The early detection of atherosclerosis in type 1 diabetes: why, how and what to do about it : Cardiovascular Endocrinology & Metabolism

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The early detection of atherosclerosis in type 1 diabetes: why, how and what to do about it

Jenkins, Aliciaa,b; Januszewski, Andrzeja,b; O’Neal, Davida,b

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Cardiovascular Endocrinology & Metabolism 8(1):p 14-27, March 2019. | DOI: 10.1097/XCE.0000000000000169
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Abstract

The major cause of morbidity and often premature mortality in people with type I diabetes (T1D) is cardiovascular disease owing to accelerated atherosclerosis. We review publications relating to the rationale behind, and clinical tests for, detecting and treating early atherosclerosis in people with T1D. Currently available tools for atherosclerosis assessment include risk equations using vascular risk factors, arterial intima–media thickness, the ankle–brachial index, coronary artery calcification and angiography, and for more advanced lesions, intravascular ultrasound and optical coherence tomography. Evolving research tools include risk equations incorporating novel clinical, biochemical and molecular tests; vascular MRI and molecular imaging. As yet there is little information available to quantify early atherosclerosis. With better means to control the vascular risk factors, such as hypertension, dyslipidaemia and glycaemic control, and emerging therapies to control novel risk factors, further epidemiologic and clinical trials are merited to facilitate the translation into clinical practice of robust means to detect, monitor and treat early atherosclerosis in those with T1D.

Incidence and prevalence of type 1 diabetes

Type 1 diabetes (T1D) incidence varies greatly, with the highest rates in Scandinavian countries [≈35 people/100 000/annum (p.a.)] versus ≈1/100 000 p.a. in low incidence countries such as China 1. T1D usually presents in youth but can occur at any age. In Australia, 40% with patients with T1D are diagnosed after the age of 25 years 2, and in China, 65.3% are diagnosed after the age of 20 years 2. T1D incidence and prevalence has increased in most countries at ≈3% p.a. 3, with contributors being better case ascertainment and survival, particularly in disadvantaged regions 4–7, and a genuine increase, postulated to relate to increased autoimmunity because of fewer early childhood infections 8,9. In addition to classical T1D, there is a slower onset form of antibody-positive T1D, latent autoimmune diabetes in adults (LADA), which is usually diagnosed after 25–40 years of age and does not require exogenous insulin for at least 6 months 10. Most publications relate to atherosclerosis in T2D, with few in T1D and LADA. A recent cross-sectional study showed greater (subclinical) carotid plaque in LADA than in age-matched and sex-matched patients with classic T1D or T2D 11.

Chronic complications

Chronic complications of diabetes relate to the vasculature and nerves. Atherosclerosis leads to coronary artery disease (CAD), cerebrovascular disease (CVD) and peripheral arterial disease (PAD), with CVD being a major cause of death in diabetes 12. Atherosclerosis is the accumulation of lipid-rich plaque in arteries, whereas arteriosclerosis refers to age-related stiffening of arteries, which may coexist with, or occur in the absence of, atherosclerosis.

People with T1D microvascular complications are at particularly high risk of developing CVD. In diabetes, CAD symptoms may be atypical or silent 13. The first manifestation of atherosclerosis may be sudden death, hence the importance of detecting subclinical (early) atherosclerosis, though this is currently challenging, and the T1D evidence base is very limited. Some recommendations regarding the more recently available clinical imaging, such as coronary artery calcification (CAC) and (noninvasive) computed tomography angiography (CTA), for people with diabetes are to follow guidelines for the general population 14,15, yet these and the 2019 American Diabetes Association (ADA) guidelines 13 do not recommend screening asymptomatic people with diabetes with such tests, as they do not have well-proven value at the individual patient level. Once atherosclerosis is clinically evident, intensive risk factor control, including lipid-lowering drugs irrespective of lipid levels, blood pressure (BP) and antiplatelet agents, is recommended. Many guidelines and clinicians recommend aggressive risk factor management for anyone with T1D with clinically evident macrovascular or microvascular disease and for those at intermediate or high CVD risk without evident complications. The latter includes people with T1D aged at least 40 years, even in the absence of vascular complications, or aged at least 30 years with more than 15 years of T1D (without vascular complications) 13–17. Recent guidelines are summarized by Lan et al. 16. Unfortunately, most patients with T1D do not meet all recommended HbA1c, BP, lipids, BMI and nonsmoking targets 18.

Questions of major clinical importance are as follows: how to detect early atherosclerosis, when and how to screen, what the risk factors are for early and late atherosclerosis and how and when to treat risk factors and existing vascular damage. Another major challenge is ensuring equitable access to proven assessment tools and treatments, as not all tests are available or subsidized, and to experienced clinicians.

Figure 1 summarizes the stages of atherosclerosis. Even before T1D develops, people may have CVD risk factors. Once T1D develops, the milieu induces and/or worsens risk factors, promoting vascular damage 19. Atherosclerosis does not usually become clinically evident until triggered by an event such as plaque rupture, thrombosis or embolus.

F1
Fig. 1:
Stages in the development of atherosclerosis in type 1 diabetes. AGE, advanced glycoxidation end product; CVD, cardiovascular disease; FFA, free fatty acid.

Increased cardiovascular disease risk and confounders

The reported relative increase of atherosclerosis in T1D versus nondiabetic individuals depends on how and when the study was conducted, event definitions, the age, sex, age of T1D onset and duration, risk factor control, microvascular complication status and characteristics of the comparator group. When the study was conducted is important owing to recent more effective drugs 13,20 and insulin pumps 21, all of which reduce vascular events. Modern care has reduced deaths and hospitalizations for atherosclerosis-related events 22. In a Swedish registry study (n=36 869 patients with T1D and 184 110 nondiabetic controls) followed between 1998 and 2013, all-cause death rates in T1D fell by 29% (vs. 23% in controls), the CVD death rate fell by 42% (vs. 38% in controls) and hospitalizations for CVD fell by 36% in T1D 22. Hospitalizations for heart failure in T1D have not declined 22. T1D mortality rates are still 2- to 8-fold that of the general population, with CVD being a major driver 14,22. Swedish data from a median 8.3-year follow-up (1998–2011) [33 170 T1D adults and 164 698 controls without a previous acute myocardial infarction (AMI)] showed relative AMI and CHD death rates in T1D ranging from 2- to 15-fold in men and 5- to 33-fold in women. The excess was less with better glycaemia and normal renal function in men, but it persisted in women, even with good glycaemic control and no renal damage 23.

PAD rates remain high in T1D. In an older 14-year follow-up study, nontraumatic lower limb amputations occurred at 0.4–7.2% p.a. 24. In a Swedish inpatient registry (1975–2004) of 31 354 patients with T1D, the hazard ratio (HR) for amputations was 85.5 versus the general population, with a cumulative probability of amputation by the age of 65 years of 11% for women and 20.7% for men 25.

Metabolic memory affects relationships between risk factors and atherosclerosis 26–29. Metabolic memory is the phenomenon by which the body’s tissues, including arteries, continue to respond to poor or good glycaemic control for years after glycaemia has improved or worsened. Metabolic memory may last decades. In the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Trial, ≈5.9 years of intensive versus conventional management reduced chronic complications, including surrogate measures of atherosclerosis [carotid intima–media thickness (cIMT)] 30–33, CAC 34 and hard clinical vascular events for at least 8–12 years 29. Vascular metabolic memory has also been demonstrated, predominantly in nondiabetic individuals for lipid and BP control 31,35,36. Metabolic memory should be considered in the design and interpretation of clinical trials and in clinical practice and will particularly affect short-term interventions.

Characteristics of atherosclerosis

Arteriosclerosis is the (age-related) stiffening of artery walls, whereas atherosclerosis narrows arteries because of plaque build-up. Post-mortem studies 37–44 show that atherosclerosis, reflected by fatty streaks, begins in childhood, particularly in Westernized countries. In diabetes, atherosclerosis begins earlier, progresses faster and extends more distally than in those without diabetes 45–47. This, and less arterial collateral formation, can make bypass procedures difficult if not impossible 48. In T1D, plaques tend to be more concentric in location, softer, more fibrous, lipid rich and inflamed (with macrophage and T-cell infiltration); have more apoptotic cells around a necrotic core, more thrombosis and more advanced glycation end-product-related immunostaining and are more unstable and rupture prone 49–51. The latter often triggers a clinical event 52. People with diabetes may have clinical events with less apparent atherosclerotic burden 53, likely related to greater plaque instability and limitations of current detection techniques. Artery walls are thicker, as reflected by increased IMT, and there is often more calcification in plaque and in the arterial media in diabetes 54,55. IMT, calcification and artery lumen encroachment form the basis of many techniques to detect atherosclerosis.

Techniques to assess atherosclerosis

Tools to detect or predict atherosclerosis are summarized in Table 1. Artery-based techniques may be functional or structural. The latter is thought to be both preceded and accompanied by functional abnormalities, usually impaired vasodilation in T1D. Functional tests and retinal measures are research tools and are not discussed further in this brief review.

T1
Table 1:
Methods for the detection of atherosclerosis or the prediction of atherosclerosis

Structural measures of atherosclerosis

Arterial intima–media thickness

cIMT is usually facilitated by (arterial wall) edge-detection software. Arterial distensibility and wall shear stress measures by ultrasound are also feasible. IMT measurement is noninvasive, painless, does not involve radiation or sedation and is relatively low cost. Its measurement can guide decisions regarding further investigations, surgery or risk factor treatment. Relative to age-matched and sex-matched apparently healthy nondiabetic individuals, both cIMT and plaque are increased in T1D 56–59. Some sex differences are noted, even in T1D youth. In 314 T1D youth (mean age: 13.7 years, 5.5 years T1D, HbA1c 8.4%, and 97% on intensive diabetes management) and 118 controls, elevated mean cIMT (>90th percentile of healthy individuals) was present in 19.5%. Mean cIMT was higher in diabetic versus nondiabetic males but not in females. No individuals had plaques 57.

Traditional risk factors of age, diabetes duration, smoking, higher BMI, total cholesterol and low-density lipoprotein-cholesterol (LDL-C), BP and albumin excretion rate have been associated with, or are predictive of, cIMT and plaque in T1D 60–63.

