Quantitative imaging biomarkers for the evaluation of cardiovascular complications in type 2 diabetes mellitus

Journal of Diabetes and Its Complications 28 (2014) 234–242

Contents lists available at ScienceDirect

Journal of Diabetes and Its Complications journal homepage: WWW.JDCJOURNAL.COM

Quantitative imaging biomarkers for the evaluation of cardiovascular complications in type 2 diabetes mellitus☆ Kai Lin a, Donald M. Lloyd-Jones b, Debiao Li a, 1, James C. Carr a,⁎ a b

Department of Radiology, Northwestern University Feinberg School of Medicine, 737 N Michigan Avenue, Suite 1600, Chicago, IL 60611, USA Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N Lake shore drive, Suite 1400, Chicago, IL 60611, USA

a r t i c l e

i n f o

Article history: Received 9 May 2013 Received in revised form 19 September 2013 Accepted 19 September 2013 Available online 14 October 2013 Keywords: T2DM Cardiovascular complications Imaging biomarkers

a b s t r a c t Type 2 diabetes mellitus (T2DM) is a prevalent condition in aged populations. Cardiovascular diseases are leading causes of death and disability in patients with T2DM. Traditional strategies for controlling the cardiovascular complications of diabetes primarily target a cluster of well-defined risk factors, such as hyperglycemia, lipid disorders and hypertension. However, there is controversy over some recent clinical trials aimed at evaluating efficacy of intensive treatments for T2DM. As a powerful tool for quantitative cardiovascular risk estimation, multi-disciplinary cardiovascular imaging have been applied to detect and quantify morphological and functional abnormalities in the cardiovascular system. Quantitative imaging biomarkers acquired with advanced imaging procedures are expected to provide new insights to stratify absolute cardiovascular risks and reduce the overall costs of health care for people with T2DM by facilitating the selection of optimal therapies. This review discusses principles of state-of-the-art cardiovascular imaging techniques and compares applications of those techniques in various clinical circumstances. Individuals measurements of cardiovascular disease burdens from multiple aspects, which are closely related to existing biomarkers and clinical outcomes, are recommended as promising candidates for quantitative imaging biomarkers to assess the responses of the cardiovascular system during diabetic regimens. © 2014 Elsevier Inc. All rights reserved.

1. Introduction The cardiovascular system serves as the key infrastructure that delivers blood to the entire body, and it is vulnerable to metabolic diseases. A spectrum of cardiovascular diseases represent the major complications of diabetes and significant contributors to death and disability among people suffering from this endocrinological disorder (Polonsky, 2012). There are complicated pathological links between diabetes and cardiovascular diseases on the cellular and molecular levels. From an epidemiological perspective, diabetes has been weighted as an “equivalence” of preexisting coronary artery disease (CAD) in the risk assessment of clinical events (Haffner, Lehto, Ronnemaa, Pyorala, & Laakso, 1998). Patients with diabetes are considered to be at an early stage in the process leading to the development of heart failure (HF) even when there is no structural evidence of heart disease or any acute symptoms (Jessup et al., 2009). ☆ No conflict of interest was reported for all authors. ⁎ Corresponding author. Department of Radiology, Northwestern University, 737 N Michigan Avenue, Suite 1600, Chicago, IL 60611, USA. Tel.: +1 312 926 5113; fax: +1 312 926 5991. E-mail address: [email protected] (J.C. Carr). 1 Current address: Cedars Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA 90048. 1056-8727/$ – see front matter © 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jdiacomp.2013.09.008

The prevalence of stroke in diabetic patients is significantly higher than in healthy controls (Janghorbani et al., 2007). Diabetic nephropathy, diabetic retinopathy and peripheral artery disease (PAD) are also frequently observed as severe and disabling manifestations of vascular damage in diabetic patients (Beckman, Creager, & Libby, 2002; de Boer et al., 2011; Zhang et al., 2010). Current strategies for controlling the cardiovascular complications of diabetes primarily target a cluster of well-defined risk factors, such as hyperglycemia, lipid disorders and hypertension (Adler et al., 2000; Holman, Paul, Bethel, Matthews, & Neil, 2008; Saydah, Fradkin, & Cowie, 2004). For type 1 diabetes mellitus (T1DM), the Diabetes Control and Complications Trial (DCCT) demonstrated that intensive interventions targeting those traditional cardiovascular risk factors satisfactorily resulted in a lower incidence of various clinical events compared to the conventional diabetic regimens (Nathan et al., 2005). However, type 2 diabetes mellitus (T2DM), which accounts for nearly 95% diabetes cases, seems to present different challenges. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) randomized clinical trial, aggressive glycemic intervention, strict lipid management and tight blood pressure control in patients with T2DM were unable to reduce the incidence of cardiovascular events or deaths to the extent that had been anticipated (Cushman et al., 2010; Gerstein et al., 2008; Ginsberg et al., 2010). These unexpected

K. Lin et al. / Journal of Diabetes and Its Complications 28 (2014) 234–242

discrepancies between the observational studies and clinical trials led to debates regarding the efficacy and necessity of intensive therapies for T2DM. Because those disappointing clinical outcomes may possibly be due to irreversible organ damage in advanced T2DM patients, alternative clinical efficacy endpoints or biomarkers are highly desired to better evaluate the responses of the cardiovascular system to diabetes therapies. Cardiovascular imaging is a powerful tool for quantitative cardiovascular risk estimation (Greenland et al., 2010). Both structural and functional imaging methods have been developed to noninvasively monitor the progression of various cardiovascular diseases (Wadwa, 2007). Recently, morphological changes associated with cardiovascular diseases have been accepted as primary endpoints in clinical trials assessing the effect of pharmacological treatments for atherosclerosis (Nicholls et al., 2011). Therefore, multi-disciplinary quantitative imaging methods are promising to provide new insights in the management of patients with T2DM from multiple aspects. Moreover, imaging biomarkers, which could be used to monitor the cardiovascular burden/risk and evaluate the benefits and harms of treatments, also have the potential to help to reduce the overall costs of health care for people with T2DM by facilitating the selection of optimal therapies. In this review, we will focus on the recent findings of cardiac and vascular measurements associated with T2DM using state-of-the-art cardiovascular imaging techniques. By listing and comparing currently available noninvasive imaging approaches in clinical situations, we will also provide an overview of the progression of the cardiovascular complications in T2DM based on the usage of potential quantitative imaging biomarkers.

2. Noninvasive cardiovascular imaging techniques 2.1. Ultrasonography Ultrasonography is a traditional method for imaging the cardiovascular system. Ultrasound imaging utilizes the interaction of high frequency (usually N20 KHz) sound waves with living tissue to produce an image of the structure or organ. Ultrasonography can also detect moving organs and blood flow in regions such as the heart based on Doppler effects. Combined with novel imaging reconstruction techniques, newer echocardiographic modalities have been introduced to clinics for the evaluation of cardiovascular diseases (Steeds, 2011). Tissue Doppler imaging (TDI) is a novel echocardiographic technique to measure the velocity of myocardial structures (Douglas et al., 2007). An new echo technique, speckle tracking echocardiography (STE), can track myocardial deformation (strain) during cardiac cycles to assess the function (both global and regional) of the left ventricle (LV) (Mondillo et al., 2011). Three-dimensional (3D) echocardiography (3DE) represents another advancement. This technique can acquire real-time 3D data for the comprehensive assessment of cardiac function and motion, including ventricular mass and ejection faction (EF) (Mor-Avi, Sugeng, & Lang, 2009). Contrast echocardiography is also applied to produce clear endocardial border definition for quantitative analysis of myocardial function, mass and blood flow (Olszewski et al., 2007; Tong et al., 2005). Intravascular ultrasound (IVUS) is an invasive imaging examination to put a special probe in the vascular lumen, such as coronary artery. It allows to detect vessel wall with a high resolution using ultrasound technology. In addition to the application in the heart, ultrasonography also plays an important role in evaluating abnormalities in the whole vascular system. For surface vessels, such as the carotid artery and femoral artery, B-mode ultrasound may detect morphological changes, including thickening of the wall and occlusion (Schiano et al., 2012; Schulte-Altedorneburg et al., 2001). Transcranial Doppler (TCD) is a routine examination that helps diagnose intracranial

