Burden of cardiovascular risk factors and disease among patients with type 1 diabetes: results of the Australian National Diabetes Audit (ANDA)

The Australian National Diabetes Audit (ANDA) is an annual cross-sectional benchmarking activity including patients of all ages and diabetes types. Diabetes centres voluntarily participate, with approximately two-thirds being tertiary centres and one-third primary or community based centres. De-identified data for our study were collected across all centres during a 1-month survey period in May or June (2015) for all consecutive patients. Patients considered for this analysis were adults (≥ 18 years) with type 1 diabetes (n = 1169) presenting to one of the 49 participating diabetes centres. The degree of patient ascertainment could not be determined because only data for those participants involved in the study were collected.

Ethical approval for our study was provided by the Monash Health Human Research Ethics Committee.

Relevant pre-specified sociodemographic (date of birth, date of diabetes diagnoses, sex, Aboriginal/Torres Strait Islander ethnicity) and clinical variables (diabetes type, weight, height, smoking status, blood pressure (BP), lipid levels, urinary albumin, serum creatinine, HbA1c, lipid lowering medications, antihypertensive medications, diabetes complications, comorbid conditions) were collected. Health care professionals participating in ANDA examined patients, reviewed medical records including pathology results during standard patient consultations and recorded the de-identified information in a standardised collection form (Additional file 1). The participating centres were later contacted to clarify missing data and invalid entries.

Age was calculated as the date of questionnaire (in 2015) minus the date of birth, and diabetes duration was calculated as the date of questionnaire minus the date of diabetes diagnosis. Provided height and weight measurements were used to calculate the BMI in kg/m². The main outcome variables for this analysis were cerebral stroke, myocardial infarction (MI), coronary artery bypass graft (CABG) surgery/angioplasty, peripheral vascular disease (PVD) and the composite of these atherosclerotic outcomes defining CVD. PVD was defined clinically as the absence of both the dorsalis pedis and posterior tibialis pulses on either foot or amputation of toe, forefoot or leg (above or below knee), not due to trauma or causes other than vascular disease. An additional outcome of interest was congestive cardiac failure (CCF) defined by clinician determined symptomatic status and responsiveness to therapy. The healthcare professional completing the questionnaire determined the presence of these complications and other comorbid conditions with access to a data dictionary of terms provided by the ANDA secretariat prior to commencing the questionnaire (Additional file 2). Cardiovascular risk factors considered in analysis include sex, age, diabetes duration, HbA1c, BMI categories, smoking status (ever smoked versus never smoked), systolic blood pressure, diastolic blood pressure, albuminuria (> 20.0 mg/L, > 20.0 μg/min, > 30.0 mg/24 h, or > 2.5 mg/mmol for women and > 3.5 mg/mmol for men), presence of retinopathy, high density lipoprotein-cholesterol (HDL-C) and estimated glomerular filtration rate (eGFR) calculated using the chronic kidney disease epidemiology collaboration (CKD-EPI) formula based on sex and collected creatinine values in μmol/L [28]. The eGFR was not adjusted for ethnicity among Aboriginal or Torres Strait Islander (ATSI) people groups in keeping with current literature [29‐31], and no other ethnicity data was collected. Total cholesterol, low density lipoprotein-cholesterol (LDL-C) and triglycerides were excluded from regression analyses a priori. BMI categories were considered as underweight (< 18.5 kg/m²), normal weight (18.5 to < 25.0 kg/m²), overweight (25.0 to < 30.0 kg/m²) and obese (≥ 30.0 kg/m²). Dyslipidaemia was defined as failure to meet current Australian treatment targets (i.e. total cholesterol ≥ 4.0 mmol/L, HDL-C < 1.0 mmol/L, LDL-C ≥ 2.0 mmol/L or triglycerides ≥ 2.0 mmol/L). Hypertension was defined as systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg. Retinopathy was recorded as absent or present for the preceding 12 months. Diabetes centre type corresponds to secondary or community/primary centres derived from the category of membership with the National Association of Diabetes Centres (NADC). Secondary centres comprised centres of excellence and tertiary diabetes centres, and community/primary centres comprise affiliate and diabetes care centres.

Continuous data were tested for normality of distribution and summarised as means with standard deviations (± SD) or medians with interquartile range (IQR; 25th–75th percentile). When comparing means or medians we used the Student’s t test or Mann–Whitney U test respectively. Categorical variables were summarised as participant numbers and percentages, and when comparing between groups we used the Chi square test. Restricted cubic splines were utilised to evaluate non-linear associations between cardiovascular outcomes and diabetes duration. Scoping review and expert opinion lead to selection of knots at 5.0, 15.0, 25.0 and 35.0 years duration. The binary logistic regression model was used to examine the association of risk factors with cardiovascular outcomes of interest and likelihood ratio test assessed for interactions between age and diabetes duration as well as diabetes centre type. The selection of variables was based on identifying all measured clinical variables of known or suspected prognostic importance for the outcomes of interest and/or exhibiting a p value ≤ 0.1 on univariable analysis. Age and sex were forced into all multivariable models as they were considered clinically significant a priori. Models also adjusted for antihypertensive and lipid lowering therapy. Multivariable regression analyses were performed for each cardiovascular outcome of interest using stepwise selection of variables (1% probability for entry and 5% probability for removal) for the remaining predictor variables. Based on the coefficients from the final parsimonious multivariable model, we calculated the ROC curve and 95% confidence intervals. Population attributable risk (PAR) and 95% confidence intervals were calculated for each significant categorical variable from the final multivariable models under the assumption that associations were causal [32]. Multiple imputation was performed for missing data (Additional file 3: Table S1 and Additional file 4: Table S2 respectively). Statistical analyses were performed using Stata software version 14.2 (StataCorp, Texas, USA) and level of significance set at 5% unless otherwise specified.

