identified 13 leading SNPs associated with CeAD at a genome-wide significance level of P < 5 × 10- 8. Among them, 10 SNPs were annotated in or near (in the upstream and downstream regions of ± 500Kb) 10 functional genes. rs34508376 (OR2L13) played a suggestive role in CeAD pathophysiology which was in line with previous observations in aortic aneurysms. The other nine genes were first-time associations in CeAD cases. GO enrichment analyses showed that these 10 genes have known roles in 20 important GO terms clustered into two groups: (1) cellular biological processes (BP); (2) molecular function (MF). We used genome-wide association data to compute PRS including 32 independent SNPs and constructed predictive model for CeAD by using age, sex and PRS as predictors both in training and validation test. The area under curve (AUC) of PRS predictive model for CeAD reached 99% and 95% in the training test and validation test respectively, which were significantly larger than the age and sex models of 83% and 86%.

CONCLUSIONS

Our study showed that ten risk loci were associated with CeAD susceptibility, and annotated functional genes had roles in 20 important GO terms clustered into biological process and molecular function. The PRS derived from risk variants was associated with CeAD incidence after adjusting for age and sex both in training test and validation.

Derivation and Validation of a Scoring System for Predicting Poor Outcome After Posterior Circulation Ischemic Stroke in China

BACKGROUND AND OBJECTIVES

Guidelines for posterior circulation ischemic stroke (PCIS) treatment are lacking and outcome prediction is crucial for patients and clinicians. We aimed to develop and validate a prognostic score to predict the poor outcome for patients with PCIS.

METHODS

The score was developed from a prospective derivation cohort named the Third China National Stroke Registry (August 2015-March 2018) and validated in a spatiotemporal independent validation cohort (December 2017-March 2023) in China. Patients with PCIS with acute infarctions defined as hyperintense lesions on diffusion-weighted imaging were included in this study. The poor outcome was measured as modified Rankin scale (mRS) score 3-6 at 3 months after PCIS. Multivariable logistic regression analysis was used to identify predictors for poor outcome. The prognostic score, namely PCIS Outcome Score (PCISOS), was developed by assigning points to variables based on their relative β-coefficients in the logistic model.

RESULTS

The PCISOS was derived from 3,294 patients (median age 62 [interquartile range (IQR) 55-70] years; 2,250 [68.3%] men) and validated in 501 patients (median age 61 [IQR 53-68] years; 404 [80.6%] men). Among them, 384 (11.7%) and 64 (12.8%) had poor outcome 3 months after stroke in respective cohorts. Age, mRS before admission, NIH Stroke Scale on admission, ischemic stroke history, infarction distribution, basilar artery, and posterior cerebral artery stenosis or occlusion were identified as independent predictors for poor outcome and included in PCISOS. This easy-to-use integer scoring system identified a marked risk gradient between 4 risk groups. PCISOS performed better than previous scores, with an excellent discrimination (C statistic) of 0.80 (95% CI 0.77-0.83) in the derivation cohort and 0.81 (95% CI 0.77-0.84) in the validation cohort. Calibration test showed high agreement between the predicted and observed outcomes in both cohorts.

DISCUSSION

PCISOS can be applied for patients with PCIS with acute infarctions to predict functional outcome at 3 months post-PCIS. This simple tool helps clinicians to identify patients with PCIS with higher risk of poor outcome and provides reliable outcome expectations for patients. This information might be used for personalized rehabilitation plan and patient selection for future clinical trials to reduce disability and mortality.

Genetics of ischemic stroke functional outcome

Ischemic stroke, which accounts for 87% of cerebrovascular accidents, is responsible for massive global burden both in terms of economic cost and personal hardship. Many stroke survivors face long-term disability-a phenotype associated with an increasing number of genetic variants. While clinical variables such as stroke severity greatly impact recovery, genetic polymorphisms linked to functional outcome may offer physicians a unique opportunity to deliver personalized care based on their patient's genetic makeup, leading to improved outcomes. A comprehensive catalogue of the variants at play is required for such an approach. In this review, we compile and describe the polymorphisms associated with outcome scores such as modified Rankin Scale and Barthel Index. Our search identified 74 known genetic polymorphisms spread across 48 features associated with various poststroke disability metrics. The known variants span diverse biological systems and are related to inflammation, vascular homeostasis, growth factors, metabolism, the p53 regulatory pathway, and mitochondrial variation. Understanding how these variants influence functional outcome may be helpful in maximizing poststroke recovery.