235

vascular diseases by measuring the velocity of blood flow through the intracranial vessels (Nakae, Yokota, Yoshida, & Teramoto, 2011). Ultrasound is generally considered safe. Currently, there are no reports of health risks associated with ultrasonography (except invasive procedures). However, not all tissues or structures in the body are suitable for the transduction of sound waves. Therefore, some tissue or organs cannot be reached by this examination because there is no optimal “acoustic window” (a pathway for sound waves between the probe and the target). Despite most ultrasonography examinations are cheap and convenient, IVUS is expensive and not available in some hospitals. 2.2. X-ray computed tomography (CT) X-ray CT was first introduced to clinical practice in 1972. Tomographic imaging is a procedure of measuring the intensity attenuation of the X-ray beams from multiple orientations. A CT scanner is typically comprised of an X-ray source (X-ray tube) and a series of detectors arranged in a matrix sounding the target. Various imaging reconstruction algorithms are used to synthesize images from the distribution of X-ray beams from multiple projections. Over the past decades, CT has technically evolved and become one of the most common medical imaging methods in most hospitals. Depicting the cardiovascular system requires fast imaging techniques. Taking advantages of spiral CT scanners equipped with multiple rows of detectors, an image can be obtained very quickly (less than 1 second) and such an advantage can “freeze” heartbeats and acquire images of the heart and its affiliated vessels. Therefore, Multidetector CT (MDCT) has wide applications in the cardiovascular system. For the heart, MDCT has been utilized for coronary CT angiography (CTA) to screen lumen stenosis, the major manifestation of CAD, in the coronary arteries (Miller et al., 2008). Recent improvements in the hardware have further shortened the imaging time for the CT scan. For example, dual-source CT (DSCT) holds two X-ray tubes located at a 90° angle, allowing acquisition and reconstruction of cross-sectional images at 82.5 ms. MDCT with 320 row detectors has reached a coverage of 16 cm on the z-axis. Such a high speed and a large coverage are sufficient for cardiovascular imaging under most physiological circumstances. Cardiac CT can be applied for scoring coronary calcification, a wellaccepted quantitative imaging biomarker for overall cardiovascular risk estimation. In addition, CT can also be applied for detecting cerebral vascular disease, PAD and renal dysfunction. However, the effects of ionizing radiation and iodinated contrast media are two major concerns for the safety of this method, especially in asymptomatic individuals at high risk of cardiovascular diseases. 2.3. Magnetic resonance imaging (MRI) In general, body tissue contains a high concentration of protons. Water is the biggest source of H + ions in the body. The spin direction of protons will be aligned with the direction of a large static magnetic field (B0, provided by the scanner). When a radio frequency (RF) field (B1) at the resonance frequency is imposed, the protons will absorbed energy and flip the spin angle to precess synchronously (in phase). After the RF field is tuned off, the spins of the protons will be realigned in B0. This process is called longitudinal relaxation (T1 decay). T1 time is defined as the duration that it takes for the longitudinal magnetization to reach 63% of its final value, assuming a 90° RF pulse. At the same time, the protons also begin to dephase due to several effects including spin-spin interactions, magnetic field inhomogeneities, magnetic susceptibility and chemical shift effects. Such a dephasing is called transverse relaxation (T2 decay). T2 time is defined as the duration that it takes for the transverse magnetization to decay to 37% of its original value Bitar et al., 2006.

236

K. Lin et al. / Journal of Diabetes and Its Complications 28 (2014) 234–242

During relaxations, the protons will emit energy (RF signal), which can be received by coils and then be processed to construct 2D and 3D images. Those characteristics are utilized to generate contrast between different body tissues by adjusting RF pulses to activate the protons with various manners. As such, MRI provides more histological information compared to CT and ultrasound. Actually, MRI comprises of multiple imaging methods (achieved by different pulse sequences and imaging parameters) to provide physiologic and anatomic information in vivo from multiple aspects at high resolution. Currently, MRI has become widely used in examining the cardiovascular system. For instance, cine MRI can detect heart structure and function. Magnetic resonance angiography (MRA) can examine coronary and peripheral artery occlusions with or without contrast media. Perfusion MRI may be used to detect cardiac or cerebral infarctions. With the help of molecular probes (tissue specified molecules), such as metal nanoparticles, MRI may also be used to detect subtle pathophysiological activities of cardiovascular diseases at the molecular level (Burtea et al., 2012). In addition, MRI may be used to noninvasively evaluate the ability of the body to produce or properly use insulin by directly imaging the β cell mass or changes in islets. Those pathological procedures are related to the etiological origins of diabetes (Medarova & Moore, 2009). MRI does not require any radiation exposure. The well-known limitations of MRI are many well-known contradictions of this examination, such as claustrophobia and paramagnetic implants. In addition, cardiac motion remains a major challenge for MRI's application. MRI examination is usually expensive and will need a long scan time. 2.4. Nuclear medicine Nuclear medicine refers to a medical imaging specialty that uses radioactive substances to diagnose diseases. Different from most other medical imaging modalities, such as CT or MRI, nuclear medicine is a cluster of diagnostic tests primarily focusing on the physiological function of the targets but not on anatomic abnormalities. In nuclear medicine imaging, radiopharmaceuticals, combinations of radionuclides (serving as tracers) and specific biologically active molecules (serving as carriers, such as metabolites or monoclonal-antibodies) for different clinical purposes, are taken intravenously or orally. Then, external detectors (such as gamma cameras) capture radiation emitted by the radiopharmaceuticals that have been taken up by target organs to reflect the functions of the tissues and generate images of the targets. Single-photon emission computed tomography (SPECT) is a nuclear medicine tomographic imaging technique using gamma rays to acquire multiple images from different angles. 3D images could be reconstructed using tomographic reconstruction algorithms, similar to those used in MRI and CT, to show the target using multiple projections (Germano & Berman, 2006). Positron emission tomography (PET) is another nuclear medicine technique that reveals functional processes in the body by detecting pairs of gamma rays emitted indirectly by a positron-emitting radionuclide. For example, a commonly used biologically active molecule for PET is fluorodeoxyglucose ( 18 F) (FDG). The structure of FDG is similar to glucose except that it contains the radioactive isotope fluorine-18. This glucose analogue, which can emits positrons, may be taken up by tissues during regular metabolic activities. Therefore, imaging the concentrations of FDG reveals that tissue's metabolic activity regarding glucose. In the cardiovascular system, PET and SPECT have been regularly used to detect myocardial infarction and blood flow in the heart. Recently, the Standardized Uptake Value (SUV), which is a semiquantitative analysis for PET (calculated as the ratio of the tissue's concentration of radioactivity and the injected dose divided by the