Although it is a commonly used research tool, there is some concern that cIMT may not perform consistently well enough to improve risk classification at the individual level in the general population 64 and may be considered for intermediate risk asymptomatic individuals in the general population 15. Major diabetes guideline bodies do not recommend routine screening of asymptomatic adults with T1D.

Aortic distensibility, which reflects arteriosclerosis, was assessed by MRI in the DCCT/EDIC cohort and was related to future vascular events over a mean of 22 years, during which 27% of participants had CVD events. The aorta became stiffer with increasing age, systolic BP, LDL-C, HbA1c and macroalbuminuria and was also associated with myocardial strain 65.

The aorta, rather than the carotid, may be more appropriate to study in youths, as it is usually the first site of atherosclerosis. High-resolution ultrasound of the carotid, brachial, femoral arteries and aorta was compared in 38 youths with T1D (median age: 13 years) and 38 controls. The aortic and femoral IMT were significantly increased in T1D, whereas carotid and brachial IMT were not 66. In another study in adolescents with T1D, higher aortic IMT correlated with carotid IMT and with retinal vessel measures 67.

Arterial calcification

In addition to advanced calcification of mature (and more stable) plaques, microcalcification can occur early in plaques. CAC measured by noninvasive computerized tomography is becoming more widely used clinically and as a surrogate end point in clinical trials. As yet it is not recommended for general clinical use or (by the ADA) in adults with T1D who are asymptomatic and deemed low risk 15. CAC scores correlate with atherosclerosis severity and in some studies predict vascular and mortality outcomes 68–70. As pointed out in the 2019 ADA guidelines, CAC scores do not improve outcomes in truly asymptomatic people with T1D who have aggressive risk factor treatment 13. There are some recommendations for CAC use and interpretation at the individual level in asymptomatic individuals at intermediate risk, including in diabetes, such as the combined statement of the American College of Cardiology Foundation/American Heart Association 71. An excellent review of the CAC scan in T1D is by Burge et al.72.

CAC testing is generally well tolerated, has excellent repeatability and is low cost compared with angiography. Specialized software aids CAC calculation, which is usually reported in Agatston scores based on measures in the four major coronary arteries. CAC scores are included in some CVD risk equations (discussed later) for assessment of asymptomatic patients. Addition of CAC scores provides better discrimination and reclassification as to the need for treatment. In asymptomatic patients, CAC scores can predict vascular events more accurately than coronary angiography. A negative CAC scan identifies someone at very low 5-year risk of a coronary event. Disadvantages of CAC testing are that it cannot detect noncalcified plaque, nor clinically significant lumen occlusion, it involves radiation (comparable to that of a mammogram) and it is not always available or subsidized by health care providers. The Agatston score increases in a nonlinear way with worsening anatomical lesions owing to jumps in the weighting factors used in its calculation as lesion density increases 72.

CAC scores are more likely to be positive and higher in T1D than in nondiabetic adults 73. In the Coronary Artery Calcification in Type 1 Diabetes (CACTI) study, CAC was present in 39% of men and 12% of women, and score positivity was associated with older age, higher BMI, waist circumference and intraabdominal and subcutaneous fat volume 73. In a Pittsburgh Epidemiology of Diabetes Complications (EDC) study (302 adults, mean age: 38 years), CAC scores were positive in 11% aged 30 years and in up to 88% of those aged 50–55 years. CAC was independently associated with clinical disease, with stronger associations in men than women 73. In 69 adults with T1D for at least 50 years versus nondiabetic controls, CAC scores were higher in T1D (median Agatston score: 1000 vs. 1.0 in controls, P<0.001). In T1D, high CAC scores (≥300 Agatston units) were associated with retinopathy and neuropathy but not with renal function 74. Positive CAC scores in T1D have also been associated with cognitive impairment 75.

Calcification in carotid and coronary arteries correlated in 53 normoalbuminuric adults with T1D (r=0.720, P<0.0001), and cardiovascular autonomic neuropathy (CAN) was associated with increased coronary and carotid artery calcium scores 76.

In a 25-month longitudinal follow-up study, CAC progression occurred more rapidly in 53 normoalbuminuric persons with long-term T1D (20 with CAN) than in a 29-month follow-up of 106 nondiabetic controls matched for age, sex and baseline CAC, with T1D having an odds ratio of 3.3 for CAC progression. CAN did not increase CAC progression 77. Plasma triglyceride levels predict CAC progression in T1D 78 and in the DCCT-EDIC cohort, CAC (which was positive in 31% at first assessment) was reduced by previous intensive glycaemic control 34. In the Pittsburgh EDC study (n=292, mean age: 39.4 years, T1D duration 29.5 years), 76 participants developed a first CAD event during mean follow-up of 10.7 years, for which CAC was an independent risk factor 79. Larger T1D-specific studies are desirable.

Peripheral artery disease

Subclinical peripheral artery disease (PAD) can sometimes be identified noninvasively by ratios of BP in the lower and upper limbs [e.g. ankle–brachial index (ABI)], though arteriosclerosis interferes, by arterial calcification and by ultrasound. Invasive techniques (e.g. angiography) are more suited in the assessment of severe PAD. Ultrasound is not recommended for clinical use in asymptomatic adults with T1D.

There are few T1D studies. In a cross-sectional study of 289 consecutive asymptomatic adults with T1D (with normal leg pulses), abnormal ABI levels of up to 0.9 or more than 1.2 were detected in 6 and 26%, respectively. Those with abnormal ABI were assessed by toe–brachial index and ultrasound to assess subclinical PAD and/or carotid plaques, with a 12.8% prevalence of silent PAD. Of those with PAD, 4.8% also had carotid disease; hence, using the ABI test, one could expect to screen three asymptomatic patients with T1D to identify one with subclinical PAD and seven patients to detect one with carotid disease 80. Miller and colleagues explored relationships between an abnormal ABI greater than 1.30 or an ankle–brachial pressure difference (ABD) more than 75 mmHg and lower limb medial arterial calcification (with typical ‘tram track’ appearance) on radiography. In 185 adults with T1D (mean age and T1D duration of 32 and 23 years, respectively), calcification was noted in 57%, but only 8% had an elevated ABI and 8% had an ABD of more than 75 mmHg. Hence, adults with T1D with an ABI of more than 1.30 or ABD of more than 50 mmHg are very likely to have arterial calcification, but many with calcification will have normal pressure-related measures 81.

Other vascular assessment tests

Intravascular ultrasound and intravascular OCT, usually of coronary arteries, are invasive, being performed by angiography, hence are usually used with more severe atheroma and in the setting of stent placement.

Coronary CTA 82 is a noninvasive angiogram that, unlike conventional dye-based coronary angiograms, assesses the vessel wall as well as the lumen. CTA can detect stenoses, calcified and noncalcified plaque, plaque size and markers of potential plaque instability such as expansive vascular remodelling and spotty calcification. CTA can detect even small plaques and also can assess the haemodynamic significance of large lesions such as with fractional flow reserve measures. Large prospective trials in the general population show the utility of CTA for ruling out obstructive CAD and for identifying subclinical atherosclerosis for medical therapy; hence, CTA is now recommended as a diagnostic test in some European guidelines. There are few T1D studies. In a small prospective study of 25 T1D and 15 T2D asymptomatic patients aged 19–35 years with at least 5 years of diabetes, CAC and CTA were performed. Abnormal scan results were present in 57.5%, being noncalcified in 35%. Plaque was almost uniformly absent below the age of 25 years and became increasingly common after the age of 25 years for both groups. A negative CAC score was not sufficient to exclude early CAD as there was a preponderance of noncalcified plaque; hence, CTA in T1D patients older than 25 years old was suggested 83. Further T1D studies are desirable.

Coronary computed tomographic angiography in asymptomatic people with T2D has found that around one-third have no coronary disease, one-third have minor or moderate disease, and one-third have at least one artery with a stenosis of more than 50%, which would usually prompt invasive angiography 84. Sensitivity in detecting a stenosis of more than 50% is ≈90% and specificity is typically 80–90%, which falls to ≈50% in the presence of high CAC scores (>400 Agatston units) 85, such as are more likely in T1D. Invasive angiography is usually recommended if lesions on CT have a stenosis of more than 50%. However, only a third of 50–70% stenosed lesions on invasive angiography are functionally significant and potentially suitable for revascularization, compared with 80% of those more than 70% stenosed 86. Therefore, many people with significant lesions on CT may undergo further investigation without any change in medical management. The FACTOR-64 trial followed asymptomatic high-risk people with T2D randomized to standard guideline-based care or guided management according to disease severity and found no differences in clinical outcomes over 4 years of follow-up 87. T1D studies are needed.

Multimodal MRI techniques that also focus on the lumen are emerging. In a cross-sectional study, greater atheroma was present in nondiabetic people with clinical CVD versus those nondiabetic and (type 2) diabetic subjects without (clinical) CVD 53. This may relate to plaque quality being worse in diabetes.

Molecular imaging with atherosclerosis-related tracers (macrophage activity, calcification, cellular apoptosis and glucose metabolism) is an evolving research tool 88–90.

Risk factors

Given the paucity of evidence and lack of robust tools to quantify early atherosclerosis, most management strategies focus on risk factor assessment and care. Several risk factors at low level, for example, suboptimal HbA1c, mildly elevated BP or LDL-C, which can often be disregarded by the patient or their clinician, can place a person at moderate or high vascular risk. This has led to recommendations to use absolute cardiovascular risk calculators in primary prevention (discussed later).

CVD risk factors and biomarkers of interest are summarized in Table 2. We and others have previously reviewed the roles of traditional and emerging risk factors in diabetic vascular disease 16,91–95. We briefly comment on several risk factors.

T2
Table 2:
Risk factors for atherosclerosis in type 1 diabetes

Traditional risk factors

The importance of HbA1c, BP and lipids is demonstrated by an assessment of the ADA guidelines and CVD and mortality in 3151 patients with T1D stratified by nephropathy status. Approximately 63% of patients with nephropathy and 34% of those without nephropathy reached none of the recommended targets. Failure to reach targets was associated with increased risk of mortality and CVD 96.

Genetics

Currently no genetic factors are used clinically in atherosclerosis risk prediction in T1D, though strong associations with some genotypes, for example, ApoE and haptoglobin 2/2, are recognized 97–109. Genome-wide association study analyses have implicated many new single nucleotide polymorphisms 110.

