Abstract
Purpose of review
Type 2 diabetes confers approximately twofold-increased risk for cardiovascular disease. Early risk stratification of these patients may help reduce cardiovascular events. This review discusses the state of the art of risk factors, biomarkers, and subclinical disease parameters potentially useful in cardiovascular risk assessment in type 2 diabetes.
Recent findings
Scientific progress in the past decade has identified a spectrum of risk in diabetic individuals rather than categorizing diabetes as a coronary heart disease equivalent as previously done. Recent data on emerging biomarkers and diagnostic imaging, along with traditional risk factors, provide evidence to help inform individualized cardiovascular risk assessment.
Summary
Comprehensive assessment of traditional risk factors, biomarkers, complications of diabetes, and subclinical atherosclerosis may help classify diabetic individuals as low, intermediate, or high risk for determining the intensity of lifestyle modification and pharmacotherapy. Further research may lead to a comprehensive pathway for cardiovascular disease risk assessment in diabetic patients.
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Introduction
Type 2 diabetes mellitus, a state of relative insulin deficiency with underlying insulin resistance, accounts for majority of cases of hyperglycemia worldwide. An estimated 422 million people worldwide have diabetes [1•], and this number is expected to reach 592 million by the year 2035 [2]. Almost 30 million Americans (9% of the population) have diabetes, with estimated total health care costs of $245 billion due to extensive complications, primarily micro- and macrovascular pathology.
Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death among individuals with type 2 diabetes, in whom adverse cardiovascular outcomes occur, on average, 14.6 years earlier [3] and with increased severity compared to individuals without diabetes mellitus. People with type 2 diabetes have twofold-increased risk of developing ASCVD [4]. The increment in the diabetic population with cardiovascular events reflects the steady increase in the number of older individuals in the USA and the improved survival of individuals with diabetes. Prevalence of obesity, which is related to risk for ASCVD and diabetes, is also on an upsurge in the USA as well as globally.
Guidelines from the American Heart Association (AHA)/American Diabetes Association (ADA) [5•] and the European Society of Cardiology [6] present different recommendations for individuals with diabetes depending on an individual’s risk profile. To identify patients who will benefit most from treatment or to determine the intensity of treatment, accurate cardiovascular risk stratification is important. Reducing ASCVD burden in diabetes is a major clinical imperative that should be prioritized to reduce myocardial infarctions, strokes, heart failure hospitalizations, and premature deaths; improve quality of life; and lessen individual and economic burdens of decreased productivity and high cost of medical care.
This review article describes traditional risk factors, emerging biomarkers, and subclinical disease parameters that may be helpful in the assessment of cardiovascular risk in patients with type 2 diabetes.
Traditional Risk Factors
Blood Pressure
Uncontrolled blood pressure in diabetes is a well-known risk factor for worse cardiovascular outcomes [7]. Unregulated blood pressure in diabetes accelerates the risk for myocardial infarction, stroke, heart failure, and all-cause mortality.
Optimal blood pressure in patients with diabetes has been a topic of debate over the past several years [8]. The Joint National Commission (JNC) 8 guidelines of 2013 liberalized the recommendation for patients with diabetes from <130/80 mmHg in the previous guidelines [9] to <140/90 mmHg [10]. A systematic review published after JNC 8 concluded that blood pressure–lowering treatment in people with diabetes and systolic blood pressure already <140 mmHg was associated with reduced risk of stroke and albuminuria, and therefore challenged the relaxation of guidelines by JNC 8 [11]. However, a more recent meta-analysis that included unpublished data in patients with diabetes supported a more liberal blood pressure range up to 140/80 mmHg and concluded that if systolic blood pressure was <140 mmHg, further treatment was associated with increased risk of cardiovascular death [12]. However, the Systolic Blood Pressure Intervention Trial (SPRINT), which showed benefits of lower blood pressures to 120/80 mmHg in patients with cardiovascular risk factors but excluded all patients with diabetes mellitus [13], has renewed the controversy regarding the optimum range of blood pressure.
Consistent with JNC 8 [10], the AHA/ADA guidelines recommend blood pressure of <140/90 mmHg for most individuals with diabetes [5•], although the optimal blood pressure for individuals with diabetes in conjunction with other cardiovascular risk factors remains controversial.
Lipids
Low-Density Lipoprotein Cholesterol
Low-density lipoprotein cholesterol (LDL-C) has been the cornerstone of cardiovascular risk assessment for the past three decades. LDL is a major atherogenic lipoprotein in the bloodstream, and LDL-C is associated with the genesis and progression of ASCVD. Numerous clinical trials, epidemiologic studies, and animal models have clearly demonstrated the role of LDL-C elevation in adverse cardiovascular outcomes.
The 2013 ACC/AHA cholesterol guidelines recommend that individuals with diabetes, aged 40–75 years, without clinical ASCVD be on moderate-intensity statin therapy if their baseline LDL-C is 70–189 mg/dL, with consideration of high-intensity statin in those with 10-year ASCVD risk ≥7.5%; high-intensity statin therapy is recommended as first-line therapy in patients aged ≤75 years who have clinical ASCVD [14]. In the Collaborative Atorvastatin Diabetes Study (CARDS), treatment with atorvastatin 10 mg (moderate intensity) resulted in significant reduction in major cardiovascular events irrespective of pretreatment LDL-C levels [15]. The 2016 ACC Expert Consensus Decision Pathway on nonstatin therapy identified individuals with diabetes who have concomitant ASCVD risk factors, 10-year ASCVD risk ≥7.5%, chronic kidney disease (CKD), albuminuria, retinopathy, evidence of subclinical atherosclerosis, elevated lipoprotein (a), or elevated high-sensitivity C-reactive protein (hs-CRP) as higher risk and therefore potential candidates for high-intensity statin therapy, with the addition of ezetimibe (or colesevelam) as needed [16••].
Low-Density Lipoprotein Particle Concentration
In diabetes, LDL-C concentration may not be a true representation of the atherogenic potential in an individual [5•], as the LDL particles are small and dense. Studies suggest that small, dense LDL particles may be more atherogenic and more readily oxidized and glycated [17]. However, the benefit of measuring LDL particle concentration (LDL-P) for ASCVD risk assessment is uncertain, and LDL-P was not included in the 2013 AHA/ACC guidelines for cholesterol [14] or cardiovascular risk assessment [18], nor in the 2016 European guidelines [19, 20].