body weight), has been considered as an important index for atherosclerosis (Tahara et al., 2007). However, PET could not provide images with high spatial resolution. Therefore, a PET scanner is structurally combined with a CT or an MR scanner as a PET-CT or a PET-MR. In such hybrid systems, functional images obtained by PET will be fused or merged with highresolution anatomic images to precisely localize the metabolic or biochemical progression (Lee et al., 2012). PET examination is expensive and may be not available in some hospitals. 2.5. Noninvasive evaluation of cardiovascular complications in T2DM using multi-disciplinary imaging 2.5.1. Heart diseases For T2DM, atherosclerotic CAD and cardiomyopathy are two major cardiovascular complications in the heart. 2.5.1.1. CAD. X-ray angiography is the standard invasive procedure for diagnosing CAD in clinical practice. MDCT is a commonly performed clinical examination to screen for and diagnose CAD in high-risk populations. With a 64-slice MDCT scanner, Sun et al. (2008) reported that CTA has a good accuracy in identifying and quantifying coronary atherosclerotic plaques using IVUS, a well accepted invasive examination method, as reference. In a multicenter clinical trial for patients without documented CAD who suffered chest pain, 64-slice MDCT has high diagnostic accuracy for detection of occlusive coronary stenosis in 230 subjects (Budoff et al., 2008). According to many clinical studies, nearly the negative predictive value at nearly 95% suggested that CTA may serve as an effective noninvasive alternative to traditional examinations, such as X-ray angiography and IVUS, to rule out suspicious CAD (Hamon et al., 2006). For asymptomatic T2DM patients, there is a severe burden of coronary atherosclerosis lesions represented by coronary calcification (Budoff et al., 2005). CTA could be used to detect and quantify such a trend and predict clinical events. Coronary artery calcium (CAC) score, calculated based on the severity of calcification of coronary tree detected with MDCT, is a strong predictor of cardiovascular events in T2DM patients (Agarwal et al., 2011). Including CAC will significantly improve the efficacy of the Framingham risk score (FRS), an established quantitative cardiovascular risk estimation system, for predicting future cardiovascular events (Kavousi et al., 2012). Recently, MRI has been adopted for detecting and quantifying clinical or subclinical CAD in clinical study. Coronary MRA has high accuracy for identifying coronary stenosis. Using a 1.5-T MRI scanner, Sakuma et al acquired non-contrast free-breathing steady-state free precession (SSFP) whole-heart coronary MRA images in 138 patients with suspected CAD. At a patient based analysis, the sensitivity, specificity, positive and negative predictive values, and accuracy of MRA for detecting coronary stenosis were 88%, 72%, 71%, 88% and 79%, respectively (Kato et al., 2010). With contrast-enhanced MRA (CEMRA), Yang et al. reported that the sensitivity, specificity, and accuracy for identifying significant coronary stenoses were 91.6%, 83.1%, and 84.1%, respectively (Yang et al., 2009). However, most acute cardiovascular events were associated with superficial erosion or rupture of plaque on the coronary wall without severe obstruction in the lumen (Burke et al., 1997; Falk, Shah, & Fuster, 1995; Virmani, Kolodgie, Burke, Farb, & Schwartz, 2000). MRI can also be used to detect and quantify subclinical atherosclerosis on the coronary wall. The biomechanical features associated with arterial remodeling, such as the stiffness, are related to atherosclerosis. Both the morphological and biomechanical features of the coronary wall may serve as quantitative imaging biomarkers for evaluating the progression of atherosclerotic lesions. The vascular stiffness can be estimated by quantifying distensibility, compliance coefficients, pulse wave velocity and wave reflection (Stehouwer, Henry, & Ferreira, 2008).

K. Lin et al. / Journal of Diabetes and Its Complications 28 (2014) 234–242

Taniwaki et al. found that there was a significant positive relationship between the carotid intima-median thickness (IMT) and arterial stiffness in diabetic patients (Taniwaki et al., 1999). Miao et al. found that MRI can quantitatively depict the positive remodeling of the coronary wall in asymptomatic elders (Miao et al., 2009). Using noncontrast 3D coronary MRA, Lin et al. detected the lower coronary artery dispensability in T2DM patients compared with healthy controls (Lin et al., 2011). Significant differences in both morphological and functional features of coronary remodeling were also detected between older hypertensive patients and healthy controls (Lin et al., 2012). MRI, CT and nuclear medicine can also be used to visualize myocardial perfusion both at rest and during adenosine stress with induced vasodilatation. Adenosine stress dynamic contrast enhanced (DCE)-MRI with delayed enhancement can be used to assess potential myocardial ischemia in patients with CAD (Parra & Vera, 2012). With a DSCT, Wang et al. (2012) proved that adenosine-stress CT perfusion detects in myocardial ischemic lesions correspond to the findings of SPECT. Using rubidium-82 as a tracer, Murthy et al. (2012) studied myocardial perfusion in 2783 consecutive patients (1172 with and 1611 without diabetes mellitus) with a PET scanner. All subjects were followed up for a median duration of 1.4 years. The primary end point was cardiac death. The authors found that among diabetic patients without CAD, those with an impaired coronary flow reserve (CFR, an index for measuring coronary vasodilator function) have cardiovascular event rates comparable to those patients with a history of CAD. 2.5.1.2. Diabetic cardiomyopathy. The term “diabetic cardiomyopathy” is defined as cardiac dysfunction or heart failure that occurs in diabetic patient and cannot be explained by common cardiovascular diseases, such as coronary artery disease or hypertension (Zarich & Nesto, 1989). The pathological mechanisms resulting in diabetic cardiomyopathy are still under study. Major manifestations of diabetic cardiomyopathy are cardiac dysfunction and structural abnormality of the heart. Doppler ultrasound is a traditional method for evaluating cardiac function and structure (Garcia, Thomas, & Klein, 1998). Both diastolic function (compliance of the heart) and systolic function (cardiac contractility) are impaired in diabetic patients. However, diastolic dysfunction is usually an early sign of diabetic cardiomyopathy, especially in asymptomatic patients (Schannwell, Schneppenheim, Perings, Plehn, & Strauer, 2002). The noninvasive assessment of diastolic dysfunction mainly relies on measuring blood flow patterns, including mitral inflow velocities, deceleration time, and isovolumic relaxation time (Sohn et al., 1997). Cerutti et al. found reduced LV compliance in children with T1DM compared with healthy controls (Cerutti et al., 1994). Poirier et al. studied 46 men with T2DM without evidence of diabetic complications, hypertension, coronary artery disease or congestive HF, and LV diastolic dysfunction was found in 28 (60%) subjects (Poirier, Bogaty, Garneau, Marois, & Dumesnil, 2001). Fang et al. demonstrated evidence of systolic dysfunction, a lower peak strain (nonlinear myocardial deformation) and a lower strain rate in 48 T2DM patients with a normal ejection fraction and no evidence of CAD (Fang et al., 2003). In addition to cardiac function, remodeling of the cardiac structure could also be evaluated by cardiac ultrasound. Dawson, Morris, & Struthers (2005) investigated 500 subjects with T2DM using echocardiography, and found left ventricle hypertrophy (LVH) in 71% participants. Patients with greater LV mass and a higher prevalence of LVH are at a high risk for sudden death (Okin et al., 2001). Over the past decade, cardiac MRI has become an alternative method (sometimes considered as a “gold standard”) to provide quantitative information of cardiac functions and morphological changes (Alfakih, Reid, Jones, & Sivananthan, 2004). Measurements performed by MRI can be used to quantify ventricular volumes, myocardial mass, ejection fraction and ventricular performance. The