Sex

Women with T1D lose their relative cardioprotection. A longitudinal (median: 12.9 years follow-up) FinnDiane study related CAD and stroke events to sex and nephropathy status 111. Event rates were similar in men and women at each level of nephropathy, with higher rates associated with worsening renal function. Relative to nondiabetic people, in T1D, the standardized incidence rate (SIR) for CAD was 17.2 in women and 5.3 in men. The female to male SIR ratio increased with worsening renal function. The SIR for stroke was 5.0, similar in men and women. The female to male ratio of SIR for stroke was 0.8, 1.3, 1.6 and 1.7, with worsening renal function. The SIR in participants with T1D with normal renal function was 3.5 for CAD and 1.6 for stroke; hence, even adults with T1D with normal renal function are at increased risk of vascular events.

Age of type 1 diabetes onset

In 27 195 T1D and 135 178 nondiabetic patients followed from 1998 to 2012 for a median of 10 years, patients who developed T1D at less than 10 years of age had the highest HRs for all-cause and CVD-mortality of 4.1 and 7.4, respectively, 30 for CAD, 31 for AMI and 6.4 for stroke. For those who developed T1D at less than 10 years old, the mean loss of life-years was 17.7 for women and 14.2 for men 112. HRs were substantially lower with T1D onset at the age of 26–30 years.

Microvascular complications are risk factors for atherosclerosis, which is recognized in treatment guidelines 13,14,16. This may relate to common risk factors and that renal dysfunction aggravates many risk factors, such as dyslipidaemia, inflammation and hypertension 91. In T1D CAN has also been associated with cIMT, hypertension 113, impaired coronary artery reactivity 114, CAC, arterial stiffness and traditional risk factors 115.

Lipoproteins

High total and LDL-C and low high-density lipoprotein cholesterol (HDL-C) levels are associated with, and predictive of, CVD and microvascular complications in T1D 92. Even though T1D is usually associated with normal or high HDL-C levels, the HDL can be dysfunctional 116 regarding its reverse cholesterol transport, anti-inflammatory, anticlotting and vasodilatory functions. NMR-determined lipoprotein subclasses have also been associated with, and predictive of, carotid IMT and CAC in T1D 117–121. Qualitative changes in lipoproteins in diabetes, such as glycation, oxidation, advanced glycation end products and immune complex formation enhance lipoprotein pathogenicity 49–51.

Glycaemia

The DCCT/EDIC study showed that lower HbA1c levels were associated with better IMT (including in adolescents), CAC and subsequently macrovascular events 30–33. Hypoglycaemia is a risk factor for atherosclerosis 31, with potential mechanisms being increased endothelial dysfunction, inflammation, oxidative stress and a prothrombotic tendency 122–125. Glucose variability (GV) is an independent risk factor in T1D for microvascular and macrovascular complications 126. Short-term GV can be assessed by multiple daily blood glucose levels, or by continuous (interstitial fluid) glucose monitoring, and at the cellular level 127,128 induces oxidative stress, inflammation and epigenetic changes 129. Long-term GV (HbA1c SD and/or coefficient of variation) is an independent risk factor for chronic complications in T1D and T2D 21,127–131. In T1D, greater GV has been associated with lower flow-mediated vasodilation, CAN, increased oxidative stress and a poorer traditional vascular risk profile 126,132–135. GV by continuous (interstitial fluid) glucose monitoring was associated with CAC in T1D in the CACTI study in men but not in women 136. Further T1D GV studies, preferably with adjustment for mean glucose or HbA1c, are of interest.

Hypertension

Ambulatory (24 h) BP monitoring can facilitate the diagnosis and management of hypertension and is recommended by many national bodies 7. The first evidence of abnormal BP in T1D is ‘non-dipping’ or loss of the usual nocturnal reduction in BP relative to daytime. In a cross-sectional study of 140 patients with T1D, hypertension detected by ambulatory BP monitoring was common (25%) and was associated with increased arterial stiffness 137.

Insulin resistance is also a feature of T1D 138,139, sometimes known as ‘double diabetes,’ and is associated with vascular complications 140 and can be estimated by several formulae using traditional risk factors 141–143. A novel NMR marker has recently been suggested 144.

Inflammation

Soluble intracellular adhesion molecule and soluble vascular cell adhesion molecule-1 levels have predicted incident hypertension and arterial stiffness in 277 patients with T1D up to 20 years later 145. Systemic inflammation, reflected by high-sensitivity C-reactive protein and plasma matrix metalloproteinase-8 levels, correlated with progression of flow-mediated vasodilation, carotid IMT and carotid compliance over ≈2 years in children with T1D 146.

Emerging biomarkers include telomere length, telomerase activity and epigenetic factors (DNA methylation, microRNAs), proteomics, lipidomics and metabolomics and autoantibodies 140,147–159.

Autoantibodies to cardiovascular tissue may represent and/or promote vascular disease. Antibodies to modified LDL promote subclinical disease in T1D 117,119,120. In a longitudinal DCCT/EDIC study 160, poor glycaemic control was associated with higher rates of antibodies to cardiac tissue, which was independently associated with high risk of CAC and clinical CVD decades later. Positivity for at least two cardiac antibodies was associated with increased risk of CVD events (HR: 16.1) and detectable CAC (odds ratio: 60.1). Patients with T1D with at least two antibodies (vs. ≤1) subsequently showed elevated high-sensitivity CRP, implicating inflammation.

Risk equations

Risk equations are often recommended to estimate future CVD event risk in asymptomatic people. Those with clinically evident disease are recommended intensive treatment. Absolute cardiovascular risk is an estimate of the chance that an individual will experience a cardiovascular event, usually reported as within the next 5–10 years. A CVD risk level of at least 20% over 10 years is usually regarded as high, and up to 10% as low, though cutoff points differ between various expert groups.

Most CVD calculators are for the general population or for T2D 13,16,161. Included risk factors vary, as do the events they predict. Common denominators are age, sex, smoking, diabetes, BP and lipids. Several longitudinal observational studies in T1D show that the relative importance of traditional risk factors for vascular complications differs by T1D duration 162,163. In a comparison of the DCCT/EDIC and EDC studies with median T1D duration at baseline of 4 versus 18 years, similar traditional risk factors predicted subsequent CVD; however, albumin excretion rate predominated in EDC and HbA1c in DCCT/EDIC 163. A DCCT/EDIC study regarding CVD showed that traditional vascular risk factors mediated the importance of glycaemia over time 164. The association of HbA1c with CVD outcomes was stable, whereas that of systolic BP, heart rate, triglycerides and LDL-C increased. At 10-year follow-up, the effect of HbA1c on 10-year CVD risk was minimally mediated by systolic BP (2.7%), increasing to 26% at 20 years. Similarly, between these time points, the proportion of HbA1c effect mediated through triglycerides increased from 2.2 to 22.4%, and through LDL-C from 9.2 to 30.7%. Hence, as people with T1D age, the relative importance of traditional (nonglucose) risk factors increases.

Generally, risk calculations developed for T2D are not accurate for T1D. They tend to underestimate risk, do not incorporate important T1D factors (age of diabetes onset, diabetes duration, HbA1c, albuminuria and treatment modalities) and hence are not recommended for use in T1D 13,14,165,166. Several T1D-specific risk calculations have been developed. An early calculator was developed from a 7-year follow-up of 1973 adults with T1D in the EURODIAB Prospective Complications Study, of whom 95 developed microvascular or macrovascular events, and was validated in three other cohorts: the Pittsburgh EDC study (n=554), the Finnish Diabetic Nephropathy study (FinnDiane, n=2999) and the CACTI Study n=580). Strong prognostic factors for the composite end point [CVD events, amputations, end-stage renal disease, blindness and (all-cause) death] were age, urinary albumin–creatinine ratio, HbA1c, waist to hip ratio and HDL-C 167.

More recently, in the Steno Study of 4306 adults with T1D, an excellent prediction model for estimating the risk of first fatal or nonfatal CVD event (ischaemic heart disease, ischaemic stroke, heart failure and PAD) was derived from registry data with external validation in 2119 patients with T1D. During a median 6.8-year follow-up, 793 patients developed a CVD event. The final prediction model (https://steno.shinyapps.io/T1RiskEngine/) includes age, sex, diabetes duration, systolic BP, LDL-C, HbA1c, albuminuria, estimated glomerular filtration rate, smoking and exercise 168.

Risk models for T1D+/− and the CAC score have been developed from the Pittsburgh EDC study 79,163. Over a mean 10.7-year follow-up of 292 adults with T1D (mean age: 39 years, 29 years T1D) free of clinically evident CAD at baseline and with at least one CAC assessment, 76 experienced a CAD event. At baseline, compared with those without CAC (Agatston score=0), the adjusted HR in CAC score groups of 1–99, 100–399 and at least 400 was substantial: 3.1, 4.4 and 4.8, respectively. CAC density was inversely associated with incident CAD in those with CAC volume of at least 100 (HR: 0.3). Among participants with repeated CAC measures (62%), annual CAC progression positively associated with incident CAD after controlling for baseline CAC. The HR for above versus below the median annual CAC volume progression was 3.2. Addition of CAC to traditional risk factors improved predictive ability for CAD.

Sometimes the use of multiple biomarkers combined can predict outcomes better than individual measures 169. In the DCCT/EDIC cohort, four composite scores were created by combining z scores of related factors: acute-phase reactants, cytokines/adipokines, thrombolytic factors and endothelial dysfunction/vascular inflammation. Composite scores were related to internal carotid IMT at EDIC years 1, 6, and 12. Although individual biomarkers did not predict IMT at last follow-up, three composite scores (acute-phase reactants, thrombolytic factors and cytokines/adipokines) did, with an OR of 2–2.8.

Simple CVD risk calculators can be a helpful assessment and educational tool, with patients (without evident CVD) being shown their risk reduction with, for example, improved lipids if they were to take a statin. Their use can be complemented and individualized by assessment of subclinical vascular damage (e.g. cIMT or CAC) 79.

Calculators may differ because of differences between the population from which the calculators were devised and that of the person(s) whose risk is being calculated. For example, insulin pump use has been associated with lower risk of CVD and mortality than multiple daily injections 21. CVD risk may change over time as major risk factors and treatments (e.g. statins, BP, smoking, HbA1c and glucose control modalities) change; hence, a calculator developed decades ago may not reflect an individual’s CVD risk and subclinical atherosclerosis well in more recent times.

Interventions in type 1 diabetes with subclinical atherosclerosis end points

Given the very long subclinical timeframe of atherosclerosis, risk equations and individual biomarkers are valuable surrogate end points in clinical trials. Ideally positive outcomes should be followed by a hard clinical end-point trial, which usually requires large numbers, years of follow-up and high financial costs. Outcomes for a given intervention may differ between surrogate and hard clinical events, and even for surrogate end points measured by different methods.

Statins

The Cholesterol Treatment Trialists Consortium meta-analysis showed the LDL-C-lowering effect of statins and proportional reduction in CVD events was similar in T1D, T2D and nondiabetic individuals 20. Although being cardioprotective against clinical events 20, statins mildly increase CAC in T1D 72.

Glycaemia

In the DCCT/EDIC study, previous intensive glucose control reduced CVD event incidence by 30% 170. Benefit was first evident in surrogate measures (cIMT 30,32,33,171 and CAC 34). In the DCCT/EDIC study, intensive glucose control did not reduce the rates of PAD but did reduce peripheral arterial calcification 172. In a meta-analysis of five T1D studies, with each 1% increase in HbA1c, the risk of PAD increased by 18% 172.

Adjunct glucose control agents

The REversing with MetfOrmin Vascular Adverse Lesions (REMOVAL) trial is the first study of adjunct metformin in T1D to evaluate a CVD end point, albeit a surrogate measure 173,174. In 428 high CVD risk adults with T1D, metformin (3 years) tended to reduce mean far wall cIMT, which excludes IMT measures of more than 1.5 mm and plaque (primary end point). However, maximal cIMT (tertiary end point), which includes plaque, was significantly reduced by metformin 173. In a prestated post-hoc analysis of never smokers versus current and ex-smokers, the primary IMT end point met statistical significance 174. Metformin significantly reduced weight, LDL-C, insulin dose and preserved estimated glomerular filtration rate. CVD results of trials of other adjunct treatments such as SGLT2 inhibitors and incretin therapies are also of interest 173.

As atherosclerosis is inflammatory, trials of anti-inflammatory drugs for CVD in T1D are also merited.

Clinical practice recommendations

Most studies randomising asymptomatic patients with silent ischaemia on stress testing to further investigation and potentially surgery or to routine care show similar outcomes for up to 5-year follow-up 175,176. ADA guidelines do not recommend routine screening of asymptomatic people with diabetes as it does not improve outcomes, as long as CVD risk factors are well treated 13,35. Unfortunately, few with T1D meet all recommended targets, which is associated with adverse outcomes 96. There are significant costs and some risks subjecting people to these investigations, particularly when invasive testing is required for clarification.

CAC scores correlate with atherosclerosis severity and can predict vascular and mortality outcomes 68–70 in the general and in some (research cohort) T1D populations, yet are not deemed powerful enough at the individual clinical patient level to screen all asymptomatic patients with T1D 13. CAC scores are not specific for the presence of severe or clinically significant occluding lesions 85. Some groups suggest the value of the CAC score in the general population may be in stratification of people at low or intermediate risk – allowing those with a CAC score of 0 (or possibly 0–10) to be potentially reclassified as truly low risk, whereas those with incrementally higher scores may be shifted upward in their risk categorization, and treated more aggressively, and that these guidelines may also be suitable in T1D 15,71,72.

Some suggest that in the asymptomatic patients with T1D, CAC scans may commence at age 30–40 years (modified on the basis of risk factor burden) 13,72. This is based on the DCCT/EDIC CAC study in which many had a positive CAC scan result at a mean age of 34 years. If positive, major modifiable risk factors should be targeted, and the CAC scan repeated in 4–5 years. CAC score progression of less than 15% per annum is desirable. A CAC score prestatin commencement is recommended, as statins mildly increase CAC (as does warfarin). In the general population, knowledge of a positive CAC score has been shown to motivate patients to adhere to recommended lifestyle and medications 177,178 and its use can guide discussions with low or intermediate CVD risk regarding statin use 179.

Situations when non-evidence-based consensus exists for coronary screening include people who are not truly asymptomatic, with (i) ‘atypical’ symptoms; (ii) other evidence of vascular disease such as carotid bruits, TIAs or claudication (for which secondary prevention is indicated); (iii) ischaemic ECG abnormalities and (iv) at high risk considering undertaking strenuous exercise in the absence of regular exercise 13. Adding an imaging component (echocardiography or nuclear perfusion) will improve specificity, particularly in patients with resting ECG abnormalities or in women, as women have a high false positive rate of ECG changes.

Conclusion and future directions

Atherosclerosis remains a major burden in T1D. Risk factor management is key. Particularly high-risk patients with T1D are those with age below 10 years of T1D onset, longer diabetes duration, microvascular complications and with multiple risk factors. On an individual basis, it is often challenging to quantify vascular risk and atherosclerosis extent, particularly early on. Sensitive and specific tools to assess atherosclerosis are desirable. The range of tools is increasing, as is the range of prevention therapies. More T1D observational and clinical trials are needed to guide clinical practice to ultimately improve both the quality and length of life of people with T1D.

Acknowledgements

Conflicts of interest

A.J.J. is supported by a NHMRC Practitioner Fellowship and is a Sydney Medical School Foundation Fellow. A.J.J. and D.N.O. were investigators on the REMOVAL Trial, which was funded by JDRF International/Canada and Australia, and for which metformin and matching placebo was provided free by Merck KGaA (Germany), and EndoPAT equipment and non-financial support was received from Itamar Medical (Israel). A.J.J. is also an investigator on the DCCT/EDIC trial. A.J.J. and D.N.O. have received peer-reviewed research grants from Medtronic, Sanofi-Aventis and Glen-Sys and are on advisory boards for Abbott (Diabetes Devices, Australia), Sanofi-Aventis (Diabetes, Australia) and Medtronic Australia. A.J.J. has also received research grants or product from Mylan. For the remaining author, there are no conflicts of interest.

