The role of coronary artery calcification testing in incident coronary artery disease risk prediction in type 1 diabetes.

ABSTRACT

AIMS/HYPOTHESIS: We sought to assess the role of coronary artery calcification (CAC) and its progression in predicting incident coronary artery disease (CAD) in individuals with type 1 diabetes using data from the Pittsburgh Epidemiology of Diabetes Complications (EDC) Study. METHODS: The present study examined 292 participants who had at least one CAC measure and were free from CAD at baseline; 181 (62%) had repeat CAC assessments 4-8 years later and did not develop CAD between the two CAC measures. The HRs of incident CAD events were estimated using Cox models in categorised or in appropriately transformed CAC scores. C statistics and net reclassification improvement (NRI) were used to assess the added predictive value of CAC for incident CAD. RESULTS: At baseline, the mean age of participants was 39.4 years and the mean diabetes duration was 29.5 years. There were 76 participants who experienced a first incident CAD event over an average follow-up of 10.7 years. At baseline, compared with those without CAC (Agatston score = 0), the adjusted HR (95% CI) in groups of 1-99, 100-399 and ≥400 was 3.1 (1.6, 6.1), 4.4 (2.0, 9.5) and 4.8 (1.9, 12.0), respectively. CAC density was inversely associated with incident CAD in those with CAC volume ≥100 (HR 0.3 [95% CI 0.1, 0.9]) after adjusting for volume score. Among participants with repeated CAC measures, annual CAC progression was positively associated with incident CAD after controlling for baseline CAC. The HR (95% CI) for above vs below the median annual CAC volume progression was 3.2 (1.2, 8.5). When compared with a model that only included established risk factors, the addition of CAC improved the predictive ability for incident CAD events in the whole group. CONCLUSIONS/INTERPRETATION: CAC is strongly associated with incident CAD events in individuals with type 1 diabetes; its inclusion in CAD risk models may lead to improvement in prediction over established risk factors.