High cvd risk
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High Cardiovascular Disease (CVD) Risk: Insights from Recent Research
Global CVD Risk Prediction Models
Laboratory-Based and Office-Based Risk Scores
Recent research has developed two cardiovascular risk scores to predict the 10-year risk of fatal and non-fatal CVD in adults aged 40-74 years across 182 countries. These models include a laboratory-based score, which factors in age, sex, smoking, blood pressure, diabetes, and total cholesterol, and an office-based score, which substitutes diabetes and total cholesterol with BMI. The study found that predicted risks were generally lower in high-income countries (HICs) compared to low- and middle-income countries (LMICs), with the highest risks observed in central and southeast Asia and eastern Europe. Notably, the office-based model underestimated risk among patients with diabetes.
CVD Risk in HIV-Positive Individuals
The D:A:D study has highlighted the increased risk of CVD and chronic kidney disease (CKD) in HIV-positive individuals. Participants at high predicted risk for both CVD and CKD had significantly higher event rates for both conditions. This suggests that CVD and CKD risks should be assessed together in HIV-positive populations to better manage and mitigate these risks .
Key Risk Factors for CVD
High Blood Pressure
High blood pressure (BP) is identified as a predominant risk factor for CVD. Studies have shown that normal BP levels are lower than traditionally considered, and a right-sided shift in the population distribution of BP is a primary cause of CVD. High BP is linked to various conditions, including heart failure, atrial fibrillation, chronic kidney disease, and stroke. Effective management of BP could significantly reduce the burden of BP-related CVD.
Biomarkers in CKD Patients
In patients with advanced CKD, several biomarkers such as IL-6, sVCAM-1, and albumin have been identified as strong predictors of CVD and all-cause mortality. These biomarkers can help classify clinical CVD and predict mortality risk, underscoring the importance of monitoring these indicators in CKD patients.
Variability in CVD Risk Prediction Models
Comparison of Risk Prediction Models
Different CVD risk prediction models, such as ATP-III, Framingham (FRS), Pooled Cohort Equations (PCE), and SCORE, show substantial variability in predicted risks and treatment recommendations. This variability can lead to different clinical decisions, highlighting the need for standardized approaches to CVD risk prediction to ensure consistent and effective preventive strategies .
Familial Hypercholesterolemia
In individuals with heterozygous familial hypercholesterolemia (FH), several risk factors, including age, male sex, hypertension, diabetes, BMI, smoking, and elevated lipoprotein(a), are significantly associated with increased CVD risk. These factors should be targeted for modification to reduce CVD risk in FH patients.
Special Populations and CVD Risk
Hematopoietic Cell Transplantation Survivors
Hematopoietic cell transplantation (HCT) survivors are at a high risk of developing CVD. Specific risk prediction models for HCT survivors have been developed, incorporating factors such as age, anthracycline dose, chest radiation, hypertension, diabetes, and smoking. These models help identify high-risk individuals and guide early screening and preventive measures.
Lifelong CVD Risk Continuum
CVD risk is influenced by factors from prenatal life through adulthood. Early adiposity rebound, social and environmental influences, and metabolic health status play crucial roles in determining CVD risk. Early intervention, especially in metabolically unhealthy obese children, is essential to reduce long-term CVD risk.
Conclusion
Understanding and managing high CVD risk requires a multifaceted approach, incorporating global risk prediction models, identification of key risk factors, and tailored strategies for special populations. Consistent and accurate risk assessment tools are crucial for effective prevention and management of CVD across diverse populations.
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