Study population

As described previously [19], the EDC study was a prospective cohort of individuals with childhood-onset type 1 diabetes. Participants were diagnosed before the age of 17 years and seen within 1 year of the diagnosis at the Children’s Hospital of Pittsburgh between 1950 and 1980. Though clinic based, this cohort has been shown to be epidemiologically representative of the type 1 diabetes population in Allegheny County, Pennsylvania [20]. A total of 658 eligible participants were examined between 1986 and 1988 and then assessed biennially over a 25 year period.

At the EDC 10 year follow-up examination (1996–1998), all participants aged 30 years or over were initially invited to undergo a CAC scan; this invitation was subsequently expanded to participants aged ≥18 years after approval was obtained from the University of Pittsburgh Institutional Review Board. A subset of participants also agreed to have repeat scans approximately 4–8 years later, at the EDC 14 year (2000–2002) or 18 year (2004–2006) follow-up examination. Informed consent was obtained from all participants.

A total of 292 participants who had at least one CAC assessment and were free from CAD at the time of the assessment was evaluated in the current report. Of these, 199 (68%) had repeated scans; 181 individuals did not develop CAD between CAC measures and were included for the evaluation of CAC progression. Compared with individuals who had a second CAC measure, participants who had no repeat CAC scan were older, more likely to use statins and had earlier onset of diabetes, longer diabetes duration, higher BMI, higher systolic blood pressure and higher baseline CAC scores (see electronic supplementary material [ESM] Table 1). The time of first CAC measurement was taken as the baseline for these analyses. Participants were followed up to first event, death or the 25th year of the EDC study (2011–2014).

Assessment of coronary artery calcification

All the CAC scans were evaluated centrally at one site. CAC was measured using electron beam computed tomography (EBCT) scans (Imatron C-150 scanner; GE, South San Francisco, CA, USA). Scans were triggered by electrocardiogram signals at 80% of the R–R interval and obtained in 3 mm contiguous sections of the heart. CAC was quantified using Agatston units (following the method described by Agatston et al [21]) and by a volume-based approach using the isotropic interpolation method [22]. The area and density scores were obtained using the algorithms described in the Multi-Ethnic Study of Atherosclerosis (MESA) [10]. The area score was obtained by dividing the volume score by the scan slice thickness of 3.0 mm. The density score was subsequently calculated by dividing the Agatston score by area score (CAC density = Agatston score / area score). The progression was determined as the annualised difference between the square root of baseline and square root of follow-up CAC score (annual CAC progression = √[follow-up CAC] − √[baseline CAC] / [t2 − t1 in years]), according to the method previously published by Radford et al [9] (t1 and t2 refer to the time at baseline and the follow-up, respectively). CAC volume might be a better measure of progression, as the Agatston score is determined using both CAC density and volume [23]. Our primary analysis of CAC progression was thus based on CAC volume. We also examined progression in CAC Agatston score associated with incident CAD.

Ascertainment of CAD

CAD status was evaluated biennially from the first CAC measure to the end of the follow-up. Incident CAD was defined as new-onset EDC physician-diagnosed angina, myocardial infarction confirmed by Q waves on an ECG (Minnesota codes 1.1 or 1.2) or hospital records, angiographic stenosis ≥50%, revascularisation or ischaemic ECG changes (Minnesota codes 1.3, 4.1–4.3, 5.1–5.3 and 7.1) [24]. Other than fatal and non-fatal myocardial infarction, all CAD events in the present analysis occurred at least 90 days after the CAC tests, suggesting that the CAD diagnoses were unlikely to have been driven by the CAC results.

Measurement of covariates

Demographic and medical history information was obtained through questionnaires at the time of first CAC measure. An ‘ever smoker’ was defined as a person who had smoked at least 100 cigarettes in their lifetime. BMI was calculated as the weight (kg) divided by the square of the height (m2). Blood pressure was measured three times using a random zero sphygmomanometer according to the Hypertension Detection and Follow-up Program protocol and the mean of second and third readings was used [25]. Hypertension was defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg or the use of antihypertensive medications [26].