Epigenetics and stroke: role of DNA methylation and effect of aging on blood-brain barrier recovery

Incomplete recovery of blood-brain barrier (BBB) function contributes to stroke outcomes. How the BBB recovers after stroke remains largely unknown. Emerging evidence suggests that epigenetic factors play a significant role in regulating post-stroke BBB recovery. This study aimed to evaluate the epigenetic and transcriptional profile of cerebral microvessels after thromboembolic (TE) stroke to define potential causes of limited BBB recovery. RNA-sequencing and reduced representation bisulfite sequencing (RRBS) analyses were performed using microvessels isolated from young (6 months) and old (18 months) mice seven days poststroke compared to age-matched sham controls. DNA methylation profiling of poststroke brain microvessels revealed 11,287 differentially methylated regions (DMR) in old and 9818 DMR in young mice, corresponding to annotated genes. These DMR were enriched in genes encoding cell structural proteins (e.g., cell junction, and cell polarity, actin cytoskeleton, extracellular matrix), transporters and channels (e.g., potassium transmembrane transporter, organic anion and inorganic cation transporters, calcium ion transport), and proteins involved in endothelial cell processes (e.g., angiogenesis/vasculogenesis, cell signaling and transcription regulation). Integrated analysis of methylation and RNA sequencing identified changes in cell junctions (occludin), actin remodeling (ezrin) as well as signaling pathways like Rho GTPase (RhoA and Cdc42ep4). Aging as a hub of aberrant methylation affected BBB recovery processes by profound alterations (hypermethylation and repression) in structural protein expression (e.g., claudin-5) as well as activation of a set of genes involved in endothelial to mesenchymal transformation (e.g., Sox9, Snai1), repression of angiogenesis and epigenetic regulation. These findings revealed that DNA methylation plays an important role in regulating BBB repair after stroke, through regulating processes associated with BBB restoration and prevalently with processes enhancing BBB injury.

Epigenetics and stroke: role of DNA methylation and effect of aging on blood-brain barrier recovery

Incomplete recovery of blood-brain barrier (BBB) function contributes to stroke outcomes. How the BBB recovers after stroke remains largely unknown. Emerging evidence suggests that epigenetic factors play a significant role in regulating post-stroke BBB recovery. This study aimed to evaluate the epigenetic and transcriptional profile of cerebral microvessels after thromboembolic (TE) stroke to define potential causes of limited BBB recovery. RNA-sequencing and reduced representation bisulfite sequencing (RRBS) analyses were performed using microvessels isolated from young (6 months) and old (18 months) mice seven days poststroke compared to age-matched sham controls. DNA methylation profiling of poststroke brain microvessels revealed 11287 differentially methylated regions (DMR) in old and 9818 DMR in young mice, corresponding to annotated genes. These DMR were enriched in genes encoding cell structural proteins (e.g., cell junction, and cell polarity, actin cytoskeleton, extracellular matrix), transporters and channels (e.g., potassium transmembrane transporter, organic anion and inorganic cation transporters, calcium ion transport), and proteins involved in endothelial cell processes (e.g., angiogenesis/vasculogenesis, cell signaling and transcription regulation). Integrated analysis of methylation and RNA sequencing identified changes in cell junctions (occludin), actin remodeling (ezrin) as well as signaling pathways like Rho GTPase (RhoA and Cdc42ep4). Aging as a hub of aberrant methylation affected BBB recovery processes by profound alterations (hypermethylation and repression) in structural protein expression (e.g., claudin-5) as well as activation of a set of genes involved in endothelial to mesenchymal transformation (e.g., Sox17 , Snail1 ), repression of angiogenesis and epigenetic regulation. These findings revealed that DNA methylation plays an important role in regulating BBB repair after stroke, through regulating processes associated with BBB restoration and prevalently with processes enhancing BBB injury.

Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome

BACKGROUND

Accurate outcome prediction is of great clinical significance in customizing personalized treatment plans, reducing the situation of poor recovery, and objectively and accurately evaluating the treatment effect. This study intended to evaluate the performance of clinical text information (CTI), radiomics features, and survival features (SurvF) for predicting functional outcomes of patients with ischemic stroke.

METHODS

SurvF was constructed based on CTI and mRS radiomics features (mRSRF) to improve the prediction of the functional outcome in 3 months (90-day mRS). Ten machine learning models predicted functional outcomes in three situations (2-category, 4-category, and 7-category) using seven feature groups constructed by CTI, mRSRF, and SurvF.

