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Background and aims: Stroke is the leading cause of adult-onset disability. Although clinical factors influence stroke outcome, there is a significant variability among individuals that may be attributed to genetics and epigenetics, including DNA methylation (DNAm). We aimed to study the association between DNAm and stroke prognosis. Methods and results: To that aim, we conducted a two-phase study (discovery-replication and meta-analysis) in Caucasian patients with ischemic stroke from two independent centers (BasicMar [discovery, N = 316] and St. Pau [replication, N = 92]). Functional outcome was assessed using the modified Rankin Scale (mRS) at three months after stroke, being poor outcome defined as mRS > 2. DNAm was determined using the 450K and EPIC BeadChips in whole-blood samples collected within the first 24 h. We searched for differentially methylated positions (DMPs) in 370,344 CpGs, and candidates below p-value < 10–5 were subsequently tested in the replication cohort. We then meta-analyzed DMP results from both cohorts and used them to identify differentially methylated regions (DMRs). After doing the epigenome-wide association study, we found 29 DMPs at p-value < 10–5 and one of them was replicated: cg24391982, annotated to thrombospondin-2 (THBS2) gene (p-valuediscovery = 1.54·10–6; p-valuereplication = 9.17·10–4; p-valuemeta-analysis = 6.39·10–9). Besides, four DMRs were identified in patients with poor outcome annotated to zinc finger protein 57 homolog (ZFP57), Arachidonate 12-Lipoxygenase 12S Type (ALOX12), ABI Family Member 3 (ABI3) and Allantoicase (ALLC) genes (p-value < 1·10–9 in all cases). Discussion: Patients with poor outcome showed a DMP at THBS2 and four DMRs annotated to ZFP57, ALOX12, ABI3 and ALLC genes. This suggests an association between stroke outcome and DNAm, which may help identify new stroke recovery mechanisms. [ABSTRACT FROM AUTHOR]

This comprehensive review explores the emerging field of epigenetics in intracranial aneurysm (IA) and aneurysmal subarachnoid hemorrhage (aSAH). Despite recent advancements, the high mortality of aSAH needs an understanding of its underlying pathophysiology, where epigenetics plays a crucial role. This review synthesizes the current knowledge, focusing on three primary epigenetic mechanisms: DNA methylation, non-coding RNA (ncRNA), and histone modification in IA and aSAH. While DNA methylation studies are relatively limited, they suggest a significant role in the pathogenesis and prognosis of IA and aSAH, highlighting differentially methylated positions in genes presumably involved in these pathologies. However, methodological limitations, including small sample sizes and a lack of diverse population studies, temper these results. The role of ncRNAs, particularly miRNAs, has been more extensively studied, but there are still few studies focused on histone modifications. Despite methodological challenges and inconsistent findings, these studies underscore the involvement of miRNAs in key pathophysiological processes, including vascular smooth muscle regulation and the inflammatory response. This review emphasizes methodological challenges in epigenetic research, advocating for large-scale epigenome-wide association studies integrating genetic and environmental factors, along with longitudinal studies. Such research could unravel the complex mechanisms behind IA and aSAH, guiding the development of targeted therapeutic approaches. [ABSTRACT FROM AUTHOR]

Introduction: Migraine with aura (MA) is a frequent stroke simulator that can lead to erroneous diagnosis and subsequent unnecessary acute or secondary prevention treatments. We analyzed clinical and laboratory data of migraine with aura and ischemic stroke patients to detect differences that could help in the diagnosis. Methods: Retrospective analysis of a consecutive register of code strokes between January 2005 and June 2020. Diagnosis of ischemic stroke or MA was collected. Multivariable logistic regression analyses were performed to test associations between clinical and blood data with ischemic stroke. Results: Of 3140 code strokes, 2424 (77.2%) were ischemic strokes and 34 (1.1%) were MA. Migraine cases were younger, more frequently females and with lower prevalence of vascular risk factors. Initial NIHSS was lower in MA cases, but no differences were seen in fibrinolysis rate (30%). Blood test showed lower levels of glucose, D-dimer, and fibrinogen in MA cases. Multivariable model showed and independent association for ischemic stroke with age [OR, (95%CI): 1.09, (1.07–1.12, p < 0.001], male sex [OR, (95%CI): 4.47, (3.80–5.13), p < 0.001], initial NIHSS [OR, (95%CI): 1.21, (1.07–1.34), p < 0.01], and fibrinogen levels [OR, (95%CI): 1.01, (1.00–1.01), p < 0.05]. A model including sex male OR: 3.55 [2.882; 4.598], p < 0.001, and cutoff points (age > 65, OR: 7.953 [7.256; 8.649], p < 0.001, NIHSS > 6, OR: 3.740 [2.882; 4.598], p < 0.01, and fibrinogen > 400 mg/dL, OR: 2.988 [2.290; 3.686], p < 0.01) showed a good global discrimination capability AUC = 0.89 (95%CI: 0.88–0.94). Conclusions: In code stroke, a model including age, sex, NIHSS, and fibrinogen showed a good discrimination capability to differentiate between MA and Ischemic stroke. Whether these variables can be implemented in a diagnostic rule should be tested in future studies. [ABSTRACT FROM AUTHOR]

