Cardiovascular disease
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Cardiovascular Disease: Global Burden and Key Risk Factors
Cardiovascular disease (CVD) is the leading cause of death worldwide, responsible for nearly one-third of all deaths, with myocardial infarction and stroke accounting for the majority of these fatalities Gaidai2023Sobey2022. The burden of CVD is increasing due to factors such as population aging, unhealthy lifestyles, and environmental exposures Gaidai2023Camici2017Cosselman2015. Major risk factors include high blood pressure, high cholesterol, family history, stress, age, gender, body mass index (BMI), and unhealthy lifestyle choices Rahim2021Camici2017.
Mitochondrial Dysfunction and Cellular Mechanisms in CVD
Proper mitochondrial function is essential for heart health, as the heart requires high energy to function. In CVD, mitochondrial adaptation to changing environments is impaired, leading to decreased ATP production, increased oxidative stress, and cell damage. Dysfunctional mitochondria produce more reactive oxygen species, which can cause further damage and cell death. The process of mitophagy, which removes damaged mitochondria, is often disrupted in heart disease, worsening the condition. Restoring mitochondrial function is a key therapeutic goal in CVD management .
Environmental and Seasonal Influences on Cardiovascular Disease
Environmental exposures, such as air pollution and heavy metals (arsenic, cadmium, lead), significantly increase the risk and severity of CVD. These exposures can disrupt blood pressure regulation, metabolism, and vascular function, contributing to disease progression even at levels below current regulatory standards. Reducing environmental exposures could substantially lower CVD-related deaths and disabilities . Additionally, CVD incidence follows a seasonal pattern, with peaks during winter and after cold snaps, and additional risks during heat waves. Both environmental and behavioral responses to seasonal changes influence susceptibility to cardiovascular events .
Genetic Factors and Advances in Risk Prediction
Genetic testing has become more accessible and is increasingly used for early identification of specific CVDs, such as familial hypercholesterolemia and certain cardiomyopathies. While some CVDs are linked to single-gene mutations, common forms like coronary artery disease are influenced by many genetic variants, each with a small effect. Polygenic risk scores are now available to help predict individual risk and guide preventive therapy .
The Role of Aging in Cardiovascular Disease
Aging is a major risk factor for CVD, with older adults being more susceptible to both cardiovascular and cerebrovascular diseases. As populations age globally, the prevalence of CVD is expected to rise, making age a critical consideration in risk assessment and prevention strategies .
Advances in Early Detection and Prediction Using Artificial Intelligence
Artificial intelligence (AI) and machine learning (ML) techniques are increasingly used to analyze large and complex medical datasets for early detection and prediction of CVD. These methods can identify hidden patterns and improve diagnostic accuracy. For example, the MaLCaDD framework, which combines logistic regression and K-nearest neighbor classifiers, has achieved high accuracy in predicting CVD using clinical data Rahim2021Pandey2025Martins2021. Data mining and AI-based approaches are promising tools for supporting clinicians in early diagnosis and risk assessment Pandey2025Martins2021.
Animal Models and Research for Understanding and Treating CVD
Animal models remain essential for understanding the complex mechanisms underlying CVD and for developing new therapies. These models help researchers study hypertension, diabetes-related vascular complications, atherosclerosis, and cardiac dysfunction, providing valuable insights for drug development and prevention strategies .
Conclusion
Cardiovascular disease remains a major global health challenge, driven by a combination of genetic, environmental, lifestyle, and aging-related factors. Advances in genetic testing, environmental health, and artificial intelligence are improving early detection, risk prediction, and management of CVD. Continued research into the underlying mechanisms and the development of targeted interventions are essential for reducing the burden of cardiovascular disease worldwide.
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