RESULTS

For 2-category, ALL (CTI + mRSRF+ SurvF) performed best, with an mAUC of 0.884, mAcc of 0.864, mPre of 0.877, mF1 of 0.86, and mRecall of 0.864. For 4-category, ALL also achieved the best mAuc of 0.787, while CTI + SurvF achieved the best score with mAcc = 0.611, mPre = 0.622, mF1 = 0.595, and mRe-call = 0.611. For 7-category, CTI + SurvF performed best, with an mAuc of 0.788, mPre of 0.519, mAcc of 0.529, mF1 of 0.495, and mRecall of 0.47.

CONCLUSIONS

The above results indicate that mRSRF + CTI can accurately predict functional outcomes in ischemic stroke patients with proper machine learning models. Moreover, combining SurvF will improve the prediction effect compared with the original features. However, limited by the small sample size, further validation on larger and more varied datasets is necessary.

Stroke Genomics: Current Knowledge, Clinical Applications and Future Possibilities

The pathophysiology of stoke involves many complex pathways and risk factors. Though there are several ongoing studies on stroke, treatment options are limited, and the prevalence of stroke is continuing to increase. Understanding the genomic variants and biological pathways associated with stroke could offer novel therapeutic alternatives in terms of drug targets and receptor modulations for newer treatment methods. It is challenging to identify individual causative mutations in a single gene because many alleles are responsible for minor effects. Therefore, multiple factorial analyses using single nucleotide polymorphisms (SNPs) could be used to gain new insight by identifying potential genetic risk factors. There are many studies, such as Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) which have identified numerous independent loci associated with stroke, which could be instrumental in developing newer drug targets and novel therapies. Additionally, using analytical techniques, such as meta-analysis and Mendelian randomization could help in evaluating stroke risk factors and determining treatment priorities. Combining SNPs into polygenic risk scores and lifestyle risk factors could detect stroke risk at a very young age and help in administering preventive interventions.

Genetic Predisposition to Mosaic Chromosomal Loss Is Associated With Functional Outcome After Ischemic Stroke

Background and Objectives

To test the hypothesis that a predisposition to acquired genetic alterations is associated with ischemic stroke outcome by investigating the association between a polygenic risk score (PRS) for mosaic loss of chromosome Y (mLOY) and outcome in a large international data set.

Methods

We used data from the genome-wide association study performed within the Genetics of Ischemic Stroke Functional Outcome network, which included 6,165 patients (3,497 men and 2,668 women) with acute ischemic stroke of mainly European ancestry. We assessed a weighted PRS for mLOY and examined possible associations with the modified Rankin Scale (mRS) score 3 months poststroke in logistic regression models. We investigated the whole study sample as well as men and women separately.

Results

Increasing PRS for mLOY was associated with poor functional outcome (mRS score >2) with an odds ratio (OR) of 1.11 (95% confidence interval [CI] 1.03–1.19) per 1 SD increase in the PRS after adjustment for age, sex, ancestry, stroke severity (NIH Stroke Scale), smoking, and diabetes mellitus. In sex-stratified analyses, we found a statistically significant association in women (adjusted OR 1.20, 95% CI 1.08–1.33). In men, the association was in the same direction (adjusted OR 1.04, 95% CI 0.95–1.14), and we observed no significant genotype-sex interaction.

Discussion

In this exploratory study, we found associations between genetic variants predisposing to mLOY and stroke outcome. The significant association in women suggests underlying mechanisms related to genomic instability that operate in both sexes. These findings need replication and mechanistic exploration.

Technological readiness and implementation of genomic-driven precision medicine for complex diseases

The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data.

Cognitive Impairment and Dementia After Stroke: Design and Rationale for the DISCOVERY Study

Stroke is a leading cause of the adult disability epidemic in the United States, with a major contribution from poststroke cognitive impairment and dementia (PSCID), the rates of which are disproportionally high among the health disparity populations. Despite the PSCID's overwhelming impact on public health, a knowledge gap exists with regard to the complex interaction between the acute stroke event and highly prevalent preexisting brain pathology related to cerebrovascular and Alzheimer disease or related dementia. Understanding the factors that modulate PSCID risk in relation to index stroke event is critically important for developing personalized prognostication of PSCID, targeted interventions to prevent it, and for informing future clinical trial design. The DISCOVERY study (Determinants of Incident Stroke Cognitive Outcomes and Vascular Effects on Recovery), a collaborative network of thirty clinical performance clinical sites with access to acute stroke populations and the expertise and capacity for systematic assessment of PSCID will address this critical challenge. DISCOVERY is a prospective, multicenter, observational, nested-cohort study of 8000 nondemented ischemic and hemorrhagic stroke patients enrolled at the time of index stroke and followed for a minimum of 2 years, with serial cognitive evaluations and assessments of functional outcome, with subsets undergoing research magnetic resonance imaging and positron emission tomography and comprehensive genetic/genomic and fluid biomarker testing. The overall scientific objective of this study is to elucidate mechanisms of brain resilience and susceptibility to PSCID in diverse US populations based on complex interplay between life-course exposure to multiple vascular risk factors, preexisting burden of microvascular and neurodegenerative pathology, the effect of strategic acute stroke lesions, and the mediating effect of genomic and epigenomic variation.