237

use of velocity encoded phase MRI techniques has been applied to the estimation myocardial function and blood flow through various cardiovascular structures. This has allowed profiling of flow acceleration jets, determination of regurgitation volumes and fractions, and calculation of cardiac shunts. Recently, velocity-encoded MRI has been used to assess systolic and diastolic velocities in the LV using TDI as a reference (Marsan et al., 2008; Paelinck et al., 2007). Ernande et al. (2012) found that Cine DENSE, a motion-encoding MRI technique for myocardial strain assessment with high spatial resolution, appears to be useful in the identification of subclinical myocardial dysfunction in patients with diabetes. Myocardial tagging is an MRI technique used for quantification of strain during cardiac cycles and it has been applied to the LV with good reproducibility (Jeung et al., 2012). It allows the visualization of transmural myocardial movement by generating a reference system with grid patterns using special magnetization saturation pulses. Recently, the emergence of fast CT techniques has further extended the application of CT to evaluate myocardial function and morphology. DSCT provides reliable measurements of mass, dimensions, volumes and myocardial strain for LV with similar results compared to cardiac echocardiography (Stolzmann et al., 2008). 2.6. Cerebral vascular diseases The duration of diabetes is independently associated with the risk of ischemic stroke after adjusting for other cardiovascular risk factors. The risk of stroke increases 3% for each year, and it triples in patients who have had diabetes for more than 10 years (Banerjee et al., 2012). Furthermore, many large clinical studies found close relationships between existing hyperglycemia and a worse clinical outcome for individuals with ischemic stroke (Bruno, Williams, & Kent, 2004; Bruno et al., 1999; Moulin et al., 1997; Weir, Murray, Dyker, & Lees, 1997). MR and CT are traditional methods for diagnosing stroke and intracerebral hemorrhage. In addition, they are useful tools for finding and quantifying possible indicators of neuropathy in diabetic patients. 2.6.1. Microbleeds in the brain For stoke risk prediction, people with both diabetes and signs of retinal microvascular lesions (such as arteriovenous nicking, hemorrhages and microaneurysms) are more likely to have multiple microbleeds in the brain detected with MRI (Qiu et al., 2008). Imamine et al. found that cerebral small vascular disease observed on MRI is a sensitive biomarker for predicting future cognitive decline, and monitoring treatment through the use of such markers is expected to help maintain a good quality of life for older T2DM patients (Imamine et al., 2011). 2.6.2. Vascular atherosclerosis The IMT is a measurement of the thickness of two inner layers of the arterial wall, including the intima and media, using ultrasound. Bots et al. studied 7983 older subjects (≥55 year-old) and demonstrated that an increased common carotid IMT is associated with future cerebrovascular and cardiovascular events (Bots, Hoes, Koudstaal, Hofman, & Grobbee, 1997). The IMT has been found to be higher in diabetic patients (Napoli et al., 2012). T2DM was associated with a 0.13 mm increase in the IMT compared with control subjects (Brohall, Oden, & Fagerberg, 2006). MRI is also a powerful method for imaging stroke-related carotid atherosclerosis in T2DM (Lin, Zhang, Detrano, Lu, & Fan, 2006). Esposito et al. (2010) studied 191 patients with moderate to high-grade carotid artery stenosis and found that T2DM is an independent predictor for the development of vulnerable carotid plaques which is associated with cerebrovascular events. However, a recent meta-analysis of existing large-scale clinical studies noted that IMT measurements may only have a limited contribution to the 10-

238

K. Lin et al. / Journal of Diabetes and Its Complications 28 (2014) 234–242

year risk assessment of first-time myocardial infarction or ischemic stroke when they are combined with the FRS (Den Ruijter et al., 2012). MRI can also be used to detect intracranial atherosclerosis related with stroke (Klein, Lavallee, Touboul, Schouman-Claeys, & Amarenco, 2006). In addition, atherosclerosis is an inflammatory disease (Ross, 1999). Inflammation can be noninvasively imaged by PET. Bucerius et al. (2012) quantified carotid artery wall FDG uptake in 134 patients and found that T2DM significantly affected the level of SUV in patients who have known or suspected cardiovascular atherosclerotic lesions. 2.7. Diabetic retinopathy The incidence of retinopathy, including retinal vascular stenosis and hemorrhage, is related to elevated plasma glucose levels and high blood pressure (Avery et al., 2012). Diabetic retinopathy was traditionally diagnosed using fundus examination. Abnormal blood flow in the retinal artery is a manifestation of early diabetic retinal artery atherosclerosis (Burgansky-Eliash et al., 2012). Hence, MRI has the potential to detect the early stages of retinopathy in patients with T2DM (Bursell et al., 1996). Color Doppler ultrasonography can detect changes of blood flow in the retinal artery (Gracner, 2004). Laser scanning flowmetry (LSF), can be used to measure the velocity of blood flows using the Doppler effect of a laser beam and this method was able to detect very early changes of flow in the retinal artery in diabetic patients (Forst et al., 2012). Optical coherence tomography (OCT), a tissue imaging technique, can also detect retinal blood flow (Wang et al., 2011). Recently, subtle differences in retinovascular function between patients with diabetes and non-diabetic individuals, including changes that occur in advance of the clinical appearance of diabetic retinopathy, can be detected with MRI using a dedicated surface coil (Trick et al., 2008). 2.8. PAD PAD, which is common in patients of T2DM, is an occlusive vascular disease caused by atherosclerosis. Various noninvasive imaging approaches can be used to diagnose PAD at lower limbs. The ankle-brachial index (ABI) is a commonly used noninvasive imaging index that is determined in the diagnosis of PAD with Doppler ultrasound (McDermott et al., 2000). Duplex ultrasound scanning is an accurate method for detection and evaluation of occlusive lesions and local blood flow in the lower limbs for PAD (Koelemay et al., 1996). MRI has become a standard technique used to diagnose PAD. CEMRA has been applied to detect stenosis or occlusion in the lower extremities with high accuracy and has become a standard method for diagnosing PAD (Koelemay, Lijmer, Stoker, Legemate, & Bossuyt, 2001). Recently, non-contrast MRA has been introduced to image clinical patients with PAD to avoid the side effects of contrast media, such as renal dysfunction or nephrogenic systemic fibrosis (NSF) (Hodnett et al., 2011; Prince et al., 2008). However, compared to other imaging techniques CE-MRA, long imaging time, which may seriously degrade image quality, is the major disadvantage of non-contrast MRA. MRI can also be used to evaluate the atherosclerotic burden on the involved vessel walls in the lower limbs (McDermott et al., 2011). Bourque et al found that diabetic patients have significantly greater atherosclerotic burden in the superficial femoral artery on MRI than healthy controls (Bourque et al., 2012). In addition, CT angiography (CTA) also offers similar accuracy compared with X-ray angiography for detecting PAD (Napoli et al., 2011). 2.9. Diabetic nephropathy Diabetic nephropathy is diagnosed based on symptoms and lab tests in clinical practice. A kidney biopsy may finally confirm the

