Precision medicine in secondary prevention of ischemic stroke: how may blood-based biomarkers help in clinical routine? An expert opinion

Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke

Ischemic stroke is a serious disease posing significant threats to human health and life, with the highest absolute and relative risks of a poor prognosis following the first occurrence, and more than 90% of strokes are attributable to modifiable risk factors. Currently, machine learning (ML) is widely used for the prediction of ischemic stroke outcomes. By identifying risk factors, predicting the risk of poor prognosis and thus developing personalized treatment plans, it effectively reduces the probability of poor prognosis, leading to more effective secondary prevention. This review includes 41 studies since 2018 that used ML algorithms to build prognostic prediction models for ischemic stroke, transient ischemic attack (TIA), and acute ischemic stroke (AIS). We analyzed in detail the risk factors used in these studies, the sources and processing methods of the required data, the model building and validation, and their application in different prediction time windows. The results indicate that among the included studies, the top five risk factors in terms of frequency were cardiovascular diseases, age, sex, national institutes of health stroke scale (NIHSS) score, and diabetes. Furthermore, 64% of the studies used single-center data, 65% of studies using imbalanced data did not perform data balancing, 88% of the studies did not utilize external validation datasets for model validation, and 72% of the studies did not provide explanations for their models. Addressing these issues is crucial for enhancing the credibility and effectiveness of the research, consequently improving the development and implementation of secondary prevention measures.

Towards a new classification of atrial fibrillation detected after a stroke or a transient ischaemic attack

Globally, up to 1·5 million individuals with ischaemic stroke or transient ischaemic attack can be newly diagnosed with atrial fibrillation per year. In the past decade, evidence has accumulated supporting the notion that atrial fibrillation first detected after a stroke or transient ischaemic attack differs from atrial fibrillation known before the occurrence of as stroke. Atrial fibrillation detected after stroke is associated with a lower prevalence of risk factors, cardiovascular comorbidities, and atrial cardiomyopathy than atrial fibrillation known before stroke occurrence. These differences might explain why it is associated with a lower risk of recurrence of ischaemic stroke than known atrial fibrillation. Patients with ischaemic stroke or transient ischaemic attack can be classified in three categories: no atrial fibrillation, known atrial fibrillation before stroke occurrence, and atrial fibrillation detected after stroke. This classification could harmonise future research in the field and help to understand the role of prolonged cardiac monitoring for secondary stroke prevention with application of a personalised risk-based approach to the selection of patients for anticoagulation.

MicroRNA profiling in dogs undergoing induced ischemic heart infarction: An experimental study

Background and Aim

MicroRNAs (miRNAs) play an important role in various biological functions. According to many studies, miRNA expression is tissue-specific, strongly controlled throughout embryogenesis, and over- or under-expressed in numerous disorders, including cardiovascular pathologies. This study aimed to screen, characterize, and profile many induced biomarkers (miRNAs) in dog serum before and after experimentally inducing a regional myocardial infarction (MI) by occluding the coronary arteries under general anesthesia.

Materials and Methods

A preclinical experimental animal study recruited 12 healthy canine dogs. The selected canine dogs were anesthetized with 1 mg/kg xylazine and 15 mg/kg ketamine before undergoing femoral arterial catheterization under fluoroscopic supervision. Commercial assay kits were used to purify total RNA and miRNA before the occlusion and 2 h after the occlusion according to the manufacturer's guidelines, and the samples were stored in RNase/DNase-free water at -80°C. Data were analyzed by GraphPad Prism 5.0 software (GraphPad Prism, San Diego, CA) SPSS, and GenEx software (www.multid.se) or (REST V3).

Results

Among 325 transcribed genes, 20 were identified in 2 h. After MI, 14 biomarkers were negative, indicating downregulation, and 6 (3-F08, 3-B10, 4-A11, 1-A06, 2-E01, 3-F10) were positive, indicating upregulation. Polymerase chain reaction assay results showed a normalized fold-change in gene expression in the test sample. Fold values >1 represented a biologically significant change.

Conclusion

Profiling of miRNAs before and after MI in a dog model revealed upregulation of six previously unidentified biomarkers (3-F08, 3-B10, 4-A11, 1-A06, 2-E01, and 3-F10), indicating various miRNA regulatory patterns.

Genetic variation within the pri-let-7f-2 in the X chromosome predicting stroke risk in a Chinese Han population from Liaoning, China: From a case-control study to a new predictive nomogram

Background and objectives

Stroke is the most common cause of disability and the second cause of death worldwide. Therefore, there is a need to identify patients at risk of developing stroke. This case-control study aimed to create and verify a gender-specific genetic signature-based nomogram to facilitate the prediction of ischemic stroke (IS) risk using only easily available clinical variables.