Using Human Genetics to Understand Mechanisms in Ischemic Stroke Outcome: From Early Brain Injury to Long-Term Recovery

There is a critical need to elucidate molecular mechanisms underlying brain injury, repair, and recovery following ischemic stroke-a global health problem with major social and economic impact. Despite 5 decades of intensive research, there are no widely accepted neuroprotective drugs that mitigate ischemic brain injury, or neuroreparative drugs, or personalized approaches that guide therapies to enhance recovery. We here explore novel reverse translational approaches that will complement traditional forward translational methods in identifying mechanisms relevant to human stroke outcome. Although genome-wide association studies have yielded over 30 genetic loci that influence ischemic stroke risk, only a few genome-wide association studies have been performed for stroke outcome. We discuss important considerations for genetic studies of ischemic stroke outcome-including carefully designed phenotypes that capture injury/recovery mechanisms, anchored in time to stroke onset. We also address recent genome-wide association studies that provide insight into mechanisms underlying brain injury and repair. There are several ongoing initiatives exploring genomic associations with novel phenotypes related to stroke outcome. To improve the understanding of the genetic architecture of ischemic stroke outcome, larger studies using standardized phenotypes, preferably embedded in standard-of-care measures, are needed. Novel techniques beyond genome-wide association studies-including exploiting informatics, multi-omics, and novel analytics-promise to uncover genetic and molecular pathways from which drug targets and other new interventions may be identified.

A Clinical-Radiomics Nomogram for Functional Outcome Predictions in Ischemic Stroke

INTRODUCTION

Stroke remains a leading cause of death and disability worldwide. Effective and prompt prognostic evaluation is vital for determining the appropriate management strategy. Radiomics is an emerging noninvasive method used to identify the quantitative imaging indicators for predicting important clinical outcomes. This study was conducted to investigate and validate a radiomics nomogram for predicting ischemic stroke prognosis using the modified Rankin scale (mRS).

METHODS

A total of 598 consecutive patients with subacute infarction confirmed by diffusion-weighted imaging (DWI), from January 2018 to December 2019, were retrospectively assessed. They were assigned to the good (mRS ≤ 2) and poor (mRS > 2) functional outcome groups, respectively. Then, 399 patients examined by MR scanner 1 and 199 patients scanned by MR scanner 2 were assigned to the training and validation cohorts, respectively. Infarction lesions underwent manual segmentation on DWI, extracting 402 radiomic features. A radiomics nomogram encompassing patient characteristics and the radiomics signature was built using a multivariate logistic regression model. The performance of the nomogram was evaluated in the training and validation cohorts. Ultimately, decision curve analysis was implemented to assess the clinical value of the nomogram. The performance of infarction lesion volume was also evaluated using univariate analysis.

RESULTS

Stroke lesion volume showed moderate performance, with an area under the curve (AUC) of 0.678. The radiomics signature, including 11 radiomics features, exhibited good prediction performance. The radiomics nomogram, encompassing clinical characteristics (age, hemorrhage, and 24 h National Institutes of Health Stroke Scale score) and the radiomics signature, presented good discriminatory potential in the training cohort [AUC = 0.80; 95% confidence interval (CI) 0.75-0.86], which was validated in the validation cohort (AUC = 0.73; 95% CI 0.63-0.82). In addition, it demonstrated good calibration in the training (p = 0.55) and validation (p = 0.21) cohorts. Decision curve analysis confirmed the clinical value of this nomogram.

CONCLUSION

This novel noninvasive clinical-radiomics nomogram shows good performance in predicting ischemic stroke prognosis.

International stroke genetics consortium recommendations for studies of genetics of stroke outcome and recovery

Numerous biological mechanisms contribute to outcome after stroke, including brain injury, inflammation, and repair mechanisms. Clinical genetic studies have the potential to discover biological mechanisms affecting stroke recovery in humans and identify intervention targets. Large sample sizes are needed to detect commonly occurring genetic variations related to stroke brain injury and recovery. However, this usually requires combining data from multiple studies where consistent terminology, methodology, and data collection timelines are essential. Our group of expert stroke and rehabilitation clinicians and researchers with knowledge in genetics of stroke recovery here present recommendations for harmonizing phenotype data with focus on measures suitable for multicenter genetic studies of ischemic stroke brain injury and recovery. Our recommendations have been endorsed by the International Stroke Genetics Consortium.

