Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients.

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.

Long-term vascular events after subarachnoid hemorrhage.

BACKGROUND: Spontaneous subarachnoid hemorrhage (SAH) long-term risk is not well known. Our aims are: describing long-term vascular event (VE) incidence rates in SAH survivors; describing VE: ischemic and/or hemorrhagic; identifying independent association of factors related to VE; and analyzing the usefulness of factors to increase predictive ability. METHODS: A prospective cohort study of consecutive patients admitted to Hospital del Mar with a diagnosis of SAH (n = 566) between January 2007 and January 2020 was carried out. They were followed up until January 2021. The study endpoint was a new VE in the follow-up. We calculated both incidence rates and cumulative rates at 5 years. Cox regression survival models including vascular risk factors with and without specific data of SAH disease were developed. We analyzed ROC curves of all multivariate models. RESULTS: The analyzed cohort included 423 non-fatal SAH cases. Total patient-years were 2468.16 years. The average follow-up was 70.03 ± 43.14; range: 1-180 months. There were 49 VE detected in 47 patients, as 2 of them had more than 1 VE. Incidence rate was 0.020 events_per_patient/year, cumulative incidence at 5 years was 11.11%. The more frequent VE that we found were cerebrovascular (28/49), mainly ischemic (21/28). Disability after SAH and the presence of multiple aneurysms were independently associated with a VE risk and improved the predictive capacity of multivariate models (AUC 0.679 vs 0.764; p = 0.0062). CONCLUSIONS: We reported a low vascular risk after SAH. We have shown the usefulness of SAH factors to identify patients with a higher risk of VE.

Genetics and Epigenetics of Spontaneous Intracerebral Hemorrhage.

Intracerebral hemorrhage (ICH) is a complex and heterogeneous disease, and there is no effective treatment. Spontaneous ICH represents the final manifestation of different types of cerebral small vessel disease, usually categorized as: lobar (mostly related to cerebral amyloid angiopathy) and nonlobar (hypertension-related vasculopathy) ICH. Accurate phenotyping aims to reflect these biological differences in the underlying mechanisms and has been demonstrated to be crucial to the success of genetic studies in this field. This review summarizes how current knowledge on genetics and epigenetics of this devastating stroke subtype are contributing to improve the understanding of ICH pathophysiology and their potential role in developing therapeutic strategies.