HbA 1c was assessed via automated HPLC (Diamat; BioRad, Hercules, CA, USA) at the time CAC was first assessed. Subsequently, HbA 1c was measured with the DCA 2000 analyser (Bayer, Tarrytown, NY, USA). Results from the two methods were highly correlated (r = 0.95) [27]. The values were then converted to DCCT-aligned HbA 1c using a regression equation derived from duplicate assays [27]. Total cholesterol was determined enzymatically [28]. HDL-cholesterol was measured by a precipitation technique (heparin and manganese chloride) using a modified version of the Lipid Research Clinics method [29]. Non-HDL-cholesterol was calculated as total cholesterol minus HDL-cholesterol. Urinary albumin was determined by immunonephelometry [30]. Urinary albumin excretion rate (AER) was calculated for each of three timed urinary samples (24 h, overnight and 4 h collections); the median of the three AERs was used in the analyses.

Statistical analysis

Demographics and risk factors were compared among those with baseline CAC = 0, 1–99 and ≥100 and between CAD incident cases and non-cases at the end of follow-up. For comparing three subgroups with different CAC values, the χ2 test for categorical variables and unbalanced analysis of variance (ANOVA) analysis for continuous variables were used, as appropriate. Unadjusted Cox proportional hazard models were applied to report p values for the comparisons between CAD cases and non-cases.

Cox proportional hazard models were constructed with follow-up years used as the time axis to assess the association of baseline CAC Agatston and volume score with the incidence of CAD. Baseline CAC was examined in three ways: four categories (0, 1–99, 100–399 and ≥400), two categories (<100 and ≥100), log-transformed continuous variables (log e [CAC+1]). The log e (CAC+1) transformation has been previously used in the MESA papers for studies both in the general population [31] and in the population with diabetes [6]. HRs and 95% CIs were presented accordingly. For multivariable analyses, the models were adjusted for risk factors that had been previously demonstrated to be important predictors of CAD [12] or that were found to be significantly associated with outcomes in the univariate analyses. Of note, age and diabetes duration were highly correlated (r = 0.84) in this childhood-onset type 1 diabetes cohort. Thus, only diabetes duration was used for the adjustment. Specifically, model 1 adjusted for sex, diabetes duration, ever smoking, BMI, HbA 1c and hypertension, and model 2 (fully adjusted model) further controlled for urinary AER, lipid profile (HDL- and non-HDL-cholesterol) and the use of statins.

Only participants with CAC >0 were analysed for CAC density because plaque density could only be quantified in those with prevalent CAC [10]. Cox proportional hazard models were performed to evaluate the association between CAC density and incident CAD after adjusting for CAC volume and the full set of covariates as described above. The adjusted model was then stratified by CAC volume (1–99 vs ≥100). Heterogeneity of effect in the association between density and clinical outcomes by CAC volume was tested by fitting interaction terms between density and categorical CAC volume (1–99 vs ≥100) in the adjusted model. A CAC score of ≥100 was chosen as it is considered to confer a moderate (or more) cardiovascular risk among asymptomatic individuals [32].

The association of CAC progression with incident CAD, adjusting for baseline CAC and the full set of the covariates, was assessed in those with repeated CAC measures also using Cox models. The primary analysis of annual CAC progression was based on a dichotomised variable (below vs above the median) in volume score.

The proportionality assumptions were confirmed using Schoenfeld residuals as well as interaction terms with follow-up time [33].

The incremental values of the CAC measures for prediction of CAD events were evaluated by the increase in the C-statistic [34] and improvement in the continuous net reclassification improvement (NRI) [35] as compared with the basic model only with established risk factors. The fit of the models was assessed using the log-likelihood ratio test.

Urinary AER was log-transformed prior to statistical testing, given the highly skewed distribution of this variable. A two-sided p < 0.05 was considered significant. All analyses were performed with SAS v 9.4 (SAS Institute, Cary, NC, USA) and R version 3.4.3 (R Core Team, Vienna, Austria). The R package ‘survIDINRI’ was applied for NRI analysis with censored survival data and the package ‘survC1’ for C statistics for risk prediction.