diagnosis. In patients with T2DM, nephropathy should be screened yearly after the diagnosis is made (Gross et al., 2005). Abnormal size and shape of the kidney usually indicate irreversible kidney failure. Recently, some non-contrast MR techniques seem to be promising for detecting early kidney malfunctions in diabetic patients. Using blood oxygen level-dependent (BOLD) MRI, Wang et al. found that R2* (1/T2*, a parameter reflecting transverse decay due to local magnetic field inhomogeneities) values of the medulla were lower in patients with diabetic nephropathy compared to healthy volunteers (Wang, Kumar, Banerjee, & Hsu, 2011). With diffusion tensor imaging (DTI), Lu et al. (2011) demonstrated that quantitative changes in medullary DTI assessments may serve as indicators of early diabetic nephrology. Renal artery stenosis (RAS) is prevalent in diabetic patients. In a clinical study, 20 (17%) patients were found to have RAS among 117 hypertensive T2DM subjects using CE-MRA (Valabhji et al., 2000). Doppler ultrasonography can also be used to diagnose RAS by detecting stenosis. RAS results in a progressive of renal damage over time and it is predict adverse coronary events in a cohort of 870 patients Edwards, Craven, Burke, Dean, & Hansen, 2005; Lao, Parasher, Cho, & Yeghiazarians, 2011. 2.10. Potential quantitative imaging biomarkers during diabetic treatments and the relationships to serum biomarkers and clinical outcomes Various molecular and biochemical biomarkers have been applied to monitor severity and progression of diabetes in clinical study/ practice. Many of quantitative imaging measures have been indentified for noninvasively monitoring the efficacy of treatments for cardiovascular diseases in T2DM. The candidate quantitative imaging biomarkers are found to tightly correlate with existing serum biomarkers and have been used to predict clinical outcomes. 2.11. Cardiac function, structure, mass and volume Cardiac function is traditional prediction index for clinical outcome in diabetic patients. Yeung et al. found that low LVEF (b30%) is an independent predictor for cardiac death in 236 diabetic patients after acute myocardial infarction (MI). Amelioration of cardiac function and regression of LVH were shown to improve the cardiovascular outcome independently of other risk factors, and thus have been suggested as intermediate endpoints for cardiovascular clinical trials (Okin et al., 2004). Niranjan, McBrayer, Ramirez, Raskin, & Hsia (1997) studied 18 subjects with T1DM and 14 normal control subjects and found that those patients with normal glycemic levels consistently showed less impairment of cardiac function than their hyperglycemic counterparts. Using echocardiography, Vintila, Roberts, Vinereanu, & Fraser (2012) showed that poor glycemic control was also related to reduced left ventricular long-axis function. Choi et al. (2012) demonstrated that elevated high sensitivity C-reactive protein (CRP), a biomarker of inflammation for predicting cardiovascular events, was related to decreased regional ventricle function. Recently, gene therapy has been applied to diabetic treatments. With echocardiography, Dong et al. (2012) observed a significant improvement of cardiac function in rat models of diabetic cardiomyopathy after treatment using angiotensin (Ang)-converting enzyme-2 (ACE2) gene transfer. 2.12. Atherosclerotic plaque burden Atherosclerosis burden in various vascular territories has been used to predict cardiovascular events (Chambless et al., 1997). In patients with T2DM, the combination of increased waist circumference and increased plasma triglyceride (TG) levels are related to more extensive CAD on CTA (de Graaf et al., 2010). Glycemic control is related to vascular remodeling in diabetes. In a study with a mouse

K. Lin et al. / Journal of Diabetes and Its Complications 28 (2014) 234–242

model, Sachidanandam et al. (2009) found that glycemic control prevents microvascular remodeling and increased vascular tone in T2DM. Suboptimal glycemic control (defined as HbA1c N 7%) is a strong risk factor for CAC progression in both T1DM and T2DM (Anand et al., 2007; Snell-Bergeon et al., 2003). The level of plasma osteoprotegerin (OPG), a biomarker of cardiovascular events, are quantitatively related to CAC in T2DM patients (Anand, Lahiri, Lim, Hopkins, & Corder, 2006). Dullaart et al. found that IMT was negatively related to serum bilirubin in T2DM (Dullaart, Kappelle, & de Vries, 2012). Kang et al. demonstrated that the serum CRP level is correlated with carotid IMT and other cardiovascular risk factors (Kang et al., 2004). Lipid management is an essential part for a diabetic regimen. Statins can significantly retard and prevent atherosclerotic vascular remodeling (Nicholls et al., 2011). In a double-blind, randomized, placebo-controlled clinical study conducted in 71 statin-treated patients with low high density lipoprotein cholesterol (HDL-C), high-dose modified-release nicotinic acid, compared with placebo, significantly reduced carotid atherosclerosis within a 1-year period (Lee et al., 2009). However, Daida et al. found that coronary atherosclerosis regression was less prominent in patients with high HbA1c levels compared with those with low glycemic levels despite similar improvements after attempts at lipid control (Daida et al., 2012). It is worth noting that atherosclerosis refers to a collection of diseases and diabetes-associated vascular alterations, including structural and functional changes involving the whole vascular system. Weckbach et al. (2009) reported the overall incidence rates of various atherosclerotic diseases in 65 patients with a long history (defined as N10 years) of diabetes without acute cardiovascular symptoms. The prevalence of PAD, myocardial infarction, cerebrovascular diseases and neuropathic disease of the lower limbs were 49%, 25%, 28% and 22%, respectively. Kim et al. (2012a) reported that arterial-stiffness (heart- femoral Pulse wave velocity) is closely related to diabetic retinopathy. Diabetic patients who have more severe coronary atherosclerosis (defined by coronary X-ray angiog-

239

raphy) are more likely to have retinopathy (Norgaz et al., 2005). Therefore, it is necessary to observe multiple places to comprehensively monitor the progression of atherosclerosis in T2DM. Using fast imaging techniques, such as parallel imaging and multicoil arrangement, whole-body CE-MRA could be used to diagnose and evaluate multiple arterial occlusive diseases with a single scan (Nael et al., 2007). 2.13. Renal dysfunction The morphological changes of kidney detected by ultrasound, MRI and abdominal CT are reliable imaging indicators of advanced renal damage. The renal resistance index and the pulsatility index measured by Doppler ultrasonography, two indices reflecting kidney dysfunction are related to urinary protein, a manifestation of nephropathy in T2DM patients (Nakamori et al., 2011). Using diffusion-weighted (DW)-MRI and BOLD-MRI, Inoue et al. (2011) investigated 142 diabetic patients with CKD and found that apparent diffusion coefficient (ADC) and T2* values appear to serve as accurate indices for evaluating both tubulointerstitial renal diseases and hypoxia of the renal parenchyma. Diabetic nephropathy is related to the development of CAD. Tonelli et al. (2012) studied a population of 1,268,029 subjects with measures of the estimated glomerular filtration rate (eGFR) and proteinuria and found that existing chronic kidney disease (CKD) could be considered as an independent risk factor for predicting future MI. With a cohort of 136 asymptomatic T1DM patients with and without diabetic nephropathy, Kim et al. (2007) revealed a greater coronary plaque burden in subjects with nephropathy compared with their counterparts without albuminuria using 3D coronary wall MRI. 2.14. Status of cerebral circulation Development of diabetes will affect cerebral circulation. Using MRS, DTI and perfusion MRI, Glaser et al. (2012) found that hyperglycemia and diabetic ketoacidosis independently cause low ration of adenosine

Table 1 Recommendations of available methods for acquiring quantitative imaging biomarkers in diabetes management. Locations

Cardio-vascular complications

Pathological changes

Heart

CAD Cardio-myopathy

Macrovascular/microvascular diseases

Brain

Stroke Hemorrhage

Macrovascular/Microvascular diseases

Kidney

Renal dysfunction Renal artery stenosis

Macrovascular/Microvascular diseases

Eye

Retinal artery stenosis/ hemorrhage

Microvascular diseases

Lower limbs and other PAD vascular territories

Macrovascular diseases

Quantitative imaging biomarker (Carriers)

Clinical methods⁎ Diagnosis

Risk/outcome estimation

Cardiac function Cardiac remodeling Vascular remodeling Vascular stiffness CAC Myocardial perfusion Myocardial T1, T2 Carotid IMT Carotid plaque features Intracranial plaque features Microbleeds Brain perfusion Renal morphology Blood oxygen level Renal artery stenosis/remodeling T2⁎/R2⁎ Retinal artery stenosis Blood flow abnormality

X-ray angio MRI CT Ultrasound Nuclear Medicine

X-ray angio MRI Ultrasound CT Nuclear Medicine

MRI CT

MRI CT Nuclear Medicine Ultrasound

Biopsy Ultrasound CT

Ultrasound CT MRI Nuclear Medicine Fundus Ultrasound OCT MRI LSF ABI Ultrasound MRI CT

Artery stenosis Vascular remodeling (Atherosclerosis plaque burden)

Fundus

ABI Ultrasound MRI CT

⁎ Only broad subject headings are listed. For diagnosis use (mainly targeting patients with cardiovascular symptoms), only most common methods in clinical practice (with nonimaging examinations) are listed. For risk estimation use (mainly targeting asymptomatic subjects), the methods include methods/applications that are still at the stage of clinical study or animal study. The selection of certain methods depends on clinical needs and circumstances.

