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

Burden of cardiovascular risk factors and disease among patients with type 1 diabetes: results of... Background: Cardiovascular risk stratification is complex in type 1 diabetes. We hypothesised that traditional and diabetes-specific cardiovascular risk factors were prevalent and strongly associated with cardiovascular disease (CVD) among adults with type 1 diabetes attending Australian diabetes centres. Methods: De-identified, prospectively collected data from patients with type 1 diabetes aged ≥ 18 years in the 2015 Australian National Diabetes Audit were analysed. The burden of cardiovascular risk factors [age, sex, diabetes dura- tion, glycated haemoglobin (HbA1c), blood pressure, lipid profile, body mass index, smoking status, retinopathy, renal function and albuminuria] and associations with CVD inclusive of stroke, myocardial infarction, coronary artery bypass graft surgery/angioplasty and peripheral vascular disease were assessed. Restricted cubic splines assessed for non- linearity of diabetes duration and likelihood ratio test assessed for interactions between age, diabetes duration, centre type and cardiovascular outcomes of interest. Discriminatory ability of multivariable models were assessed with area under the receiver operating characteristic (ROC) curves. Results: Data from 1169 patients were analysed. Mean (± SD) age and median diabetes duration was 40.0 (± 16.7) and 16.0 (8.0–27.0) years respectively. Cardiovascular risk factors were prevalent including hypertension (21.9%), dyslipidaemia (89.4%), overweight/obesity (56.4%), ever smoking (38.5%), albuminuria (31.1%), estimated glomerular filtration rate < 60 mL/min/1.73 m (10.3%) and HbA1c > 7.0% (53 mmol/mol) (81.0%). Older age, longer diabetes dura- tion, smoking and antihypertensive therapy use were positively associated with CVD, while high density lipoprotein- cholesterol and diastolic blood pressure were negatively associated (p < 0.05). Association with CVD and diabetes duration remained constant until 20 years when a linear increase was noted. Longer diabetes duration also had the highest population attributable risk of 6.5% (95% CI 1.4, 11.6). Further, the models for CVD demonstrated good dis- criminatory ability (area under the ROC curve 0.88; 95% CI 0.84, 0.92). Conclusions: Cardiovascular risk factors were prevalent and strongly associated with CVD among adults with type 1 diabetes attending Australian diabetes centres. Given the approximate J-shaped association between type 1 diabetes duration and CVD, the impact of cardiovascular risk stratification and management before and after 20 years dura- tion needs to be further assessed longitudinally. Diabetes specific cardiovascular risk stratification tools incorporating diabetes duration should be an important consideration in future guideline development. Keywords: Type 1 diabetes mellitus, Cardiovascular disease, Epidemiology *Correspondence: sophia.zoungas@monash.edu School of Public Health and Preventive Medicine, Monash University, 5th Floor, The Alfred Centre, 99 Commercial Road, Melbourne, VIC 3004, Australia Full list of author information is available at the end of the article © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/ publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 2 of 12 We thus examined the burden of cardiovascular risk Background factors and their associations with cardiovascular com- Cardiovascular disease (CVD) is the leading cause of plications among patients with type 1 diabetes attend- death among people with type 1 diabetes [1–6]. Fur- ing diabetes centres across Australia. Traditionally thermore, people with type 1 diabetes experience car- considered and diabetes specific cardiovascular risk fac - diovascular events about 10 years earlier than a matched tors were hypothesised to be prevalent and strongly asso- population without diabetes [7]. This is juxtaposed with ciated with CVD in this vulnerable population. current national strategies in primary prevention of CVD that focus on absolute cardiovascular risk stratification Methods from around 40  years of age regardless of comorbidities Subjects [1, 8–11]. This strategy fails to integrate the duration of The Australian National Diabetes Audit (ANDA) is an exposure to risk factors which may be of particular rel- annual cross-sectional benchmarking activity including evance to younger people diagnosed with type 1 diabetes patients of all ages and diabetes types. Diabetes centres in their youth. voluntarily participate, with approximately two-thirds While traditional cardiovascular risk factors are being tertiary centres and one-third primary or com- expected to contribute to the observed increased risk of munity based centres. De-identified data for our study CVD, the relative strength of associations in type 1 dia- were collected across all centres during a 1-month sur- betes is not clear. The protective association of female sex vey period in May or June (2015) for all consecutive with CVD, for example, appears to be negated in at least patients. Patients considered for this analysis were adults those women aged less than 40  years with type 1 diabe- (≥ 18  years) with type 1 diabetes (n = 1169) present- tes [5, 12, 13]. Similarly, while obesity is recognised as an ing to one of the 49 participating diabetes centres. The independent risk factor for CVD in the general popula- degree of patient ascertainment could not be determined tion [14–18], the impact of increasing body mass index because only data for those participants involved in the (BMI) in type 1 diabetes is not firmly established. Fur - study were collected. thermore, recommendations for pharmacotherapy to Ethical approval for our study was provided by the manage risk factors are largely extrapolated from trials in Monash Health Human Research Ethics Committee. adults with type 2 diabetes that may not be generalisable to those with type 1 diabetes [1, 8, 9, 19]. Data collection Understanding relationships between risk factors and Relevant pre-specified sociodemographic (date of birth, cardiovascular outcomes is pivotal for informing preven- date of diabetes diagnoses, sex, Aboriginal/Torres Strait tive strategies. Current risk stratification models utilise Islander ethnicity) and clinical variables (diabetes type, traditional cardiovascular risk factors from risk equa- weight, height, smoking status, blood pressure (BP), lipid tions, which have been extensively validated in the gen- levels, urinary albumin, serum creatinine, HbA1c, lipid eral population [1, 8–10]. However, this approach has lowering medications, antihypertensive medications, been shown to be a poor predictor of cardiovascular diabetes complications, comorbid conditions) were col- events in adults with type 1 diabetes, generally underes- lected. Health care professionals participating in ANDA timating risk in this group [1, 9, 20]. Risk stratification examined patients, reviewed medical records including models specifically for adults with type 1 diabetes as pathology results during standard patient consultations well as investigational biomarkers have been developed and recorded the de-identified information in a standard - but are not in widespread clinical use [21–26]. Elements ised collection form (Additional file  1). The participating of these models that differ from those currently recom - centres were later contacted to clarify missing data and mended include consideration of diabetes duration, invalid entries. glycaemic control (HbA1c) and albuminuria [21–23]. However, there is a paucity of contemporary data on Variables the prevalence of cardiovascular risk factors and dis- Age was calculated as the date of questionnaire (in 2015) ease among people with type 1 diabetes. Follow-up of minus the date of birth, and diabetes duration was cal- the landmark diabetes control and complications trial culated as the date of questionnaire minus the date of cohort also suggests there may be gaps in managing car- diabetes diagnosis. Provided height and weight measure- diovascular risk factors as only 7.6% attained all four of ments were used to calculate the BMI in kg/m . The main the American Diabetes Association recommendations outcome variables for this analysis were cerebral stroke, for complication prevention [27]. Until further studies myocardial infarction (MI), coronary artery bypass graft can corroborate any associations and the reliability of (CABG) surgery/angioplasty, peripheral vascular dis- new risk stratification models, only individual risk factor ease (PVD) and the composite of these atherosclerotic assessment and clinical judgment can direct clinical care. Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 3 of 12 outcomes defining CVD. PVD was defined clinically as participant numbers and percentages, and when com- the absence of both the dorsalis pedis and posterior tibi- paring between groups we used the Chi square test. alis pulses on either foot or amputation of toe, forefoot Restricted cubic splines were utilised to evaluate non- or leg (above or below knee), not due to trauma or causes linear associations between cardiovascular outcomes other than vascular disease. An additional outcome of and diabetes duration. Scoping review and expert interest was congestive cardiac failure (CCF) defined by opinion lead to selection of knots at 5.0, 15.0, 25.0 and clinician determined symptomatic status and responsive- 35.0 years duration. The binary logistic regression model ness to therapy. The healthcare professional completing was used to examine the association of risk factors with the questionnaire determined the presence of these com- cardiovascular outcomes of interest and likelihood ratio plications and other comorbid conditions with access to test assessed for interactions between age and diabetes a data dictionary of terms provided by the ANDA secre- duration as well as diabetes centre type. The selection tariat prior to commencing the questionnaire (Additional of variables was based on identifying all measured clini- file  2). Cardiovascular risk factors considered in analysis cal variables of known or suspected prognostic impor- include sex, age, diabetes duration, HbA1c, BMI catego- tance for the outcomes of interest and/or exhibiting a ries, smoking status (ever smoked versus never smoked), p value ≤ 0.1 on univariable analysis. Age and sex were systolic blood pressure, diastolic blood pressure, albu- forced into all multivariable models as they were consid- minuria (> 20.0  mg/L, > 20.0  μg/min, > 30.0  mg/24  h, ered clinically significant a priori. Models also adjusted or > 2.5  mg/mmol for women and > 3.5  mg/mmol for for antihypertensive and lipid lowering therapy. Multivar- men), presence of retinopathy, high density lipoprotein- iable regression analyses were performed for each cardio- cholesterol (HDL-C) and estimated glomerular filtration vascular outcome of interest using stepwise selection of rate (eGFR) calculated using the chronic kidney disease variables (1% probability for entry and 5% probability for epidemiology collaboration (CKD-EPI) formula based removal) for the remaining predictor variables. Based on on sex and collected creatinine values in μmol/L [28]. the coefficients from the final parsimonious multivariable The eGFR was not adjusted for ethnicity among Abo - model, we calculated the ROC curve and 95% confidence riginal or Torres Strait Islander (ATSI) people groups intervals. Population attributable risk (PAR) and 95% in keeping with current literature [29–31], and no other confidence intervals were calculated for each significant ethnicity data was collected. Total cholesterol, low den- categorical variable from the final multivariable models sity lipoprotein-cholesterol (LDL-C) and triglycerides under the assumption that associations were causal [32]. were excluded from regression analyses a priori. BMI Multiple imputation was performed for missing data categories were considered as underweight (< 18.5  kg/ (Additional file  3: Table S1 and Additional file  4: Table S2 2 2 m ), normal weight (18.5 to < 25.0  kg/m ), overweight respectively). Statistical analyses were performed using 2 2 (25.0 to < 30.0  kg/m ) and obese (≥ 30.0  kg/m ). Dyslip- Stata software version 14.2 (StataCorp, Texas, USA) and idaemia was defined as failure to meet current Austral - level of significance set at 5% unless otherwise specified. ian treatment targets (i.e. total cholesterol ≥ 4.0  mmol/L, HDL-C < 1.0  mmol/L, LDL-C ≥ 2.0  mmol/L or triglycer- Results ides ≥ 2.0 mmol/L). Hypertension was defined as systolic Patient characteristics blood pressure ≥ 140  mmHg or diastolic blood pres- Data from 1169 patients were included in this study. Car- sure ≥ 90  mmHg. Retinopathy was recorded as absent diovascular risk factors were highly prevalent, including or present for the preceding 12 months. Diabetes centre hypertension (21.9%), dyslipidaemia (89.4%), overweight type corresponds to secondary or community/primary or obesity (56.4%), ever smoking (38.5%), albuminuria centres derived from the category of membership with (31.1%), eGFR < 45  mL/min/1.73  m (6.5%) or < 60  mL/ the National Association of Diabetes Centres (NADC). min/1.73 m (10.3%) and HbA1c exceeding 7.0% (81.0%). Secondary centres comprised centres of excellence and Patients with CVD tended to be male (61.5%) with a tertiary diabetes centres, and community/primary cen- mean age of 58.5 ± 13.7 years. Median diabetes duration tres comprise affiliate and diabetes care centres. 