Data from 1169 patients were included in this study. Cardiovascular risk factors were highly prevalent, including hypertension (21.9%), dyslipidaemia (89.4%), overweight or obesity (56.4%), ever smoking (38.5%), albuminuria (31.1%), eGFR < 45 mL/min/1.73 m² (6.5%) or < 60 mL/min/1.73 m² (10.3%) and HbA1c exceeding 7.0% (81.0%). Patients with CVD tended to be male (61.5%) with a mean age of 58.5 ± 13.7 years. Median diabetes duration was 35.0 (24.5–45.0) years, mean HbA1c was 8.6 ± 1.5% and the mean HDL-C was 1.35 ± 0.42 mmol/L. Most patients with CVD were overweight/obese (56.4%), had smoked (64.2%) or had retinopathy (56.2%). The mean eGFR for patients with CVD was 71 (± 29) mL/min/1.73 m² and around half of the patients with CVD had albuminuria (47.9%). Secondary prevention prescribing of lipid lowering therapy and antihypertensive therapy was noted in up to 75.3 and 72.9% respectively. Mean systolic and diastolic blood pressure levels were 132 ± 21 and 72 ± 10 mmHg respectively. A summary of cardiovascular outcomes with risk factor levels is provided in Table 1 and Additional file 5: Table S3a and b.

A non-linear association between diabetes duration and CVD was demonstrated (Fig. 1). Odds of CVD were low and static until approximately 20 years duration, at which point a positive linear association emerged (Fig. 1). As a categorical variable, diabetes duration ≥ 20.0 years was significantly associated with the composite outcome of CVD (no interaction with age; likelihood ratio test p-value 0.816) in multivariable analysis [adjusted odds ratio (aOR) 1.05 (95% CI 1.01, 1.10); p 0.018] (Table 2).

Increasing age [aOR 1.06 (95% CI 1.03, 1.09)], diabetes duration ≥ 20.0 years [aOR 1.05 (95% CI 1.01, 1.10)], smoking status [aOR 2.40 (95% CI 1.26, 4.58)] and prescription of antihypertensive therapy [aOR 2.44 (95% CI 1.15, 5.18)] were all positively associated with CVD. Increasing HDL-C and diastolic blood pressure were negatively associated with CVD [aOR 0.43 (95% CI 0.21, 0.90) and aOR 0.96 (95% CI 0.93–1.00) respectively]. The model’s discriminatory ability was demonstrated with area under the ROC curve of 0.88 (95% CI 0.84, 0.92) (Table 2).

When stroke was considered, there was significant positive association with increasing age [aOR 1.05 (95% CI 1.01, 1.08)]. Increasing eGFR was negatively associated with stroke [aOR 0.98 (95% CI 0.96, 1.00)]. The area under the ROC curve was 0.81 (95% CI 0.74, 0.88) (Table 2).

When the outcome of MI or CABG/angioplasty was considered, there were significant positive associations with increasing age [aOR 1.09 (95% CI 1.05, 1.13) or aOR 1.08 (95% CI 1.03, 1.13)] and antihypertensive therapy [aOR 5.06 (95% CI 1.38, 18.54) or aOR 8.96 (95% CI 1.12, 71.54)], and negative associations with increasing HDL-C [aOR 0.20 (95% CI 0.06, 0.68) or aOR 0.23 (95% CI 0.06, 0.92)]. The area under the ROC curve for MI was 0.90 (95% CI 0.87, 0.94) and for CABG/angioplasty, it was 0.92 (95% CI 0.89, 0.95) (Table 2).

When PVD was considered, there were significant positive associations with increasing age [aOR 1.04 (95% CI 1.01, 1.07)], retinopathy [aOR 2.47 (95% CI 1.06, 5.74)], and negative association with eGFR [aOR 0.97 (95% CI 0.96, 0.99)]. The area under the ROC curve was 0.85 (95% CI 0.81, 0.90) (Table 2).

When the additional outcome of CCF was considered, there was significant association with increasing age [aOR 1.15 (95% CI 1.05, 1.25)]. The area under the ROC curve was 0.90 (95% CI 0.84, 0.95) (Table 2).

Adding diabetes centre type into the final multivariable CVD models had minimal impact on the associations. There was also no significant interaction between diabetes centre type and any atherosclerotic cardiovascular outcome. Further, excluding patients with CCF resulted in diastolic blood pressure and antihypertensive therapy being removed from the final parsimonious model for CVD (data not shown).

After multiple imputation for missing data there was an increase in the magnitude of the association between antihypertensive therapy and MI or CABG/angioplasty [aOR 15.91 (95% CI 7.65, 33.12; p < 0.001) and aOR 21.90 (95% CI 9.79, 48.99; p < 0.001) respectively] and HDL-cholesterol was no longer significantly associated with CABG/angioplasty [aOR 0.45 (95% CI 0.19, 1.08; p 0.072)] (Additional file 3: Table S1).

In the study population, the estimated proportions of CVD attributable to diabetes duration ≥ 20 years, use of antihypertensive therapy and smoking were 6.5% (95% CI 1.4, 11.6), 5.1% (95% CI 0.9, 9.3) and 3.9% (95% CI 1.0, 6.7), respectively. The estimated proportion of PVD attributable to presence of retinopathy was 2.7% (95% CI 0.2, 5.2). The estimated proportions of MI or CABG/angioplasty attributable to use of antihypertensive therapy were 4.8% (95% CI 1.8, 7.8) and 11.2% (95% CI 5.0, 17.5), respectively (Additional file 6: Table S4).