240

K. Lin et al. / Journal of Diabetes and Its Complications 28 (2014) 234–242

triphosphate (ATP) to inorganic phosphate (Pi), reduced cerebral blood flow (CBF), and decreased ADC values of the cortex. Kim et al. (2012b) demonstrated that the atherosclerotic plaque burden on the basilar artery, which is directly related with ischemic stroke, was quantitatively associated with old age, history of stroke, diabetes, low HDL-C and high levels of homocysteine using MRI. See Table 1 for a brief description of the clinical imaging methods available for quantitatively monitoring cardiovascular complications in diabetic patients. 3. Conclusion Although the clinical outcome is the ultimate “gold standard” for evaluating the efficacy of a certain therapy, quantitative biomarkers, such as HbA1c, lipid levels, and blood pressure, have long been applied in clinical studies/trials of diabetes as surrogate endpoints to evaluate the absolute risk. However, none of the current quantitative biomarkers is ideal. Certain biomarkers may be unreliable for indicating a pathological process or therapeutic intervention under specific pathophysiological circumstances for many reasons. Actually, only a very small part of published biomarkers enter clinical practice eventually Kern, 2012. Given the complicated pathological conditions in diabetic patients, the existing knowledge still cannot fully explain the relationships among diabetes treatments, variances of biomarkers (metabolic features and other physiological indices) and prognoses using a single equation. Hence, the involvement of interdisciplinary quantitative biomarkers for diabetic management has become an emerging trend. In contrast to existing metabolic biomarkers, which mostly function at a cellular or molecular level, imaging biomarkers immediately reflects the anatomic and functional changes of an organ. Therefore, they are expected to contribute to systemic prediction and assessment of cardiovascular complications in diabetic patients from a unique standpoint. More clinical studies are needed in the fields of endocrinology, cardiology and radiology to refine and validate efficient quantitative imaging biomarkers for various pathophysiological features. References Adler, A. I., et al. (2000). Association of systolic blood pressure with macrovascular and microvascular complications of type 2 diabetes (UKPDS 36): prospective observational study. BMJ, 321, 412–419. Agarwal, S., et al. (2011). Coronary calcium score and prediction of all-cause mortality in diabetes: the diabetes heart study. Diabetes Care, 34, 1219–1224. Alfakih, K., Reid, S., Jones, T., & Sivananthan, M. (2004). Assessment of ventricular function and mass by cardiac magnetic resonance imaging. European Radiology, 14, 1813–1822. Anand, D. V., Lahiri, A., Lim, E., Hopkins, D., & Corder, R. (2006). The relationship between plasma osteoprotegerin levels and coronary artery calcification in uncomplicated type 2 diabetic subjects. Journal of the American College of Cardiology, 47, 1850–1857. Anand, D. V., et al. (2007). Determinants of progression of coronary artery calcification in type 2 diabetes role of glycemic control and inflammatory/vascular calcification markers. Journal of the American College of Cardiology, 50, 2218–2225. Avery, C. L., et al. (2012). Impact of long-term measures of glucose and blood pressure on the retinal microvasculature. Atherosclerosis, 225(2), 412–417. Banerjee, C., et al. (2012). Duration of diabetes and risk of ischemic stroke: the Northern Manhattan Study. Stroke, 43, 1212–1217. Beckman, J. A., Creager, M. A., & Libby, P. (2002). Diabetes and atherosclerosis: epidemiology, pathophysiology, and management. JAMA, 287, 2570–2581. Bitar, R., et al. (2006). MR pulse sequences: what every radiologist wants to know but is afraid to ask. Radiographics, 26, 513–537. Bots, M. L., Hoes, A. W., Koudstaal, P. J., Hofman, A., & Grobbee, D. E. (1997). Common carotid intima-media thickness and risk of stroke and myocardial infarction: the Rotterdam Study. Circulation, 96, 1432–1437. Bourque, J. M., et al. (2012). Usefulness of cardiovascular magnetic resonance imaging of the superficial femoral artery for screening patients with diabetes mellitus for atherosclerosis. The American Journal of Cardiology, 110, 50–56. Brohall, G., Oden, A., & Fagerberg, B. (2006). Carotid artery intima-media thickness in patients with Type 2 diabetes mellitus and impaired glucose tolerance: a systematic review. Diabetic Medicine, 23, 609–616. Bruno, A., Williams, L. S., & Kent, T. A. (2004). How important is hyperglycemia during acute brain infarction? The Neurologist, 10, 195–200.