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 Statistical analysis patients with CVD were overweight/obese (56.4%), Continuous data were tested for normality of distribu- had smoked (64.2%) or had retinopathy (56.2%). The tion and summarised as means with standard deviations mean eGFR for patients with CVD was 71 (± 29)  mL/ (± SD) or medians with interquartile range (IQR; 25th– min/1.73  m and around half of the patients with CVD 75th percentile). When comparing means or medians had albuminuria (47.9%). Secondary prevention prescrib- we used the Student’s t test or Mann–Whitney U test ing of lipid lowering therapy and antihypertensive ther- respectively. Categorical variables were summarised as apy was noted in up to 75.3 and 72.9% respectively. Mean Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 4 of 12 systolic and diastolic blood pressure levels were 132 ± 21 and 72 ± 10  mmHg respectively. A summary of cardio- vascular outcomes with risk factor levels is provided in Table 1 and Additional file 5: Table S3a and b. Cardiovascular complications 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 Fig. 1 Restricted cubic spline of type 1 diabetes duration and ratio (aOR) 1.05 (95% CI 1.01, 1.10); p 0.018] (Table 2). cardiovascular disease Increasing age [aOR 1.06 (95% CI 1.03, 1.09)], dia- betes duration ≥ 20.0  years [aOR 1.05 (95% CI 1.01, 1.10)], smoking status [aOR 2.40 (95% CI 1.26, 4.58)] with CVD. Increasing HDL-C and diastolic blood pres- and prescription of antihypertensive therapy [aOR sure were negatively associated with CVD [aOR 0.43 2.44 (95% CI 1.15, 5.18)] were all positively associated (95% CI 0.21, 0.90) and aOR 0.96 (95% CI 0.93–1.00) Table 1 Distribution of variables for cardiovascular outcomes of interest Variables Cardiovascular outcomes of interest p-value Total CVD No CVD N = 1169 N = 148 N = 1013 Female sex, n (%) 609 (53.3%) 57 (38.5%) 550 (55.7%) < 0.001 Age (years), mean (± SD) 40.0 (± 16.7) 58.5 (± 13.7) 37.3 (± 15.4) < 0.001 Age (years), median (IQR) 37.0 (24.9-52.0) 59.0 (49.0-68.2) 33.8 (23.9-47.2) < 0.001 Diabetes duration (years), mean (SD) 19.2 (± 14.4) 34.8 (± 15.5) 17.1 (± 12.8) < 0.001 Diabetes duration (years), median (IQR) 16.0 (8.0-27.0) 35.0 (24.5-45.0) 15.0 (8.0-24.0) < 0.001 Diabetes duration (≥ 20.0 years), n (%) 476 (41.3%) 121 (84.0%) 354 (35.4%) < 0.001 HbA1c (%), mean (± SD) 8.5 (± 1.8) 8.6 (± 1.5) 8.5 (± 1.9) 0.386 HDL-C , mean (± SD) 1.53 (± 0.54) 1.35 (± 0.42) 1.56 (± 0.56) < 0.001 LDL-C , mean (± SD) 2.55 (± 0.95) 2.15 (± 0.82) 2.62 (± 0.95) < 0.001 Total-C , mean (± SD) 4.73 (± 1.09) 4.18 (± 1.12) 4.83 (± 1.06) < 0.001 Triglycerides , mean (± SD) 1.37 (± 1.42) 1.54 (± 1.84) 1.34 (± 1.34) 0.190 Systolic BP , mean (± SD) 124 (± 17) 132 (± 21) 123 (± 16) < 0.001 Diastolic BP , mean (± SD) 74 (± 10) 72 (± 10) 74 (± 10) 0.073 BMI categories, n (%) (kg/m ) 0.372 < 18.5 20 (2.0%) 4 (3.2%) 15 (1.7%) 18.5 to < 25 419 (41.6%) 51 (40.5%) 365 (41.8%) 25 to < 30 315 (31.3%) 34 (27.0%) 279 (31.9%) ≥ 30 253 (25.1%) 37 (29.4%) 215 (24.6%) Ever smoked, n (%) 397 (38.5%) 86 (64.2%) 309 (34.6%) < 0.001 Albuminuria, n (%) 220 (31.1%) 46 (47.9%) 174 (28.6%) < 0.001 eGFR , mean (± SD) 97 (± 28) 71 (± 29) 101 (± 25) < 0.001 Antihypertensive Rx, n (%) 320 (28.1%) 105 (72.9%) 214 (21.7%) < 0.001 Lipid lowering Rx, n (%) 342 (29.7%) 110 (75.3%) 232 (23.2%) < 0.001 Retinopathy, n (%) 284 (24.7%) 82 (56.2%) 202 (20.2%) < 0.001 Rx: treatment a b c 2 mmol/L, mmHg, mL/min/1.73 m Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 5 of 12 Table 2 Risk factors associated with cardiovascular outcomes of interest Variables Univariable analyses Multivariable analyses OR (95% CI) p-value OR (95% CI) p-value ROC 95% CI Cardiovascular disease (composite) Female sex 0.50 (0.35–0.71) < 0.001 0.90 (0.46–1.78) 0.764 0.88 0.84–0.92 Age (years) 1.08 (1.07–1.09) < 0.001 1.06 (1.03–1.09) < 0.001 Diabetes duration group 1.12 (1.09–1.15) < 0.001 1.05 (1.01–1.10) 0.018 HbA1c (%) 1.04 (0.95–1.15) 0.386 HDL-cholesterol (mmol/L) 0.36 (0.21–0.62) < 0.001 0.43 (0.21–0.90) 0.025 Systolic BP (mmHg) 1.03 (1.02–1.04) < 0.001 Diastolic BP (mmHg) 0.98 (0.97–1.00) 0.073 0.96 (0.93–1.00) 0.048 BMI categories 1.00 (0.97–1.03) 0.937 Ever smoked 3.39 (2.32–4.95) < 0.001 2.40 (1.26–4.58) 0.008 Albuminuria 2.30 (1.49–3.56) < 0.001 eGFR (mL/min/1.73 m ) 0.96 (0.96–0.97) < 0.001 Antihypertensive Rx 9.74 (6.54–14.49) < 0.001 2.44 (1.15–5.18) 0.020 Lipid lowering Rx 10.13 (6.76–15.17) < 0.001 Retinopathy 5.07 (3.53–7.28) < 0.001 Stroke Female sex 0.33 (0.15–0.72) 0.006 0.49 (0.16–1.47) 0.201 0.81 0.74–0.88 Age (years) 1.06 (1.04–1.09) < 0.001 1.05 (1.01–1.08) 0.006 Diabetes duration group 1.08 (1.03–1.12) < 0.001 HbA1c (%) 1.17 (0.99–1.38) 0.062 HDL-cholesterol (mmol/L) 0.26 (0.09–0.77) 0.015 Systolic BP (mmHg) 1.02 (1.00–1.04) 0.016 Diastolic BP (mmHg) 1.00 (0.97–1.04) 0.928 BMI categories 1.02 (0.96–1.10) 0.514 Ever smoked 2.32 (1.10–4.92) 0.027 Albuminuria 2.27 (0.97–5.31) 0.059 eGFR (mL/min/1.73 m ) 0.97 (0.96–0.99) < 0.001 0.98 (0.96–1.00) 0.030 Antihypertensive Rx 7.85 (3.47–17.74) < 0.001 Lipid lowering Rx 7.54 (3.35–16.95) < 0.001 Retinopathy 3.87 (1.88–7.96) < 0.001 Myocardial infarction Female sex 0.41 (0.23–0.72) 0.002 0.97 (0.39–2.41) 0.943 0.90 0.87–0.94 Age (years) 1.08 (1.06–1.10) < 0.001 1.09 (1.05–1.13) < 0.001 Diabetes duration group 1.12 (1.08–1.17) < 0.001 HbA1c (%) 0.96 (0.82–1.13) 0.647 HDL-cholesterol (mmol/L) 0.24 (0.10–0.58) 0.002 0.20 (0.06–0.68) 0.010 Systolic BP (mmHg) 1.03 (1.02–1.05) < 0.001 Diastolic BP (mmHg) 1.01 (0.98–1.03) 0.707 BMI categories 1.01 (0.95–1.06) 0.842 Ever smoked 2.31 (1.30–4.10) 0.004 Albuminuria 2.20 (1.15–4.21) 0.017 eGFR (mL/min/1.73 m ) 0.97 (0.96–0.98) < 0.001 Antihypertensive Rx 23.91 (10.12–56.49) < 0.001 5.06 (1.38–18.54) 0.014 Lipid lowering Rx 18.21 (8.14–40.73) < 0.001 Retinopathy 3.67 (2.12–6.35) < 0.001 Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 6 of 12 Table 2 (continued) Variables Univariable analyses Multivariable analyses OR (95% CI) p-value OR (95% CI) p-value ROC 95% CI Coronary artery bypass graft/angioplasty Female sex 0.38 (0.22–0.68) 0.001 0.93 (0.34–2.52) 0.884 0.92 0.89-0.95 Age (years) 1.09 (1.07–1.12) < 0.001 1.08 (1.03–1.13) 0.001 Diabetes duration group 1.21 (1.13–1.30) < 0.001 HbA1c (%) 0.96 (0.81–1.12) 0.586 HDL-cholesterol (mmol/L) 0.27 (0.11–0.67) 0.005 0.23 (0.06–0.92) 0.038 Systolic BP (mmHg) 1.03 (1.01–1.04) < 0.001 Diastolic BP (mmHg) 0.98 (0.96–1.01) 0.171 BMI categories 1.03 (0.98–1.09) 0.263 Ever smoked 2.18 (1.25–3.81) 0.006 Albuminuria 1.82 (0.93–3.59) 0.082 eGFR (mL/min/1.73 m ) 0.97 (0.96–0.98) < 0.001 Antihypertensive Rx 38.63 (13.83–107.88) < 0.001 8.96 (1.12–71.54) 0.039 Lipid lowering Rx 36.00 (12.91–100.41) < 0.001 Retinopathy 4.45 (2.57–7.69) < 0.001 Peripheral vascular disease Female sex 0.73 (0.46–1.16) 0.180 1.08 (0.49–2.39) 0.851 0.85 0.81–0.90 Age (years) 1.07 (1.05–1.09) < 0.001 1.04 (1.01–1.07) 0.005 Diabetes duration group 1.11 (1.08–1.15) < 0.001 HbA1c (%) 1.09 (0.96–1.24) 0.171 HDL-cholesterol (mmol/L) 0.37 (0.18–0.77) 0.008 Systolic BP (mmHg) 1.02 (1.01–1.04) < 0.001 Diastolic BP (mmHg) 0.97 (0.95–0.99) 0.009 BMI categories 0.99 (0.95–1.03) 0.653 Ever smoked 3.67 (2.22–6.08) < 0.001 Albuminuria 2.97 (1.65–5.34) < 0.001 eGFR (mL/min/1.73 m ) 0.96 (0.95–0.97) < 0.001 0.97 (0.96–0.99) 0.002 Antihypertensive Rx 5.26 (3.25–8.53) < 0.001 Lipid lowering Rx 5.22 (3.21–8.46) < 0.001 Retinopathy 6.41 (3.95–10.41) < 0.001 2.47 (1.06–5.74) 0.036 Congestive cardiac failure Female sex 1.00 (0.36–2.78) 0.964 1.51 (0.30–7.47) 0.614 0.90 0.84–0.95 Age (years) 1.10 (1.06–1.14) < 0.001 1.15 (1.05–1.25) 0.002 Diabetes duration group 1.16 (1.05–1.29) 0.004 HbA1c (%) 0.97 (0.70–1.34) 0.858 HDL-cholesterol (mmol/L) 1.68 (0.90–3.16) 0.104 Systolic BP (mmHg) 1.02 (0.99–1.05) 0.157 Diastolic BP (mmHg) 0.97 (0.92–1.02) 0.195 BMI categories 1.20 (1.05–1.38) 0.008 Ever smoked 1.40 (0.50–3.90) 0.515 Albuminuria 5.30 (1.36–20.70) 0.016 eGFR (mL/min/1.73 m ) 0.95 (0.94–0.97) < 0.001 Antihypertensive Rx 37.42 (4.90–285.81) < 0.001 Lipid Lowering Rx 6.70 (2.12–21.21) 0.001 Retinopathy 20.75 (4.65–92.51) < 0.001 Rx: treatment Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 7 of 12 Population attributable risks for factors associated respectively]. The model’s discriminatory ability was with cardiovascular outcomes demonstrated with area under the ROC curve of 0.88 In the study population, the estimated proportions of (95% CI 0.84, 0.92) (Table 2). CVD attributable to diabetes duration ≥ 20  years, use of When stroke was considered, there was significant antihypertensive therapy and smoking were 6.5% (95% positive association with increasing age [aOR 1.05 CI 1.4, 11.6), 5.1% (95% CI 0.9, 9.3) and 3.9% (95% CI (95% CI 1.01, 1.08)]. Increasing eGFR was negatively 1.0, 6.7), respectively. The estimated proportion of PVD associated with stroke [aOR 0.98 (95% CI 0.96, 1.00)]. attributable to presence of retinopathy was 2.7% (95% The area under the ROC curve was 0.81 (95% CI 0.74, CI 0.2, 5.2). The estimated proportions of MI or CABG/ 0.88) (Table 2). angioplasty attributable to use of antihypertensive ther- When the outcome of MI or CABG/angioplasty apy were 4.8% (95% CI 1.8, 7.8) and 11.2% (95% CI 5.0, was considered, there were significant positive asso- 17.5), respectively (Additional file 6: Table S4). ciations with increasing age [aOR 1.09 (95% CI 1.05, 1.13) or aOR 1.08 (95% CI 1.03, 1.13)] and antihyper- tensive therapy [aOR 5.06 (95% CI 1.38, 18.54) or aOR 8.96 (95% CI 1.12, 71.54)], and negative associations Discussion with increasing HDL-C [aOR 0.20 (95% CI 0.06, 0.68) This study reports for the first time the large burden of or aOR 0.23 (95% CI 0.06, 0.92)]. The area under the cardiovascular risk factors among patients with type ROC curve for MI was 0.90 (95% CI 0.87, 0.94) and 1 diabetes attending diabetes centres across Australia. for CABG/angioplasty, it was 0.92 (95% CI 0.89, 0.95) Furthermore it shows that a group of traditional car- (Table 2). diovascular risk factors (age, sex, HDL-cholesterol level, When PVD was considered, there were significant smoking status, diastolic blood pressure and use of anti- positive associations with increasing age [aOR 1.04 hypertensive therapy) and diabetes specific risk factors (95% CI 1.01, 1.07)], retinopathy [aOR 2.47 (95% CI (type 1 diabetes duration), provide good discriminatory 1.06, 5.74)], and negative association with eGFR [aOR ability for the presence of CVD. The individual outcomes 0.97 (95% CI 0.96, 0.99)]. The area under the ROC of MI, CABG/angioplasty and CCF share similar asso- curve was 0.85 (95% CI 0.81, 0.90) (Table 2). ciations, while stroke is also associated with declining When the additional outcome of CCF was consid- renal function and PVD is associated with declining renal ered, there was significant association with increasing function and retinopathy. This suggests that informa - age [aOR 1.15 (95% CI 1.05, 1.25)]. The area under the tion required for cardiovascular risk stratification among ROC curve was 0.90 (95% CI 0.84, 0.95) (Table 2). patients with type 1 diabetes may not differ substantively from other high risk populations aside from the need to consider diabetes duration. Sensitivity analyses The significant non-linear association between diabe - Adding diabetes centre type into the final multivari- tes duration and CVD (independent of patient age) and able CVD models had minimal impact on the asso- the threshold effect seen at approximately 20 years, is an ciations. There was also no significant interaction important finding and consistent with previous model - between diabetes centre type and any atherosclerotic ling, prospective cohort and registry studies [12, 13, 22, cardiovascular outcome. Further, excluding patients 33–35]. Indeed, population based cohort studies and with CCF resulted in diastolic blood pressure and anti- national registry studies have all observed increased rates hypertensive therapy being removed from the final of CVD with longer diabetes duration. Some have also parsimonious model for CVD (data not shown). reported that CVD becomes the leading cause of death After multiple imputation for missing data there after about 20 years duration [12, 13, 22, 34, 35]. The sub - was an increase in the magnitude of the association stantive PAR related to longer diabetes duration strongly between antihypertensive therapy and MI or CABG/ supports the assessment and management of cardiovas- angioplasty [aOR 15.91 (95% CI 7.65, 33.12; p < 0.001) cular risk among people with long diabetes duration irre- and aOR 21.90 (95% CI 9.79, 48.99; p < 0.001) respec- spective of their current age and the older age thresholds tively] and HDL-cholesterol was no longer significantly recommended by current CVD guidelines. associated with CABG/angioplasty [aOR 0.45 (95% CI The negative association of HDL-cholesterol with CVD, 0.19, 1.08; p 0.072)] (Additional file 3: Table S1). MI and CABG/angioplasty [aOR 0.43 (95% CI 0.21, 0.90); 0.20 (95% CI 0.06, 0.68) and 0.23 (95% CI 0.06, 0.92) Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 8 of 12 respectively] is in keeping with current understanding this relationship is not firmly established in patients with of a protective role for HDL-cholesterol and HDL func- type 1 diabetes [52] and no association was noted in our tion [1, 36–40]. While it is unknown whether increasing analyses. Nonetheless, the finding that 56.4% of patients HDL-cholesterol will improve cardiovascular outcomes with type 1 diabetes were either overweight or obese is among patients with type 1 diabetes, the importance of alarming but consistent with other studies in this popula- this lipid variable for risk stratification is consistent with tion that report rates as high as 78% [53–55]. In addition, data from the Framingham Heart Study and a number of we found that female sex was not independently associ- meta-analyses which have also reported an inverse asso- ated with any cardiovascular outcome. This supports the ciation with CVD in other populations [36–38, 41]. The premise that the protective effect of female sex on car - observation that pharmacotherapies were strongly asso- diovascular disease is negated among women with type ciated with CVD likely relates to secondary prevention 1 diabetes as reported by previous cohort and registry strategies. studies [5, 12, 13]. The lack of an independent positive association The association between CVD and diastolic blood pres - between CVD and HbA1c, systolic blood pressure, BMI, sure is complex and may be impacted by patient age, albuminuria or negative association with renal function arterial stiffness, vascular resistance, endothelial dysfunc - was unexpected. In particular, our finding of no associa - tion, diastolic dysfunction and antihypertensive therapy tion with HbA1c conflicts with other evidence of a lin - [25, 26, 56–58]. This may be of particular relevance to ear relationship between hyperglycaemia or glycaemic our heterogeneous cohort ranging from 18 to 91 years of exposure and cardiovascular risk [33, 42–48]. This may age, including patients with CCF and those taking multi- be explained by differences in the study designs as we ple antihypertensive agents. The observed negative asso - were unable to assess glycaemic control over time. We ciation between diastolic blood pressure and CVD may also noted no significant difference in glycaemic control also represent reverse causation [59–63]. It was thus not among adults with or without cardiovascular disease (p surprising that diastolic blood pressure was removed 0.386; Table 1). from the prediction model when patients with CCF were Systolic blood pressure, albuminuria and declining excluded. eGFR were significantly associated with increased risk of The finding that diabetic retinopathy and declining CVD in univariable analysis [OR 1.03 (95% CI 1.02, 1.04), renal function was associated with peripheral vascular OR 2.30 (95% CI 1.49, 3.56) and OR 0.96 (95% CI 0.96, disease was not surprising and may relate to shared risk 0.97) respectively], but not in the multivariable analy- factors [64–66]. In our cross-sectional study, microvas- sis, suggesting these effects were accounted for by other cular complications such as retinopathy or nephropathy variables in the model. Interestingly, the mean systolic provided an indication of long term risk factor exposure, and diastolic blood pressures among all patients and but cohort studies have suggested PVD may also predict among those with CVD were within or close to recom- cardiovascular outcomes and end stage kidney disease mended blood pressure targets measuring 124 ± 17 and [64, 67]. Further, the negative association between renal 74 ± 10 mmHg, and 132 ± 21 and 72 ± 10  mmHg respec- function and stroke that we observed is in keeping with tively. Albuminuria was also noted to be prevalent in studies among the general population [68–71]. However, 31.1% of our cohort, affecting around half (47.9%) of the we found no independent association between stroke and patients with a history of CVD and is consistent with albuminuria in contrast to prior studies [72–77]. international estimates of 28–52% prevalence among A strength of this