Population distribution of traditional and the emerging cardiovascular risk factors carotid plaque and IMT: The REFINE-Reykjavik study with comparison with the Tromsø study

Objectives: Population statistics for carotid plaque and cardiovascular risk factors reported in scientific journals are usually presented as averages for the population or age and sex adjusted, rather than sex and age groups. Important population differences about atherosclerosis and cardiovascular risk factors may thus be missed. We compare the distribution of cardiovascular risk factors, carotids plaque and carotid intima-media thickness (CIMT) in two population-based studies. Methods: Carotid artery atherosclerotic plaque prevalence and risk factors levels for cardiovascular disease by sex in 5-year age groups from the Risk Evaluation For Infarct Estimates Reykjavik study (REFINE-Reykjavik study) were compared with data from the Tromsø 6 study. Results: The threshold of carotid plaque presence in the Tromsø 6 study fell between minimal and moderate plaque defined in the REFINE-Reykjavik study reflecting carotid plaque prevalence. The prevalence of minimal carotid plaque in the REFINE-Reykjavik study was 47% in men (40–69 years old) and 38% in women and 11% in men and 7% in women of moderate plaque. The prevalence of any plaque in the Tromsø 6 study was 35% in men and 27% in women. The mean (CIMT) was similar in the studies. In the Tromsø 6 study mean systolic blood pressure was 8 mm Hg higher in men and 10 mm Hg higher in women, mean low-density lipoprotein was 0.5 mmol/L higher in men and 0.3 mmol/L higher in women and the prevalence of smoking was 4% higher in men and 9% higher in women. However, body mass index was 0.8 kg/m2 higher in men and 0.9 kg/m2 in women in the REFINE-Reykjavik study. Conclusion: Comparison between Iceland and Norway revealed differences in the prevalence of carotid plaque, which was assumed to be due to different definition of plaque. However, clinically significant differences in conventional cardiovascular risk factors were seen. This underscores the importance of detailed comparison of population data across different populations.