Bruno, A., et al. (1999). Acute blood glucose level and outcome from ischemic stroke. Trial of ORG 10172 in Acute Stroke Treatment (TOAST) Investigators. Neurology, 52, 280–284. Bucerius, J., et al. (2012). Impact of noninsulin-dependent type 2 diabetes on carotid wall 18 F-fluorodeoxyglucose positron emission tomography uptake. Journal of the American College of Cardiology, 59, 2080–2088. Budoff, M. J., et al. (2005). Diabetes and progression of coronary calcium under the influence of statin therapy. American Heart Journal, 149, 695–700. Budoff, M. J., et al. (2008). 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. Journal of the American College of Cardiology, 52, 1724–1732. Burgansky-Eliash, Z., et al. (2012). Increased retinal blood flow velocity in patients with early diabetes mellitus. Retina, 32, 112–119. Burke, A. P., et al. (1997). Coronary risk factors and plaque morphology in men with coronary disease who died suddenly. The New England Journal of Medicine, 336, 1276–1282. Bursell, S. E., et al. (1996). Retinal blood flow changes in patients with insulindependent diabetes mellitus and no diabetic retinopathy. Investigative Ophthalmology & Visual Science, 37, 886–897. Burtea, C., et al. (2012). Development of a magnetic resonance imaging protocol for the characterization of atherosclerotic plaque by using vascular cell adhesion molecule-1 and apoptosis-targeted ultrasmall superparamagnetic iron oxide derivatives. Arteriosclerosis, thrombosis, and vascular biology, 32, e36–e48. Cerutti, F., et al. (1994). Evaluation of left ventricular diastolic function in insulin dependent diabetic children by M-mode and Doppler echocardiography. Panminerva Medica, 36, 109–114. Chambless, L. E., et al. (1997). Association of coronary heart disease incidence with carotid arterial wall thickness and major risk factors: the Atherosclerosis Risk in Communities (ARIC) Study, 1987-1993. American Journal of Epidemiology, 146, 483–494. Choi, E. Y., et al. (2012). High-sensitivity C-reactive protein as an independent predictor of progressive myocardial functional deterioration: the multiethnic study of atherosclerosis. American Heart Journal, 164, 251–258. Cushman, W. C., et al. (2010). Effects of intensive blood-pressure control in type 2 diabetes mellitus. The New England Journal of Medicine, 362, 1575–1585. Daida, H., et al. (2012). High HbA1c levels correlate with reduced plaque regression during statin treatment in patients with stable coronary artery disease: Results of the coronary atherosclerosis study measuring effects of rosuvastatin using intravascular ultrasound in Japanese subjects (COSMOS). Cardiovascular Diabetology, 11, 87. Dawson, A., Morris, A. D., & Struthers, A. D. (2005). The epidemiology of left ventricular hypertrophy in type 2 diabetes mellitus. Diabetologia, 48, 1971–1979. de Boer, I. H., et al. (2011). Temporal trends in the prevalence of diabetic kidney disease in the United States. JAMA, 305, 2532–2539. de Graaf, F. R., et al. (2010). Usefulness of hypertriglyceridemic waist phenotype in type 2 diabetes mellitus to predict the presence of coronary artery disease as assessed by computed tomographic coronary angiography. The American Journal of Cardiology, 106, 1747–1753. Den Ruijter, H. M., et al. (2012). Common carotid intima-media thickness measurements in cardiovascular risk prediction: a meta-analysis. JAMA, 308, 796–803. Dong, B., et al. (2012). Angiotensin-converting enzyme-2 overexpression improves left ventricular remodeling and function in a rat model of diabetic cardiomyopathy. Journal of the American College of Cardiology, 59, 739–747. Douglas, P. S., et al. (2007). ACCF/ASE/ACEP/ASNC/SCAI/SCCT/SCMR 2007 appropriateness criteria for transthoracic and transesophageal echocardiography: a report of the American College of Cardiology Foundation Quality Strategic Directions Committee Appropriateness Criteria Working Group, American Society of Echocardiography, American College of Emergency Physicians, American Society of Nuclear Cardiology, Society for Cardiovascular Angiography and Interventions, Society of Cardiovascular Computed Tomography, and the Society for Cardiovascular Magnetic Resonance endorsed by the American College of Chest Physicians and the Society of Critical Care Medicine. Journal of the American College of Cardiology, 50, 187–204. Dullaart, R. P., Kappelle, P. J., & de Vries, R. (2012). Lower carotid intima media thickness is predicted by higher serum bilirubin in both non-diabetic and Type 2 diabetic subjects. Clinica Chimica Acta, 414C, 161–165. Edwards, M. S., Craven, T. E., Burke, G. L., Dean, R. H., & Hansen, K. J. (2005). Renovascular disease and the risk of adverse coronary events in the elderly: a prospective, population-based study. Archives of Internal Medicine, 165, 207–213. Ernande, L., et al. (2012). Systolic myocardial dysfunction in patients with Type-2 diabetes mellitus: identification at MR imaging with cine displacement encoding with stimulated echoes. Radiology, 265(2), 402–409. Esposito, L., et al. (2010). MRI plaque imaging reveals high-risk carotid plaques especially in diabetic patients irrespective of the degree of stenosis. BMC Medical Imaging, 10, 27. Falk, E., Shah, P. K., & Fuster, V. (1995). Coronary plaque disruption. Circulation, 92, 657–671. Fang, Z. Y., et al. (2003). Echocardiographic detection of early diabetic myocardial disease. Journal of the American College of Cardiology, 41, 611–617. Forst, T., et al. (2012). Pilot study for the evaluation of morphological and functional changes in retinal blood flow in patients with insulin resistance and/or type 2 diabetes mellitus. Journal of Diabetes Science and Technology, 6, 163–168. Garcia, M. J., Thomas, J. D., & Klein, A. L. (1998). New Doppler echocardiographic applications for the study of diastolic function. Journal of the American College of Cardiology, 32, 865–875.

K. Lin et al. / Journal of Diabetes and Its Complications 28 (2014) 234–242 Germano, G., & Berman, D. S. (2006). Clinical gated cardiac SPECT. Malden, Mass.: Blackwell Pub. Gerstein, H. C., et al. (2008). Effects of intensive glucose lowering in type 2 diabetes. The New England Journal of Medicine, 358, 2545–2559. Ginsberg, H. N., et al. (2010). Effects of combination lipid therapy in type 2 diabetes mellitus. The New England Journal of Medicine, 362, 1563–1574. Glaser, N., et al. (2012). Effects of hyperglycemia and effects of ketosis on cerebral perfusion, cerebral water distribution, and cerebral metabolism. Diabetes, 61, 1831–1837. Gracner, T. (2004). Ocular blood flow velocity determined by color Doppler imaging in diabetic retinopathy. Ophthalmologica, 218, 237–242. Greenland, P., et al. (2010). 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. Journal of the American College of Cardiology, 56, e50–e103. Gross, J. L., et al. (2005). Diabetic nephropathy: diagnosis, prevention, and treatment. Diabetes Care, 28, 164–176. Haffner, S. M., Lehto, S., Ronnemaa, T., Pyorala, K., & Laakso, M. (1998). Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. The New England Journal of Medicine, 339, 229–234. Hamon, M., et al. (2006). Diagnostic performance of multislice spiral computed tomography of coronary arteries as compared with conventional invasive coronary angiography: a meta-analysis. Journal of the American College of Cardiology, 48, 1896–1910. Hodnett, P. A., et al. (2011). Peripheral arterial disease in a symptomatic diabetic population: prospective comparison of rapid unenhanced MR angiography (MRA) with contrast-enhanced MRA. AJR. American Journal of Roentgenology, 197, 1466–1473. Holman, R. R., Paul, S. K., Bethel, M. A., Matthews, D. R., & Neil, H. A. (2008). 10-year follow-up of intensive glucose control in type 2 diabetes. The New England Journal of Medicine, 359, 1577–1589. Imamine, R., et al. (2011). Does cerebral small vessel disease predict future decline of cognitive function in elderly people with type 2 diabetes? Diabetes Research and Clinical Practice, 94, 91–99. Inoue, T., et al. (2011). Noninvasive evaluation of kidney hypoxia and fibrosis using magnetic resonance imaging. Journal of the American Society of Nephrology, 22, 1429–1434. Janghorbani, M., et al. (2007). Prospective study of type 1 and type 2 diabetes and risk of stroke subtypes: the Nurses' Health Study. Diabetes Care, 30, 1730–1735. Jessup, M., et al. (2009). 2009 focused update: ACCF/AHA Guidelines for the Diagnosis and Management of Heart Failure in Adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines: developed in collaboration with the International Society for Heart and Lung Transplantation. Circulation, 119, 1977–2016. Jeung, M. Y., et al. (2012). Myocardial tagging with MR imaging: overview of normal and pathologic findings. Radiographics, 32, 1381–1398. Kang, E. S., et al. (2004). Serum high sensitivity C-reactive protein is associated with carotid intima-media thickness in type 2 diabetes. Diabetes Research and Clinical Practice, 66(Suppl 1), S115–S120. Kato, S., et al. (2010). Assessment of coronary artery disease using magnetic resonance coronary angiography: a national multicenter trial. Journal of the American College of Cardiology, 56, 983–991. Kavousi, M., et al. (2012). Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study. Annals of Internal Medicine, 156, 438–444. Kern, S. E. (2012). Why your new cancer biomarker may never work: recurrent patterns and remarkable diversity in biomarker failures. Cancer Research, 72, 6097–6101. Kim, W. Y., et al. (2007). Subclinical coronary and aortic atherosclerosis detected by magnetic resonance imaging in type 1 diabetes with and without diabetic nephropathy. Circulation, 115, 228–235. Kim, W. J., et al. (2012a). The association between regional arterial stiffness and diabetic retinopathy in type 2 diabetes. Atherosclerosis, 225, 237–241. Kim, Y. S., et al. (2012b). The advantage of high-resolution MRI in evaluating basilar plaques: a comparison study with MRA. Atherosclerosis, 224, 411–416. Klein, I. F., Lavallee, P. C., Touboul, P. J., Schouman-Claeys, E., & Amarenco, P. (2006). In vivo middle cerebral artery plaque imaging by high-resolution MRI. Neurology, 67, 327–329. Koelemay, M. J., Lijmer, J. G., Stoker, J., Legemate, D. A., & Bossuyt, P. M. (2001). Magnetic resonance angiography for the evaluation of lower extremity arterial disease: a meta-analysis. JAMA, 285, 1338–1345. Koelemay, M. J., et al. (1996). Diagnosis of arterial disease of the lower extremities with duplex ultrasonography. British Journal of Surgery, 83, 404–409. Lao, D., Parasher, P. S., Cho, K. C., & Yeghiazarians, Y. (2011). Atherosclerotic renal artery stenosis – diagnosis and treatment. Mayo Clinic Proceedings, 86, 649–657. Lee, J. M., et al. (2009). Effects of high-dose modified-release nicotinic acid on atherosclerosis and vascular function: a randomized, placebo-controlled, magnetic resonance imaging study. Journal of the American College of Cardiology, 54, 1787–1794. Lee, W. W., et al. (2012). PET/MRI of inflammation in myocardial infarction. Journal of the American College of Cardiology, 59, 153–163. Lin, K., Zhang, Z. Q., Detrano, R., Lu, B., & Fan, Z. M. (2006). Carotid vulnerable lesions are related to accelerated recurrence for cerebral infarction magnetic resonance imaging study. Academic Radiology, 13, 1180–1186. Lin, K., et al. (2011). Noninvasive evaluation of coronary distensibility in older adults: a feasibility study with MR angiography. Radiology, 261, 771–778.