analysis includes the large dataset of patients with type 1 diabetes [49, 50]. This highlights the patients with type 1 diabetes taken from a nation-wide current prioritisation of blood pressure control among benchmarking activity. Furthermore, participants are diabetes centres in Australia [1, 2] as well as the impor- likely to be representative of patients attending diabetes tance of routine screening for renal dysfunction and centres throughout Australia as data were collected from albuminuria. every state and territory. Data were also collected for a Our finding that 38.5% of adult patients with type 1 broad range of cardiovascular risk factors and clinically diabetes had been smokers is consistent with a recent significant outcomes, with consideration of non-linear report that 38% of all Australians over 14  years of age associations and precision of risk prediction using area have been smokers [51]. As expected, the proportion was under the ROC curve. Key study limitations comprise the much higher among those patients with a history CVD cross-sectional nature of data collection, possible refer- (64.2%), reinforcing the need for diabetes centres to offer ral bias, and the reliance on healthcare worker reports patients assistance with smoking cessation efforts. as we were unable to independently verify diagnoses, While elevated BMI is recognised as an independent treatments or biochemistry. Also, the pre-specified clini - risk factor for CVD in the general population [14–18], cal questionnaire in ANDA did not provide scope to Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 9 of 12 Abbreviations differentiate those patients who were normotensive or ANDA: Australian National Diabetes Audit; aOR: adjusted odds ratio; AQCA: had normal lipid profiles due to medication or if pharma - Australian Quality Clinical Audit; BMI: body mass index; BP: blood pressure; cotherapy was solely part of secondary prevention strat- CABG: coronary artery bypass graft; CCF: congestive cardiac failure; CKD-EPI: chronic kidney disease epidemiology collaboration; CVD: cardiovascular egies, and these groups may confer different degrees of disease; eGFR: estimated glomerular filtration rate; HbA1c: glycated haemo - cardiovascular risk. Another limitation is that albuminu- globin; HDL-C: high density lipoprotein-cholesterol; IQR: interquartile range ria was defined by a single biochemistry result within the (25th–75th percentile); LDL-C: low density lipoprotein-cholesterol; HDL-C: high density lipoprotein-cholesterol; MI: myocardial infarction; NADC: National 12  months prior to participation in ANDA. Single false Association of Diabetes Centres; PVD: peripheral vascular disease; ROC: positive results or resolution of albuminuria with block- receiver operating characteristic; Rx: treatment; SD: standard deviation; Total-C: ade of the renin–angiotensin–aldosterone-system there- total-cholesterol; TRIPOD: Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis. fore could not be captured by this study. The association between adiposity and CVD was assessed only with BMI, Authors’ contributions but other measures such as waist circumference may AP drafted the manuscript and performed statistical analysis under the supervision of senior authors, SZ and AE. Manuscript drafting and review add to future studies. Finally, the calculation of PAR was was assisted by SR, NN, DL, NW, SA and SZ. SR also provided advice regarding based on the assumption that there was a causal rela- statistical analysis. All authors read and approved the final manuscript. tionship between the risk factors identified in our study Author details and CVD outcomes. Despite these limitations, this study School of Public Health and Preventive Medicine, Monash University, 5th provides important data on CVD among a large popula- Floor, The Alfred Centre, 99 Commercial Road, Melbourne, VIC 3004, Australia. tion with type 1 diabetes and informs future longitudinal Diabetes and Vascular Medicine Unit, Monash Health, Clayton, VIC 3168, Australia. Department of Medicine, The University of Melbourne, Melbourne, analyses of cardiovascular risk stratification. Our find - VIC 3010, Australia. ings also suggest that future cardiovascular risk stratifica - tion models will need to examine the impact of diabetes Acknowledgements We thank the participating diabetes centres for their time and generous specific risk factors for populations with type 1 diabe - contribution to the Australian National Diabetes Audit. tes using the ‘Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis’ Competing interests AP, AE, SR and NN report no competing interests in relation to this study. DL (TRIPOD) statement [78]. reports having received honoraria and study grants from AbbVie, AstraZeneca, Bayer, Bristol Myers Squibb, Pfizer, Sanofi and Shire. NW reports institutional Conclusions contract work outside the submitted work from AstraZeneca, Novo Nordisk, Roche, Eli Lilly and MSD Australia. SA reports past participation in advi- Our study demonstrates that the adult population with sory boards and/or receiving honoraria outside the submitted work from type 1 diabetes attending diabetes centres bears a sig- GlaxoSmithKline, Novartis, AstraZeneca/Bristol-Myers Squibb Australia, Eli Lilly nificant cardiovascular burden. Further, analysis reveals Australia, Janssen Cilag, Merck Sharp & Dohme (Australia), Sanofi Aventis, Novo Nordisk and Servier Laboratories. SZ reports institutional contract work out- associations between a number of traditionally consid- side the submitted work from AstraZeneca, Novo Nordisk and MSD Australia. ered and diabetes specific risk factors with CVD, which together provide good discriminatory ability for presence Availability of data and materials The datasets used and/or analysed during the current study are available from of disease. Given the substantial population risk of CVD the ANDA secretariat on reasonable request in line with the research data attributable to long diabetes duration, the impact of new enquiry procedures. cardiovascular risk stratification tools and interventions Consent for publication to manage risk factors before and after 20 years duration Not applicable. will need to be further assessed by prospective studies. Ethics approval and consent to participate Additional files Ethics approval was provided by Monash Health Human Research Ethics Com- mittee (Monash Health Reference: RES-17-0000-164L). Additional file 1. ‘ANDA-AQCA 2015’ provides the questionnaire that was Funding completed as part of the Australian Quality Clinical Audit [AQCA]. The Commonwealth Department of Health and Ageing funds the Australian Additional file 2. ‘ANDA-AQCA 2015 Data Definitions’ outlines the defini- National Diabetes Audit. This research has received no specific grant from any tions used by healthcare professionals who completed the questionnaire. funding agency in the public, commercial or not-for profit sectors. Additional file 3: Table S1. Multiple imputation for cardiovascular outcomes of interest. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- Additional file 4: Table S2. Missing data for cardiovascular risk factors lished maps and institutional affiliations. and outcomes of interest. Additional file 5: Table S3. a Distribution of variables for cardiovascular Received: 15 March 2018 Accepted: 26 May 2018 outcomes of interest and b Distribution of variables for congestive cardiac failure. Additional file 6: Table S4. Population attributable risk for cardiovascular outcomes of interest. Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 10 of 12 References experience. Arch Intern Med. 2002;162(16):1867–72. https ://jaman etwor 1. National Vascular Disease Prevention Alliance. Guidelines for the manage-k.com/journ als/jamai ntern almed icine /fulla rticl e/21279 6. Accessed 14 ment of absolute cardiovascular disease risk. 2012. https ://www.aihw. May 2018. gov.au/getme dia/0ce5f 234-0abf-41b9-a392-be5dd 1e94c 54/17034 .pdf. 17. Malik S, Wong ND, Franklin SS, Kamath TV, L’Italien GJ, Pio JR, et al. Impact aspx?inlin e=true. Accessed 5 Mar 2018. of the metabolic syndrome on mortality from coronary heart disease, 2. Australian Institute of Health and Welfare 2014. Cardiovascular disease, cardiovascular disease, and all causes in United States adults. Circulation. diabetes and chronic kidney disease—Australian facts: prevalence and 2004;110(10):1245–50. incidence. Cardiovascular, diabetes and chronic kidney disease series 18. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Eec ff t of no. 2. Cat. no. CDK 2. Canberra: AIHW. https ://www.aihw.gov.au/getme potentially modifiable risk factors associated with myocardial infarction dia/0ce5f 234-0abf-41b9-a392-be5dd 1e94c 54/17034 .pdf.aspx?inlin in 52 countries (the INTERHEART study): case–control study. Lancet. e=true. Accessed 5 Mar 2018. 2004;364(9438):937–52. https ://www.thela ncet.com/journ als/lance t/artic 3. Schnell O, Cappuccio F, Genovese S, Standl E, Valensi P, Ceriello A. Type 1 le/PIIS0 140-6736(04)17018 -9/fullt ext. Accessed 14 May 2018. diabetes and cardiovascular disease. Cardiovasc Diabetol. 2013;12(1):156. 19. Craig ME, Twigg SM, Donaghue KC, Cheung NW, Cameron FJ, Conn J, 4. de Ferranti SD, de Boer IH, Fonseca V, Fox CS, Golden SH, Lavie CJ, et al. et al for the Australian Type 1 Diabetes Guidelines Expert Advisory Group. Type 1 diabetes mellitus and cardiovascular disease: a scientific state - National evidence-based clinical care guidelines for type 1 diabetes in ment from the American Heart Association and American Diabetes children, adolescents and adults. In: Australian Government Department Association. Diab Care. 2014;37(10):2843–63. of Health and Ageing. Canberra 2011. https ://www.nhmrc .gov.au/_files 5. Laing SP, Swerdlow AJ, Slater SD, Burden AC, Morris A, Waugh NR, et al. _nhmrc /publi catio ns/attac hment s/ext00 4_type1 _diabe tes_child ren_ Mortality from heart disease in a cohort of 23,000 patients with insulin-adole scent s_adult s.pdf. Accessed 5 Mar 2018. treated diabetes. Diabetologia. 2003;46(6):760–5. https ://doi.org/10.1007/ 20. Zgibor JC, Piatt GA, Ruppert K, Orchard TJ, Roberts MS. Deficiencies of s0012 5-003-1116-6. cardiovascular risk prediction models for type 1 diabetes. Diab Care. 6. Morrish NJ, Wang SL, Stevens LK, Fuller JH, Keen H. Mortality and causes 2006;29(8):1860–5. http://care.diabe tesjo urnal s.org/conte nt/diaca of death in the WHO multinational study of vascular disease in diabetes. re/29/8/1860.full.pdf. Accessed 10 May 2018. Diabetologia. 2001;44(Suppl 2):S14–21. 21. Vistisen D, Andersen GS, Hansen CS, Hulman A, Henriksen JE, Bech- 7. Soedamah-Muthu SS, Fuller JH, Mulnier HE, Raleigh VS, Lawrenson RA, Nielsen H, et al. Prediction of first cardiovascular disease event in Colhoun HM. High risk of cardiovascular disease in patients with type 1 type 1 diabetes mellitus: the steno type 1 risk engine. Circulation. diabetes in the U.K.: a cohort study using the general practice research 2016;133(11):1058–66. http://circ.ahajo urnal s.org/conte nt/133/11/1058. database. Diab Care. 2006;29(4):798–804. http://care.diabe tesjo urnal long. Accessed 15 May 2018. s.org/conte nt/29/4/798.long. Accessed 15 May 2018. 22. Cederholm J, Eeg-Olofsson K, Eliasson B, Zethelius B, Gudbjornsdot- 8. Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, tir S. A new model for 5-year risk of cardiovascular disease in type 1 et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk. diabetes; from the Swedish National Diabetes Register (NDR). Diab Med. A report of the American College of Cardiology/American Heart Associa- 2011;28(10):1213–20. https ://doi.org/10.1111/j.1464-5491.2011.03342 .x. tion Task Force on practice guidelines. Circulation. 2014;129:S49–73. 23. Zgibor JC, Ruppert K, Orchard TJ, Soedamah-Muthu SS, Fuller J, Chatur- http://circ.ahajo urnal s.org/conte nt/129/25_suppl _2/S49.long. Accessed vedi N, et al. Development of a coronary heart disease risk prediction 16 May 2018. model for type 1 diabetes: the Pittsburgh CHD in type 1 diabetes risk 9. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. model. Diab Res Clin Pract. 2010;88(3):314–21. 2016 European guidelines on cardiovascular disease prevention in clinical 24. Hernández M, López C, Real J, Valls J, Ortega-Martinez de Victoria E, practice. The sixth joint task force of the European Society of Cardiol- Vázquez F, et al. Preclinical carotid atherosclerosis in patients with latent ogy and other societies on cardiovascular disease prevention in clinical autoimmune diabetes in adults (LADA), type 2 diabetes and classical type practice (constituted by representatives of 10 societies and by invited 1 diabetes. Cardiovasc Diabetol. 2017;16:94. experts). Developed with the special contribution of the European Asso- 25. Ferreira I, Hovind P, Schalkwijk CG, Parving HH, Stehouwer CDA, Rossing ciation for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart P. Biomarkers of inflammation and endothelial dysfunction as predictors J. 2016;37(29):2315–81. of pulse pressure and incident hypertension in type 1 diabetes: a 20 year 10. National Institute for Health and Care Excellence. Cardiovascular disease: life-course study in an inception cohort. Diabetologia. 2018;61(1):231–41. risk assessment and reduction, including lipid modification. Clinical https ://doi.org/10.1007/s0012 5-017-4470-5. guideline [CG181]. 2014. http://www.nice.org.uk/Guida nce/CG181 . 26. Bradley TJ, Slorach C, Mahmud FH, Dunger DB, Deanfield J, Deda L, et al. Accessed 17 Feb 2018. Early changes in cardiovascular structure and function in adolescents 11. Jaiswal M, Divers J, Urbina EM, Dabelea D, Bell RA, Pettitt DJ, et al. with type 1 diabetes. Cardiovasc Diabetol. 2016;15:31. Cardiovascular autonomic neuropathy in adolescents and young adults 27. Swasey KK, Orchard TJ, Costacou T. Trends in cardiovascular risk factor with type 1 and type 2 diabetes: the SEARCH for diabetes in youth cohort management in type 1 diabetes by sex. J Diab Compl. 2018;32(4):411–7. study. Pediatr Diab. 2018;19(4):680–9. https ://doi.org/10.1111/pedi.12633 .https ://www.jdcjo urnal .com/artic le/S1056 -8727(17)30931 -5/fullt ext. 12. Skrivarhaug T, Bangstad HJ, Stene LC, Sandvik L, Hanssen KF, Joner G. Accessed 14 May 2018. Long-term mortality in a nationwide cohort of childhood-onset type 1 28. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, diabetic patients in Norway. Diabetologia. 2006;49(2):298–305. https :// et al. A new equation to estimate glomerular filtration rate. Ann Intern doi.org/10.1007/s0012 5-005-0082-6. Med. 2009;150(9):604–12. 13. Secrest AM, Becker DJ, Kelsey SF, Laporte RE, Orchard TJ. Cause-specific 29. Maple-Brown LJ, Hughes JT, Ritte R, Barzi F, Hoy WE, Lawton PD, et al. Pro- mortality trends in a large population-based cohort with long-standing gression of kidney disease in indigenous Australians: the eGFR follow-up childhood-onset type 1 diabetes. Diabetes. 2010;59(12):3216–22. http:// study. Clin J Am Soc Nephrol. 2016;11(6):993–1004. diabe tes.diabe tesjo urnal s.org/conte nt/59/12/3216. Accessed 14 May 30. Maple-Brown LJ, Cunningham J, Hodge AM, Weeramanthri T, Dunbar 2018. T, Lawton PD, et al. High rates of albuminuria but not of low eGFR in 14. Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an inde- urban indigenous Australians: the DRUID Study. BMC Public Health. pendent risk factor for cardiovascular disease: a 26-year follow-up of 2011;11(1):346. participants in the Framingham heart study. Circulation. 