Non-High-Density Lipoprotein Cholesterol
Non-high-density lipoprotein cholesterol (non-HDL-C) is the sum of cholesterol in LDL, triglyceride-rich lipoproteins such as very-low-density lipoprotein (VLDL), chylomicrons, and their remnants, and lipoprotein (a) [21], therefore including the cholesterol content of all the atherogenic lipoproteins. Literature published in the last decade has shown that non-HDL-C level is a strong marker for ASCVD and may have a stronger association with ASCVD risk than LDL-C concentration [22, 23]. Several meta-analyses have shown that apolipoprotein B-100 (apoB) and non-HDL-C are better markers for ASCVD risk in statin-treated individuals [24, 25]. Non-HDL-C may be useful in patients with high triglycerides, as commonly seen in patients with diabetes, in whom the calculation of LDL-C is problematic, and can be calculated from a nonfasting sample.
The 2016 European Society of Cardiology (ESC)/European Atherosclerosis Society (EAS) lipid guidelines recommend calculating non-HDL-C for risk assessment, especially in patients with hypertriglyceridemia, and defined desirable non-HDL-C in individuals with diabetes or metablic syndrome as <130 mg/dL in high-risk patients and <100 mg/dL in very-high-risk patients [19]. The International Atherosclerosis Society defined optimal non-HDL-C as <130 mg/dL for primary prevention (particularly in high-risk patients, including those with diabetes with other risk factors) and <100 mg/dL for secondary prevention [26].
In patients with diabetes, non-HDL-C may remain elevated despite near-normal levels of LDL-C; therefore, non-HDL-C thresholds are included in the 2016 ACC Expert Consensus Decision Pathway on nonstatin therapy, in which non-HDL-C levels ≥130 mg/dL are considered higher risk in patients in diabetes [16••].
Apolipoprotein B
Measurement of apoB signifies the total burden of atherogenic particles; each chylomicron, VLDL, intermediate-density lipoprotein (IDL), LDL, and lipoprotein (a) particle has one molecule of apoB [21]. In several studies and post hoc analyses, apoB was a better predictor of ASCVD than LDL-C [27, 28]. ApoB and LDL-P also appear to be more closely associated with diabetes [29,30,31]. In a recently published retrospective analysis of 851 patients, with a subset of 419 individuals with diabetes or metabolic syndrome, the correlation between apoB and non-HDL-C was lower in individuals with diabetes or metabolic syndrome [32]. This study and others [21] concluded that apoB is likely a better marker to assess ASCVD risk in diabetic patients with elevated triglycerides. However, in an analysis of 9026 participants with obesity and insulin resistance syndromes, including diabetes, in the Atherosclerosis Risk in Communities (ARIC) study, apoB was not a superior prognostic marker of incident coronary heart disease (CHD) risk to non-HDL-C [33].
The 2016 European Guidelines on Cardiovascular Disease Prevention found no evidence that apoB was better than LDL-C for ASCVD risk prediction [20], and in the 2013 ACC/AHA cholesterol guidelines, apoB measurement for assessment of ASCVD risk was considered of uncertain value [14]. However, for individuals with diabetes or metabolic syndrome, the 2016 ESC/EAS lipid guidelines defined desirable apoB concentration as <100 mg/dL in high-risk patients and <80 mg/dL in very-high-risk patients [19].
Triglycerides
The role of triglycerides as a direct measure of ASCVD risk is elusive. In a meta-analysis of prospective studies including 300,000 men and women, triglyceride was correlated with ASCVD; however, this association was significantly lowered after adjustment for non-HDL-C and HDL-C levels [34]. In the Pravastatin or Atorvastatin Evaluation and Infection Therapy (PROVE-IT) study, lower on-treatment triglyceride level (<150 mg/dL) was associated with reduced ASCVD risk, compared with higher triglyceride level, independent of the level of LDL-C [35]. A recent review of the literature and genome-wide association studies suggested that triglycerides and triglyceride-rich lipoproteins are in the causal pathway of ASCVD [36].
The AHA scientific statement on triglycerides and ASCVD classified fasting triglyceride levels <100 mg/dL as optimal and <150 mg/dL as normal [37]. In the 2016 ADA guidelines, triglyceride levels ≥150 mg/dL are considered elevated [38]. The presence of hypertriglyceridemia is a marker for elevated triglyceride-rich lipoproteins, which, because of their atherogenic potential, should be included in ASCVD risk assessment especially in patients with diabetes, who often have increased production and impaired clearance of triglyceride-rich lipoproteins [39].
High-Density Lipoprotein Cholesterol
Low HDL-C, typically in conjunction with elevated triglycerides, is the most common diabetic dyslipidemia [38]. Although low HDL-C is a marker of ASCVD risk, it has not been established as a risk factor in clinical trials of HDL-C–raising therapies. The Atherothrombosis Intervention in Metabolic Syndrome With Low HDL/High Triglycerides: Impact on Global Health Outcomes (AIM-HIGH) trial evaluated 3414 patients (34% of whom had diabetes) who were randomly assigned to receive niacin or placebo in addition to simvastatin ± ezetimibe. The trial was stopped prematurely for lack of benefit on the composite endpoint of ASCVD events, despite the rise in the HDL-C with niacin combination therapy [40]. Similarly, in the Heart Protection Study 2–Treatment of HDL to Reduce the Incidence of Vascular Events (HPS2-THRIVE) in 25,673 patients (32% with diabetes), adding extended-release niacin–laropiprant to statin did not reduce ASCVD event risk and increased risk for serious adverse events [41]. Increasing HDL-C with cholesteryl ester transfer protein (CETP) inhibition has also failed to reduce ASCVD event rates in clinical trials [42, 43]. Genetic studies also have not shown the expected ASCVD benefit of polymorphisms that increase HDL-C levels [44].
The 2016 ADA guidelines define low HDL-C levels as <40 mg/dL in men and <50 mg/dL in women [38].
Hemoglobin A1c
Glycosylated hemoglobin (HbA1c) reflects the glycemic index of the hemoglobin for the past 8–12 weeks. It is the most commonly used test in diabetes assessment along with fasting glucose. Mounting evidence supports the association of elevated HbA1c, even below the threshold for diagnosis of diabetes, with adverse cardiovascular outcomes after adjustment for traditional cardiovascular risk factors [45,46,47]. However, as with HDL-C, clinical trials of interventions to improve HbA1c have failed to demonstrate ASCVD benefit with intensive verus standard glycemic control [48,49,50], and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study was stopped early as a result of increased total and cardiovasular mortality in the intensive–glucose lowering group [48]; the cause for the excess mortality has not been determined [48]. On the basis of these trials, the ADA, ACC, and AHA issued a joint statement emphasizing an individualized approach to HbA1c evaluation [51].