Materials and methods

A total of 1,803 IS patients and 1,456 healthy controls from the Liaoning province in China (Han population) were included which randomly divided into training cohort (70%) and validation cohort (30%) using the sample function in R software. The distribution of the pri-let-7f-2 rs17276588 variant genotype was analyzed. Following genotyping analysis, statistical analysis was used to identify relevant features. The features identified from the multivariate logistic regression, the least absolute shrinkage and selection operator (LASSO) regression, and univariate regression were used to create a multivariate prediction nomogram model. A calibration curve was used to determine the discrimination accuracy of the model in the training and validation cohorts. External validity was also performed.

Results

The genotyping analysis identified the A allele as a potential risk factor for IS in both men and women. The nomogram identified the rs17276588 variant genotype and several clinical parameters, including age, diabetes mellitus, body mass index (BMI), hypertension, history of alcohol use, history of smoking, and hyperlipidemia as risk factors for developing IS. The calibration curves for the male and female models showed good consistency and applicability.

Conclusion

The pri-let-7f-2 rs17276588 variant genotype is highly linked to the incidence of IS in the northern Chinese Han population. The nomogram we devised, which combines genetic fingerprints and clinical data, has a lot of promise for predicting the risk of IS within the Chinese Han population.

Investigational drugs for ischemic stroke: what's in the clinical development pipeline for acute phase and prevention?

INTRODUCTION

Stroke is a leading cause of disability and mortality and its burden expected to increase. The only approved drug for acute ischemic stroke is the intravenous thrombolytic alteplase. The risk of bleeding complications is one of the reasons for the undertreatment of eligible patients. Numerous drugs are currently being developed to improve safety-efficacy.

AREAS COVERED

We reviewed literature from January 1^st, 2000, to 15^th January 2022 for the development and testing of novel drugs with the aim of targeting treatment at prevention of ischemic stroke: PubMed, MEDLINE, Google Scholar, and ClinicalTrial.gov.

EXPERT OPINION

The pathophysiology of ischemic stroke involves multiple pathways causing cerebral artery obstruction and brain tissue ischemia. Data suggest that tenecteplase is a more promising fibrinolytic agent with a superior efficacy-safety profile, compared to the currently approved alteplase. Current guidelines consider a short-term cycle of mannitol or hypertonic saline to be advisable in patients with space-occupying hemispheric infarction. Regarding primary and secondary prevention, research is primarily focused on identifying mechanisms to improve the safety-efficacy profile using a "hemostasis-sparing" approach. Further evaluation on those agents that have already shown promise for their risk/benefit profiles, would benefit greatly a neurologist's capacity to successfully prevent and treat ischemic stroke patients.

A novel biomarker panel index improves risk stratification after ischemic stroke

Background:

We investigated 92 blood biomarkers implicated in the pathophysiological pathways of ischemic injury, inflammation, hemostasis, and regulation of vascular resistance to predict post-stroke mortality.

Aim:

Based on the most promising markers, we aimed to create a novel Biomarker Panel Index (BPI) for risk stratification.

Methods:

In this prospective study, we measured 92 biomarkers in 320 stroke patients. The primary outcome measure was mortality within 90 days. We estimated the association of each biomarker using logistic regression adjusting for multiple testing. The most significant 16 biomarkers were used to create the BPI. We fitted regression models to estimate the association and the discriminatory accuracy of the BPI with mortality and stroke etiology.

Results:

Adjusted for demographic and vascular covariates, the BPI remained independently associated with mortality (odds ratio (OR) 1.68, 95% confidence interval (CI): 1.29–2.18) and cardioembolic stroke etiology (OR 1.38, 95% CI: 1.10–1.74), and improved the discriminatory accuracy to predict mortality (area under the receiver operating characteristic curve (AUC) 0.93, 95% CI: 0.89–0.96) and cardioembolic stroke etiology (AUC 0.70, 95% CI: 0.64–0.77) as compared to the best clinical prediction models alone (AUC 0.89, 95% CI: 0.84–0.94 and AUC 0.66, 95% CI: 0.60-0.73, respectively).

Conclusions:

We identified a novel BPI improving risk stratification for mortality after ischemic stroke beyond established demographic and vascular risk factors. Furthermore, the BPI is associated with underlying cardioembolic stroke etiology. These results need external validation.

|Therapeutic and preventive effects of apigenin in cerebral ischemia: a review

APG can exert various protective effects against cerebral ischemia. Moreover, APG has shown a highly promising ability to prevent cerebral ischemia in terms of regulating blood glucose, blood pressure, lipids and gut microbes.