Risk Factors for Cardiovascular Disease: Epidemiology, Screening, Prevention, and Therapeutic Interventions

Area of Science:

• Cardiology
• Public Health
• Genetics

Background:

• Cardiovascular diseases (CVDs) are the leading cause of global mortality.
• Traditional risk factors (hypertension, dyslipidemia, diabetes, smoking) are well-known.
• Emerging factors like inflammation, environmental exposure, stress, gut microbiota, and genetics significantly influence CVD risk.

Purpose of the Study:

• To comprehensively review CVD epidemiology, pathophysiology, screening, prevention, and treatment.
• To emphasize the integration of novel technologies for risk prediction and personalized prevention.
• To discuss clinical evidence, ongoing trials, and implementation in diverse socioeconomic settings.

Main Methods:

• Literature review of epidemiology and pathophysiological mechanisms.
• Analysis of multiomics methods, artificial intelligence (AI), and digital health technologies.
• Synthesis of preclinical and clinical evidence, including ongoing trials.

Main Results:

• Identification of novel risk factors beyond traditional ones.
• Demonstration of AI and digital health transforming CVD risk prediction and personalized prevention.
• Highlighting challenges in translating new biomarkers and technologies into clinical practice.

Conclusions:

• Precision cardiovascular medicine requires integrating mechanistic insights with practical applications.
• Addressing knowledge gaps is crucial for reducing the global burden of CVDs.
• Scalable and equitable clinical applications of new technologies remain a key challenge.