Biomarkers
High-Sensitivity C-Reactive Protein
Hs-CRP is a marker of inflammation that has been associated with the development of diabetes [52] and atherosclerosis [53]. Several large studies have reported an association between hs-CRP concentration and ASCVD outcomes [54,55,56]. In the Women’s Health Study, the addition of hs-CRP to traditional risk factors improved ASCVD risk prediction [57, 58]. In the ARIC cohort, comparison of 6-year change in hs-CRP with incident diabetes and ASCVD indicated that individuals with increased hs-CRP or sustained hs-CRP elevation had increased risk for incident diabetes compared with individuals whose hs-CRP remained low/moderate; individuals with sustained hs-CRP elevations also had increased risk for CHD, ischemic stroke, heart failure, and mortality [59•].
The 2013 ACC/AHA guidelines did not include hs-CRP as a routine measurement but recommended selective use by clinicians [14]. The Centers for Disease Control and Prevention and AHA recommend using the mean of two hs-CRP measurements performed 2 weeks apart to minimize within-person variability and defined hs-CRP >3.0 mg/L as a high relative risk level [60]. Two large trials of anti-inflammatory therapies, Cardiovascular Inflammation Reduction Trial (CIRT) [61] and Canakinumab Anti-inflammatory Thrombosis Outcomes Study (CANTOS) [62], are ongoing.
N-terminal Pro–B-Type Natriuretic Peptide
N-terminal pro–B-type natriuretic peptide (NT-proBNP) is a hormone with natriuretic and vasodilatory properties that is secreted by cardiac ventricular myocytes in response to elevated ventricular filling pressures and increased wall stress [63]. NT-proBNP has a powerful association with ASCVD in both high-risk patients with established ASCVD and the general population [64].
Natriuretic peptides are inversely correlated with obesity. Patients with higher body mass index tend to have lower NT-proBNP levels [65], which may be secondary to increased NT-proBNP clearance receptors in adipose tissue [65], whereas higher NT-proBNP levels are associated with enhanced lipolysis and metabolism [66]. In the ARIC study, higher NT-proBNP levels were associated with increased risk of heart failure even among individuals with obesity [67].
The predictive value of NT-proBNP for ASCVD events has also been shown in individuals with diabetes [68, 69]. Normal NT-proBNP (<125 pg/mL) was a strong negative predictor of short-term ASCVD events and of higher predictive value than traditional ASCVD risk markers in 631 consecutive diabetic outpatients [68], and among elderly individuals with diabetes in the population-based Cassale Monferrato study, NT-proBNP was predictive of ASCVD events and of additive value to the albumin excretion rate for ASCVD risk prediction [69]. A clinic-based prospective study in diabetic patients also established the superiority of NT-proBNP to albuminuria for prediction of cardiac events [70]. This finding was successfully translated into clinical practice, using NT-proBNP to identify patients with diabetes warranting intensive work-up and primary preventive cardiovascular therapy in the NT-proBNP Selected Prevention of Cardiac Events in a Population of Diabetic Patients without a History of Cardiac Disease (PONTIAC) trial [71].
High-Sensitivity Cardiac Troponins
The development of high-sensitivity assays for cardiac troponins T (hs-cTnT) and I (hs-cTnI) enables earlier and more-sensitive detection, which facilitates the diagnosis of myocardial infarction [72,73,74] and the evaluation of these biomarkers for ASCVD risk prediction. Community-based studies have established strong associations between hs-cTnT and incident CHD, stroke, heart failure, and all-cause mortality. In the ARIC study, adding hs-cTnT and NT-proBNP to clinical characteristics significantly improved heart failure prediction [75], and among ARIC participants without clinical ASCVD, detectable hs-cTnT levels (≥3–13.9 ng/L) were more frequent in individuals with diabetes [76]. Elevated hs-cTnT (≥14 ng/L) also occurred more frequently in ARIC participants with diabetes and was associated with substantially increased risks for heart failure, mortality, and CHD [77], and combined assessment of hs-cTnT and NT-proBNP elevation in ARIC participants with diabetes identified a subgroup with twofold-increased risk for incident ASCVD after adjustment for traditional risk factors [78,79,80]. In the observational Zwolle Outpatient Diabetes Project Integrating Available Care (ZODIAC-37) in stable outpatients with diabetes, hs-cTnT levels were related to mortality; at 11-year follow-up of 1133 patients, 84% of those with elevated hs-cTnT (≥14 ng/L) had died, compared with 58% of those with low-detectable hs-cTnT (3–14 ng/L) and only 23% of those with undetectable hs-cTnT levels (<3 ng/L), suggesting the potential use of hs-cTnT as a marker for mortality in individuals with diabetes [81].
Hs-cTnI was evaluated for ASCVD risk prediction in a cohort study among asymptomatic adults and shown to improve prediction of incident CHD events beyond traditional risk factors combined with hs-CRP and estimated glomerular filtration rate (GFR) [79]. In a secondary-prevention study of pravastatin, both baseline hs-cTnI and 1-year change in hs-cTnI improved CHD risk prediction in models that included traditional risk factors and other biomarkers [82]. Among individuals with diabetes, a case–control study found significantly higher hs-cTnI levels in those with CHD than in those without CHD [80], and in the Cleveland Clinic GeneBank study, detectable hs-cTnI below the diagnostic threshold for myocardial infarction (9–29 ng/L) was strongly associated with 3-year incident ASCVD events in individuals with diabetes even after adjustment for traditional and other risk factors [83], suggesting a role for this biomarker in ASCVD risk assessment in diabetic patients.
Microalbuminuria and Chronic Kidney Disease
Microalbuminuria predicts increased risk for vascular disease complications [84, 85] as well as for the progression to overt nephropathy in patients with diabetes. Microalbuminuria was also a predictor of inducible ischemia in asymptomatic diabetes patients [86].
Diabetic nephropathy leads to overt CKD. Diabetic kidney disease (DKD), present in 34.5% of US adults with diabetes [87], is associated with substantially increased ASCVD morbidity and mortality [88]. Even mild albuminuria and slightly decreased GFR are strongly linked to elevated ASCVD and death risks [89]. It is important to note that while diabetes is the major contributor to CKD in patients with diabetes, other causes of CKD also need to be evaluated.
The ADA [90] and National Kidney Foundation [91] recommend measuring both urine albumin excretion and GFR annually to screen for DKD in all patients with type 2 diabetes.