References

1. International Diabetes Federation. IDF Diabetes Atlas, 8th edition; 2018. Available at: http://diabetesatlas.org/resources/2017-atlas.html. [Accessed 19 December 2018].
2. Diabetes Snapshot. AIHW Report updated; 2018. Available at: https://www.aihw.gov.au/reports/diabetes/diabetes-snapshot/contents/how-many-australians-have-diabetes/type-1-diabetes. [Accessed 19 December 2018].
3. Weng J, Zhou Z, Guo L, Zhu D, Ji L, Luo X, et al. T1D China Study Group. Incidence of type 1 diabetes in China, 2010–13: population based study. BMJ 2018; 360:j5295.
4. Duarte Gómez E, Gregory GA, Castrati Nostas M, Middlehurst AC, Jenkins AJ, Ogle GD. Incidence and mortality rates and clinical characteristics of type 1 diabetes among children and young adults in Cochabamba, Bolivia. J Diabetes Res 2017; 2017:8454757.
5. Marshall SL, Edidin D, Arena VC, Becker DJ, Bunker CH, Gishoma C. Prevalence and incidence of clinically recognized cases of Type 1 diabetes in children and adolescents in Rwanda, Africa. Diabet Med 2015; 32:1186–1192.
6. Piloya-Were T, Sunni M, Ogle GD, Moran A. Childhood diabetes in Africa. Curr Opin Endocrinol Diabetes Obes 2016; 23:306–311.
7. Rakhimova GN, Alimova NU, Ryaboshtan A, Waldman B, Ogle GD, Ismailov SI. Epidemiological data of type 1 diabetes mellitus in children in Uzbekistan, 1998–2014. Pediatr Diabetes 2018; 19:158–165.
8. Jakobsen OAJ, Szereday L. The ‘Three Amigos’ lurking behind type 1 diabetes: hygiene, gut microbiota and viruses. Acta Microbiol Immunol Hung 2018; 28:1–18.
9. Xia Y, Xie Z, Huang G, Zhou Z. Incidence and trend of type 1 diabetes and the underlying environmental determinants. Diabetes Metab Res Rev 2019; 35:3075.
10. Fourlanos S, Dotta F, Greenbaum CJ, Palmer JP, Rolandsson O, Colman PG, Harrison LC. Latent autoimmune diabetes in adults (LADA) should be less latent. Diabetologia 2005; 48:2206–2212.
11. Hernandez M, López C, Real J, Valls J, Ortega-Martinez de Victoria E, Vázquez F, et al. Preclinical carotid atherosclerosis in patients with latent autoimmune diabetes in adults (LADA), type 2 diabetes and classical type 1 diabetes. Cardiovasc Diabetol 2017; 16:94.
12. Lee SI, Patel M, Jones CM, Narendran P. Cardiovascular disease and type 1 diabetes: prevalence, prediction and management in an ageing population. Ther Adv Chronic Dis 2015; 6:347–374.
13. American Diabetes Association. 10. Cardiovascular disease and risk management: standards of medical care in diabetes – 2019. Diabetes Care 2019; 42 (Suppl 1):S103–S123.
14. de Ferranti SD, de Boer IH, Fonseca V, Fox CS, Golden SH, Lavie CJ, et al. Type 1 diabetes mellitus and cardiovascular disease: a scientific statement from the American Heart Association and American Diabetes Association. Diabetes Care 2014; 37:2843–2863.
15. Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation 2010; 122:2748–2764.
16. Lan NSR, Fegan PG, Yeap BB, Bell DA, Watts GF, et al. Dyslipidaemia in adults with type 1 diabetes-when to treat? Diabetes Metab Res Rev 2019; 35:e3090.
17. Jellinger PS, Handelsman Y, Rosenblit PD, Bloomgarden ZT, Fonseca VA, Garber AJ, et al. American Association of Clinical Endocrinologists and American College of Endocrinology Guidelines for Management of Dyslipidemia and Prevention of Cardiovascular Disease. Endocr Pract 2017; 23 (Suppl 2):1–87.
18. Livingstone SJ, Looker HC, Hothersall EJ, Wild SH, Lindsay RS, Chalmers J, et al. Risk of cardiovascular disease and total mortality in adults with type 1 diabetes: Scottish registry linkage study. PLoS Med 2012; 9:e1001321.
19. Brownlee M. The pathobiology of diabetic complications: a unifying mechanism. Diabetes 2005; 54:1615–1625.
20. Cholesterol Treatment Trialists’ (CTT) Collaborators, Kearney PM, Blackwell L, Collins R, Keech A, Simes J, Peto R, et al. Efficacy of cholesterol-lowering therapy in 18 686 people with diabetes in 14 randomised trials of statins: a meta-analysis. Lancet 2008; 371:117–125.
21. Steineck I, Cederholm J, Eliasson B, Rawshani A, Eeg-Olofsson K, Svensson AM, et al. Insulin pump therapy, multiple daily injections, and cardiovascular mortality in 18 168 people with type 1 diabetes: observational study. BMJ 2015; 350:3234.
22. Rawshani A, Rawshani A, Franzén S, Eliasson B, Svensson AM, Miftaraj M, et al. Mortality and cardiovascular disease in type 1 and type 2 diabetes. N Engl J Med 2017; 376:1407–1418.
23. Matuleviciene-Anangen V, Rosengren A, Svensson A-M, Pivodic A, Svensson AM, Gudbjörnsdottir S, et al. Glycaemic control and excess risk of major coronary events in persons with type 1 diabetes. Heart 2017; 103:1687–1695.
24. Moss SE, Klein R, Klein BE. The 14-year incidence of lower-extremity amputations in a diabetic population. The Wisconsin Epidemiologic Study of Diabetic Retinopathy. Diabetes Care 1999; 22:951–959.
25. Jonasson JM, Ye W, Sparén P, Apelqvist J, Nyrén O, Brismar K, et al. Risks of nontraumatic lower-extremity amputations in patients with type 1 diabetes: a population-based cohort study in Sweden. Diabetes Care 2008; 31:1536–1540.
26. Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Research Group, Lachin JM, White NH, Hainsworth DP, Sun W, Cleary PA, Nathan DM. Effect of intensive diabetes therapy on the progression of diabetic retinopathy in patients with type 1 diabetes: 18 years of follow-up in the DCCT/EDIC. Diabetes 2015; 64:631–642.
27. Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HAW. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med 2008; 359:1577–1589.
28. Jermendy G. Vascular memory: can we broaden the concept of the metabolic memory? Cardiovasc Diabetol 2012; 11:44.
29. White NH, Sun W, Cleary PA, Danis RP, Davis MD, Hainsworth DP, et al. Prolonged effect of intensive therapy on the risk of retinopathy complications in patients with type 1 diabetes mellitus: 10 years after the Diabetes Control and Complications Trial. Arch Ophthalmol 2008; 126:1707–1715.
30. [No authors listed]. Effect of intensive diabetes treatment on carotid artery wall thickness in the epidemiology of diabetes interventions and complications. Epidemiology of Diabetes Interventions and Complications (EDIC) Research Group. Diabetes 1999; 48:383–390.
31. Fährmann ER, Adkins L, Loader CJ, Han H, Rice KM, Denvir J, Driscoll HK, et al. Severe hypoglycemia and coronary artery calcification during the diabetes control and complications trial/epidemiology of diabetes interventions and complications (DCCT/EDIC) study. Diabetes Res Clin Pract 2015; 107:280–289.
32. Lachin JM, Orchard TJ, Nathan DM. DCCT/EDIC Research Group. Update on cardiovascular outcomes at 30 years of the diabetes control and complications trial/epidemiology of diabetes interventions and complications study. Diabetes Care 2014; 37:39–43.
33. Polak JF, Backlund JY, Cleary PA, Harrington AP, O’Leary DH, Lachin JM, Nathan DM. DCCT/EDIC Research Group. Progression of carotid artery intima-media thickness during 12 years in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study. Diabetes 2011; 60:607–613.
34. Cleary PA, Orchard TJ, Genuth S, Wong ND, Detrano R, Backlund JY, et al. The effect of intensive glycemic treatment on coronary artery calcification in type 1 diabetic participants of the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study. Diabetes 2006; 55:3556–65.
35. Elam MB, Ginsberg HN, Lovato LC, Corson M, Largay J, Leiter LA, et al. Association of fenofibrate therapy with long-term cardiovascular risk in statin-treated patients with type 2 diabetes. JAMA Cardiol 2017; 2:370–380.
36. ACCORD Study Group, Ginsberg HN, Elam MB, Lovato LC, Crouse JR, Leiter LA, Linz P, et al. Effects of combination lipid therapy in type 2 diabetes mellitus. N Engl J Med 2010; 362:1563–1574.
37. Conte MS. Challenges of distal bypass surgery in patients with diabetes: patient selection, techniques, and outcomes. J Vasc Surg 2010; 52 (Suppl):96S–103S.
38. Haffner SM, Lehto S, Rönnemaa T, Pyörälä K, Laakso M. Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. N Engl J Med 1998; 339:229–234.
39. Kapur A, De Palma R. Mortality after myocardial infarction in patients with diabetes mellitus. Heart 2007; 93:1504–1506.
40. McNamara JJ, Molot MA, Stremple JF, Cutting RT. Coronary artery disease in combat casualties in Vietnam. JAMA 1971; 216:1185–1187.
41. Menegazzo L, Poncina N, Albiero M, Menegolo M, Grego F, Avogaro A, Fadini GP. Diabetes modifies the relationships among carotid plaque calcification, composition and inflammation. Atherosclerosis 2015; 241:533–538.
42. Stary HC. Evolution and progression of atherosclerotic lesions in coronary arteries of children and young adults. Arteriosclerosis 1989; 9 (Suppl):19–32.
43. Tanaka K, Masuda J, Imamura T, Sueishi K, Nakashima T, Sakurai I, et al. A nation-wide study of atherosclerosis in infants, children and young adults in Japan. Atherosclerosis 1988; 72:143–156.
44. Tuzcu EM, Kapadia SR, Tutar E, Ziada KM, Hobbs RE, McCarthy PM, et al. High prevalence of coronary atherosclerosis in asymptomatic teenagers and young adults: evidence from intravascular ultrasound. Circulation 2001; 103:2705–2710.
45. Valsania P, Zarich SW, Kowalchuk GJ, Kosinski E, Warram JH, Krolewski AS. Severity of coronary artery disease in young patients with insulin-dependent diabetes mellitus. Am Heart J 1991; 122 (Pt 1):695–700.
46. Pajunen P, Taskinen MR, Nieminen MS, Syvänne M. Angiographic severity and extent of coronary artery disease in patients with type 1 diabetes mellitus. Am J Cardiol 2000; 86:1080–1085.
47. Larsen JR, Tsunoda T, Tuzcu EM, Schoenhagen P, Brekke M, Arnesen H, et al. Intracoronary ultrasound examinations reveal significantly more advanced coronary atherosclerosis in people with type 1 diabetes than in age- and sex-matched non-diabetic controls. Diab Vasc Dis Res 2007; 4:62–65.
48. Bulugahapitiya U, Siyambalapitiya S, Sithole J, Idris I. Is diabetes a coronary risk equivalent? Systematic review and meta-analysis. Diabet Med 2009; 26:142–148.
