ACS researcher Manon van den Bogaart and colleagues have published new findings from the EPIC-Norfolk study, tracking over 23,000 adults for more than 20 years. Their work uncovers how traditional risk factors - high cholesterol, high blood pressure, and smoking - differently influence the risk of developing various types of cardiovascular disease (CVD).
Risk Factors Affect Disease Types Differently
The study shows that high LDL cholesterol, elevated systolic blood pressure, and smoking all raise the risk of a first CVD event, but their effects vary by disease type. High blood pressure was the strongest predictor for both ischemic and hemorrhagic stroke, while smoking had the greatest impact on abdominal aortic aneurysm, even more than high cholesterol and blood pressure combined.
Multiple Risk Factors Compound the Danger
Having more than one risk factor led to a stepwise increase in CVD risk. For ischemic heart disease, the risk nearly tripled for those with all three risk factors compared to those with none. This pattern was also seen for other types of CVD, highlighting the importance of addressing multiple risk factors.
Sex Differences in Risk Associations
The research identified notable differences between men and women. High cholesterol was more strongly linked to CVD in men, while high blood pressure had a greater effect in women, especially for ischemic heart disease and peripheral artery disease. Smoking increased risk similarly in both sexes across all disease types.
A broader perspective
“While many previous studies have explored the links between LDL cholesterol, blood pressure, smoking, and cardiovascular events, most have focused on single risk factors or outcomes, lacked analyses of risk factor combinations, or did not include sex-specific results,” says Manon van den Bogaart, PhD Candidate Cardiology. This new study stands out by addressing these gaps, providing a comprehensive view across multiple risk factors, disease types, and sexes over more than two decades.
Implications for Prevention and Research
These findings provide insights into potential pathophysiological mechanisms. Also, it highlights how different vascular territories may vary in their susceptibility to specific risk factors.
The next step
Currently, the research team is advancing this work by using latent class analysis to identify data-driven clusters of commonly co-occurring risk factors. This approach aims to uncover underlying patient phenotypes with similar risk profiles and to determine whether these phenotypes differ in their association with cardiovascular events in specific arterial territories.
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