Cardiac and Subclinical Atherosclerosis Evaluation
Electrocardiography
Asymptomatic patients with diabetes may have signs of previously unrecognized myocardial infarction on resting electrocardiography (ECG). In the United Kingdom Prospective Diabetes Study (UKPDS), one in every six newly diagnosed diabetic patients had ECG evidence of silent myocardial infarction [92]. Typical ECG abnormalities include abnormal Q-waves, deep T-wave inversions, left bundle branch block, and nonspecific ST-T wave changes, and warrant evaluation for ASCVD and inducible ischemia.
The AHA/ADA guidelines concluded that obtaining a resting ECG for cardiovascular risk stratification in asymptomatic adults with diabetes was reasonable [5•].
Coronary Calcium Score
Coronary artery calcium (CAC) screening can enhance risk prediction in asymptomatic individuals and increase the predictive value of the Framingham Risk Score [93]. In the Multi-Ethnic Study of Atherosclerosis (MESA), the adjusted risk for coronary events among participants without ASCVD at baseline was ∼7 times higher for CAC score >300 compared with CAC score of 0 [94]. The role of CAC scoring has also been well established in ASCVD risk stratification in diabetes; nearly 20% of asymptomatic patients with diabetes had markedly elevated CAC scores in several well-powered studies, and the absence of CAC indicated low risk of mortality among this high-risk population [95,96,97], suggesting that the use of CAC may help improve risk assessment in individuals with diabetes [98].
In the 2013 AHA/ACC guidelines, CAC scoring was recommended for further risk assessment in intermediate-risk patients [14]. Commonly used CAC score categories for plaque burden estimation are 0 (no identifiable disease), 1–99 (mild disease), 100–399 (moderate disease), and ≥400 (severe disease).
Carotid Intima–Media Thickness
Carotid intima–media thickness (CIMT) assessment is noninvasive and uses nonionizing-radiation ultrasound to measure the combined thickness of the intima and media of the carotid artery wall. In numerous studies, CIMT was shown to be a surrogate marker of atherosclerosis and was associated with incident CHD and improved CHD risk prediction [99,100,101,102]. In the ARIC study, the addition of CIMT and ultrasound-assessed presence or absence of plaque to traditional risk factors improved CHD risk prediction, with a net reclassification index of 9.9% overall [103]. However, a meta-analysis of 14 population-based studies including 45,828 individuals found little improvement in prediction of first myocardial infarction or stroke with the addition of common CIMT to Framingham Risk Score [104]. Another meta-analysis, which included 16 population-based studies and 36,984 individuals without known ASCVD, showed that the mean of CIMT measurements at baseline and follow-up, but not change in CIMT, was predictive of ASCVD events [105].
The role of CIMT in ASCVD risk assessment in diabetes is also unclear. In an analysis from MESA, CIMT in individuals with diabetes, metabolic syndrome, or neither was not associated with CHD or ASCVD after adjustment for traditional risk factors [106]. However, in a Japanese study in 287 diabetic patients with carotid plaque but without any known ASCVD, ultrasound assessment of plaque thickness and, using gray-scale median, plaque echogenicity to determine presence of lipid-rich plaque showed that carotid plaque thickness was an independent predictor of ASCVD events after adjustment for traditional risk factors, and that inclusion of plaque echogenicity further improved risk prediction [107].
The 2010 ACCF/AHA Guideline for Assessment of Cardiovascular Risk in Asymptomatic Adults recommended CIMT for further cardiovascular risk assessment in asymptomatic adults at intermediate risk, based on clinical judgment [93]. However, the 2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk [18] and 2016 European Guidelines on Cardiovascular Disease Prevention in Clinical Practice [20] do not recommend routine CIMT testing for ASCVD risk assessment.
Myocardial Perfusion Scintigraphy and Other Imaging Modalities
Numerous studies have screened asymptomatic diabetes patients for ASCVD risk with nuclear scintigraphy [108]. The Detection of Ischemia in Asymptomatic Diabetics (DIAD) trial, a large randomized controlled study, showed that screening asymptomatic patients with diabetes with myocardial perfusion scintigraphy (MPS) was predictive of ASCVD events but did not lead to improved clinical outcomes [109]. Similarly, another large trial, Do You Need to Assess Myocardial Ischemia in Type 2 Diabetes (DYNAMIT), did not show improved clinical outcomes with screening for silent ischemia and was ended prematurely [110]. Current evidence does not support routine screening of patients with MPS.
Other imaging modalities used for ASCVD risk assessment in patients with diabetes include cardiac computed tomography angiography (CCTA) and cardiac magnetic resonance imaging; however, the risk–benefit profiles of these tests have not been established [5•]. The Screening for Asymptomatic Obstructive Coronary Artery Disease among High-Risk Diabetic Patients Using CT Angiography, Following Core 64 (FACTOR-64) trial, in which 900 asymptomatic patients with type 1 or type 2 diabetes were randomized to CCTA (and CCTA-directed standard or aggressive therapy) or standard care, showed no difference in the primary outcome of fatal or nonfatal cardiovascular events at 4-year follow-up [111]. Although some small studies had promising results in detection of occult CHD with CCTA [112], the current technology limits the utility of CCTA for general screening [113].
Testing for Other Diabetic Complications
Retinal Examination
Diabetic retinopathy is a sign of macrovascular disease and an indicator of ASCVD risk both type 1 and type 2 diabetes [114]. In a study in which 557 asymptomatic patients with type 2 diabetes were assessed for CHD by CCTA, retinopathy was an independent clinical predictor of significant CHD [115]. In ACCORD, severe retinopathy more than doubled the risk for ASCVD events, and each categorical increase in retinopathy increased ASCVD risk by 38% [116]. Retinopathy has also been linked to inducible ischemia [117].
The ADA recommends screening for diabetic retinopathy at the time of diagnosis, with reexaminations every 1–2 years depending on presence, progression, or severity of retinopathy [90].
Neuropathy
Cardiovascular neuropathy is divided into autonomic and peripheral neuropathies. Cardiovascular autonomic neuropathy has been studied more widely in diabetes and has been associated with poor prognosis [118] and elevated incidence of additional microvascular complications, including peripheral neuropathy [119]. Cardiovascular autonomic neuropathy is an independent risk factor for cardiovascular death and silent myocardial ischemia [120]. Peripheral neuropathy has also been associated with increased cardiovascular risk in individuals with diabetes and no prior history of ASCVD, and improved risk prediction when added to a model based on standard ASCVD risk factors [121].
The ADA recommends screening for diabetic neuropathy, including autonomic and peripheral, beginning at diagnosis of type 2 diabetes [90].
