Blood Biomarkers Unveil Stroke Risk Factors in Chinese Population

Stroke is a significant public health challenge globally, with China experiencing high rates of incidence and mortality. Geographical disparities in stroke rates within China suggest the influence of environmental, genetic, and lifestyle factors. Lifestyle choices like smoking and pre-existing health conditions such as hypertension and diabetes are known risk factors for stroke. Traditionally, stroke diagnosis relies on imaging techniques like computed tomography and magnetic resonance imaging. However, these methods have limitations, including delays in diagnosis and resource-intensive procedures.

To address these challenges, researchers are exploring blood biomarkers like C-reactive protein (CRP), osteopontin (OPN), osteoprotegerin (OPG), and lectin-like oxidized low-density lipoprotein receptor 1 (LOX1) as potential indicators of stroke risk and severity. These biomarkers could enhance early diagnosis and treatment strategies for stroke. A retrospective analysis of 156 patients with arterial stenosis leading to ischemic stroke was conducted. Carotid artery stenosis was evaluated using Doppler ultrasound, and biomarker levels were measured and analyzed to explore their correlation with stroke risk.

The study findings revealed nuanced roles for CRP, OPN, OPG, and LOX1 in stroke risk. CRP and OPG were identified as protective factors against carotid artery occlusion, while OPN and LOX1 were recognized as risk factors. The study underscores the complexity of atherosclerosis and highlights potential therapeutic targets. By elucidating the associations of these biomarkers with atherosclerosis leading to stroke, the study contributes to a better understanding of stroke etiology and opens avenues for the development of more effective diagnostic tools and treatment protocols.

Further research, including longitudinal studies and clinical trials, is crucial to confirm these findings and explore their therapeutic implications. The study’s use of a Support Vector Machine (SVM) optimization model showcases the potential of machine learning algorithms in data analysis for stroke classification and risk assessment. The SVM model demonstrated high accuracy in predicting carotid artery occlusion risk and plaque stability based on biomarker levels. The study’s outcomes provide valuable insights into the pathophysiology of carotid artery disease and stroke, offering a basis for future research and clinical applications.