Carotid artery imaging and cardiovascular disease risk

Abstract

Part 1 of this thesis focuses on echogenicity of the carotid artery. This measure represents the color of the intima-media complex on ultrasound images. Echolucent vascular walls appear dark, echodense vascular walls light. We showed in a group of young adults that higher age, BMI, systolic blood pressure, triglycerides and carotid intima media thickness (CIMT) were associated with a lower grey-scale median (GSM) and thus a more echolucent carotid artery wall. Higher HDL-cholesterol and male sex were associated with a higher GSM and thus a more echodense carotid artery wall. Lower GSM was associated with a higher estimated cardiovascular disease risk. Furthermore, we showed that in a cohort of elderly men that when the population was split into thirds of GSM, individuals in the highest third had (independent of cardiovascular risk factors and CIMT) a significantly higher risk of all-cause mortality and cardiovascular mortality compared to those with a GSM in the lowest third.

Part 2 of this thesis focuses on CIMT, both in association with risk factors and disease and as a possible marker to improve cardiovascular risk prediction. Ethnicity appeared to slightly but significantly modify the associations between risk factors and CIMT and cardiovascular events. This has possible implications for the development of cardiovascular risk prediction models. In a population of adults under 45 years of age, we found that age, sex, diastolic blood pressure, body mass index, total cholesterol and high-density lipoprotein cholesterol were associated with higher CIMT. Furthermore, higher CIMT was independently associated with increased risk of first-time myocardial infarction or stroke in this population. We did not find clinically relevant incremental prognostic value, either for measuring mean common CIMT in individuals with hypertension, or for measuring CIMT in the internal carotid artery and/or the carotid bulb in the general population.

Part 3 of this thesis focuses on vascular age and on the comparison of prediction models. First, we conducted a review of the literature to explore published concepts, definitions and clinical applications of vascular age. Vascular age is the expression of cardiovascular disease risk in years instead of a percentage. Despite sharing a common name, various studies have proposed distinct ways to define and measure vascular age. While we were unable to examine the effect of using vascular age in the communication of risk, we did examine the claim of vascular age as a way to improve risk prediction. We applied four methods of determining vascular age (two based on existing risk scores and two based on CIMT) to a study population representing the general population. Irrespective of definition vascular age does not improve cardiovascular risk prediction.

Finally, we examined the metrics used to calculate incremental prognostic value of prediction models. We did this by calculating several incremental value statistics for biomarkers currently used in risk predictions and clinical practice, when they were added as ‘new biomarker’ to the remaining set. Most of these biomarkers seem to show little benefit in risk prediction when using current metrics that test added value of novel biomarkers.