Conclusion
In summary, although type 2 diabetes was previously considered a CHD risk equivalent, more recent scientific and clinical data have revealed that individuals with diabetes have a spectrum of risk dependent on other risk factors [5•], therefore warranting an individualized approach to risk assessment. Enormous progress has been made in the prevention of ASCVD in diabetes, as reflected in reduced mortality from ASCVD causes among individuals with diabetes in the past two decades [122], but the increased risk in individuals with diabetes still lingers. Individuals with diabetes are 1.7 times more likely to suffer from ASCVD-related death and 1.8 times more likely to have a myocardial infarction than their nondiabetic counterparts [123]. Evaluation of concurrent traditional risk factors, other biomarkers, and imaging parameters may help provide more-comprehensive risk assessment in patients with diabetes (Table 1), although limited evidence on emerging markers warrants clinician judgment and individualized case-based selection of appropriate measures. Continuing research in this field will enable further progress toward a comprehensive risk assessment algorithm for ASCVD in diabetes.
References
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
• World Health Organization. Global report on diabetes. In: Geneva, Switzerland: WHO Press; 2016. p. 88. This is the first global report by the World Health Organization on the current prevalence and clinical complications of diabetes mellitus. It also describes the present economic burden of diabetes in countries by region and income, and government efforts in prevention and treatment worldwide.
Guariguata L, Whiting DR, Hambleton I, et al. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014;103:137–49.
Booth GL, Kapral MK, Fung K, Tu JV. Relation between age and cardiovascular disease in men and women with diabetes compared with non-diabetic people: a population-based retrospective cohort study. Lancet. 2006;368:29–36.
Emerging Risk Factors Collaboration. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375:2215–22.
• Fox CS, Golden SH, Anderson C, et al. Update on prevention of cardiovascular disease in adults with type 2 diabetes mellitus in light of recent evidence: a scientific statement from the American Heart Association and the American Diabetes Association. Circulation. 2015;132:691–718. The authors summarize findings from recent key clinical trials pertaining to lifestyle, blood glucose, blood pressure, and cholesterol for the primary prevention of cardiovascular disease in diabetes mellitus. Additionally, they provide a summary table for the current recommendations for managing cardiovascular disease risk factors (nutrition, obesity, blood glucose, blood pressure, and cholesterol) in type 2 diabetes.
Task Force on Diabetes, Pre-diabetes, and Cardiovascular Diseases of the European Society of Cardiology (ESC) et al. ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J. 2013;34:3035–87.
Mogensen CE. New treatment guidelines for a patient with diabetes and hypertension. J Hypertens Suppl. 2003;21:S25–30.
Deedwania PC. Blood pressure control in diabetes mellitus: is lower always better, and how low should it go? Circulation. 2011;123:2776–8.
Chobanian AV, Bakris GL, Black HR, et al. The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289:2560–72.
James PA, Oparil S, Carter BL, et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA. 2014;311:507–20.
Emdin CA, Rahimi K, Neal B, et al. Blood pressure lowering in type 2 diabetes: a systematic review and meta-analysis. JAMA. 2015;313:603–15.
Brunström M, Carlberg B. Effect of antihypertensive treatment at different blood pressure levels in patients with diabetes mellitus: systematic review and meta-analyses. BMJ. 2016;352:i717.
SPRINT Research Group. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med. 2015;373:2103–16.
Stone NJ, Robinson JG, Lichtenstein AH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63:2889–934.
Colhoun HM, Betteridge DJ, Durrington PN, et al. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet. 2004;364:685–96.
•• Lloyd-Jones DM, Morris PB, Ballantyne CM, et al. ACC Expert Consensus Decision Pathway on the role of non-statin therapies for LDL-cholesterol lowering in the management of atherosclerotic cardiovascular disease risk: a report of the American College of Cardiology Task Force on Clinical Expert Consensus Documents. J Am Coll Cardiol. 2016;68:92–125. This document discusses the role of nonstatin therapies for LDL-C lowering in the management of cardiovascular disease risk in light of data that became available after the 2013 US guidelines were published, including clinical trials of statin–nonstatin combinations and PCSK9 inhibitors, and provides specific algorithms for clinical practice.
Soran H, Durrington PN. Susceptibility of LDL and its subfractions to glycation. Curr Opin Lipidol. 2011;22:254–61.
Goff Jr DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013;63:2935–59.
Task Force for the Management of Dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS). 2016 ESC/EAS guidelines for the management of dyslipidaemias. Eur Heart J 2016 prepub.
Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice. European guidelines on cardiovascular disease prevention in clinical practice. Eur Heart J. 2016;37:2315–81.
Sniderman A, Williams K, Cobbaert C. ApoB versus non-HDL-C: what to do when they disagree. Curr Atheroscler Rep. 2009;11:358–63.
Ramjee V, Sperling LS, Jacobson TA. Non-high-density lipoprotein cholesterol versus apolipoprotein B in cardiovascular risk stratification: do the math. J Am Coll Cardiol. 2011;58:457–63.
Robinson JG, Wang S, Smith BJ, Jacobson TA. Meta-analysis of the relationship between non-high-density lipoprotein cholesterol reduction and coronary heart disease risk. J Am Coll Cardiol. 2009;53:316–22.
Thanassoulis G, Williams K, Ye K, et al. Relations of change in plasma levels of LDL-C, non-HDL-C and apoB with risk reduction from statin therapy: a meta-analysis of randomized trials. J Am Heart Assoc. 2014;3, e000759.
Boekholdt SM, Arsenault BJ, Mora S, et al. Association of LDL cholesterol, non-HDL cholesterol, and apolipoprotein B levels with risk of cardiovascular events among patients treated with statins: a meta-analysis. JAMA. 2012;307:1302–9.
Expert Dyslipidemia Panel. An International Atherosclerosis Society Position Paper: global recommendations for the management of dyslipidemia. J Clin Lipidol. 2013;7:561–5.
Walldius G, Jungner I, Holme I, et al. High apolipoprotein B, low apolipoprotein A-I, and improvement in the prediction of fatal myocardial infarction (AMORIS study): a prospective study. Lancet. 2001;358:2026–33.
van Lennep JE, Westerveld HT, van Lennep HW, et al. Apolipoprotein concentrations during treatment and recurrent coronary artery disease events. Arterioscler Thromb Vasc Biol. 2000;20:2408–13.