The copy number variation and stroke (CaNVAS) risk and outcome study

BACKGROUND AND PURPOSE

The role of copy number variation (CNV) variation in stroke susceptibility and outcome has yet to be explored. The Copy Number Variation and Stroke (CaNVAS) Risk and Outcome study addresses this knowledge gap.

METHODS

Over 24,500 well-phenotyped IS cases, including IS subtypes, and over 43,500 controls have been identified, all with readily available genotyping on GWAS and exome arrays, with case measures of stroke outcome. To evaluate CNV-associated stroke risk and stroke outcome it is planned to: 1) perform Risk Discovery using several analytic approaches to identify CNVs that are associated with the risk of IS and its subtypes, across the age-, sex- and ethnicity-spectrums; 2) perform Risk Replication and Extension to determine whether the identified stroke-associated CNVs replicate in other ethnically diverse datasets and use biomarker data (e.g. methylation, proteomic, RNA, miRNA, etc.) to evaluate how the identified CNVs exert their effects on stroke risk, and lastly; 3) perform outcome-based Replication and Extension analyses of recent findings demonstrating an inverse relationship between CNV burden and stroke outcome at 3 months (mRS), and then determine the key CNV drivers responsible for these associations using existing biomarker data.

RESULTS

The results of an initial CNV evaluation of 50 samples from each participating dataset are presented demonstrating that the existing GWAS and exome chip data are excellent for the planned CNV analyses. Further, some samples will require additional considerations for analysis, however such samples can readily be identified, as demonstrated by a sample demonstrating clonal mosaicism.

CONCLUSION

The CaNVAS study will cost-effectively leverage the numerous advantages of using existing case-control data sets, exploring the relationships between CNV and IS and its subtypes, and outcome at 3 months, in both men and women, in those of African and European-Caucasian descent, this, across the entire adult-age spectrum.

Texture analysis based on ADC maps and T2-FLAIR images for the assessment of the severity and prognosis of ischaemic stroke

OBJECTIVES

To explore the feasibility of texture analysis based on T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images and apparent diffusion coefficient (ADC) maps in the assessment of the severity and prognosis of ischaemic stroke using the National Institutes of Health Stroke Scale (NIHSS) and modified Rankin scale (mRS) scores, respectively.

METHODS

Overall, 116 patients diagnosed with subacute ischaemic stroke were included in this retrospective study. Based on T2-FLAIR images and ADC maps, 15 texture features were extracted from the ROIs of each patient using grey-level co-occurrence matrix (GLCM) and local binary pattern histogram Fourier (LBP-HF) methods. The correlations of NIHSS score on admission (NIHSS_baseline), NIHSS score 24 h after stroke onset (NIHSS_24h) and mRS score with the texture features were evaluated using Spearman's partial correlations. The receiver operating characteristic (ROC) curve was used to compare the performance of the selected texture features in the evaluation of stroke severity and prognosis.

RESULTS

Texture features derived from the T2-FLAIR images and ADC maps were correlated with NIHSS score and mRS score. Entropy_ADC and 0.75Quantile_T2-FLAIR showed the best diagnostic performance for assessing stroke severity. The combination of Entropy_ADC and 0.75Quantile_T2-FLAIR achieved a better performance in the evaluation of stroke severity (AUC = 0.7, p = 0.01) than either feature alone. Only 0.05Quantile_T2-FLAIR was found to be correlated with mRS score, and none of the texture features were predictive of mRS score.

CONCLUSION

Texture features derived from T2-FLAIR images and ADC maps might serve as biomarkers to evaluate stroke severity, but were insufficient to predict stroke prognosis.

Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke

Despite many years of research, no biomarkers for stroke are available to use in clinical practice. Progress in high-throughput technologies has provided new opportunities to understand the pathophysiology of this complex disease, and these studies have generated large amounts of data and information at different molecular levels. The integration of these multi-omics data means that thousands of proteins (proteomics), genes (genomics), RNAs (transcriptomics) and metabolites (metabolomics) can be studied simultaneously, revealing interaction networks between the molecular levels. Integrated analysis of multi-omics data will provide useful insight into stroke pathogenesis, identification of therapeutic targets and biomarker discovery. In this Review, we detail current knowledge on the pathology of stroke and the current status of biomarker research in stroke. We summarize how proteomics, metabolomics, transcriptomics and genomics are all contributing to the identification of new candidate biomarkers that could be developed and used in clinical stroke management.