1983;67(5):968– 31. Johnson DW, Jones GRD, Mathew TH, Ludlow MJ, Doogue MP, Jose 77. http://circ.ahajo urnal s.org/conte nt/67/5/968.long. Accessed 12 May MD, et al. Chronic kidney disease and automatic reporting of estimated 2018. glomerular filtration rate: new developments and revised recommenda- 15. Manson JE, Colditz GA, Stampfer MJ, Willett WC, Rosner B, Monson RR, tions. Med J Aust. 2012. 197(4):222–3. https ://www.mja.com.au/journ et al. A prospective study of obesity and risk of coronary heart disease al/2012/197/4/chron ic-kidne y-disea se-and-autom atic-repor ting-estim in women. N Engl J Med. 1990;322(13):882–9. https ://doi.org/10.1056/ated-glome rular -filtr ation . Accessed 4 May 2018. NEJM1 99003 29322 1303. 32. Gordis L. Epidemiology. 2nd ed. Philadelphia: W.B. Saunders Company; 16. Wilson PW, D’Agostino RB, Sullivan L, Parise H, Kannel WB. Overweight 2000. and obesity as determinants of cardiovascular risk: the Framingham Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 11 of 12 33. Nathan DM, Genuth S, Lachin J, Cleary P, Crofford O, Davis M, et al. The 49. Newman DJ, Mattock MB, Dawnay AB, Kerry S, McGuire A, Yaqoob M, effect of intensive treatment of diabetes on the development and et al. Systematic review on urine albumin testing for early detection of progression of long-term complications in insulin-dependent diabetes diabetic complications. Health Technol Assess. 2005;9(30):iii–vi, xiii–163. mellitus. N Engl J Med. 1993;329(14):977–86. https ://www.nejm.org/https ://www.journ alsli brary .nihr.ac.uk/hta/hta93 00#/full-repor t. Accessed doi/10.1056/NEJM1 99309 30329 1401?url_ver=Z39.88-2003&rfr_ 15 May 2018. id=ori:rid:cross ref.org&rfr_dat=cr_pub%3dwww .ncbi.nlm.nih.gov. 50. Warram JH, Gearin G, Laffel L, Krolewski AS. Eec ff t of duration of type I Accessed 14 May 2018. diabetes on the prevalence of stages of diabetic nephropathy defined 34. Morimoto A, Onda Y, Nishimura R, Sano H, Utsunomiya K, Tajima N. by urinary albumin/creatinine ratio. J Am Soc Nephrol. 1996;7(6):930–7. Cause-specific mortality trends in a nationwide population-based cohort http://jasn.asnjo urnal s.org/conte nt/7/6/930.long. Accessed 15 May 2018. of childhood-onset type 1 diabetes in Japan during 35 years of follow-up: 51. Australian Institute of Health and Welfare 2017. National Drug Strategy the DERI mortality study. Diabetologia. 2013;56(10):2171–5. https ://doi. Household Survey 2016: detailed findings. Drug Statistics series no. org/10.1007/s0012 5-013-3001-2. 31. Cat. no. PHE 214. Canberra: AIHW. https ://www.aihw.gov.au/getme 35. Livingstone SJ, Looker HC, Hothersall EJ, Wild SH, Lindsay RS, Chalm-dia/15db8 c15-7062-4cde-bfa4-3c207 9f30a f3/21028 .pdf.aspx?inlin e=true. ers J, et al. Risk of cardiovascular disease and total mortality in adults Accessed 5 Mar 2018. with type 1 diabetes: Scottish registry linkage study. PLoS Med. 52. Purnell JQ, Braffett BH, Zinman B, Gubitosi-Klug RA, Sivitz W, Bantle JP, 2012;9(10):e1001321. et al. Impact of excessive weight gain on cardiovascular outcomes in 36. Gordon T, Castelli WP, Hjortland MC, Kannel WB, Dawber TR. High density type 1 diabetes: results from the diabetes control and complications trial/ lipoprotein as a protective factor against coronary heart disease: the epidemiology of diabetes interventions and complications (DCCT/EDIC) Framingham study. Am J Med. 1977;62(5):707–14. https ://www.amjme study. Diab Care. 2017;40(12):1756–62. http://care.diabe tesjo urnal s.org/ d.com/artic le/0002-9343(77)90874 -9/pdf. Accessed 15 May 2018.conte nt/40/12/1756.long. Accessed 15 May 2018. 37. Cooney MT, Dudina A, De Bacquer D, Wilhelmsen L, Sans S, Menotti 53. Conway B, Miller RG, Costacou T, Fried L, Kelsey S, Evans RW, et al. Tem- A, et al. HDL cholesterol protects against cardiovascular disease in poral patterns in overweight and obesity in type 1 diabetes. Diab Med. both genders, at all ages and at all levels of risk. Atherosclerosis. 2010;27(4):398–404. 2009;206(2):611–6. https ://www.ather oscle rosis -journ al.com/artic le/ 54. Price SA, Gorelik A, Fourlanos S, Colman PG, Wentworth JM. Obesity is S0021 -9150(09)00163 -4/fullt ext. Accessed 14 May 2018. associated with retinopathy and macrovascular disease in type 1 diabe- 38. Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, tes. Obes Res Clin Pract. 2014;8(2):e178–82. https ://www.obesi tyres earch et al. Major lipids, apolipoproteins, and risk of vascular disease. JAMA. clini calpr actic e.com/artic le/S1871 -403X(13)00032 -X/fullt ext. Accessed 15 2009;302(18):1993–2000. May 2018. 39. Emerging risk factors collaboration, De Angelantonio E, Gao P, Pennells 55. Holt SK, Lopushnyan N, Hotaling J, Sarma AV, Dunn RL, Cleary PA, et al. L, Kaptoge S, Caslake M, et al. Lipid-related markers and cardiovascular Prevalence of low testosterone and predisposing risk factors in men with disease prediction. JAMA. 2012;307(23):2499–506. type 1 diabetes mellitus: findings from the DCCT/EDIC. J Clin Endocrinol 40. Heier M, Borja MS, Brunborg C, Seljeflot I, Margeirsdottir HD, Hanssen KF, Metab. 2014;99(9):E1655–60. et al. Reduced HDL function in children and young adults with type 1 56. Franklin SS. Beyond blood pressure: arterial stiffness as a new biomarker diabetes. Cardiovasc Diabetol. 2017;16:85. of cardiovascular disease. J Am Soc Hypertens. 2008;2(3):140–51. https :// 41. Propsective studies collaboration, Lewington S, Whitlock G, Clarke R, www.ashjo urnal .com/artic le/S1933 -1711(07)00194 -5/fullt ext. Accessed Sherliker P, Emberson J, et al. Blood cholesterol and vascular mortal- 15 May 2018. ity by age, sex, and blood pressure: a meta-analysis of individual data 57. Franklin SS, Wong ND. Hypertension and cardiovascular disease: con- from 61 prospective studies with 55,000 vascular deaths. Lancet. tributions of the Framingham heart study. Glob Heart. 2013;8(1):49–57. 2007;370(9602):1829–39. https ://www.thela ncet.com/journ als/lance t/https ://www.scien cedir ect.com/scien ce/artic le/pii/S2211 81601 20026 artic le/PIIS0 140-6736(07)61778 -4/fullt ext. Accessed 14 May 2018.45?via%3Dihu b. Accessed 15 May 2018. 42. Adler AI, Erqou S, Lima TA, Robinson AH. Association between gly- 58. Marti CN, Gheorghiade M, Kalogeropoulos AP, Georgiopoulou VV, cated haemoglobin and the risk of lower extremity amputation in Quyyumi AA, Butler J. Endothelial dysfunction, arterial stiffness, and heart patients with diabetes mellitus-review and meta-analysis. Diabetologia. failure. J Am Coll Cardiol. 2012;60(16):1455–69. https ://www.scien cedir 2010;53(5):840–9. https ://doi.org/10.1007/s0012 5-009-1638-7.ect.com/scien ce/artic le/pii/S0735 10971 20252 35?via%3Dihu b. Accessed 43. Larsen J, Brekke M, Sandvik L, Arnesen H, Hanssen KF, Dahl-Jorgensen K. 15 May 2018. Silent coronary atheromatosis in type 1 diabetic patients and its relation 59. Hansson L, Zanchetti A, Carruthers SG, Dahlof B, Elmfeldt D, Julius S, to long-term glycemic control. Diabetes. 2002;51(8):2637–41. http://diabe et al. Eec ff ts of intensive blood-pressure lowering and low-dose aspirin tes.diabe tesjo urnal s.org/conte nt/51/8/2637.long. Accessed 15 May 2018. in patients with hypertension: principal results of the hypertension 44. Eeg-Olofsson K, Cederholm J, Nilsson PM, Zethelius B, Svensson AM, Gud- optimal treatment (HOT ) randomised trial. HOT study group. Lancet. bjornsdottir S, et al. Glycemic control and cardiovascular disease in 7454 1998;351(9118):1755–62. https ://www.thela ncet.com/journ als/lance t/ patients with type 1 diabetes: an observational study from the Swedish artic le/PIIS0 140-6736(98)04311 -6/fullt ext. Accessed 15 May 2018. National Diabetes Register (NDR). Diab Care. 2010;33(7):1640–6. 60. SPRINT Research Group, Wright JT Jr., Williamson JD, Whelton PK, 45. Singer DE, Nathan DM, Anderson KM, Wilson PW, Evans JC. Association Snyder JK, Sink KM, et al. A randomized trial of intensive versus standard of HbA1c with prevalent cardiovascular disease in the original cohort of blood-pressure control. N Engl J Med. 2015;373(22):2103–16. https :// the Framingham heart study. Diabetes. 1992;41(2):202–8. http://diabe tes.www.nejm.org/doi/10.1056/NEJMo a1511 939?url_ver=Z39.88-2003&rfr_ diabe tesjo urnal s.org/conte nt/41/2/202.long. Accessed 14 May 2018. id=ori:rid:cross ref.org&rfr_dat=cr_pub%3dwww .ncbi.nlm.nih.gov. 46. Zhao W, Katzmarzyk PT, Horswell R, Wang Y, Johnson J, Hu G. HbA1c Accessed 15 May 2018. and coronary heart disease risk among diabetic patients. Diab Care. 61. Pepine CJ, Handberg EM, Cooper-DeHoff RM, Marks RG, Kowey P, Messerli 2014;37(2):428–35. FH, et al. A calcium antagonist vs a non-calcium antagonist hypertension 47. Chen YY, Lin YJ, Chong E, Chen PC, Chao TF, Chen SA, et al. The impact treatment strategy for patients with coronary artery disease. The inter- of diabetes mellitus and corresponding HbA1c levels on the future risks national verapamil–trandolapril study (INVEST ): a randomized controlled of cardiovascular disease and mortality: a representative cohort study in trial. JAMA. 2003;290(21):2805–16. https ://jaman etwor k.com/journ als/ Taiwan. PLoS ONE. 2015;10(4):e0123116. http://journ als.plos.org/ploso ne/jama/fulla rticl e/19776 1. Accessed 15 May 2018. artic le?id=10.1371/journ al.pone.01231 16. Accessed 14 May 2018. 62. Satish S, Zhang DD, Goodwin JS. Clinical significance of falling blood 48. Miller RG, Anderson SJ, Costacou T, Sekikawa A, Orchard TJ. Hemoglobin pressure among older adults. J Clin Epidemiol. 2001;54(9):961–7. https :// A1c and cardiovascular disease incidence in type 1 diabetes: an applica-www.jclin epi.com/artic le/S0895 -4356(01)00360 -2/fullt ext. Accessed 15 tion of joint modeling of longitudinal and time-to-event data in the May 2018. pittsburgh epidemiology of diabetes complications (EDC) study. Am J 63. Vidal-Petiot E, Ford I, Greenlaw N, Ferrari R, Fox KM, Tardif JC, et al. Car- Epidemiol. 2018. https ://acade mic.oup.com/aje/advan ce-artic le-abstr act/ diovascular event rates and mortality according to achieved systolic and doi/10.1093/aje/kwx38 6/48312 47?redir ected From=fullt ext. Accessed 15 diastolic blood pressure in patients with stable coronary artery disease: May 2018. (Epub ahead of print). an international cohort study. Lancet. 2016;388(10056):2142–52. https :// Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 12 of 12 www.thela ncet.com/journ als/lance t/artic le/PIIS0 140-6736(16)31326 -5/ 71. Abramson JL, Jurkovitz CT, Vaccarino V, Weintraub WS, McClellan fullt ext. Accessed 15 May 2018. W. Chronic kidney disease, anemia, and incident stroke in a middle- 64. Mohammedi K, Potier L, Belhatem N, Matallah N, Hadjadj S, Roussel R, aged, community-based population: the ARIC study. Kidney Int. et al. Lower-extremity amputation as a marker for renal and cardiovascu- 2003;64(2):610–5. https ://www.kidne y-inter natio nal.org/artic le/S0085 lar events and mortality in patients with long standing type 1 diabetes. -2538(15)49368 -8/fullt ext. Accessed 15 May 2018. Cardiovasc Diabetol. 2016;15:5. 72. Sandsmark DK, Messe SR, Zhang X, Roy J, Nessel L, Lee Hamm L, et al. 65. Mohammedi K, Woodward M, Hirakawa Y, Zoungas S, Williams B, Proteinuria, but not eGFR, predicts stroke risk in chronic kidney disease: Lisheng L, et al. Microvascular and macrovascular disease and risk for chronic renal insufficiency cohort study. Stroke. 2015;46(8):2075–80. major peripheral arterial disease in patients with type 2 diabetes. Diab 73. Bouchi R, Babazono T, Nyumura I, Toya K, Hayashi T, Ohta M, et al. Is a Care. 2016;39(10):1796–803. http://care.diabe tesjo urnal s.org/conte reduced estimated glomerular filtration rate a risk factor for stroke in nt/39/10/1796.long. Accessed 15 May 2018. patients with type 2 diabetes? Hypertens Res. 2009;32(5):381–6. https :// 66. Garimella PS, Hart PD, O’Hare A, DeLoach S, Herzog CA, Hirsch AT. www.natur e.com/artic les/hr200 930. Accessed 15 May 2018. Peripheral artery disease and CKD: a focus on peripheral artery disease 74. Sundquist K, Li X. Type 1 diabetes as a risk factor for stroke in men and as a critical component of CKD care. Am J Kidney Dis. 2012;60(4):641–54. women aged 15–49: a nationwide study from Sweden. Diab Med. https ://www.ajkd.org/artic le/S0272 -6386(12)00665 -8/fullt ext. Accessed 2006;23(11):1261–7. https ://doi.org/10.1111/j.1464-5491.2006.01959 .x. 15 May 2018. 75. Hagg S, Thorn LM, Putaala J, Liebkind R, Harjutsalo V, Forsblom CM, 67. Criqui MH, Ninomiya JK, Wingard DL, Ji M, Fronek A. Progression of et al. Incidence of stroke according to presence of diabetic nephropa- peripheral arterial disease predicts cardiovascular disease morbidity and thy and severe diabetic retinopathy in patients with type 1 diabetes. mortality. J Am Coll Cardiol. 2008;52(21):1736–42. https ://www.scien cedir Diab Care. 2013;36(12):4140–6. http://care.diabe tesjo urnal s.org/conte ect.com/scien ce/artic le/pii/S0735 10970 80295 98?via%3Dihu b. Accessed nt/36/12/4140.long. Accessed 15 May 2018. 15 May 2018. 76. Chang Y T, Wu JL, Hsu CC, Wang JD, Sung JM. Diabetes and end-stage 68. Chronic kidney disease prognosis consortium, Matsushita K, van der renal disease synergistically contribute to increased incidence of Velde M, Astor BC, Woodward M, Levey AS, et al. Association of estimated cardiovascular events: a nationwide follow-up study during 1998–2009. glomerular filtration rate and albuminuria with all-cause and cardiovascu- Diab Care. 2014;37(1):277–85. http://care.diabe tesjo urnal s.org/conte lar mortality in general population cohorts: a collaborative meta-analysis. nt/37/1/277.long. Accessed 16 May 2018. Lancet. 2010;375(9731):2073–81. https ://www.thela ncet.com/journ als/ 77. Tuomilehto J, Borch-Johnsen K, Molarius A, Forsen T, Rastenyte D, Sarti C, lance t/artic le/PIIS0 140-6736(10)60674 -5/fullt ext. Accessed 15 May 2018. et al. Incidence of cardiovascular disease in Type 1 (insulin-dependent) 69. Masson P, Webster AC, Hong M, Turner R, Lindley RI, Craig JC. Chronic kid- diabetic subjects with and without diabetic nephropathy in Finland. ney disease and the risk of stroke: a systematic review and meta-analysis. Diabetologia. 1998;41(7):784–90. https ://doi.org/10.1007/s0012 50050 988. Nephrol Dial Transplant. 2015;30(7):1162–9. https ://acade mic.oup.com/ 78. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg ndt/artic le/30/7/1162/23248 40. Accessed 15 May 2018. EW, et al. Transparent reporting of a multivariable prediction model for 70. Ovbiagele B, Bath PM, Cotton D, Sha N, Diener HC, PRoFESS investigators. individual prognosis or diagnosis ( TRIPOD): explanation and elabora- Low glomerular filtration rate, recurrent stroke risk, and effect of renin- tion. Ann Intern Med. 2015;162(1):W1–73. http://annal s.org/aim/fulla rticl angiotensin system modulation. Stroke. 2013;44(11):3223–5. http://strok e/20885 42/trans paren t-repor ting-multi varia ble-predi ction -model -indiv e.ahajo urnal s.org/conte nt/44/11/3223.long. Accessed 15 May 2018.idual -progn osis-diagn osis-tripo d-expla natio n. Accessed 15 May 2018. Ready to submit your research ? 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Burden of cardiovascular risk factors and disease among patients with type 1 diabetes: results of the Australian National Diabetes Audit (ANDA)