Sattar N, Williams K, Sniderman AD, et al. Comparison of the associations of apolipoprotein B and non-high-density lipoprotein cholesterol with other cardiovascular risk factors in patients with the metabolic syndrome in the Insulin Resistance Atherosclerosis Study. Circulation. 2004;110:2687–93.
Garvey WT, Kwon S, Zheng D, et al. Effects of insulin resistance and type 2 diabetes on lipoprotein subclass particle size and concentration determined by nuclear magnetic resonance. Diabetes. 2003;52:453–62.
Kathiresan S, Otvos JD, Sullivan LM, et al. Increased small low-density lipoprotein particle number: a prominent feature of the metabolic syndrome in the Framingham Heart Study. Circulation. 2006;113:20–9.
Barkas F, Elisaf M, Liberopoulos E, et al. High triglyceride levels alter the correlation of apolipoprotein B with low- and non-high-density lipoprotein cholesterol mostly in individuals with diabetes or metabolic syndrome. Atherosclerosis. 2016;247:58–63.
Ndumele CE, Matsushita K, Astor B, et al. Apolipoproteins do not add prognostic information beyond lipoprotein cholesterol measures among individuals with obesity and insulin resistance syndromes: the ARIC study. Eur J Prev Cardiol. 2014;21:866–75.
Emerging Risk Factors Collaboration. Major lipids, apolipoproteins, and risk of vascular disease. JAMA. 2009;302:1993–2000.
Miller M, Cannon CP, Murphy SA, et al. Impact of triglyceride levels beyond low-density lipoprotein cholesterol after acute coronary syndrome in the PROVE IT-TIMI 22 trial. J Am Coll Cardiol. 2008;51:724–30.
Budoff M. Triglycerides and triglyceride-rich lipoproteins in the causal pathway of cardiovascular disease. Am J Cardiol. 2016;118:138–45.
Miller M, Stone NJ, Ballantyne C, et al. Triglycerides and cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2011;123:2292–333.
American Diabetes Association. 8 Cardiovascular disease and risk management. Diabetes Care. 2016;39(1):S60–71.
Kreisberg RA. Diabetic dyslipidemia. Am J Cardiol. 1998;82:67U–73U. discussion 85U-86U.
Investigators AIM-HIGH. Niacin in patients with low HDL cholesterol levels receiving intensive statin therapy. N Engl J Med. 2011;365:2255–67.
HPS2-THRIVE Collaborative Group. Effects of extended-release niacin with laropiprant in high-risk patients. N Engl J Med. 2014;371:203–12.
Barter PJ, Caulfield M, Eriksson M, et al. Effects of torcetrapib in patients at high risk for coronary events. N Engl J Med. 2007;357:2109–22.
Schwartz GG, Olsson AG, Abt M, et al. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N Engl J Med. 2012;367:2089–99.
Voight BF, Peloso GM, Orho-Melander M, et al. Plasma HDL cholesterol and risk of myocardial infarction: a Mendelian randomisation study. Lancet. 2012;380:572–80.
Emerging Risk Factors Collaboration. Glycated hemoglobin measurement and prediction of cardiovascular disease. JAMA. 2014;311:1225–33.
Matsushita K, Blecker S, Pazin-Filho A, et al. The association of hemoglobin A1C with incident heart failure among people without diabetes: the Atherosclerosis Risk in Communities study. Diabetes. 2010;59:2020–6.
Selvin E, Steffes MW, Zhu H, et al. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. N Engl J Med. 2010;362:800–11.
Action to Control Cardiovascular Risk in Diabetes Study Group. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008;358:2545–59.
ADVANCE Collaborative Group. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med. 2008;358:2560–72.
Duckworth W, Abraira C, Moritz T, et al. Glucose control and vascular complications in veterans with type 2 diabetes. N Engl J Med. 2009;360:129–39.
Skyler JS, Bergenstal R, Bonow RO, et al. Intensive glycemic control and the prevention of cardiovascular events: implications of the ACCORD, ADVANCE, and VA diabetes trials: a position statement of the American Diabetes Association and a scientific statement of the American College of Cardiology Foundation and the American Heart Association. J Am Coll Cardiol. 2009;53:298–304.
Donath MY. Multiple benefits of targeting inflammation in the treatment of type 2 diabetes. Diabetologia. 2016;59:679–82.
Hotamisligil GS. Inflammation and metabolic disorders. Nature. 2006;444:860–7.
Ballantyne CM, Hoogeveen RC, Bang H, et al. Lipoprotein-associated phospholipase A2, high-sensitivity C-reactive protein, and risk for incident coronary heart disease in middle-aged men and women in the Atherosclerosis Risk in Communities (ARIC) study. Circulation. 2004;109:837–42.
Kuller LH, Tracy RP, Shaten J, et al. Relation of C-reactive protein and coronary heart disease in the MRFIT nested case-control study. Am J Epidemiol. 1996;144:537–47.
Pradhan AD, Manson JE, Rossouw JE, et al. Inflammatory biomarkers, hormone replacement therapy, and incident coronary heart disease: prospective analysis from the Women’s Health Initiative observational study. JAMA. 2002;288:980–7.
Ridker PM, Rifai N, Rose L, et al. Comparison of C-reactive protein and low-density lipoprotein cholesterol levels in the prediction of first cardiovascular events. N Engl J Med. 2002;347:1557–65.
Ridker PM, Paynter NP, Rifai N, et al. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation. 2008;118:2243–51. 2244p following 2251.
• Parrinello CM, Lutsey PL, Ballantyne CM, et al. Six-year change in high-sensitivity C-reactive protein and risk of diabetes, cardiovascular disease, and mortality. Am Heart J. 2015;170:380–9. This study analyzed hs-CRP in a large cohort of 10,160 ARIC participants over 6 years and found that persons with sustained elevations in hs-CRP were at the highest risk for incident cardiovascular disease and mortality, and those with increased hs-CRP or sustained hs-CRP elevations were at an increased risk for incident diabetes. The authors concluded that 2 hs-CRP measurements over a time are better for cardiovascular risk assessment.
Pearson TA, Mensah GA, Alexander RW, et al. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 2003;107:499–511.
Everett BM, Pradhan AD, Solomon DH, et al. Rationale and design of the Cardiovascular Inflammation Reduction Trial: a test of the inflammatory hypothesis of atherothrombosis. Am Heart J. 2013;166:199–207. e115.
Ridker PM, Thuren T, Zalewski A, Libby P. Interleukin-1b inhibition and the prevention of recurrent cardiovascular events: rationale and design of the Canakinumab Anti-inflammatory Thrombosis Outcomes Study (CANTOS). Am Heart J. 2011;162:597–605.
