Assessing CVD Risk: Traditional ApproachesEvaluating CMR
Cardiovascular disease (CVD) is a leading cause of disability and death. Primary and secondary prevention measures help reduce cardiovascular events and improve the overall health of patients. In an attempt to understand the factors contributing to the development of CVD, several epidemiological and prospective studies have been conducted. These studies have followed thousands of individuals over a number of years to pinpoint a first or recurrent cardiovascular event. One of the first studies was the Framingham Heart Study. This landmark U.S. study followed men and women who were initially free of CVD in order to gain initial insight on the major cause(s) of heart disease. It has enabled researchers to identify major CVD risk factors such as hypertension, smoking, elevated cholesterol or LDL cholesterol (bad cholesterol) concentrations, reduced levels of HDL cholesterol (good cholesterol), and type 2 diabetes. Many international prospective studies have confirmed that these risk factors have a significant impact on the development of heart disease. Because they were identified early on, these variables are referred to as “traditional” risk factors. Further analyses from the Framingham Heart Study have led to the development of a CVD risk prediction model based on these traditional risk factors: the Framingham risk score. Another well-recognized epidemiological prospective study—the PROCAM study—has also developed a risk prediction model that uses some of the risk factors included in the Framingham risk score along with other variables. The risk of subsequent CVD is categorized as low, intermediate, or high depending on the result obtained. Other organizations and groups have also developed CVD risk prediction algorithms. With the obesity and type 2 diabetes epidemics sweeping the world, it remains unresolved whether these global risk assessment tools fully capture the risk of abdominal obesity and the related abnormalities of the metabolic syndrome.
The Framingham Study
- The seminal Framingham Heart Study has helped identify many major CHD risk factors by providing a simple CHD prediction algorithm that uses categorical variables to help physicians evaluate their patients’ CHD risk.
- The Framingham risk score is a useful tool to predict the absolute CHD risk of men and women who are initially free of CHD.
Some of the most significant milestones of the Framingham study include the following:
- 1960: Cigarette smoking found to increase CHD risk
- 1961: Cholesterol levels, blood pressure, and electrocardiogram abnormalities found to increase the risk of heart disease
- 1967: Physical activity found to reduce the risk of CHD and obesity to increase the risk of CHD
- 1977: High HDL cholesterol levels found to protect against CHD
- 1981: Low LDL cholesterol concentrations linked to low incidence of CHD
Read more on The Framingham Study.
The PROCAM Study
- The PROCAM study showed that HDL cholesterol is an important driver of CHD risk and provided evidence that HDL cholesterol levels exert a much stronger influence on CHD risk in individuals with elevated global cardiovascular risk.
- One of the most important findings of the PROCAM study was that CHD risk factors do not act in isolation but rather in conjunction.
- The PROCAM study demonstrated that fasting triglyceride levels are an independent risk factor for CHD events, regardless of HDL or LDL cholesterol concentrations.
Read more on The PROCAM Study.
- Identifying individuals at high risk of CVD is one of the main objectives of primary prevention and the first step towards treating high-risk patients with modifiable risk factors.
- When evaluating a patient’s global CHD risk, it is important to consider all risk factors simultaneously.
- The SCORE risk estimation system directly estimates total fatal CVD risk for clinical management of CVD risk in European populations.
- The UKPDS risk engine is a good CHD risk estimation model for primary prevention of CHD in type 2 diabetic patients.
- CUORE has produced a predictive equation specific to the Italian population.
Read more on Other Studies.