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Copyright © 2018 by The Author(s)
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Medicine & Public Health; Diabetes; Angiology; Cardiology
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Abstract

Background: Cardiovascular risk stratification is complex in type 1 diabetes. We hypothesised that traditional and diabetes-specific cardiovascular risk factors were prevalent and strongly associated with cardiovascular disease (CVD) among adults with type 1 diabetes attending Australian diabetes centres. Methods: De-identified, prospectively collected data from patients with type 1 diabetes aged ≥ 18 years in the 2015 Australian National Diabetes Audit were analysed. The burden of cardiovascular risk factors [age, sex, diabetes dura- tion, glycated haemoglobin (HbA1c), blood pressure, lipid profile, body mass index, smoking status, retinopathy, renal function and albuminuria] and associations with CVD inclusive of stroke, myocardial infarction, coronary artery bypass graft surgery/angioplasty and peripheral vascular disease were assessed. Restricted cubic splines assessed for non- linearity of diabetes duration and likelihood ratio test assessed for interactions between age, diabetes duration, centre type and cardiovascular outcomes of interest. Discriminatory ability of multivariable models were assessed with area under the receiver operating characteristic (ROC) curves. Results: Data from 1169 patients were analysed. Mean (± SD) age and median diabetes duration was 40.0 (± 16.7) and 16.0 (8.0–27.0) years respectively. Cardiovascular risk factors were prevalent including hypertension (21.9%), dyslipidaemia (89.4%), overweight/obesity (56.4%), ever smoking (38.5%), albuminuria (31.1%), estimated glomerular filtration rate < 60 mL/min/1.73 m (10.3%) and HbA1c > 7.0% (53 mmol/mol) (81.0%). Older age, longer diabetes dura- tion, smoking and antihypertensive therapy use were positively associated with CVD, while high density lipoprotein- cholesterol and diastolic blood pressure were negatively associated (p < 0.05). Association with CVD and diabetes duration remained constant until 20 years when a linear increase was noted. Longer diabetes duration also had the highest population attributable risk of 6.5% (95% CI 1.4, 11.6). Further, the models for CVD demonstrated good dis- criminatory ability (area under the ROC curve 0.88; 95% CI 0.84, 0.92). Conclusions: Cardiovascular risk factors were prevalent and strongly associated with CVD among adults with type 1 diabetes attending Australian diabetes centres. Given the approximate J-shaped association between type 1 diabetes duration and CVD, the impact of cardiovascular risk stratification and management before and after 20 years dura- tion needs to be further assessed longitudinally. Diabetes specific cardiovascular risk stratification tools incorporating diabetes duration should be an important consideration in future guideline development. Keywords: Type 1 diabetes mellitus, Cardiovascular disease, Epidemiology *Correspondence: sophia.zoungas@monash.edu School of Public Health and Preventive Medicine, Monash University, 5th Floor, The Alfred Centre, 99 Commercial Road, Melbourne, VIC 3004, Australia Full list of author information is available at the end of the article © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/ publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 2 of 12 We thus examined the burden of cardiovascular risk Background factors and their associations with cardiovascular com- Cardiovascular disease (CVD) is the leading cause of plications among patients with type 1 diabetes attend- death among people with type 1 diabetes [1–6]. Fur- ing diabetes centres across Australia. Traditionally thermore, people with type 1 diabetes experience car- considered and diabetes specific cardiovascular risk fac - diovascular events about 10 years earlier than a matched tors were hypothesised to be prevalent and strongly asso- population without diabetes [7]. This is juxtaposed with ciated with CVD in this vulnerable population. current national strategies in primary prevention of CVD that focus on absolute cardiovascular risk stratification Methods from around 40  years of age regardless of comorbidities Subjects [1, 8–11]. This strategy fails to integrate the duration of The Australian National Diabetes Audit (ANDA) is an exposure to risk factors which may be of particular rel- annual cross-sectional benchmarking activity including evance to younger people diagnosed with type 1 diabetes patients of all ages and diabetes types. Diabetes centres in their youth. voluntarily participate, with approximately two-thirds While traditional cardiovascular risk factors are being tertiary centres and one-third primary or com- expected to contribute to the observed increased risk of munity based centres. De-identified data for our study CVD, the relative strength of associations in type 1 dia- were collected across all centres during a 1-month sur- betes is not clear. The protective association of female sex vey period in May or June (2015) for all consecutive with CVD, for example, appears to be negated in at least patients. Patients considered for this analysis were adults those women aged less than 40  years with type 1 diabe- (≥ 18  years) with type 1 diabetes (n = 1169) present- tes [5, 12, 13]. Similarly, while obesity is recognised as an ing to one of the 49 participating diabetes centres. The independent risk factor for CVD in the general popula- degree of patient ascertainment could not be determined tion [14–18], the impact of increasing body mass index because only data for those participants involved in the (BMI) in type 1 diabetes is not firmly established. Fur - study were collected. thermore, recommendations for pharmacotherapy to Ethical approval for our study was provided by the manage risk factors are largely extrapolated from trials in Monash Health Human Research Ethics Committee. adults with type 2 diabetes that may not be generalisable to those with type 1 diabetes [1, 8, 9, 19]. Data collection Understanding relationships between risk factors and Relevant pre-specified sociodemographic (date of birth, cardiovascular outcomes is pivotal for informing preven- date of diabetes diagnoses, sex, Aboriginal/Torres Strait tive strategies. Current risk stratification models utilise Islander ethnicity) and clinical variables (diabetes type, traditional cardiovascular risk factors from risk equa- weight, height, smoking status, blood pressure (BP), lipid tions, which have been extensively validated in the gen- levels, urinary albumin, serum creatinine, HbA1c, lipid eral population [1, 8–10]. However, this approach has lowering medications, antihypertensive medications, been shown to be a poor predictor of cardiovascular diabetes complications, comorbid conditions) were col- events in adults with type 1 diabetes, generally underes- lected. Health care professionals participating in ANDA timating risk in this group [1, 9, 20]. Risk stratification examined patients, reviewed medical records including models specifically for adults with type 1 diabetes as pathology results during standard patient consultations well as investigational biomarkers have been developed and recorded the de-identified information in a standard - but are not in widespread clinical use [21–26]. Elements ised collection form (Additional file  1). The participating of these models that differ from those currently recom - centres were later contacted to clarify missing data and mended include consideration of diabetes duration, invalid entries. glycaemic control (HbA1c) and albuminuria [21–23]. However, there is a paucity of contemporary data on Variables the prevalence of cardiovascular risk factors and dis- Age was calculated as the date of questionnaire (in 2015) ease among people with type 1 diabetes. Follow-up of minus the date of birth, and diabetes duration was cal- the landmark diabetes control and complications trial culated as the date of questionnaire minus the date of cohort also suggests there may be gaps in managing car- diabetes diagnosis. Provided height and weight measure- diovascular risk factors as only 7.6% attained all four of ments were used to calculate the BMI in kg/m . The main the American Diabetes Association recommendations outcome variables for this analysis were cerebral stroke, for complication prevention [27]. Until further studies myocardial infarction (MI), coronary artery bypass graft can corroborate any associations and the reliability of (CABG) surgery/angioplasty, peripheral vascular dis- new risk stratification models, only individual risk factor ease (PVD) and the composite of these atherosclerotic assessment and clinical judgment can direct clinical care. Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 3 of 12 outcomes defining CVD. PVD was defined clinically as participant numbers and percentages, and when com- the absence of both the dorsalis pedis and posterior tibi- paring between groups we used the Chi square test. alis pulses on either foot or amputation of toe, forefoot Restricted cubic splines were utilised to evaluate non- or leg (above or below knee), not due to trauma or causes linear associations between cardiovascular outcomes other than vascular disease. An additional outcome of and diabetes duration. Scoping review and expert interest was congestive cardiac failure (CCF) defined by opinion lead to selection of knots at 5.0, 15.0, 25.0 and clinician determined symptomatic status and responsive- 35.0 years duration. The binary logistic regression model ness to therapy. The healthcare professional completing was used to examine the association of risk factors with the questionnaire determined the presence of these com- cardiovascular outcomes of interest and likelihood ratio plications and other comorbid conditions with access to test assessed for interactions between age and diabetes a data dictionary of terms provided by the ANDA secre- duration as well as diabetes centre type. The selection tariat prior to commencing the questionnaire (Additional of variables was based on identifying all measured clini- file  2). Cardiovascular risk factors considered in analysis cal variables of known or suspected prognostic impor- include sex, age, diabetes duration, HbA1c, BMI catego- tance for the outcomes of interest and/or exhibiting a ries, smoking status (ever smoked versus never smoked), p value ≤ 0.1 on univariable analysis. Age and sex were systolic blood pressure, diastolic blood pressure, albu- forced into all multivariable models as they were consid- minuria (> 20.0  mg/L, > 20.0  μg/min, > 30.0  mg/24  h, ered clinically significant a priori. Models also adjusted or > 2.5  mg/mmol for women and > 3.5  mg/mmol for for antihypertensive and lipid lowering therapy. Multivar- men), presence of retinopathy, high density lipoprotein- iable regression analyses were performed for each cardio- cholesterol (HDL-C) and estimated glomerular filtration vascular outcome of interest using stepwise selection of rate (eGFR) calculated using the chronic kidney disease variables (1% probability for entry and 5% probability for epidemiology collaboration (CKD-EPI) formula based removal) for the remaining predictor variables. Based on on sex and collected creatinine values in μmol/L [28]. the coefficients from the final parsimonious multivariable The eGFR was not adjusted for ethnicity among Abo - model, we calculated the ROC curve and 95% confidence riginal or Torres Strait Islander (ATSI) people groups intervals. Population attributable risk (PAR) and 95% in keeping with current literature [29–31], and no other confidence intervals were calculated for each significant ethnicity data was collected. Total cholesterol, low den- categorical variable from the final multivariable models sity lipoprotein-cholesterol (LDL-C) and triglycerides under the assumption that associations were causal [32]. were excluded from regression analyses a priori. BMI Multiple imputation was performed for missing data categories were considered as underweight (< 18.5  kg/ (Additional file  3: Table S1 and Additional file  4: Table S2 2 2 m ), normal weight (18.5 to < 25.0  kg/m ), overweight respectively). Statistical analyses were performed using 2 2 (25.0 to < 30.0  kg/m ) and obese (≥ 30.0  kg/m ). Dyslip- Stata software version 14.2 (StataCorp, Texas, USA) and idaemia was defined as failure to meet current Austral - level of significance set at 5% unless otherwise specified. ian treatment targets (i.e. total cholesterol ≥ 4.0  mmol/L, HDL-C < 1.0  mmol/L, LDL-C ≥ 2.0  mmol/L or triglycer- Results ides ≥ 2.0 mmol/L). Hypertension was defined as systolic Patient characteristics blood pressure ≥ 140  mmHg or diastolic blood pres- Data from 1169 patients were included in this study. Car- sure ≥ 90  mmHg. Retinopathy was recorded as absent diovascular risk factors were highly prevalent, including or present for the preceding 12 months. Diabetes centre hypertension (21.9%), dyslipidaemia (89.4%), overweight type corresponds to secondary or community/primary or obesity (56.4%), ever smoking (38.5%), albuminuria centres derived from the category of membership with (31.1%), eGFR < 45  mL/min/1.73  m (6.5%) or < 60  mL/ the National Association of Diabetes Centres (NADC). min/1.73 m (10.3%) and HbA1c exceeding 7.0% (81.0%). Secondary centres comprised centres of excellence and Patients with CVD tended to be male (61.5%) with a tertiary diabetes centres, and community/primary cen- mean age of 58.5 ± 13.7 years. Median diabetes duration tres comprise affiliate and diabetes care centres. 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 Statistical analysis patients with CVD were overweight/obese (56.4%), Continuous data were tested for normality of distribu- had smoked (64.2%) or had retinopathy (56.2%). The tion and summarised as means with standard deviations mean eGFR for patients with CVD was 71 (± 29)  mL/ (± SD) or medians with interquartile range (IQR; 25th– min/1.73  m and around half of the patients with CVD 75th percentile). When comparing means or medians had albuminuria (47.9%). Secondary prevention prescrib- we used the Student’s t test or Mann–Whitney U test ing of lipid lowering therapy and antihypertensive ther- respectively. Categorical variables were summarised as apy was noted in up to 75.3 and 72.9% respectively. Mean Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 4 of 12 systolic and diastolic blood pressure levels were 132 ± 21 and 72 ± 10  mmHg respectively. A summary of cardio- vascular outcomes with risk factor levels is provided in Table 1 and Additional file 5: Table S3a and b. Cardiovascular complications 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 Fig. 1 Restricted cubic spline of type 1 diabetes duration and ratio (aOR) 1.05 (95% CI 1.01, 1.10); p 0.018] (Table 2). cardiovascular disease Increasing age [aOR 1.06 (95% CI 1.03, 1.09)], dia- betes duration ≥ 20.0  years [aOR 1.05 (95% CI 1.01, 1.10)], smoking status [aOR 2.40 (95% CI 1.26, 4.58)] with CVD. Increasing HDL-C and diastolic blood pres- and prescription of antihypertensive therapy [aOR sure were negatively associated with CVD [aOR 0.43 2.44 (95% CI 1.15, 5.18)] were all positively associated (95% CI 0.21, 0.90) and aOR 0.96 (95% CI 0.93–1.00) Table 1 Distribution of variables for cardiovascular outcomes of interest Variables Cardiovascular outcomes of interest p-value Total CVD No CVD N = 1169 N = 148 N = 1013 Female sex, n (%) 609 (53.3%) 57 (38.5%) 550 (55.7%) < 0.001 Age (years), mean (± SD) 40.0 (± 16.7) 58.5 (± 13.7) 37.3 (± 15.4) < 0.001 Age (years), median (IQR) 37.0 (24.9-52.0) 59.0 (49.0-68.2) 33.8 (23.9-47.2) < 0.001 Diabetes duration (years), mean (SD) 19.2 (± 14.4) 34.8 (± 15.5) 17.1 (± 12.8) < 0.001 Diabetes duration (years), median (IQR) 16.0 (8.0-27.0) 35.0 (24.5-45.0) 15.0 (8.0-24.0) < 0.001 Diabetes duration (≥ 20.0 years), n (%) 476 (41.3%) 121 (84.0%) 354 (35.4%) < 0.001 HbA1c (%), mean (± SD) 8.5 (± 1.8) 8.6 (± 1.5) 8.5 (± 1.9) 0.386 HDL-C , mean (± SD) 1.53 (± 0.54) 1.35 (± 0.42) 1.56 (± 0.56) < 0.001 LDL-C , mean (± SD) 2.55 (± 0.95) 2.15 (± 0.82) 2.62 (± 0.95) < 0.001 Total-C , mean (± SD) 4.73 (± 1.09) 4.18 (± 1.12) 4.83 (± 1.06) < 0.001 Triglycerides , mean (± SD) 1.37 (± 1.42) 1.54 (± 1.84) 1.34 (± 1.34) 0.190 Systolic BP , mean (± SD) 124 (± 17) 132 (± 21) 123 (± 16) < 0.001 Diastolic BP , mean (± SD) 74 (± 10) 72 (± 10) 74 (± 10) 0.073 BMI categories, n (%) (kg/m ) 0.372 < 18.5 20 (2.0%) 4 (3.2%) 15 (1.7%) 18.5 to < 25 419 (41.6%) 51 (40.5%) 365 (41.8%) 25 to < 30 315 (31.3%) 34 (27.0%) 279 (31.9%) ≥ 30 253 (25.1%) 37 (29.4%) 215 (24.6%) Ever smoked, n (%) 397 (38.5%) 86 (64.2%) 309 (34.6%) < 0.001 Albuminuria, n (%) 220 (31.1%) 46 (47.9%) 174 (28.6%) < 0.001 eGFR , mean (± SD) 97 (± 28) 71 (± 29) 101 (± 25) < 0.001 Antihypertensive Rx, n (%) 320 (28.1%) 105 (72.9%) 214 (21.7%) < 0.001 Lipid lowering Rx, n (%) 342 (29.7%) 110 (75.3%) 232 (23.2%) < 0.001 Retinopathy, n (%) 284 (24.7%) 82 (56.2%) 202 (20.2%) < 0.001 Rx: treatment a b c 2 mmol/L, mmHg, mL/min/1.73 m Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 5 of 12 Table 2 Risk factors associated with cardiovascular outcomes of interest Variables Univariable analyses Multivariable analyses OR (95% CI) p-value OR (95% CI) p-value ROC 95% CI Cardiovascular disease (composite) Female sex 0.50 (0.35–0.71) < 0.001 0.90 (0.46–1.78) 0.764 0.88 0.84–0.92 Age (years) 1.08 (1.07–1.09) < 0.001 1.06 (1.03–1.09) < 0.001 Diabetes duration group 1.12 (1.09–1.15) < 0.001 1.05 (1.01–1.10) 0.018 HbA1c (%) 1.04 (0.95–1.15) 0.386 HDL-cholesterol (mmol/L) 0.36 (0.21–0.62) < 0.001 0.43 (0.21–0.90) 0.025 Systolic BP (mmHg) 1.03 (1.02–1.04) < 0.001 Diastolic BP (mmHg) 0.98 (0.97–1.00) 0.073 0.96 (0.93–1.00) 0.048 BMI categories 1.00 (0.97–1.03) 0.937 Ever smoked 3.39 (2.32–4.95) < 0.001 2.40 (1.26–4.58) 0.008 Albuminuria 2.30 (1.49–3.56) < 0.001 eGFR (mL/min/1.73 m ) 0.96 (0.96–0.97) < 0.001 Antihypertensive Rx 9.74 (6.54–14.49) < 0.001 2.44 (1.15–5.18) 0.020 Lipid lowering Rx 10.13 (6.76–15.17) < 0.001 Retinopathy 5.07 (3.53–7.28) < 0.001 Stroke Female sex 0.33 (0.15–0.72) 0.006 0.49 (0.16–1.47) 0.201 0.81 0.74–0.88 Age (years) 1.06 (1.04–1.09) < 0.001 1.05 (1.01–1.08) 0.006 Diabetes duration group 1.08 (1.03–1.12) < 0.001 HbA1c (%) 1.17 (0.99–1.38) 0.062 HDL-cholesterol (mmol/L) 0.26 (0.09–0.77) 0.015 Systolic BP (mmHg) 1.02 (1.00–1.04) 0.016 Diastolic BP (mmHg) 1.00 (0.97–1.04) 0.928 BMI categories 1.02 (0.96–1.10) 0.514 Ever smoked 2.32 (1.10–4.92) 0.027 Albuminuria 2.27 (0.97–5.31) 0.059 eGFR (mL/min/1.73 m ) 0.97 (0.96–0.99) < 0.001 0.98 (0.96–1.00) 0.030 Antihypertensive Rx 7.85 (3.47–17.74) < 0.001 Lipid lowering Rx 7.54 (3.35–16.95) < 0.001 Retinopathy 3.87 (1.88–7.96) < 0.001 Myocardial infarction Female sex 0.41 (0.23–0.72) 0.002 0.97 (0.39–2.41) 0.943 0.90 0.87–0.94 Age (years) 1.08 (1.06–1.10) < 0.001 1.09 (1.05–1.13) < 0.001 Diabetes duration group 1.12 (1.08–1.17) < 0.001 HbA1c (%) 0.96 (0.82–1.13) 0.647 HDL-cholesterol (mmol/L) 0.24 (0.10–0.58) 0.002 0.20 (0.06–0.68) 0.010 Systolic BP (mmHg) 1.03 (1.02–1.05) < 0.001 Diastolic BP (mmHg) 1.01 (0.98–1.03) 0.707 BMI categories 1.01 (0.95–1.06) 0.842 Ever smoked 2.31 (1.30–4.10) 0.004 Albuminuria 2.20 (1.15–4.21) 0.017 eGFR (mL/min/1.73 m ) 0.97 (0.96–0.98) < 0.001 Antihypertensive Rx 23.91 (10.12–56.49) < 0.001 5.06 (1.38–18.54) 0.014 Lipid lowering Rx 18.21 (8.14–40.73) < 0.001 Retinopathy 3.67 (2.12–6.35) < 0.001 Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 6 of 12 Table 2 (continued) Variables Univariable analyses Multivariable analyses OR (95% CI) p-value OR (95% CI) p-value ROC 95% CI Coronary artery bypass graft/angioplasty Female sex 0.38 (0.22–0.68) 0.001 0.93 (0.34–2.52) 0.884 0.92 0.89-0.95 Age (years) 1.09 (1.07–1.12) < 0.001 1.08 (1.03–1.13) 0.001 Diabetes duration group 1.21 (1.13–1.30) < 0.001 HbA1c (%) 0.96 (0.81–1.12) 0.586 HDL-cholesterol (mmol/L) 0.27 (0.11–0.67) 0.005 0.23 (0.06–0.92) 0.038 Systolic BP (mmHg) 1.03 (1.01–1.04) < 0.001 Diastolic BP (mmHg) 0.98 (0.96–1.01) 0.171 BMI categories 1.03 (0.98–1.09) 0.263 Ever smoked 2.18 (1.25–3.81) 0.006 Albuminuria 1.82 (0.93–3.59) 0.082 eGFR (mL/min/1.73 m ) 0.97 (0.96–0.98) < 0.001 Antihypertensive Rx 38.63 (13.83–107.88) < 0.001 8.96 (1.12–71.54) 0.039 Lipid lowering Rx 36.00 (12.91–100.41) < 0.001 Retinopathy 4.45 (2.57–7.69) < 0.001 Peripheral vascular disease Female sex 0.73 (0.46–1.16) 0.180 1.08 (0.49–2.39) 0.851 0.85 0.81–0.90 Age (years) 1.07 (1.05–1.09) < 0.001 1.04 (1.01–1.07) 0.005 Diabetes duration group 1.11 (1.08–1.15) < 0.001 HbA1c (%) 1.09 (0.96–1.24) 0.171 HDL-cholesterol (mmol/L) 0.37 (0.18–0.77) 0.008 Systolic BP (mmHg) 1.02 (1.01–1.04) < 0.001 Diastolic BP (mmHg) 0.97 (0.95–0.99) 0.009 BMI categories 0.99 (0.95–1.03) 0.653 Ever smoked 3.67 (2.22–6.08) < 0.001 Albuminuria 2.97 (1.65–5.34) < 0.001 eGFR (mL/min/1.73 m ) 0.96 (0.95–0.97) < 0.001 0.97 (0.96–0.99) 0.002 Antihypertensive Rx 5.26 (3.25–8.53) < 0.001 Lipid lowering Rx 5.22 (3.21–8.46) < 0.001 Retinopathy 6.41 (3.95–10.41) < 0.001 2.47 (1.06–5.74) 0.036 Congestive cardiac failure Female sex 1.00 (0.36–2.78) 0.964 1.51 (0.30–7.47) 0.614 0.90 0.84–0.95 Age (years) 1.10 (1.06–1.14) < 0.001 1.15 (1.05–1.25) 0.002 Diabetes duration group 1.16 (1.05–1.29) 0.004 HbA1c (%) 0.97 (0.70–1.34) 0.858 HDL-cholesterol (mmol/L) 1.68 (0.90–3.16) 0.104 Systolic BP (mmHg) 1.02 (0.99–1.05) 0.157 Diastolic BP (mmHg) 0.97 (0.92–1.02) 0.195 BMI categories 1.20 (1.05–1.38) 0.008 Ever smoked 1.40 (0.50–3.90) 0.515 Albuminuria 5.30 (1.36–20.70) 0.016 eGFR (mL/min/1.73 m ) 0.95 (0.94–0.97) < 0.001 Antihypertensive Rx 37.42 (4.90–285.81) < 0.001 Lipid Lowering Rx 6.70 (2.12–21.21) 0.001 Retinopathy 20.75 (4.65–92.51) < 0.001 Rx: treatment Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 7 of 12 Population attributable risks for factors associated respectively]. The model’s discriminatory ability was with cardiovascular outcomes demonstrated with area under the ROC curve of 0.88 In the study population, the estimated proportions of (95% CI 0.84, 0.92) (Table 2). CVD attributable to diabetes duration ≥ 20  years, use of When stroke was considered, there was significant antihypertensive therapy and smoking were 6.5% (95% positive association with increasing age [aOR 1.05 CI 1.4, 11.6), 5.1% (95% CI 0.9, 9.3) and 3.9% (95% CI (95% CI 1.01, 1.08)]. Increasing eGFR was negatively 1.0, 6.7), respectively. The estimated proportion of PVD associated with stroke [aOR 0.98 (95% CI 0.96, 1.00)]. attributable to presence of retinopathy was 2.7% (95% The area under the ROC curve was 0.81 (95% CI 0.74, CI 0.2, 5.2). The estimated proportions of MI or CABG/ 0.88) (Table 2). angioplasty attributable to use of antihypertensive ther- When the outcome of MI or CABG/angioplasty apy were 4.8% (95% CI 1.8, 7.8) and 11.2% (95% CI 5.0, was considered, there were significant positive asso- 17.5), respectively (Additional file 6: Table S4). ciations with increasing age [aOR 1.09 (95% CI 1.05, 1.13) or aOR 1.08 (95% CI 1.03, 1.13)] and antihyper- tensive therapy [aOR 5.06 (95% CI 1.38, 18.54) or aOR 8.96 (95% CI 1.12, 71.54)], and negative associations Discussion with increasing HDL-C [aOR 0.20 (95% CI 0.06, 0.68) This study reports for the first time the large burden of or aOR 0.23 (95% CI 0.06, 0.92)]. The area under the cardiovascular risk factors among patients with type ROC curve for MI was 0.90 (95% CI 0.87, 0.94) and 1 diabetes attending diabetes centres across Australia. for CABG/angioplasty, it was 0.92 (95% CI 0.89, 0.95) Furthermore it shows that a group of traditional car- (Table 2). diovascular risk factors (age, sex, HDL-cholesterol level, When PVD was considered, there were significant smoking status, diastolic blood pressure and use of anti- positive associations with increasing age [aOR 1.04 hypertensive therapy) and diabetes specific risk factors (95% CI 1.01, 1.07)], retinopathy [aOR 2.47 (95% CI (type 1 diabetes duration), provide good discriminatory 1.06, 5.74)], and negative association with eGFR [aOR ability for the presence of CVD. The individual outcomes 0.97 (95% CI 0.96, 0.99)]. The area under the ROC of MI, CABG/angioplasty and CCF share similar asso- curve was 0.85 (95% CI 0.81, 0.90) (Table 2). ciations, while stroke is also associated with declining When the additional outcome of CCF was consid- renal function and PVD is associated with declining renal ered, there was significant association with increasing function and retinopathy. This suggests that informa - age [aOR 1.15 (95% CI 1.05, 1.25)]. The area under the tion required for cardiovascular risk stratification among ROC curve was 0.90 (95% CI 0.84, 0.95) (Table 2). patients with type 1 diabetes may not differ substantively from other high risk populations aside from the need to consider diabetes duration. Sensitivity analyses The significant non-linear association between diabe - Adding diabetes centre type into the final multivari- tes duration and CVD (independent of patient age) and able CVD models had minimal impact on the asso- the threshold effect seen at approximately 20 years, is an ciations. There was also no significant interaction important finding and consistent with previous model - between diabetes centre type and any atherosclerotic ling, prospective cohort and registry studies [12, 13, 22, cardiovascular outcome. Further, excluding patients 33–35]. Indeed, population based cohort studies and with CCF resulted in diastolic blood pressure and anti- national registry studies have all observed increased rates hypertensive therapy being removed from the final of CVD with longer diabetes duration. Some have also parsimonious model for CVD (data not shown). reported that CVD becomes the leading cause of death After multiple imputation for missing data there after about 20 years duration [12, 13, 22, 34, 35]. The sub - was an increase in the magnitude of the association stantive PAR related to longer diabetes duration strongly between antihypertensive therapy and MI or CABG/ supports the assessment and management of cardiovas- angioplasty [aOR 15.91 (95% CI 7.65, 33.12; p < 0.001) cular risk among people with long diabetes duration irre- and aOR 21.90 (95% CI 9.79, 48.99; p < 0.001) respec- spective of their current age and the older age thresholds tively] and HDL-cholesterol was no longer significantly recommended by current CVD guidelines. associated with CABG/angioplasty [aOR 0.45 (95% CI The negative association of HDL-cholesterol with CVD, 0.19, 1.08; p 0.072)] (Additional file 3: Table S1). MI and CABG/angioplasty [aOR 0.43 (95% CI 0.21, 0.90); 0.20 (95% CI 0.06, 0.68) and 0.23 (95% CI 0.06, 0.92) Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 8 of 12 respectively] is in keeping with current understanding this relationship is not firmly established in patients with of a protective role for HDL-cholesterol and HDL func- type 1 diabetes [52] and no association was noted in our tion [1, 36–40]. While it is unknown whether increasing analyses. Nonetheless, the finding that 56.4% of patients HDL-cholesterol will improve cardiovascular outcomes with type 1 diabetes were either overweight or obese is among patients with type 1 diabetes, the importance of alarming but consistent with other studies in this popula- this lipid variable for risk stratification is consistent with tion that report rates as high as 78% [53–55]. In addition, data from the Framingham Heart Study and a number of we found that female sex was not independently associ- meta-analyses which have also reported an inverse asso- ated with any cardiovascular outcome. This supports the ciation with CVD in other populations [36–38, 41]. The premise that the protective effect of female sex on car - observation that pharmacotherapies were strongly asso- diovascular disease is negated among women with type ciated with CVD likely relates to secondary prevention 1 diabetes as reported by previous cohort and registry strategies. studies [5, 12, 13]. The lack of an independent positive association The association between CVD and diastolic blood pres - between CVD and HbA1c, systolic blood pressure, BMI, sure is complex and may be impacted by patient age, albuminuria or negative association with renal function arterial stiffness, vascular resistance, endothelial dysfunc - was unexpected. In particular, our finding of no associa - tion, diastolic dysfunction and antihypertensive therapy tion with HbA1c conflicts with other evidence of a lin - [25, 26, 56–58]. This may be of particular relevance to ear relationship between hyperglycaemia or glycaemic our heterogeneous cohort ranging from 18 to 91 years of exposure and cardiovascular risk [33, 42–48]. This may age, including patients with CCF and those taking multi- be explained by differences in the study designs as we ple antihypertensive agents. The observed negative asso - were unable to assess glycaemic control over time. We ciation between diastolic blood pressure and CVD may also noted no significant difference in glycaemic control also represent reverse causation [59–63]. It was thus not among adults with or without cardiovascular disease (p surprising that diastolic blood pressure was removed 0.386; Table 1). from the prediction model when patients with CCF were Systolic blood pressure, albuminuria and declining excluded. eGFR were significantly associated with increased risk of The finding that diabetic retinopathy and declining CVD in univariable analysis [OR 1.03 (95% CI 1.02, 1.04), renal function was associated with peripheral vascular OR 2.30 (95% CI 1.49, 3.56) and OR 0.96 (95% CI 0.96, disease was not surprising and may relate to shared risk 0.97) respectively], but not in the multivariable analy- factors [64–66]. In our cross-sectional study, microvas- sis, suggesting these effects were accounted for by other cular complications such as retinopathy or nephropathy variables in the model. Interestingly, the mean systolic provided an indication of long term risk factor exposure, and diastolic blood pressures among all patients and but cohort studies have suggested PVD may also predict among those with CVD were within or close to recom- cardiovascular outcomes and end stage kidney disease mended blood pressure targets measuring 124 ± 17 and [64, 67]. Further, the negative association between renal 74 ± 10 mmHg, and 132 ± 21 and 72 ± 10  mmHg respec- function and stroke that we observed is in keeping with tively. Albuminuria was also noted to be prevalent in studies among the general population [68–71]. However, 31.1% of our cohort, affecting around half (47.9%) of the we found no independent association between stroke and patients with a history of CVD and is consistent with albuminuria in contrast to prior studies [72–77]. international estimates of 28–52% prevalence among A strength of this analysis includes the large dataset of patients with type 1 diabetes [49, 50]. This highlights the patients with type 1 diabetes taken from a nation-wide current prioritisation of blood pressure control among benchmarking activity. Furthermore, participants are diabetes centres in Australia [1, 2] as well as the impor- likely to be representative of patients attending diabetes tance of routine screening for renal dysfunction and centres throughout Australia as data were collected from albuminuria. every state and territory. Data were also collected for a Our finding that 38.5% of adult patients with type 1 broad range of cardiovascular risk factors and clinically diabetes had been smokers is consistent with a recent significant outcomes, with consideration of non-linear report that 38% of all Australians over 14  years of age associations and precision of risk prediction using area have been smokers [51]. As expected, the proportion was under the ROC curve. Key study limitations comprise the much higher among those patients with a history CVD cross-sectional nature of data collection, possible refer- (64.2%), reinforcing the need for diabetes centres to offer ral bias, and the reliance on healthcare worker reports patients assistance with smoking cessation efforts. as we were unable to independently verify diagnoses, While elevated BMI is recognised as an independent treatments or biochemistry. Also, the pre-specified clini - risk factor for CVD in the general population [14–18], cal questionnaire in ANDA did not provide scope to Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 9 of 12 Abbreviations differentiate those patients who were normotensive or ANDA: Australian National Diabetes Audit; aOR: adjusted odds ratio; AQCA: had normal lipid profiles due to medication or if pharma - Australian Quality Clinical Audit; BMI: body mass index; BP: blood pressure; cotherapy was solely part of secondary prevention strat- CABG: coronary artery bypass graft; CCF: congestive cardiac failure; CKD-EPI: chronic kidney disease epidemiology collaboration; CVD: cardiovascular egies, and these groups may confer different degrees of disease; eGFR: estimated glomerular filtration rate; HbA1c: glycated haemo - cardiovascular risk. Another limitation is that albuminu- globin; HDL-C: high density lipoprotein-cholesterol; IQR: interquartile range ria was defined by a single biochemistry result within the (25th–75th percentile); LDL-C: low density lipoprotein-cholesterol; HDL-C: high density lipoprotein-cholesterol; MI: myocardial infarction; NADC: National 12  months prior to participation in ANDA. Single false Association of Diabetes Centres; PVD: peripheral vascular disease; ROC: positive results or resolution of albuminuria with block- receiver operating characteristic; Rx: treatment; SD: standard deviation; Total-C: ade of the renin–angiotensin–aldosterone-system there- total-cholesterol; TRIPOD: Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis. fore could not be captured by this study. The association between adiposity and CVD was assessed only with BMI, Authors’ contributions but other measures such as waist circumference may AP drafted the manuscript and performed statistical analysis under the supervision of senior authors, SZ and AE. Manuscript drafting and review add to future studies. Finally, the calculation of PAR was was assisted by SR, NN, DL, NW, SA and SZ. SR also provided advice regarding based on the assumption that there was a causal rela- statistical analysis. All authors read and approved the final manuscript. tionship between the risk factors identified in our study Author details and CVD outcomes. Despite these limitations, this study School of Public Health and Preventive Medicine, Monash University, 5th provides important data on CVD among a large popula- Floor, The Alfred Centre, 99 Commercial Road, Melbourne, VIC 3004, Australia. tion with type 1 diabetes and informs future longitudinal Diabetes and Vascular Medicine Unit, Monash Health, Clayton, VIC 3168, Australia. Department of Medicine, The University of Melbourne, Melbourne, analyses of cardiovascular risk stratification. Our find - VIC 3010, Australia. ings also suggest that future cardiovascular risk stratifica - tion models will need to examine the impact of diabetes Acknowledgements We thank the participating diabetes centres for their time and generous specific risk factors for populations with type 1 diabe - contribution to the Australian National Diabetes Audit. tes using the ‘Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis’ Competing interests AP, AE, SR and NN report no competing interests in relation to this study. DL (TRIPOD) statement [78]. reports having received honoraria and study grants from AbbVie, AstraZeneca, Bayer, Bristol Myers Squibb, Pfizer, Sanofi and Shire. NW reports institutional Conclusions contract work outside the submitted work from AstraZeneca, Novo Nordisk, Roche, Eli Lilly and MSD Australia. SA reports past participation in advi- Our study demonstrates that the adult population with sory boards and/or receiving honoraria outside the submitted work from type 1 diabetes attending diabetes centres bears a sig- GlaxoSmithKline, Novartis, AstraZeneca/Bristol-Myers Squibb Australia, Eli Lilly nificant cardiovascular burden. Further, analysis reveals Australia, Janssen Cilag, Merck Sharp & Dohme (Australia), Sanofi Aventis, Novo Nordisk and Servier Laboratories. SZ reports institutional contract work out- associations between a number of traditionally consid- side the submitted work from AstraZeneca, Novo Nordisk and MSD Australia. ered and diabetes specific risk factors with CVD, which together provide good discriminatory ability for presence Availability of data and materials The datasets used and/or analysed during the current study are available from of disease. Given the substantial population risk of CVD the ANDA secretariat on reasonable request in line with the research data attributable to long diabetes duration, the impact of new enquiry procedures. cardiovascular risk stratification tools and interventions Consent for publication to manage risk factors before and after 20 years duration Not applicable. will need to be further assessed by prospective studies. Ethics approval and consent to participate Additional files Ethics approval was provided by Monash Health Human Research Ethics Com- mittee (Monash Health Reference: RES-17-0000-164L). Additional file 1. ‘ANDA-AQCA 2015’ provides the questionnaire that was Funding completed as part of the Australian Quality Clinical Audit [AQCA]. The Commonwealth Department of Health and Ageing funds the Australian Additional file 2. ‘ANDA-AQCA 2015 Data Definitions’ outlines the defini- National Diabetes Audit. This research has received no specific grant from any tions used by healthcare professionals who completed the questionnaire. funding agency in the public, commercial or not-for profit sectors. Additional file 3: Table S1. Multiple imputation for cardiovascular outcomes of interest. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- Additional file 4: Table S2. Missing data for cardiovascular risk factors lished maps and institutional affiliations. and outcomes of interest. Additional file 5: Table S3. a Distribution of variables for cardiovascular Received: 15 March 2018 Accepted: 26 May 2018 outcomes of interest and b Distribution of variables for congestive cardiac failure. Additional file 6: Table S4. Population attributable risk for cardiovascular outcomes of interest. Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 10 of 12 References experience. Arch Intern Med. 2002;162(16):1867–72. https ://jaman etwor 1. National Vascular Disease Prevention Alliance. Guidelines for the manage-k.com/journ als/jamai ntern almed icine /fulla rticl e/21279 6. Accessed 14 ment of absolute cardiovascular disease risk. 2012. https ://www.aihw. May 2018. gov.au/getme dia/0ce5f 234-0abf-41b9-a392-be5dd 1e94c 54/17034 .pdf. 17. Malik S, Wong ND, Franklin SS, Kamath TV, L’Italien GJ, Pio JR, et al. Impact aspx?inlin e=true. Accessed 5 Mar 2018. of the metabolic syndrome on mortality from coronary heart disease, 2. Australian Institute of Health and Welfare 2014. Cardiovascular disease, cardiovascular disease, and all causes in United States adults. Circulation. diabetes and chronic kidney disease—Australian facts: prevalence and 2004;110(10):1245–50. incidence. Cardiovascular, diabetes and chronic kidney disease series 18. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Eec ff t of no. 2. Cat. no. CDK 2. Canberra: AIHW. https ://www.aihw.gov.au/getme potentially modifiable risk factors associated with myocardial infarction dia/0ce5f 234-0abf-41b9-a392-be5dd 1e94c 54/17034 .pdf.aspx?inlin in 52 countries (the INTERHEART study): case–control study. Lancet. e=true. Accessed 5 Mar 2018. 2004;364(9438):937–52. https ://www.thela ncet.com/journ als/lance t/artic 3. Schnell O, Cappuccio F, Genovese S, Standl E, Valensi P, Ceriello A. Type 1 le/PIIS0 140-6736(04)17018 -9/fullt ext. Accessed 14 May 2018. diabetes and cardiovascular disease. Cardiovasc Diabetol. 2013;12(1):156. 19. Craig ME, Twigg SM, Donaghue KC, Cheung NW, Cameron FJ, Conn J, 4. de Ferranti SD, de Boer IH, Fonseca V, Fox CS, Golden SH, Lavie CJ, et al. et al for the Australian Type 1 Diabetes Guidelines Expert Advisory Group. Type 1 diabetes mellitus and cardiovascular disease: a scientific state - National evidence-based clinical care guidelines for type 1 diabetes in ment from the American Heart Association and American Diabetes children, adolescents and adults. In: Australian Government Department Association. Diab Care. 2014;37(10):2843–63. of Health and Ageing. Canberra 2011. https ://www.nhmrc .gov.au/_files 5. Laing SP, Swerdlow AJ, Slater SD, Burden AC, Morris A, Waugh NR, et al. _nhmrc /publi catio ns/attac hment s/ext00 4_type1 _diabe tes_child ren_ Mortality from heart disease in a cohort of 23,000 patients with insulin-adole scent s_adult s.pdf. Accessed 5 Mar 2018. treated diabetes. Diabetologia. 2003;46(6):760–5. https ://doi.org/10.1007/ 20. Zgibor JC, Piatt GA, Ruppert K, Orchard TJ, Roberts MS. Deficiencies of s0012 5-003-1116-6. cardiovascular risk prediction models for type 1 diabetes. Diab Care. 6. Morrish NJ, Wang SL, Stevens LK, Fuller JH, Keen H. Mortality and causes 2006;29(8):1860–5. http://care.diabe tesjo urnal s.org/conte nt/diaca of death in the WHO multinational study of vascular disease in diabetes. re/29/8/1860.full.pdf. Accessed 10 May 2018. Diabetologia. 2001;44(Suppl 2):S14–21. 21. Vistisen D, Andersen GS, Hansen CS, Hulman A, Henriksen JE, Bech- 7. Soedamah-Muthu SS, Fuller JH, Mulnier HE, Raleigh VS, Lawrenson RA, Nielsen H, et al. Prediction of first cardiovascular disease event in Colhoun HM. High risk of cardiovascular disease in patients with type 1 type 1 diabetes mellitus: the steno type 1 risk engine. Circulation. diabetes in the U.K.: a cohort study using the general practice research 2016;133(11):1058–66. http://circ.ahajo urnal s.org/conte nt/133/11/1058. database. Diab Care. 2006;29(4):798–804. http://care.diabe tesjo urnal long. Accessed 15 May 2018. s.org/conte nt/29/4/798.long. Accessed 15 May 2018. 22. Cederholm J, Eeg-Olofsson K, Eliasson B, Zethelius B, Gudbjornsdot- 8. Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, tir S. A new model for 5-year risk of cardiovascular disease in type 1 et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk. diabetes; from the Swedish National Diabetes Register (NDR). Diab Med. A report of the American College of Cardiology/American Heart Associa- 2011;28(10):1213–20. https ://doi.org/10.1111/j.1464-5491.2011.03342 .x. tion Task Force on practice guidelines. Circulation. 2014;129:S49–73. 23. Zgibor JC, Ruppert K, Orchard TJ, Soedamah-Muthu SS, Fuller J, Chatur- http://circ.ahajo urnal s.org/conte nt/129/25_suppl _2/S49.long. Accessed vedi N, et al. Development of a coronary heart disease risk prediction 16 May 2018. model for type 1 diabetes: the Pittsburgh CHD in type 1 diabetes risk 9. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. model. Diab Res Clin Pract. 2010;88(3):314–21. 2016 European guidelines on cardiovascular disease prevention in clinical 24. Hernández M, López C, Real J, Valls J, Ortega-Martinez de Victoria E, practice. The sixth joint task force of the European Society of Cardiol- Vázquez F, et al. Preclinical carotid atherosclerosis in patients with latent ogy and other societies on cardiovascular disease prevention in clinical autoimmune diabetes in adults (LADA), type 2 diabetes and classical type practice (constituted by representatives of 10 societies and by invited 1 diabetes. Cardiovasc Diabetol. 2017;16:94. experts). Developed with the special contribution of the European Asso- 25. Ferreira I, Hovind P, Schalkwijk CG, Parving HH, Stehouwer CDA, Rossing ciation for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart P. Biomarkers of inflammation and endothelial dysfunction as predictors J. 2016;37(29):2315–81. of pulse pressure and incident hypertension in type 1 diabetes: a 20 year 10. National Institute for Health and Care Excellence. Cardiovascular disease: life-course study in an inception cohort. Diabetologia. 2018;61(1):231–41. risk assessment and reduction, including lipid modification. Clinical https ://doi.org/10.1007/s0012 5-017-4470-5. guideline [CG181]. 2014. http://www.nice.org.uk/Guida nce/CG181 . 26. Bradley TJ, Slorach C, Mahmud FH, Dunger DB, Deanfield J, Deda L, et al. Accessed 17 Feb 2018. Early changes in cardiovascular structure and function in adolescents 11. Jaiswal M, Divers J, Urbina EM, Dabelea D, Bell RA, Pettitt DJ, et al. with type 1 diabetes. Cardiovasc Diabetol. 2016;15:31. Cardiovascular autonomic neuropathy in adolescents and young adults 27. Swasey KK, Orchard TJ, Costacou T. Trends in cardiovascular risk factor with type 1 and type 2 diabetes: the SEARCH for diabetes in youth cohort management in type 1 diabetes by sex. J Diab Compl. 2018;32(4):411–7. study. Pediatr Diab. 2018;19(4):680–9. https ://doi.org/10.1111/pedi.12633 .https ://www.jdcjo urnal .com/artic le/S1056 -8727(17)30931 -5/fullt ext. 12. Skrivarhaug T, Bangstad HJ, Stene LC, Sandvik L, Hanssen KF, Joner G. Accessed 14 May 2018. Long-term mortality in a nationwide cohort of childhood-onset type 1 28. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, diabetic patients in Norway. Diabetologia. 2006;49(2):298–305. https :// et al. A new equation to estimate glomerular filtration rate. Ann Intern doi.org/10.1007/s0012 5-005-0082-6. Med. 2009;150(9):604–12. 13. Secrest AM, Becker DJ, Kelsey SF, Laporte RE, Orchard TJ. Cause-specific 29. Maple-Brown LJ, Hughes JT, Ritte R, Barzi F, Hoy WE, Lawton PD, et al. Pro- mortality trends in a large population-based cohort with long-standing gression of kidney disease in indigenous Australians: the eGFR follow-up childhood-onset type 1 diabetes. Diabetes. 2010;59(12):3216–22. http:// study. Clin J Am Soc Nephrol. 2016;11(6):993–1004. diabe tes.diabe tesjo urnal s.org/conte nt/59/12/3216. Accessed 14 May 30. Maple-Brown LJ, Cunningham J, Hodge AM, Weeramanthri T, Dunbar 2018. T, Lawton PD, et al. High rates of albuminuria but not of low eGFR in 14. Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an inde- urban indigenous Australians: the DRUID Study. BMC Public Health. pendent risk factor for cardiovascular disease: a 26-year follow-up of 2011;11(1):346. participants in the Framingham heart study. Circulation. 1983;67(5):968– 31. Johnson DW, Jones GRD, Mathew TH, Ludlow MJ, Doogue MP, Jose 77. http://circ.ahajo urnal s.org/conte nt/67/5/968.long. Accessed 12 May MD, et al. Chronic kidney disease and automatic reporting of estimated 2018. glomerular filtration rate: new developments and revised recommenda- 15. Manson JE, Colditz GA, Stampfer MJ, Willett WC, Rosner B, Monson RR, tions. Med J Aust. 2012. 197(4):222–3. https ://www.mja.com.au/journ et al. A prospective study of obesity and risk of coronary heart disease al/2012/197/4/chron ic-kidne y-disea se-and-autom atic-repor ting-estim in women. N Engl J Med. 1990;322(13):882–9. https ://doi.org/10.1056/ated-glome rular -filtr ation . Accessed 4 May 2018. NEJM1 99003 29322 1303. 32. Gordis L. Epidemiology. 2nd ed. Philadelphia: W.B. Saunders Company; 16. Wilson PW, D’Agostino RB, Sullivan L, Parise H, Kannel WB. Overweight 2000. and obesity as determinants of cardiovascular risk: the Framingham Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 11 of 12 33. Nathan DM, Genuth S, Lachin J, Cleary P, Crofford O, Davis M, et al. The 49. Newman DJ, Mattock MB, Dawnay AB, Kerry S, McGuire A, Yaqoob M, effect of intensive treatment of diabetes on the development and et al. Systematic review on urine albumin testing for early detection of progression of long-term complications in insulin-dependent diabetes diabetic complications. Health Technol Assess. 2005;9(30):iii–vi, xiii–163. mellitus. N Engl J Med. 1993;329(14):977–86. https ://www.nejm.org/https ://www.journ alsli brary .nihr.ac.uk/hta/hta93 00#/full-repor t. Accessed doi/10.1056/NEJM1 99309 30329 1401?url_ver=Z39.88-2003&rfr_ 15 May 2018. id=ori:rid:cross ref.org&rfr_dat=cr_pub%3dwww .ncbi.nlm.nih.gov. 50. Warram JH, Gearin G, Laffel L, Krolewski AS. Eec ff t of duration of type I Accessed 14 May 2018. diabetes on the prevalence of stages of diabetic nephropathy defined 34. Morimoto A, Onda Y, Nishimura R, Sano H, Utsunomiya K, Tajima N. by urinary albumin/creatinine ratio. J Am Soc Nephrol. 1996;7(6):930–7. Cause-specific mortality trends in a nationwide population-based cohort http://jasn.asnjo urnal s.org/conte nt/7/6/930.long. Accessed 15 May 2018. of childhood-onset type 1 diabetes in Japan during 35 years of follow-up: 51. Australian Institute of Health and Welfare 2017. National Drug Strategy the DERI mortality study. Diabetologia. 2013;56(10):2171–5. https ://doi. Household Survey 2016: detailed findings. Drug Statistics series no. org/10.1007/s0012 5-013-3001-2. 31. Cat. no. PHE 214. Canberra: AIHW. https ://www.aihw.gov.au/getme 35. Livingstone SJ, Looker HC, Hothersall EJ, Wild SH, Lindsay RS, Chalm-dia/15db8 c15-7062-4cde-bfa4-3c207 9f30a f3/21028 .pdf.aspx?inlin e=true. ers J, et al. Risk of cardiovascular disease and total mortality in adults Accessed 5 Mar 2018. with type 1 diabetes: Scottish registry linkage study. PLoS Med. 52. Purnell JQ, Braffett BH, Zinman B, Gubitosi-Klug RA, Sivitz W, Bantle JP, 2012;9(10):e1001321. et al. Impact of excessive weight gain on cardiovascular outcomes in 36. Gordon T, Castelli WP, Hjortland MC, Kannel WB, Dawber TR. High density type 1 diabetes: results from the diabetes control and complications trial/ lipoprotein as a protective factor against coronary heart disease: the epidemiology of diabetes interventions and complications (DCCT/EDIC) Framingham study. Am J Med. 1977;62(5):707–14. https ://www.amjme study. Diab Care. 2017;40(12):1756–62. http://care.diabe tesjo urnal s.org/ d.com/artic le/0002-9343(77)90874 -9/pdf. Accessed 15 May 2018.conte nt/40/12/1756.long. Accessed 15 May 2018. 37. Cooney MT, Dudina A, De Bacquer D, Wilhelmsen L, Sans S, Menotti 53. Conway B, Miller RG, Costacou T, Fried L, Kelsey S, Evans RW, et al. Tem- A, et al. HDL cholesterol protects against cardiovascular disease in poral patterns in overweight and obesity in type 1 diabetes. Diab Med. both genders, at all ages and at all levels of risk. Atherosclerosis. 2010;27(4):398–404. 2009;206(2):611–6. https ://www.ather oscle rosis -journ al.com/artic le/ 54. Price SA, Gorelik A, Fourlanos S, Colman PG, Wentworth JM. Obesity is S0021 -9150(09)00163 -4/fullt ext. Accessed 14 May 2018. associated with retinopathy and macrovascular disease in type 1 diabe- 38. Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, tes. Obes Res Clin Pract. 2014;8(2):e178–82. https ://www.obesi tyres earch et al. Major lipids, apolipoproteins, and risk of vascular disease. JAMA. clini calpr actic e.com/artic le/S1871 -403X(13)00032 -X/fullt ext. Accessed 15 2009;302(18):1993–2000. May 2018. 39. Emerging risk factors collaboration, De Angelantonio E, Gao P, Pennells 55. Holt SK, Lopushnyan N, Hotaling J, Sarma AV, Dunn RL, Cleary PA, et al. L, Kaptoge S, Caslake M, et al. Lipid-related markers and cardiovascular Prevalence of low testosterone and predisposing risk factors in men with disease prediction. JAMA. 2012;307(23):2499–506. type 1 diabetes mellitus: findings from the DCCT/EDIC. J Clin Endocrinol 40. Heier M, Borja MS, Brunborg C, Seljeflot I, Margeirsdottir HD, Hanssen KF, Metab. 2014;99(9):E1655–60. et al. Reduced HDL function in children and young adults with type 1 56. Franklin SS. Beyond blood pressure: arterial stiffness as a new biomarker diabetes. Cardiovasc Diabetol. 2017;16:85. of cardiovascular disease. J Am Soc Hypertens. 2008;2(3):140–51. https :// 41. Propsective studies collaboration, Lewington S, Whitlock G, Clarke R, www.ashjo urnal .com/artic le/S1933 -1711(07)00194 -5/fullt ext. Accessed Sherliker P, Emberson J, et al. Blood cholesterol and vascular mortal- 15 May 2018. ity by age, sex, and blood pressure: a meta-analysis of individual data 57. Franklin SS, Wong ND. Hypertension and cardiovascular disease: con- from 61 prospective studies with 55,000 vascular deaths. Lancet. tributions of the Framingham heart study. Glob Heart. 2013;8(1):49–57. 2007;370(9602):1829–39. https ://www.thela ncet.com/journ als/lance t/https ://www.scien cedir ect.com/scien ce/artic le/pii/S2211 81601 20026 artic le/PIIS0 140-6736(07)61778 -4/fullt ext. Accessed 14 May 2018.45?via%3Dihu b. Accessed 15 May 2018. 42. Adler AI, Erqou S, Lima TA, Robinson AH. Association between gly- 58. Marti CN, Gheorghiade M, Kalogeropoulos AP, Georgiopoulou VV, cated haemoglobin and the risk of lower extremity amputation in Quyyumi AA, Butler J. Endothelial dysfunction, arterial stiffness, and heart patients with diabetes mellitus-review and meta-analysis. Diabetologia. failure. J Am Coll Cardiol. 2012;60(16):1455–69. https ://www.scien cedir 2010;53(5):840–9. https ://doi.org/10.1007/s0012 5-009-1638-7.ect.com/scien ce/artic le/pii/S0735 10971 20252 35?via%3Dihu b. Accessed 43. Larsen J, Brekke M, Sandvik L, Arnesen H, Hanssen KF, Dahl-Jorgensen K. 15 May 2018. Silent coronary atheromatosis in type 1 diabetic patients and its relation 59. Hansson L, Zanchetti A, Carruthers SG, Dahlof B, Elmfeldt D, Julius S, to long-term glycemic control. Diabetes. 2002;51(8):2637–41. http://diabe et al. Eec ff ts of intensive blood-pressure lowering and low-dose aspirin tes.diabe tesjo urnal s.org/conte nt/51/8/2637.long. Accessed 15 May 2018. in patients with hypertension: principal results of the hypertension 44. Eeg-Olofsson K, Cederholm J, Nilsson PM, Zethelius B, Svensson AM, Gud- optimal treatment (HOT ) randomised trial. HOT study group. Lancet. bjornsdottir S, et al. Glycemic control and cardiovascular disease in 7454 1998;351(9118):1755–62. https ://www.thela ncet.com/journ als/lance t/ patients with type 1 diabetes: an observational study from the Swedish artic le/PIIS0 140-6736(98)04311 -6/fullt ext. Accessed 15 May 2018. National Diabetes Register (NDR). Diab Care. 2010;33(7):1640–6. 60. SPRINT Research Group, Wright JT Jr., Williamson JD, Whelton PK, 45. Singer DE, Nathan DM, Anderson KM, Wilson PW, Evans JC. Association Snyder JK, Sink KM, et al. A randomized trial of intensive versus standard of HbA1c with prevalent cardiovascular disease in the original cohort of blood-pressure control. N Engl J Med. 2015;373(22):2103–16. https :// the Framingham heart study. Diabetes. 1992;41(2):202–8. http://diabe tes.www.nejm.org/doi/10.1056/NEJMo a1511 939?url_ver=Z39.88-2003&rfr_ diabe tesjo urnal s.org/conte nt/41/2/202.long. Accessed 14 May 2018. id=ori:rid:cross ref.org&rfr_dat=cr_pub%3dwww .ncbi.nlm.nih.gov. 46. Zhao W, Katzmarzyk PT, Horswell R, Wang Y, Johnson J, Hu G. HbA1c Accessed 15 May 2018. and coronary heart disease risk among diabetic patients. Diab Care. 61. Pepine CJ, Handberg EM, Cooper-DeHoff RM, Marks RG, Kowey P, Messerli 2014;37(2):428–35. FH, et al. A calcium antagonist vs a non-calcium antagonist hypertension 47. Chen YY, Lin YJ, Chong E, Chen PC, Chao TF, Chen SA, et al. The impact treatment strategy for patients with coronary artery disease. The inter- of diabetes mellitus and corresponding HbA1c levels on the future risks national verapamil–trandolapril study (INVEST ): a randomized controlled of cardiovascular disease and mortality: a representative cohort study in trial. JAMA. 2003;290(21):2805–16. https ://jaman etwor k.com/journ als/ Taiwan. PLoS ONE. 2015;10(4):e0123116. http://journ als.plos.org/ploso ne/jama/fulla rticl e/19776 1. Accessed 15 May 2018. artic le?id=10.1371/journ al.pone.01231 16. Accessed 14 May 2018. 62. Satish S, Zhang DD, Goodwin JS. Clinical significance of falling blood 48. Miller RG, Anderson SJ, Costacou T, Sekikawa A, Orchard TJ. Hemoglobin pressure among older adults. J Clin Epidemiol. 2001;54(9):961–7. https :// A1c and cardiovascular disease incidence in type 1 diabetes: an applica-www.jclin epi.com/artic le/S0895 -4356(01)00360 -2/fullt ext. Accessed 15 tion of joint modeling of longitudinal and time-to-event data in the May 2018. pittsburgh epidemiology of diabetes complications (EDC) study. Am J 63. Vidal-Petiot E, Ford I, Greenlaw N, Ferrari R, Fox KM, Tardif JC, et al. Car- Epidemiol. 2018. https ://acade mic.oup.com/aje/advan ce-artic le-abstr act/ diovascular event rates and mortality according to achieved systolic and doi/10.1093/aje/kwx38 6/48312 47?redir ected From=fullt ext. Accessed 15 diastolic blood pressure in patients with stable coronary artery disease: May 2018. (Epub ahead of print). an international cohort study. Lancet. 2016;388(10056):2142–52. https :// Pease et al. Cardiovasc Diabetol (2018) 17:77 Page 12 of 12 www.thela ncet.com/journ als/lance t/artic le/PIIS0 140-6736(16)31326 -5/ 71. Abramson JL, Jurkovitz CT, Vaccarino V, Weintraub WS, McClellan fullt ext. Accessed 15 May 2018. W. Chronic kidney disease, anemia, and incident stroke in a middle- 64. Mohammedi K, Potier L, Belhatem N, Matallah N, Hadjadj S, Roussel R, aged, community-based population: the ARIC study. Kidney Int. et al. Lower-extremity amputation as a marker for renal and cardiovascu- 2003;64(2):610–5. https ://www.kidne y-inter natio nal.org/artic le/S0085 lar events and mortality in patients with long standing type 1 diabetes. -2538(15)49368 -8/fullt ext. Accessed 15 May 2018. Cardiovasc Diabetol. 2016;15:5. 72. Sandsmark DK, Messe SR, Zhang X, Roy J, Nessel L, Lee Hamm L, et al. 65. Mohammedi K, Woodward M, Hirakawa Y, Zoungas S, Williams B, Proteinuria, but not eGFR, predicts stroke risk in chronic kidney disease: Lisheng L, et al. Microvascular and macrovascular disease and risk for chronic renal insufficiency cohort study. Stroke. 2015;46(8):2075–80. major peripheral arterial disease in patients with type 2 diabetes. Diab 73. Bouchi R, Babazono T, Nyumura I, Toya K, Hayashi T, Ohta M, et al. Is a Care. 2016;39(10):1796–803. http://care.diabe tesjo urnal s.org/conte reduced estimated glomerular filtration rate a risk factor for stroke in nt/39/10/1796.long. Accessed 15 May 2018. patients with type 2 diabetes? Hypertens Res. 2009;32(5):381–6. https :// 66. Garimella PS, Hart PD, O’Hare A, DeLoach S, Herzog CA, Hirsch AT. www.natur e.com/artic les/hr200 930. Accessed 15 May 2018. Peripheral artery disease and CKD: a focus on peripheral artery disease 74. Sundquist K, Li X. Type 1 diabetes as a risk factor for stroke in men and as a critical component of CKD care. Am J Kidney Dis. 2012;60(4):641–54. women aged 15–49: a nationwide study from Sweden. Diab Med. https ://www.ajkd.org/artic le/S0272 -6386(12)00665 -8/fullt ext. Accessed 2006;23(11):1261–7. https ://doi.org/10.1111/j.1464-5491.2006.01959 .x. 15 May 2018. 75. Hagg S, Thorn LM, Putaala J, Liebkind R, Harjutsalo V, Forsblom CM, 67. Criqui MH, Ninomiya JK, Wingard DL, Ji M, Fronek A. Progression of et al. Incidence of stroke according to presence of diabetic nephropa- peripheral arterial disease predicts cardiovascular disease morbidity and thy and severe diabetic retinopathy in patients with type 1 diabetes. mortality. J Am Coll Cardiol. 2008;52(21):1736–42. https ://www.scien cedir Diab Care. 2013;36(12):4140–6. http://care.diabe tesjo urnal s.org/conte ect.com/scien ce/artic le/pii/S0735 10970 80295 98?via%3Dihu b. Accessed nt/36/12/4140.long. Accessed 15 May 2018. 15 May 2018. 76. Chang Y T, Wu JL, Hsu CC, Wang JD, Sung JM. Diabetes and end-stage 68. Chronic kidney disease prognosis consortium, Matsushita K, van der renal disease synergistically contribute to increased incidence of Velde M, Astor BC, Woodward M, Levey AS, et al. Association of estimated cardiovascular events: a nationwide follow-up study during 1998–2009. glomerular filtration rate and albuminuria with all-cause and cardiovascu- Diab Care. 2014;37(1):277–85. http://care.diabe tesjo urnal s.org/conte lar mortality in general population cohorts: a collaborative meta-analysis. nt/37/1/277.long. Accessed 16 May 2018. Lancet. 2010;375(9731):2073–81. https ://www.thela ncet.com/journ als/ 77. Tuomilehto J, Borch-Johnsen K, Molarius A, Forsen T, Rastenyte D, Sarti C, lance t/artic le/PIIS0 140-6736(10)60674 -5/fullt ext. Accessed 15 May 2018. et al. Incidence of cardiovascular disease in Type 1 (insulin-dependent) 69. Masson P, Webster AC, Hong M, Turner R, Lindley RI, Craig JC. Chronic kid- diabetic subjects with and without diabetic nephropathy in Finland. ney disease and the risk of stroke: a systematic review and meta-analysis. Diabetologia. 1998;41(7):784–90. https ://doi.org/10.1007/s0012 50050 988. Nephrol Dial Transplant. 2015;30(7):1162–9. https ://acade mic.oup.com/ 78. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg ndt/artic le/30/7/1162/23248 40. Accessed 15 May 2018. EW, et al. Transparent reporting of a multivariable prediction model for 70. Ovbiagele B, Bath PM, Cotton D, Sha N, Diener HC, PRoFESS investigators. individual prognosis or diagnosis ( TRIPOD): explanation and elabora- Low glomerular filtration rate, recurrent stroke risk, and effect of renin- tion. Ann Intern Med. 2015;162(1):W1–73. http://annal s.org/aim/fulla rticl angiotensin system modulation. Stroke. 2013;44(11):3223–5. http://strok e/20885 42/trans paren t-repor ting-multi varia ble-predi ction -model -indiv e.ahajo urnal s.org/conte nt/44/11/3223.long. Accessed 15 May 2018.idual -progn osis-diagn osis-tripo d-expla natio n. Accessed 15 May 2018. Ready to submit your research ? 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Cardiovascular DiabetologySpringer Journals

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