Braunwald E. Biomarkers in heart failure. N Engl J Med. 2008;358:2148–59.
Blankenberg S, Zeller T, Saarela O, et al. Contribution of 30 biomarkers to 10-year cardiovascular risk estimation in 2 population cohorts: the MONICA, Risk, Genetics, Archiving, and Monograph (MORGAM) biomarker project. Circulation. 2010;121:2388–97.
Wang TJ, Larson MG, Levy D, et al. Impact of obesity on plasma natriuretic peptide levels. Circulation. 2004;109:594–600.
Wang TJ. The natriuretic peptides and fat metabolism. N Engl J Med. 2012;367:377–8.
Ndumele CE, Matsushita K, Sang Y, et al. N-terminal pro-brain natriuretic peptide and heart failure risk among individuals with and without obesity: the Atherosclerosis Risk in Communities (ARIC) study. Circulation. 2016;133:631–8.
Huelsmann M, Neuhold S, Strunk G, et al. NT-proBNP has a high negative predictive value to rule-out short-term cardiovascular events in patients with diabetes mellitus. Eur Heart J. 2008;29:2259–64.
Bruno G, Landi A, Barutta F, et al. N-terminal probrain natriuretic peptide is a stronger predictor of cardiovascular mortality than C-reactive protein and albumin excretion rate in elderly patients with type 2 diabetes: the Casale Monferrato population-based study. Diabetes Care. 2013;36:2677–82.
Clodi M, Resl M, Neuhold S, et al. A comparison of NT-proBNP and albuminuria for predicting cardiac events in patients with diabetes mellitus. Eur J Prev Cardiol. 2012;19:944–51.
Huelsmann M, Neuhold S, Resl M, et al. PONTIAC (NT-proBNP selected PreventiOn of cardiac eveNts in a populaTion of dIabetic patients without a history of Cardiac disease): a prospective randomized controlled trial. J Am Coll Cardiol. 2013;62:1365–72.
Reichlin T, Hochholzer W, Bassetti S, et al. Early diagnosis of myocardial infarction with sensitive cardiac troponin assays. N Engl J Med. 2009;361:858–67.
Aldous SJ, Richards M, Cullen L, et al. Diagnostic and prognostic utility of early measurement with high-sensitivity troponin T assay in patients presenting with chest pain. CMAJ. 2012;184:E260–268.
Hochholzer W, Reichlin T, Twerenbold R, et al. Incremental value of high-sensitivity cardiac troponin T for risk prediction in patients with suspected acute myocardial infarction. Clin Chem. 2011;57:1318–26.
Nambi V, Liu X, Chambless LE, et al. Troponin T and N-terminal pro-B-type natriuretic peptide: a biomarker approach to predict heart failure risk—the Atherosclerosis Risk in Communities study. Clin Chem. 2013;59:1802–10.
Rubin J, Matsushita K, Lazo M, et al. Determinants of minimal elevation in high-sensitivity cardiac troponin T in the general population. Clin Biochem. 2016;49:657–62.
Selvin E, Lazo M, Chen Y, et al. Diabetes mellitus, prediabetes, and incidence of subclinical myocardial damage. Circulation. 2014;130:1374–82.
Gori M, Gupta DK, Claggett B, et al. Natriuretic peptide and high-sensitivity troponin for cardiovascular risk prediction in diabetes: the Atherosclerosis Risk in Communities (ARIC) study. Diabetes Care. 2016;39:677–85.
Iribarren C, Chandra M, Rana JS, et al. High-sensitivity cardiac troponin I and incident coronary heart disease among asymptomatic older adults. Heart. 2016;102:1177–82.
Segre CA, Hueb W, Garcia RM, et al. Troponin in diabetic patients with and without chronic coronary artery disease. BMC Cardiovasc Disord. 2015;15:72.
Hendriks SH, van Dijk PR, van Hateren KJ, et al. High-sensitive troponin T is associated with all-cause and cardiovascular mortality in stable outpatients with type 2 diabetes (ZODIAC-37). Am Heart J. 2016;174:43–50.
Tonkin AM, Blankenberg S, Kirby A, et al. Biomarkers in stable coronary heart disease, their modulation and cardiovascular risk: the LIPID biomarker study. Int J Cardiol. 2015;201:499–507.
Tang WH, Wu Y, Britt Jr EB, et al. Detectable subclinical myocardial necrosis is associated with cardiovascular risk in stable patients with diabetes. Diabetes Care. 2013;36:1126–31.
Gerstein HC, Mann JF, Yi Q, et al. Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA. 2001;286:421–6.
Grimm Jr RH, Svendsen KH, Kasiske B, et al. Proteinuria is a risk factor for mortality over 10 years of follow-up. Kidney Int Suppl. 1997;63:S10–14.
Rutter MK, McComb JM, Brady S, Marshall SM. Silent myocardial ischemia and microalbuminuria in asymptomatic subjects with non-insulin-dependent diabetes mellitus. Am J Cardiol. 1999;83:27–31.
de Boer IH, Rue TC, Hall YN, et al. Temporal trends in the prevalence of diabetic kidney disease in the United States. JAMA. 2011;305:2532–9.
Palsson R, Patel UD. Cardiovascular complications of diabetic kidney disease. Adv Chronic Kidney Dis. 2014;21:273–80.
Ninomiya T, Perkovic V, de Galan BE, et al. Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes. J Am Soc Nephrol. 2009;20:1813–21.
American Diabetes Association, Suppl 1. 9. Microvascular complications and foot care. Diabetes Care. 2016;39:S72–80.
Foundation NK. KDOQI clinical practice guideline for diabetes and CKD: 2012 update. Am J Kidney Dis. 2012;60:850–86.
Davis TM, Coleman RL, Holman RR, Group U. Prognostic significance of silent myocardial infarction in newly diagnosed type 2 diabetes mellitus: United Kingdom Prospective Diabetes Study (UKPDS) 79. Circulation. 2013;127:980–7.
Greenland P, Alpert JS, Beller GA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2010;122:2748–64.
Detrano R, Guerci AD, Carr JJ, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med. 2008;358:1336–45.
Hoff JA, Quinn L, Sevrukov A, et al. The prevalence of coronary artery calcium among diabetic individuals without known coronary artery disease. J Am Coll Cardiol. 2003;41:1008–12.
Scholte AJ, Bax JJ, Wackers FJ. Screening of asymptomatic patients with type 2 diabetes mellitus for silent coronary artery disease: combined use of stress myocardial perfusion imaging and coronary calcium scoring. J Nucl Cardiol. 2006;13:11–8.
Schurgin S, Rich S, Mazzone T. Increased prevalence of significant coronary artery calcification in patients with diabetes. Diabetes Care. 2001;24:335–8.
Agarwal S, Morgan T, Herrington DM, et al. Coronary calcium score and prediction of all-cause mortality in diabetes: the Diabetes Heart Study. Diabetes Care. 2011;34:1219–24.
Polak JF, Pencina MJ, Pencina KM, et al. Carotid-wall intima-media thickness and cardiovascular events. N Engl J Med. 2011;365:213–21.
O’Leary DH, Polak JF, Kronmal RA, et al. Carotid-artery intima and media thickness as a risk factor for myocardial infarction and stroke in older adults. N Engl J Med. 1999;340:14–22.
Bots ML, Hoes AW, Koudstaal PJ, et al. Common carotid intima-media thickness and risk of stroke and myocardial infarction: the Rotterdam study. Circulation. 1997;96:1432–7.
Chambless LE, Folsom AR, Clegg LX, et al. Carotid wall thickness is predictive of incident clinical stroke: the Atherosclerosis Risk in Communities (ARIC) study. Am J Epidemiol. 2000;151:478–87.
Nambi V, Chambless L, Folsom AR, et al. Carotid intima-media thickness and presence or absence of plaque improves prediction of coronary heart disease risk: the ARIC (Atherosclerosis Risk In Communities) study. J Am Coll Cardiol. 2010;55:1600–7.
Den Ruijter HM, Peters SA, Anderson TJ, et al. Common carotid intima-media thickness measurements in cardiovascular risk prediction: a meta-analysis. JAMA. 2012;308:796–803.
Lorenz MW, Polak JF, Kavousi M, et al. Carotid intima-media thickness progression to predict cardiovascular events in the general population (the PROG-IMT collaborative project): a meta-analysis of individual participant data. Lancet. 2012;379:2053–62.
Malik S, Budoff MJ, Katz R, et al. Impact of subclinical atherosclerosis on cardiovascular disease events in individuals with metabolic syndrome and diabetes: the multi-ethnic study of atherosclerosis. Diabetes Care. 2011;34:2285–90.
Irie Y, Katakami N, Kaneto H, et al. The utility of ultrasonic tissue characterization of carotid plaque in the prediction of cardiovascular events in diabetic patients. Atherosclerosis. 2013;230:399–405.
Moralidis E, Didangelos T, Arsos G, et al. Myocardial perfusion scintigraphy in asymptomatic diabetic patients: a critical review. Diabetes Metab Res Rev. 2010;26:336–47.
Young LH, Wackers FJ, Chyun DA, 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;301:1547–55.
Lievre MM, Moulin P, Thivolet C, et al. Detection of silent myocardial ischemia in asymptomatic patients with diabetes: results of a randomized trial and meta-analysis assessing the effectiveness of systematic screening. Trials. 2011;12:23.
Muhlestein JB, Lappe DL, Lima JA, 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;312:2234–43.
Scholte AJ, Schuijf JD, Kharagjitsingh AV, et al. Prevalence of coronary artery disease and plaque morphology assessed by multi-slice computed tomography coronary angiography and calcium scoring in asymptomatic patients with type 2 diabetes. Heart. 2008;94:290–5.
Rivera JJ, Nasir K, Choi EK, et al. Detection of occult coronary artery disease in asymptomatic individuals with diabetes mellitus using non-invasive cardiac angiography. Atherosclerosis. 2009;203:442–8.
Klein BE, Klein R, McBride PE, et al. Cardiovascular disease, mortality, and retinal microvascular characteristics in type 1 diabetes: Wisconsin Epidemiologic Study of Diabetic Retinopathy. Arch Intern Med. 2004;164:1917–24.
Park GM, Lee SW, Cho YR, et al. Coronary computed tomographic angiographic findings in asymptomatic patients with type 2 diabetes mellitus. Am J Cardiol. 2014;113:765–71.
Gerstein HC, Ambrosius WT, Danis R, et al. Diabetic retinopathy, its progression, and incident cardiovascular events in the ACCORD trial. Diabetes Care. 2013;36:1266–71.
Akasaka T, Yoshida K, Hozumi T, et al. Retinopathy identifies marked restriction of coronary flow reserve in patients with diabetes mellitus. J Am Coll Cardiol. 1997;30:935–41.
Vinik AI, Maser RE, Mitchell BD, Freeman R. Diabetic autonomic neuropathy. Diabetes Care. 2003;26:1553–79.
Valensi P, Huard JP, Giroux C, Attali JR. Factors involved in cardiac autonomic neuropathy in diabetic patients. J Diabetes Complicat. 1997;11:180–7.
Pop-Busui R, Evans GW, Gerstein HC, et al. Effects of cardiac autonomic dysfunction on mortality risk in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Diabetes Care. 2010;33:1578–84.
Brownrigg JR, de Lusignan S, McGovern A, et al. Peripheral neuropathy and the risk of cardiovascular events in type 2 diabetes mellitus. Heart. 2014;100:1837–43.
Gore MO, Patel MJ, Kosiborod M, et al. Diabetes mellitus and trends in hospital survival after myocardial infarction, 1994 to 2006: data from the national registry of myocardial infarction. Circ Cardiovasc Qual Outcomes. 2012;5:791–7.
Centers for Disease Control and Prevention. National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in the United States, 2014. In: Atlanta, GA: U.S. Department of Health and Human Services; 2014.
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Anum Saeed declares that she has no conflict of interest.
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Christie Ballantyne has received grant/research support (all paid to institution, not individual) from Abbott Diagnostic, Amarin, Amgen, Eli Lilly, Esperion, Ionis, Novartis, Pfizer, Regeneron, Roche Diagnostic, Sanofi-Synthelabo, NIH, AHA, and ADA, and is a consultant for Abbott Diagnostics, Amarin, Amgen, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Esperion, Ionis, Matinas BioPharma Inc, Merck, Novartis, Pfizer, Regeneron, Roche Diagnostic, and Sanofi-Synthelabo; provisional patent (patent no. 61721475) entitled “Biomarkers to Improve Prediction of Heart Failure Risk” has been filed by Baylor College of Medicine and Roche.
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Saeed, A., Ballantyne, C.M. Assessing Cardiovascular Risk and Testing in Type 2 Diabetes. Curr Cardiol Rep 19, 19 (2017). https://doi.org/10.1007/s11886-017-0831-4
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DOI: https://doi.org/10.1007/s11886-017-0831-4