Age acceleration (Age-A) is a useful tool that is able to predict a broad range of health outcomes. It is necessary to determine DNA methylation levels to estimate it, and it is known that Age-A is influenced by environmental, lifestyle, and vascular risk factors (VRF). The aim of this study is to estimate the contribution of these easily measurable factors to Age-A in patients with cerebrovascular disease (CVD), using different machine learning (ML) approximations, and try to find a more accessible model able to predict Age-A. We studied a CVD cohort of 952 patients with information about VRF, lifestyle habits, and target organ damage. We estimated Age-A using Hannum's epigenetic clock, and trained six different models to predict Age-A: a conventional linear regression model, four ML models (elastic net regression (EN), K-Nearest neighbors, random forest, and support vector machine models), and one deep learning approximation (multilayer perceptron (MLP) model). The best-performing models were EN and MLP; although, the predictive capability was modest (R2 0.358 and 0.378, respectively). In conclusion, our results support the influence of these factors on Age-A; although, they were not enough to explain most of its variability. [ABSTRACT FROM AUTHOR]

Simple Summary: Biological age (B-age), or the degree of aging of an individual, can differ from chronological age. B-age is affected by epigenetics, and we calculate it based on the degree of methylation of multiple specific regions of human DNA. For previous research, we know that patients with ischemic stroke are biologically older than healthy individuals without stroke. On the other hand, white matter hyperintensities (WMH) observed in brain magnetic resonance images are an unspecific sign that has been associated with brain aging and also with the increased risk of stroke or dementia. It is unknown whether epigenetic biological age is associated with this sign of brain aging. In this manuscript, we interrogated the association between B-age and WMH volume and found that patients with high WMH burden are biologically older. Moreover, we found that 42.7% of the effects of chronological age on WMH can be explained by B-age, suggesting a role of epigenetics in WMH pathophysiology. Our study also generates a potential number of questions that might be addressed in further articles, such as whether this relationship depends on WMH location. In this manuscript we studied the relationship between WMH and biological age (B-age) in patients with acute stroke. We included in this study 247 patients with acute stroke recruited at Hospital del Mar having both epigenetic (DNA methylation) and magnetic resonance imaging data. WMH were measured using a semi-automated method. B-age was calculated using two widely used methods: the Hannum and Horvath formulas. We used multiple linear regression models to interrogate the role of B-age on WMH volume after adjusting for chronological age (C-age) and other covariables. Average C-age of the sample was 68.4 (±11.8) and we observed a relatively high median WMH volume (median = 8.8 cm3, Q1–Q3 = 4.05–18.8). After adjusting for potential confounders, we observed a significant effect of B-ageHannum on WMH volume (βHannum = 0.023, p-value = 0.029) independently of C-age, which remained significant (βC-age = 0.021, p-value = 0.036). Finally, we performed a mediation analysis, which allowed us to discover that 42.7% of the effect of C-age on WMH is mediated by B-ageHannum. On the other hand, B-ageHoarvath showed no significant associations with WMH after being adjusted for C-age. In conclusion, we show for the first time that biological age, measured through DNA methylation, contributes substantially to explain WMH volumetric burden irrespective of chronological age. [ABSTRACT FROM AUTHOR]