49. Jenkins AJ, Best JD, Klein RL, Lyons TJ. Lipoproteins, glycoxidation and diabetic angiopathy. Diabetes Metab Res Rev 2004; 20:349–368.
50. Jenkins AJ, Toth PP, Lyons TJ. Jenkins AJ, Toth PP, Lyons TJ. Lipoproteins in diabetes mellitus. Contemporary Diabetes. New York, NY: Humana Press; 2014.
51. Lyons TJ, Jenkins AJ. Lipoprotein glycation and its metabolic consequences. Curr Opin Lipidol 1997; 8:174–180.
52. Mautner SL, Lin F, Roberts WC. Composition of atherosclerotic plaques in the epicardial coronary arteries in juvenile (type I) diabetes mellitus. Am J Cardiol 1992; 70:1264–1268.
53. Duce SL, Weir-McCall JR, Gandy SJ, Matthew SZ, Cassidy DB, McCormick L, et al. Cohort comparison study of cardiac disease and atherosclerotic burden in type 2 diabetic adults using whole body cardiovascular magnetic resonance imaging. Cardiovasc Diabetol 2015; 14:122.
54. Matsuzawa Y, Kwon TG, Lennon RJ, Lerman LO, Lerman A. Prognostic value of flow-mediated vasodilation in brachial artery and fingertip artery for cardiovascular events: a systematic review and meta-analysis. J Am Heart Assoc 2015; 4:11.
55. Djaberi R, Schuijf JD, Boersma E, Kroft LJ, Pereira AM, Romijn JA, et al. Differences in atherosclerotic plaque burden and morphology between type 1 and 2 diabetes as assessed by multislice computed tomography. Diabetes Care 2009; 32:1507–1512.
56. Dalla Pozza R, Bechtold S, Bonfig W, Putzker S, Kozlik-Feldmann R, Netz H, Schwarz HP. Age of onset of type 1 diabetes in children and carotid intima medial thickness. J Clin Endocrinol Metab 2007; 92:2053–2057.
57. Margeirsdottir HD, Stensaeth KH, Larsen JR, Brunborg C, Dahl-Jørgensen K. Early signs of atherosclerosis in diabetic children on intensive insulin treatment: a population-based study. Diabetes Care 2010; 33:2043–2048.
58. Yamasaki Y, Kawamori R, Matsushima H, Nishizawa H, Kodama M, Kajimoto Y, et al. Atherosclerosis in carotid artery of young IDDM patients monitored by ultrasound high-resolution B-mode imaging. Diabetes 1994; 43:634–639.
59. Jarvisalo MJ, Putto-Laurila A, Jartti L, Lehtimäki T, Solakivi T, Rönnemaa T, Raitakari OT, et al. Carotid artery intima-media thickness in children with type 1 diabetes. Diabetes 2002; 51:493–498.
60. Distiller LA, Joffe BI, Melville V, Welman T, Disteller GB. Carotid artery intima–media complex thickening in patients with relatively long-surviving type 1 diabetes mellitus. J Diabetes Complications 2006; 20:280–284.
61. Larsen JR, Brekke M, Bergengen L, Sandvik L, Arnesen H, Hanssen KF, Dahl-Jorgensen K. Mean HbA1c over 18 years predicts carotid intima media thickness in women with type 1 diabetes. Diabetologia 2005; 48:776–779.
62. Nathan DM, Lachin J, Cleary P, Orchard T, Brillon DJ, Backlund JY, et al. Diabetes Control and Complications Trial; Epidemiology of Diabetes Interventions and Complications Research Group. Intensive diabetes therapy and carotid intima–media thickness in type 1 diabetes mellitus. N Engl J Med 2003; 348:2294–303.
63. Ogawa Y, Uchigata Y, Iwamoto Y. Progression factors of carotid intima-media thickness and plaque in patients with long-term, early-onset type 1 diabetes mellitus in Japan: simultaneous comparison with diabetic retinopathy. J Atheroscler Thromb 2009; 16:821–828.
64. Lorenz MW, Schaefer C, Steinmetz H, Sitzer M. Is carotid intima media thickness useful for individual prediction of cardiovascular risk? Ten-year results from the Carotid Atherosclerosis Progression Study (CAPS). Eur Heart J 2010; 31:2041–2048.
65. Turkbey EB, Redheuil A, Backlund JY, Small AC, Cleary PA, Lachin JM, et al. Aortic distensibility in type 1 diabetes. Diabetes Care 2013; 36:2380–2387.
66. Lilje C, Cronan JC, Schwartzenburg EJ, Owers EM, Clesi P, Gomez R, et al. Intima-media thickness at different arterial segments in pediatric type 1 diabetes patients and its relationship with advanced glycation end products. Pediatr Diabetes 2018; 19:450–456.
67. Pena AS, Liew G, Anderson J, Giles LC, Gent R, Wong TY, Couper JJ. Early atherosclerosis is associated with retinal microvascular changes in adolescents with type 1 diabetes. Pediatr Diabetes 2018; 19:1467–1470.
68. Carr JJ, Jacobs DR Jr, Terry JG, Shay CM, Sidney S, Liu K, et al. Association of coronary artery calcium in adults aged 32 to 46 years with incident coronary heart disease and death. JAMA Cardiol 2017; 2:391–399.
69. Elkeles RS, Godsland IF, Feher MD, Rubens MB, Roughton M, Nugara F, et al. Coronary calcium measurement improves prediction of cardiovascular events in asymptomatic patients with type 2 diabetes: the PREDICT study. Eur Heart J 2008; 29:2244–2251.
70. Raggi P, Shaw LJ, Berman DS, Callister TQ. Prognostic value of coronary artery calcium screening in subjects with and without diabetes. J Am Coll Cardiol 2004; 43:1663–1669.
71. de Ferranti SD, de Boer IH, Fonseca V, Fox CS, Golden SH, Lavie CJ, et al. Type 1 diabetes mellitus and cardiovascular disease: a scientific statement from the American Heart Association and American Diabetes Association. Circulation 2014; 130:1110–1130.
72. Burge MR, Eaton RP, Schade DS. The role of a coronary artery calcium scan in type 1 diabetes. Diabetes Technol Ther 2016; 18:594–603.
73. Dabelea D, Kinney G, Snell-Bergeon JK, Hokanson JE, Eckel RH, Ehrlich J, et al. Effect of type 1 diabetes on the gender difference in coronary artery calcification: a role for insulin resistance? The Coronary Artery Calcification in Type 1 Diabetes (CACTI) Study. Diabetes 2003; 52:2833–2839.
74. Lovshin JA, Bjornstad P, Lovblom LE, Bai JW, Lytvyn Y, Boulet G, et al. Atherosclerosis and microvascular complications: results from the canadian study of longevity in type 1 diabetes. Diabetes Care 2018; 41:2570–2578.
75. Guo J, Nunley KA, Costacou T, Miller RG, Rosano C, Edmundowicz D, Orchard TJ. Greater progression of coronary artery calcification is associated with clinically relevant cognitive impairment in type 1 diabetes. Atherosclerosis 2019; 280:58–65.
76. Hjortkjaer HO, Jensen T, Hilsted J, Mogensen UM, Rossing P, Køber L, Kofoed KF. Generalised arterial calcification in normoalbuminuric patients with type 1 diabetes with and without cardiovascular autonomic neuropathy. Diab Vasc Dis Res 2019; 16:98–102.
77. Hjortkjaer HO, Jensen T, Hilsted J, Corinth H, Mogensen UM, Køber L, et al. Possible early detection of coronary artery calcium progression in type 1 diabetes: a case–control study of normoalbuminuric type 1 diabetes patients and matched controls. Diabetes Res Clin Pract 2018; 141:18–25.
78. Bjornstad P, Maahs DM, Wadwa RP, Pyle L, Rewers M, Eckel RH, Snell-Bergeon JK. Plasma triglycerides predict incident albuminuria and progression of coronary artery calcification in adults with type 1 diabetes: the Coronary Artery Calcification in Type 1 Diabetes Study. J Clin Lipidol 2014; 8:576–583.
79. Guo J, Erqou SA, Miller RG, Edmundowicz D, Orchard TJ, Costacou T. The role of coronary artery calcification testing in incident coronary artery disease risk prediction in type 1 diabetes. Diabetologia 2019; 62:259–268.
80. Nattero-Chavez L, Redondo López S, Alonso Díaz S, Garnica Ureña M, Fernández-Durán E, Escobar-Morreale HF, Luque-Ramírez M. The peripheral atherosclerotic profile in patients with type 1 diabetes warrants a thorough vascular assessment of asymptomatic patients. Diabetes Metab Res Rev 2019; 35:3088.
81. Ix JH, Miller RG, Criqui MH, Orchard TJ. Test characteristics of the ankle-brachial index and ankle-brachial difference for medial arterial calcification on X-ray in type 1 diabetes. J Vasc Surg 2012; 56:721–727.
82. Schmermund A, Eckert J, Schmidt M, Magedanz A, Voigtländer T. Coronary computed tomography angiography: a method coming of age. Clin Res Cardiol 2018; 107 (Suppl 2):40–48.
83. Madaj PM, Budoff MJ, Li D, Tayek JA, Karlsberg RP, Karpman HL. Identification of noncalcified plaque in young persons with diabetes: an opportunity for early primary prevention of coronary artery disease identified with low-dose coronary computed tomographic angiography. Acad Radiol 2012; 19:889–893.
84. Park GM, Lee SW, Cho YR, Kim CJ, Cho JS, Park MW, et al. Coronary computed tomographic angiographic findings in asymptomatic patients with type 2 diabetes mellitus. Am J Cardiol 2014; 113:765–771.
85. Budoff MJ, Dowe D, Jollis JG, Gitter M, Sutherland J, Halamert E, et al. Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial. J Am Coll Cardiol 2008; 52:1724–1732.
86. Tonino PA, Fearon WF, De Bruyne B, Oldroyd KG, Leesar MA, Ver Lee PN, et al. Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation. J Am Coll Cardiol 2010; 55:2816–2821.
87. Muhlestein JB, Lappé DL, Lima JA, Rosen BD, May HT, Knight S, et al. Effect of screening for coronary artery disease using CT angiography on mortality and cardiac events in high-risk patients with diabetes: the FACTOR-64 randomized clinical trial. JAMA 2014; 312:2234–2243.
88. Andrews JPM, Fayad ZA, Dweck MR. New methods to image unstable atherosclerotic plaques. Atherosclerosis 2018; 272:118–128.
89. Celeng C, de Keizer B, Merkely B, de Jong P, Leiner T, Takx RAP. PET molecular targets and near-infrared fluorescence imaging of atherosclerosis. Curr Cardiol Rep 2018; 20:11.
90. Vigne J, Thackeray J, Essers J, Makowski M, Varasteh Z, Curaj A, et al. Current and emerging preclinical approaches for imaging-based characterization of atherosclerosis. Mol Imaging Biol 2018; 20:869–887.
91. Jenkins AJ, Joglekar MV, Hardikar AA, Keech AC, O’Neal DN, Januszewski AS. Biomarkers in diabetic retinopathy. Rev Diabet Stud 2015; 12:159–195.
92. Jenkins AJ, Rowley KG, Lyons TJ, Best JD, Hill MA, Klein RL. Lipoproteins and diabetic microvascular complications. Curr Pharm Des 2004; 10:3395–3418.
93. Jenkins AJ, Welsh P, Petrie JR. Metformin, lipids and atherosclerosis prevention. Curr Opin Lipidol 2018; 29:346–353.
94. Stitt AW, Jenkins AJ, Cooper ME. Advanced glycation end products and diabetic complications. Expert Opin Investig Drugs 2002; 11:1205–1223.
95. Bjornstad P, Donaghue KC, Maahs DM. Macrovascular disease and risk factors in youth with type 1 diabetes: time to be more attentive to treatment? Lancet Diabetes Endocrinol 2018; 6:809–820.
96. Lithovius R, Harjutsalo V, Forsblom C, Saraheimo M, Groop PH. The consequences of failure to achieve targets of guidelines for prevention and treatment of diabetic complications in patients with type 1 diabetes. Acta Diabetol 2015; 52:31–38.
97. Costacou T, Evans RW, Orchard TJ. Does the concentration of oxidative and inflammatory biomarkers differ by haptoglobin genotype in type 1 diabetes? Antioxid Redox Signal 2015; 23:1439–1444.
98. Costacou T, Evans RW, Orchard TJ. Glycaemic control modifies the haptoglobin 2 allele-conferred susceptibility to coronary artery disease in type 1 diabetes. Diabet Med 2016; 33:1524–1527.
99. Dalan R, Liuh Ling G. The protean role of haptoglobin and haptoglobin genotypes on vascular complications in diabetes mellitus. Eur J Prev Cardiol 2018; 25:1502–1519.
100. Hochberg I, Berinstein EM, Milman U, Shapira C, Levy AP. Interaction between the haptoglobin genotype and vitamin E on cardiovascular disease in diabetes. Curr Diab Rep 2017; 17:42.
101. Llaurado G, Gutiérrez C, Giménez-Palop O, Cano A, Pareja R, Berlanga Escalera E, et al. Haptoglobin genotype is associated with increased endothelial dysfunction serum markers in type 1 diabetes. Eur J Clin Invest 2015; 45:932–939.
102. Orchard TJ, Backlund JC, Costacou T, Cleary P, Lopes-Virella M, Levy AP, et al. Haptoglobin 2-2 genotype and the risk of coronary artery disease in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study (DCCT/EDIC). J Diabetes Complications 2016; 30:1577–1584.
103. Costacou T, Ferrell RE, Ellis D, Orchard TJ. Haptoglobin genotype and renal function decline in type 1 diabetes. Diabetes 2009; 58:2904–2909.
104. Costacou T, Ferrell RE, Orchard TJ. Haptoglobin genotype: a determinant of cardiovascular complication risk in type 1 diabetes. Diabetes 2008; 57:1702–1706.
105. Costacou T, Secrest AM, Ferrell RE, Orchard TJ. Haptoglobin genotype and cerebrovascular disease incidence in type 1 diabetes. Diab Vasc Dis Res 2014; 11:335–342.
106. Levy AP, Roguin A, Hochberg I, Herer P, Marsh S, Nakhoul FM, Skorecki K. Haptoglobin phenotype and vascular complications in patients with diabetes. N Engl J Med 2000; 343:969–970.
107. Orchard TJ, Sun W, Cleary PA, Genuth SM, Lachin JM, McGee P, et al. Haptoglobin genotype and the rate of renal function decline in the diabetes control and complications trial/epidemiology of diabetes interventions and complications study. Diabetes 2013; 62:3218–3223.
108. Simpson M, Snell-Bergeon JK, Kinney GL, Lache O, Miller-Lotan R, Anbinder Y, et al. Haptoglobin genotype predicts development of coronary artery calcification in a prospective cohort of patients with type 1 diabetes. Cardiovasc Diabetol 2011; 10:99.
109. Costacou T, Orchard TJ. The haptoglobin genotype predicts cardio-renal mortality in type 1 diabetes. J Diabetes Complications 2016; 30:221–226.
110. Charmet R, Duffy S, Keshavarzi S, Gyorgy B, Marre M, Rossing P, et al. Novel risk genes identified in a genome-wide association study for coronary artery disease in patients with type 1 diabetes. Cardiovasc Diabetol 2018; 17:61.
111. Harjutsalo V, Thomas MC, Forsblom C, Groop PH. FinnDiane Study Group. Risk of coronary artery disease and stroke according to sex and presence of diabetic nephropathy in type 1 diabetes. Diabetes Obes Metab 2018; 20:2759–2767.
112. Rawshani A, Sattar N, Franzén S, Rawshani A, Hattersley AT, Svensson AM, et al. Excess mortality and cardiovascular disease in young adults with type 1 diabetes in relation to age at onset: a nationwide, register-based cohort study. Lancet 2018; 392:477–486.
113. Mala S, Potockova V, Hoskovcova L, Pithova P, Brabec M, Kulhankova J, et al. Cardiac autonomic neuropathy may play a role in pathogenesis of atherosclerosis in type 1 diabetes mellitus. Diabetes Res Clin Pract 2017; 134:139–144.
114. Mogensen UM, Jensen T, Køber L, Kelbæk H, Mathiesen AS, Dixen U, et al. Cardiovascular autonomic neuropathy and subclinical cardiovascular disease in normoalbuminuric type 1 diabetic patients. Diabetes 2012; 61:1822–1830.
115. Kempler P, Tesfaye S, Chaturvedi N, Stevens LK, Webb DJ, Eaton S, et al. Autonomic neuropathy is associated with increased cardiovascular risk factors: the EURODIAB IDDM Complications Study. Diabet Med 2002; 19:900–909.
116. Ganjali S, Dallinga-Thie GM, Simental-Mendía LE, Banach M, Pirro M, Sahebkar A. HDL functionality in type 1 diabetes. Atherosclerosis 2017; 267:99–109.
117. Basu A, Jenkins AJ, Zhang Y, Stoner JA, Klein RL, Lopes-Virella MF, et al. Nuclear magnetic resonance-determined lipoprotein subclasses and carotid intima–media thickness in type 1 diabetes. Atherosclerosis 2016; 244:93–100.
118. Basu A, Jenkins AJ, Zhang Y, Stoner JA, Klein RL, Lopes-Virella MF, et al. Data on carotid intima–media thickness and lipoprotein subclasses in type 1 diabetes from the Diabetes Control and Complications Trial and the Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC). Data Brief 2016; 6:33–38.
119. Lyons TJ, Jenkins AJ, Zheng D, Klein RL, Otvos JD, Yu Y, et al. Nuclear magnetic resonance-determined lipoprotein subclass profile in the DCCT/EDIC cohort: associations with carotid intima–media thickness. Diabet Med 2006; 23:955–966.
120. DCCT/EDIC Research Group, Zhang Y, Jenkins AJ, Basu A, Stoner JA, Lopes-Virella MF, Klein RL, Lyons TJ. DCCT/EDIC Research Group. Associations between intensive diabetes therapy and NMR-determined lipoprotein subclass profiles in type 1 diabetes. J Lipid Res 2016; 57:310–317.
121. Colhoun HM, Otvos JD, Rubens MB, Taskinen MR, Underwood SR, Fuller JH. Lipoprotein subclasses and particle sizes and their relationship with coronary artery calcification in men and women with and without type 1 diabetes. Diabetes 2002; 51:1949–1956.
122. Chow E, Iqbal A, Walkinshaw E, Phoenix F, Macdonald IA, Storey RF, et al. Prolonged prothrombotic effects of antecedent hypoglycemia in individuals with type 2 diabetes. Diabetes Care 2018; 41:2625–2633.
123. Joy NG, Mikeladze M, Younk LM, Tate DB, Davis SN. Effects of equivalent sympathetic activation during hypoglycemia on endothelial function and pro-atherothrombotic balance in healthy individuals and obese standard treated type 2 diabetes. Metabolism 2016; 65:1695–1705.
124. Ratter JM, Rooijackers HM, Tack CJ, Hijmans AG, Netea MG, de Galan BE, Stienstra R. Proinflammatory effects of hypoglycemia in humans with or without diabetes. Diabetes 2017; 66:1052–1061.
125. Ratter JM, Rooijackers HMM, Jacobs CWM, de Galan BE, Tack CJ, Stienstra R, et al. Hypoglycaemia induces recruitment of non-classical monocytes and cytotoxic lymphocyte subsets in type 1 diabetes. Diabetologia 2018; 61:2069–2071.
126. Jun JE, Lee SE, Lee YB, Ahn JY, Kim G, Hur KY, et al. Continuous glucose monitoring defined glucose variability is associated with cardiovascular autonomic neuropathy in type 1 diabetes. Diabetes Metab Res Rev 2019; 35:3092.
127. Piconi L, Quagliaro L, Assaloni R, Da Ros R, Maier A, Zuodar G, Ceriello A. Constant and intermittent high glucose enhances endothelial cell apoptosis through mitochondrial superoxide overproduction. Diabetes Metab Res Rev 2006; 22:198–203.
128. Polhill TS, Saad S, Poronnik P, Fulcher GR, Pollock CA. Short-term peaks in glucose promote renal fibrogenesis independently of total glucose exposure. Am J Physiol Renal Physiol 2004; 287:268–273.
129. Ceriello A, Esposito K, Piconi L, Ihnat MA, Thorpe JE, Testa R, et al. Oscillating glucose is more deleterious to endothelial function and oxidative stress than mean glucose in normal and type 2 diabetic patients. Diabetes 2008; 57:1349–1354.
130. Kilpatrick ES, Rigby AS, Atkin SL. A1C variability and the risk of microvascular complications in type 1 diabetes: data from the Diabetes Control and Complications Trial. Diabetes Care 2008; 31:2198–2202.
131. Virk SA, Donaghue KC, Cho YH, Benitez-Aguirre P, Hing S, Pryke A, et al. Association between HbA1c variability and risk of microvascular complications in adolescents with type 1 diabetes. J Clin Endocrinol Metab 2016; 101:3257–3263.
132. Farabi SS, Quinn L, Phillips S, Mihailescu D, Park C, Ali M, Martyn-Nemeth P. Endothelial dysfunction is related to glycemic variability and quality and duration of sleep in adults with type 1 diabetes. J Cardiovasc Nurs 2018; 33:E21–E25.
133. Guarnotta V, Di Bella G, Pillitteri G, Ciresi A, Giordano C. Improved cardiovascular and cardiometabolic risk in patients with type 1 diabetes and autoimmune polyglandular syndrome switched from glargine to degludec due to hypoglycaemic variability. Front Endocrinol (Lausanne) 2018; 9:428.
134. Nyiraty S, Pesei F, Orosz A, Coluzzi S, Vági OE, Lengyel C, et al. Cardiovascular autonomic neuropathy and glucose variability in patients with type 1 diabetes: is there an association? Front Endocrinol (Lausanne) 2018; 9:174.
135. Rodrigues R, de Medeiros LA, Cunha LM, Garrote-Filho MDS, Bernardino Neto M, Jorge PT, et al. Correlations of the glycemic variability with oxidative stress and erythrocytes membrane stability in patients with type 1 diabetes under intensive treatment. Diabetes Res Clin Pract 2018; 144:153–160.
136. Snell-Bergeon JK, Roman R, Rodbard D, Garg S, Maahs DM, Schauer IE, et al. Glycaemic variability is associated with coronary artery calcium in men with type 1 diabetes: the Coronary Artery Calcification in Type 1 Diabetes study. Diabet Med 2010; 27:1436–1442.
137. Lithovius R, Gordin D, Forsblom C, Saraheimo M, Harjutsalo V. Groop PH; FinnDiane Study Group. Ambulatory blood pressure and arterial stiffness in individuals with type 1 diabetes. Diabetologia 2018; 61:1935–1945.
138. Pitocco D, Zaccardi F, Tarzia P, Milo M, Scavone G, Rizzo P, et al. Metformin improves endothelial function in type 1 diabetic subjects: a pilot, placebo-controlled randomized study. Diabetes Obes Metab 2013; 15:427–431.