241

Lin, K., et al. (2012). Potential quantitative magnetic resonance imaging biomarkers of coronary remodeling in older hypertensive patients. Arteriosclerosis, Thrombosis, and Vascular Biology, 32, 1742–1747. Lu, L., et al. (2011). Use of diffusion tensor MRI to identify early changes in diabetic nephropathy. American Journal of Nephrology, 34, 476–482. Marsan, N. A., et al. (2008). Comparison between tissue Doppler imaging and velocityencoded magnetic resonance imaging for measurement of myocardial velocities, assessment of left ventricular dyssynchrony, and estimation of left ventricular filling pressures in patients with ischemic cardiomyopathy. The American Journal of Cardiology, 102, 1366–1372. McDermott, M. M., et al. (2000). Lower ankle/brachial index, as calculated by averaging the dorsalis pedis and posterior tibial arterial pressures, and association with leg functioning in peripheral arterial disease. Journal of Vascular Surgery, 32, 1164–1171. McDermott, M. M., et al. (2011). Superficial femoral artery plaque and functional performance in peripheral arterial disease: walking and leg circulation study (WALCS III). JACC. Cardiovascular Imaging, 4, 730–739. Medarova, Z., & Moore, A. (2009). MRI in diabetes: first results. AJR. American Journal of Roentgenology, 193, 295–303. Miao, C., et al. (2009). Positive remodeling of the coronary arteries detected by magnetic resonance imaging in an asymptomatic population: MESA (Multi-Ethnic Study of Atherosclerosis). Journal of the American College of Cardiology, 53, 1708–1715. Miller, J. M., et al. (2008). Diagnostic performance of coronary angiography by 64-row CT. The New England Journal of Medicine, 359, 2324–2336. Mondillo, S., et al. (2011). Speckle-tracking echocardiography: a new technique for assessing myocardial function. Journal of Ultrasound in Medicine, 30, 71–83. Mor-Avi, V., Sugeng, L., & Lang, R. M. (2009). Real-time 3-dimensional echocardiography: an integral component of the routine echocardiographic examination in adult patients? Circulation, 119, 314–329. Moulin, T., et al. (1997). The Besancon Stroke Registry: an acute stroke registry of 2,500 consecutive patients. European Neurology, 38, 10–20. Murthy, V. L., et al. (2012). Association between coronary vascular dysfunction and cardiac mortality in patients with and without diabetes mellitus. Circulation, 126, 1858–1868. Nael, K., et al. (2007). Multistation whole-body high-spatial-resolution MR angiography using a 32-channel MR system. AJR. American Journal of Roentgenology, 188, 529–539. Nakae, R., Yokota, H., Yoshida, D., & Teramoto, A. (2011). Transcranial Doppler ultrasonography for diagnosis of cerebral vasospasm after aneurysmal subarachnoid hemorrhage: mean blood flow velocity ratio of the ipsilateral and contralateral middle cerebral arteries. Neurosurgery, 69, 876–883 (discussion 883). Nakamori, A., et al. (2011). Influence of proteinuria on renal Doppler sonographic measurements in chronic kidney disease and in diabetes mellitus. Journal of Clinical Ultrasound, 39, 506–511. Napoli, A., et al. (2011). Peripheral arterial occlusive disease: diagnostic performance and effect on therapeutic management of 64-section CT angiography. Radiology, 261, 976–986. Napoli, N., et al. (2012). Increased carotid thickness in subjects with recently-diagnosed diabetes from rural Cameroon. PLoS One, 7, e41316. Nathan, D. M., et al. (2005). Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. The New England Journal of Medicine, 353, 2643–2653. Nicholls, S. J., et al. (2011). Effect of two intensive statin regimens on progression of coronary disease. The New England Journal of Medicine, 365, 2078–2087. Niranjan, V., McBrayer, D. G., Ramirez, L. C., Raskin, P., & Hsia, C. C. (1997). Glycemic control and cardiopulmonary function in patients with insulin-dependent diabetes mellitus. The American Journal of Medicine, 103, 504–513. Norgaz, T., et al. (2005). Retinopathy is related to the angiographically detected severity and extent of coronary artery disease in patients with type 2 diabetes mellitus. International Heart Journal, 46, 639–646. Okin, P. M., et al. (2001). Relation of echocardiographic left ventricular mass and hypertrophy to persistent electrocardiographic left ventricular hypertrophy in hypertensive patients: the LIFE Study. American Journal of Hypertension, 14, 775–782. Okin, P. M., et al. (2004). Regression of electrocardiographic left ventricular hypertrophy during antihypertensive treatment and the prediction of major cardiovascular events. JAMA, 292, 2343–2349. Olszewski, R., et al. (2007). The clinical applications of contrast echocardiography. European Journal of Echocardiography, 8, S13–S23. Paelinck, B. P., et al. (2007). Tissue cardiovascular magnetic resonance demonstrates regional diastolic dysfunction in remote tissue early after inferior myocardial infarction. Journal of Cardiovascular Magnetic Resonance, 9, 877–882. Parra, D. A., & Vera, K. (2012). New imaging modalities to assess cardiac function: not just pretty pictures. Current Opinion in Pediatrics, 24, 557–564. Poirier, P., Bogaty, P., Garneau, C., Marois, L., & Dumesnil, J. G. (2001). Diastolic dysfunction in normotensive men with well-controlled type 2 diabetes: importance of maneuvers in echocardiographic screening for preclinical diabetic cardiomyopathy. