Global stroke statistics 2022

Association between genetic predisposition and disease burden of stroke in China: a genetic epidemiological study

BACKGROUND

Stroke ranks second worldwide and first in China as a leading cause of death and disability. It has a polygenic architecture and is influenced by environmental and lifestyle factors. However, it remains unknown as to whether and how much the genetic predisposition of stroke is associated with disease burden.

METHODS

Allele frequency from the whole genome sequencing data in the Chinese Millionome Database of 141,418 individuals and trait-specific polygenic risk score models were applied to estimate the provincial genetic predisposition to stroke, stroke-related risk factors and stroke-related drug response. Disease burden including mortality, disability-adjusted life years (DALYs), years of life lost(YLLs), years lived with disability (YLDs) and prevalence in China was collected from the Global Burden Disease study. The association between stroke genetic predisposition and the epidemiological burden was assessed and then quantified in both regression-based models and machine learning-based models at a provincial resolution.

FINDINGS

Among the 30 administrative divisions in China, the genetic predisposition of stroke was characterized by a north-higher-than-south gradient (p < 0.0001). Genetic predisposition to stroke, blood pressure, body mass index, and alcohol use were strongly intercorrelated (rho >0.6; p < 0.05 after Bonferroni correction for each comparison). Genetic risk imposed an independent effect of approximately 1-6% on mortality, DALYs and YLLs.

INTERPRETATION

The distribution pattern of stroke genetic predisposition is different at a macroscopic level, and it subtly but significantly impacts the epidemiological burden. Further research is warranted to identify the detailed aetiology and potential translation into public health measures.

FUNDING

Beijing Municipal Science and Technology Commission (Z191100006619106), CAMS Innovation Fund for Medical Sciences (CAMS-I2M, 2023-I2M-1-001), the National High Level Hospital Clinical Research Funding (2022-GSP-GG-17), National Natural Science Foundation of China (32000398, 32171441 to X.J.), Natural Science Foundation of Guangdong Province, China (2017A030306026 to X.J.), and National Key R&D Program of China (2022YFC2502402).

A Light-Weight Artificial Neural Network for Recognition of Activities of Daily Living

Human activity recognition (HAR) is essential for the development of robots to assist humans in daily activities. HAR is required to be accurate, fast and suitable for low-cost wearable devices to ensure portable and safe assistance. Current computational methods can achieve accurate recognition results but tend to be computationally expensive, making them unsuitable for the development of wearable robots in terms of speed and processing power. This paper proposes a light-weight architecture for recognition of activities using five inertial measurement units and four goniometers attached to the lower limb. First, a systematic extraction of time-domain features from wearable sensor data is performed. Second, a small high-speed artificial neural network and line search method for cost function optimization are used for activity recognition. The proposed method is systematically validated using a large dataset composed of wearable sensor data from seven activities (sitting, standing, walking, stair ascent/descent, ramp ascent/descent) associated with eight healthy subjects. The accuracy and speed results are compared against methods commonly used for activity recognition including deep neural networks, convolutional neural networks, long short-term memory and convolutional-long short-term memory hybrid networks. The experiments demonstrate that the light-weight architecture can achieve a high recognition accuracy of 98.60%, 93.10% and 84.77% for seen data from seen subjects, unseen data from seen subjects and unseen data from unseen subjects, respectively, and an inference time of 85 μs. The results show that the proposed approach can perform accurate and fast activity recognition with a reduced computational complexity suitable for the development of portable assistive devices.

Common Signaling Pathways Involved in Alzheimer's Disease and Stroke: Two Faces of the Same Coin

Alzheimer's disease (AD) and stroke are two interrelated neurodegenerative disorders which are the leading cause of death and affect the neurons in the brain and central nervous system. Although amyloid-β aggregation, tau hyperphosphorylation, and inflammation are the hallmarks of AD, the exact cause and origin of AD are still undefined. Recent enormous fundamental discoveries suggest that the amyloid hypothesis of AD has not been proven and anti-amyloid therapies that remove amyloid deposition have not yet slowed cognitive decline. However, stroke, mainly ischemic stroke (IS), is caused by an interruption in the cerebral blood flow. Significant features of both disorders are the disruption of neuronal circuitry at different levels of cellular signaling, leading to the death of neurons and glial cells in the brain. Therefore, it is necessary to find out the common molecular mechanisms of these two diseases to understand their etiological connections. Here, we summarized the most common signaling cascades including autotoxicity, ApoE4, insulin signaling, inflammation, mTOR-autophagy, notch signaling, and microbiota-gut-brain axis, present in both AD and IS. These targeted signaling pathways reveal a better understanding of AD and IS and could provide a distinguished platform to develop improved therapeutics for these diseases.

Virtual and Augmented Reality in Post-stroke Rehabilitation: A Narrative Review

Virtual reality (VR) and augmented reality (AR) are noble adjunctive technologies currently being studied for the neuro-rehabilitation of post-stroke patients, potentially enhancing conventional therapy. We explored the literature to find if VR/AR improves neuroplasticity in stroke rehabilitation for a better quality of life. This modality can lay the foundation for telerehabilitation services in remote areas. We analyzed four databases, namely Cochrane Library, PubMed, Google Scholar, and Science Direct, by searching the following keywords: ("Stroke Rehabilitation" [Majr]) AND ("Augmented Reality" [Majr]), Virtual Augmented Reality in Stroke Rehabilitation. All the available open articles were reviewed and outlined. The studies conclude that VR/AR can help in early rehabilitation and yield better results in post-stroke patients in adjunct to conventional therapy. However, due to the limited research on this subject, we cannot conclude that this information is absolute. Moreover, VR/AR was seldom customized according to the needs of stroke survivors, which would have given us the full extent of its application. Around the world, stroke survivors are being studied to verify the accessibility and practicality of these innovative technologies. Observations conclude that further exploration of the extent of the implementations and efficacy of VR and AR, combined with conventional rehabilitation, is fundamental.

Mobile App Interventions for Parkinson's Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review

Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson's disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient's physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.

Increasing burden of stroke in China: A systematic review and meta-analysis of prevalence, incidence, mortality, and case fatality

BACKGROUND

The epidemiology of stroke is evolving in China as the population undergoes demographic, lifestyle, and economic transitions. An updated review is pertinent to providing feedback on current, and in planning future, prevention and management strategies.

AIMS

To identify high-quality epidemiological studies for quantifying the prevalence, incidence, mortality, and case fatality for stroke in China.

METHODS

A search was undertaken across a range of bibliographic databases on 30 November 2021 without time limitation. Assessments were made of the risk of bias of the included studies. The outcomes were synthesized using a random-effects model. Subgroup analysis and meta-regression models were used to define the source of heterogeneity.

RESULTS

Of 9407 identified records, 26 population-based studies were included. Due to significant heterogeneity across the studies, the original range for crude rates of indices was wide. The pooled annual prevalence was 1329.5/100,000 (95% confidence interval (CI) 713.6-2131.9, p < 0.001), incidence 442.1/100,000 (327.6-573.8, p < 0.001), mortality 154.1/100,000 (52.6-308.8, I² = 100%, p < 0.001), and case fatality 35.8% (26.1% to 46.1%, I² = 97%, p < 0.001). The prevalence and incidence of stroke have increased, but stroke-related case fatality has declined in China over recent decades. There are significant regional and rural-urban differences in incidence rates.

CONCLUSION

Despite improved public health policies and healthcare delivery, the burden of stroke remains high in China. Further coordinated efforts are required in prevention and community care to offset the likelihood of further expansion in the absolute number of stroke cases in this large population.

Explainable machine learning for long-term outcome prediction in two-center stroke patients after intravenous thrombolysis

Objective

Neurological outcome prediction in patients with ischemic stroke is very critical in treatment strategy and post-stroke management. Machine learning techniques with high accuracy are increasingly being developed in the medical field. We studied the application of machine learning models to predict long-term neurological outcomes in patients with after intravenous thrombolysis.

Methods

A retrospective cohort study was performed to review all stroke patients with intravenous thrombolysis. Patients with modified Rankin Score (mRs) less than two at three months post-thrombolysis were considered as good outcome. The clinical features between stroke patients with good and with poor outcomes were compared using three different machine learning models (Random Forest, Support Vector Machine and Logistic Regression) to identify which performed best. Two datasets from the other stroke center were included accordingly for external verification and performed with explainable AI models.

Results

Of the 488 patients enrolled in this study, and 374 (76.6%) patients had favorable outcomes. Patients with higher mRs at 3 months had increased systolic pressure, blood glucose, cholesterol (TC), and 7-day National Institute of Health Stroke Scale (NIHSS) score compared to those with lower mRs. The predictability and the areas under the curves (AUC) for the random forest model was relatively higher than support vector machine and LR models. These findings were further validated in the external dataset and similar results were obtained. The explainable AI model identified the risk factors as well.

Conclusion

Explainable AI model is able to identify NIHSS_Day7 is independently efficient in predicting neurological outcomes in patients with ischemic stroke after intravenous thrombolysis.

Cerebral small vessel disease: Recent advances and future directions

Cerebral small vessel disease (SVD) causes lacunar stroke and intracerebral hemorrhage, and is the most common pathology underlying vascular cognitive impairment. Increasingly, the importance of other clinical features of SVD is being recognized including motor impairment, (vascular) parkinsonism, impaired balance, falls, and behavioral symptoms, such as depression, apathy, and personality change. Epidemiological data show a high prevalence of the characteristic magnetic resonance imaging (MRI) features of white matter hyperintensities and lacunar infarcts in community studies, and recent data suggest that it is also a major health burden in low- and middle-income countries. In this review, we cover advances in diagnosis, imaging, clinical presentations, pathogenesis, and treatment.The two most common pathologies underlying SVD are arteriolosclerosis caused by aging, hypertension, and other conventional vascular risk factors, and cerebral amyloid angiopathy (CAA) caused by vascular deposition of β-amyloid. We discuss the revised Boston criteria of CAA based on MRI features, which have been recently validated. Imaging is providing important insights into pathogenesis, including improved detection of tissue damage using diffusion tensor imaging (DTI) leading to its use to monitor progression and surrogate endpoints in clinical trials. Advanced MRI techniques can demonstrate functional or dynamic abnormalities of the blood vessels, while the high spatial resolution provided by ultrahigh field MRI at 7 T allows imaging of individual perforating arteries for the first time, and the measurement of flow velocity and pulsatility within these arteries. DTI and structural network analysis have highlighted the importance of network disruption in mediating the effect of different SVD pathologies in causing a number of symptoms, including cognitive impairment, apathy, and gait disturbance.Despite the public health importance of SVD, there are few proven treatments. We review the evidence for primary prevention, and recent data showing how intensive blood pressure lowering reduces white matter hyperintensities (WMH) progression and delays the onset of cognitive impairment. There are few treatments for secondary prevention, but a number of trials are currently evaluating novel treatment approaches. Recent advances have implicated molecular processes related to endothelial dysfunction, nitric oxide synthesis, blood-brain barrier integrity, maintenance and repair of the extracellular matrix, and inflammation. Novel treatment approaches are being developed to a number of these targets. Finally, we highlight the importance of large International collaborative initiatives in SVD to address important research questions and cover a number which have recently been established.

Mesenchymal stem cell therapy for neurological disorders: The light or the dark side of the force?

Neurological disorders are recognized as major causes of death and disability worldwide. Because of this, they represent one of the largest public health challenges. With awareness of the massive burden associated with these disorders, came the recognition that treatment options were disproportionately scarce and, oftentimes, ineffective. To address these problems, modern research is increasingly looking into novel, more effective methods to treat neurological patients; one of which is cell-based therapies. In this review, we present a critical analysis of the features, challenges, and prospects of one of the stem cell types that can be employed to treat numerous neurological disorders-mesenchymal stem cells (MSCs). Despite the fact that several studies have already established the safety of MSC-based treatment approaches, there are still some reservations within the field regarding their immunocompatibility, heterogeneity, stemness stability, and a range of adverse effects-one of which is their tumor-promoting ability. We additionally examine MSCs' mechanisms of action with respect to in vitro and in vivo research as well as detail the findings of past and ongoing clinical trials for Parkinson's and Alzheimer's disease, ischemic stroke, glioblastoma multiforme, and multiple sclerosis. Finally, this review discusses prospects for MSC-based therapeutics in the form of biomaterials, as well as the use of electromagnetic fields to enhance MSCs' proliferation and differentiation into neuronal cells.

HTRA1 methylation in peripheral blood as a potential marker for the preclinical detection of stroke: a case-control study and a prospective nested case-control study

BACKGROUND

Stroke is the leading cause of mortality in China. DNA methylation has essential roles in multiple diseases, but its association with stroke was barely studied. We hereby explored the association between blood-based HTRA serine protease 1 (HTRA1) methylation and the risk of stroke.

RESULTS

The association was discovered in a hospital-based case-control study (cases/controls = 190:190) and further validated in a prospective nested case-control study including 139 cases who developed stroke within 2 years after recruitment and 144 matched stroke-free controls. We observed stroke-related altered HTRA1 methylation and expression in both case-control study and prospective study. This blood-based HTRA1 methylation was associated with stroke independently from the known risk factors and mostly affected the older population. The prospective results further showed that the altered HTRA1 methylation was detectable 2 years before the clinical determination of stroke and became more robust with increased discriminatory power for stroke along with time when combined with other known stroke-related variables [onset time ≤ 1 year: area under the curve (AUC) = 0.76].

CONCLUSIONS

In our study, altered HTRA1 methylation was associated with stroke at clinical and preclinical stages and thus may provide a potential biomarker in the blood for the risk evaluation and preclinical detection of stroke.