139. Rask-Madsen C, Kahn CR. Tissue-specific insulin signaling, metabolic syndrome, and cardiovascular disease. Arterioscler Thromb Vasc Biol 2012; 32:2052–2059.
140. Astrup AS, Tarnow L, Jorsal A, Lajer M, Nzietchueng R, Benetos A, et al. Telomere length predicts all-cause mortality in patients with type 1 diabetes. Diabetologia 2010; 53:45–48.
141. Kilpatrick ES, Rigby AS, Atkin SL. Insulin resistance, the metabolic syndrome, and complication risk in type 1 diabetes: ‘double diabetes’ in the Diabetes Control and Complications Trial. Diabetes Care 2007; 30:707–712.
142. McGill M, Molyneaux L, Twigg SM, Yue DK. The metabolic syndrome in type 1 diabetes: does it exist and does it matter? J Diabetes Complications 2008; 22:18–23.
143. Pambianco G, Costacou T, Orchard TJ. The prediction of major outcomes of type 1 diabetes: a 12-year prospective evaluation of three separate definitions of the metabolic syndrome and their components and estimated glucose disposal rate: the Pittsburgh Epidemiology of Diabetes Complications Study experience. Diabetes Care 2007; 30:1248–1254.
144. Connelly MA, Otvos JD, Zhang Q, Zhang S, Antalis CJ, Chang AM, Hoogwerf BJ, et al. Effects of hepato-preferential basal insulin peglispro on nuclear magnetic resonance biomarkers lipoprotein insulin resistance index and GlycA in patients with diabetes. Biomark Med 2017; 11:991–1001.
145. Ferreira I, Hovind P, Schalkwijk CG, Parving HH, Stehouwer CDA, Rossing P. Biomarkers of inflammation and endothelial dysfunction as predictors of pulse pressure and incident hypertension in type 1 diabetes: a 20 year life-course study in an inception cohort. Diabetologia 2018; 61:231–241.
146. Odermarsky M, Pesonen E, Sorsa T, Lernmark Å, Pussinen PJ, Liuba P. HLA, infections and inflammation in early stages of atherosclerosis in children with type 1 diabetes. Acta Diabetol 2018; 55:41–47.
147. Handy DE, Castro R, Loscalzo J. Epigenetic modifications: basic mechanisms and role in cardiovascular disease. Circulation 2011; 123:2145–2156.
148. Januszewski AS, Sutanto SS, McLennan S, O’Neal DN, Keech AC, Twigg SM, Jenkins AJ. Shorter telomeres in adults with type 1 diabetes correlate with diabetes duration, but only weakly with vascular function and risk factors. Diabetes Res Clin Pract 2016; 117:4–11.
149. Wang J, Dong X, Cao L, Sun Y, Qiu Y, Zhang Y, et al. Association between telomere length and diabetes mellitus: a meta-analysis. J Int Med Res 2016; 44:1156–1173.
150. Tesovnik T, Kovac J, Hovnik T, Kotnik P, Battelino T, Trebusak Podkrajsek K. Association of average telomere length with body-mass index and vitamin D status in juvenile population with type 1 diabetes. Zdr Varst 2015; 54:74–78.
151. Tesovnik T, Kovac J, Hovnik T, Dovc K, Bratina N, Battelino T, et al. Association of glycemic control and cell stress with telomere attrition in type 1 diabetes. JAMA Pediatr 2018; 172:879–881.
152. Fyhrquist F, Tiitu A, Saijonmaa O, Forsblom C, Groop PH. FinnDiane Study Group. Telomere length and progression of diabetic nephropathy in patients with type 1 diabetes. J Intern Med 2010; 267:278–286.
153. Mastropasqua R, Toto L, Cipollone F, Santovito D, Carpineto P, Mastropasqua L. Role of microRNAs in the modulation of diabetic retinopathy. Prog Retin Eye Res 2014; 43:92–107.
154. Satake E, Pezzolesi MG, Md Dom ZI, Smiles AM, Niewczas MA, Krolewski AS. Circulating miRNA profiles associated with hyperglycemia in patients with type 1 diabetes. Diabetes 2018; 67:1013–1023.
155. Barutta F, Bruno G, Matullo G, Chaturvedi N, Grimaldi S, Schalkwijk C, et al. MicroRNA-126 and micro-/macrovascular complications of type 1 diabetes in the EURODIAB Prospective Complications Study. Acta Diabetol 2017; 54:133–139.
156. El-Samahy MH, Adly AA, Elhenawy YI, Ismail EA, Pessar SA, Mowafy ME, et al. Urinary miRNA-377 and miRNA-216a as biomarkers of nephropathy and subclinical atherosclerotic risk in pediatric patients with type 1 diabetes. J Diabetes Complications 2018; 32:185–192.
157. Ghai V, Wu X, Bheda-Malge A, Argyropoulos CP, Bernardo JF, Orchard T, et al. Genome-wide profiling of urinary extracellular vesicle microRNAs associated with diabetic nephropathy in type 1 diabetes. Kidney Int Rep 2018; 3:555–572.
158. Kaidonis G, Gillies MC, Abhary S, Liu E, Essex RW, Chang JH, et al. A single-nucleotide polymorphism in the MicroRNA-146a gene is associated with diabetic nephropathy and sight-threatening diabetic retinopathy in Caucasian patients. Acta Diabetol 2016; 53:643–650.
159. Zampetaki A, Willeit P, Burr S, Yin X, Langley SR, Kiechl S, et al. Angiogenic microRNAs linked to incidence and progression of diabetic retinopathy in type 1 diabetes. Diabetes 2016; 65:216–227.
160. Sousa GR, Pober D, Galderisi A, Lv H, Yu L, Pereira AC, et al. Glycemic control, cardiac autoimmunity, and long-term risk of cardiovascular disease in type 1 diabetes mellitus: a DCCT/EDIC Cohort-Based Study. Circulation 2019; 139:730–743.
161. Jenkins AJ, Scott E, Fulcher J, Kilov G, Januszewski AC. Management of diabetes mellitus. In: Toth PP, Christopher P, editors. Comprehensive cardiovascular medicine in the primary care setting. Springer Press: Switzerland; 2018. 113–177.
162. Karamos B, Porta M, Songini M, Metelko Z, Kerenyi Z, Tamas R. Different risk factors of microangiopathy in patients with type I diabetes mellitus of short versus long duration. The EURODIAB IDDM Complications Study. Diabetologia 2000; 43:348–355.
163. Miller RG, Costacou T, Orchard TJ. Risk factor modeling for cardiovascular disease in type i diabetes in the Pittsburgh Epidemiology of Diabetes Complications (EDC) Study: a comparison to the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study. Diabetes 2019; 68:409–419.
164. Bebu I, Braffett BH, Pop-Busui R, Orchard TJ, Nathan DM, Lachin JM. DCCT/EDIC Research Group. The relationship of blood glucose with cardiovascular disease is mediated over time by traditional risk factors in type 1 diabetes: the DCCT/EDIC study. Diabetologia 2017; 60:2084–2091.
165. Catapano AL, Graham I, De Backer G, Wiklund O, Chapman MJ, Drexel H, et al. 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias. Eur Heart J 2016; 37:2999–3058.
166. Jacobson TA, Ito MK, Maki KC, Orringer CE, Bays HE, Jones PH, et al. National lipid association recommendations for patient-centered management of dyslipidemia: part 1: full report. J Clin Lipidol 2015; 9:129–169.
167. Soedamah-Muthu SS, Vergouwe Y, Costacou T, Miller RG, Zgibor J, Chaturvedi N, et al. Predicting major outcomes in type 1 diabetes: a model development and validation study. Diabetologia 2014; 57:2304–2314.
168. Vistisen D, Andersen GS, Hansen CS, Hulman A, Henriksen JE, Bech-Nielsen H, Jørgensen ME, et al. Prediction of first cardiovascular disease event in type 1 diabetes mellitus: the steno type 1 risk engine. Circulation 2016; 133:1058–1066.
169. Hunt KJ, Baker NL, Cleary PA, Klein R, Virella G, Lopes-Virella MF. DCCT/EDIC Group of Investigators. Longitudinal association between endothelial dysfunction, inflammation, and clotting biomarkers with subclinical atherosclerosis in type 1 diabetes: an evaluation of the DCCT/EDIC Cohort. Diabetes Care 2015; 38:1281–1289.
170. Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Study Research Group. Intensive diabetes treatment and cardiovascular outcomes in type 1 diabetes: The DCCT/EDIC Study 30-Year Follow-up. Diabetes Care 2016; 39:686–693.
171. Purnell JQ, Zinman B, Brunzell JD. DCCT/EDIC Research Group. The effect of excess weight gain with intensive diabetes mellitus treatment on cardiovascular disease risk factors and atherosclerosis in type 1 diabetes mellitus: results from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study (DCCT/EDIC) study. Circulation 2013; 127:180–187.
172. Adler AI, Erqou S, Lima TA, Robinson AH. Association between glycated haemoglobin and the risk of lower extremity amputation in patients with diabetes mellitus-review and meta-analysis. Diabetologia 2010; 53:840–849.
173. Petrie JR, Chaturvedi N, Ford I, Brouwers MCGJ, Greenlaw N, Tillin T, et al. Cardiovascular and metabolic effects of metformin in patients with type 1 diabetes (REMOVAL): a double-blind, randomised, placebo-controlled trial. Lancet Diabetes Endocrinol 2017; 5:597–609.
174. Petrie JR, Chaturvedi N, Ford I, Hramiak I, Hughes AD, Jenkins AJ, et al. Metformin in adults with type 1 diabetes: design and methods of REducing with MetfOrmin Vascular Adverse Lesions (REMOVAL): an international multicentre trial. Diabetes Obes Metab 2017; 19:509–516.
175. Young LH, Wackers FJ, Chyun DA, Davey JA, Barrett EJ, Taillefer R, et al. Cardiac outcomes after screening for asymptomatic coronary artery disease in patients with type 2 diabetes: the DIAD study: a randomized controlled trial. JAMA 2009; 301:1547–1555.
176. Zellweger MJ, Maraun M, Osterhues HH, Keller U, Müller-Brand J, Jeger R, et al. Progression to overt or silent CAD in asymptomatic patients with diabetes mellitus at high coronary risk: main findings of the prospective multicenter BARDOT trial with a pilot randomized treatment substudy. JACC Cardiovasc Imaging 2014; 7:1001–1010.
177. Kalia NK, Miller LG, Nasir K, Blumenthal RS, Agrawal N, Budoff MJ. Visualizing coronary calcium is associated with improvements in adherence to statin therapy. Atherosclerosis 2006; 185:394–399.
178. Mamudu HM, Paul TK, Veeranki SP, Budoff M. The effects of coronary artery calcium screening on behavioral modification, risk perception, and medication adherence among asymptomatic adults: a systematic review. Atherosclerosis 2014; 236:338–350.
179. Michos ED, Blaha MJ, Blumenthal RS. Use of the coronary artery calcium score in discussion of initiation of statin therapy in primary prevention. Mayo Clin Proc 2017; 92:1831–1841.
Keywords:

atherosclerosis; biomarkers; coronary artery calcification; intima–media thickness; risk factors; type 1 diabetes; vascular function

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