Effectiveness of Lifestyle Modification vs. Therapeutic, Preventative Strategies for Reducing Cardiovascular Risk in...

Cardiovascular diseases (CVD) are still the leading cause of death in developed countries. The aim of this study was to calculate the potential for CV risk reduction when using three different prevention strategies to evaluate the effect of primary prevention.

A total of 931 individuals aged 20-79 years old from the Bialystok PLUS Study were analyzed. The study population was divided into CV risk classes. The Systematic Coronary Risk Estimation (SCORE), Framingham Risk Score (FRS), and LIFE-CVD were used to assess CV risk. The optimal prevention strategy assumed the attainment of therapeutic goals according to the European guidelines. The moderate strategy assumed therapeutic goals in participants with increased risk factors: a reduction in systolic blood pressure by 10 mmHg when it was above 140 mmHg, a reduction in total cholesterol by 25% when it was above 190 mg/dL, and a reduction in body mass index below 30. The minimal prevention strategy assumed that CV risk would be lowered by lifestyle modifications. The greatest CV risk reduction was achieved in the optimal model and then in the minimal model, and the lowest risk reduction was achieved in the moderate model, e.g., using the optimal model of prevention (Model 1). In the total population, we achieved a reduction of -1.74% in the 10-year risk of CVD death (SCORE) in relation to the baseline model, a -0.85% reduction when using the moderate prevention model (Model 2), and a -1.11% reduction when using the minimal prevention model (Model 3). However, in the low CV risk class, the best model was the minimal one (risk reduction of -0.72%), which showed even better results than the optimal one (reduction of -0.69%) using the FRS.

A strategy based on lifestyle modifications in a population without established CVD could be more effective than the moderate strategy used in the present study. Moreover, applying a minimal strategy to the low CV risk class population may even be beneficial for an optimal model.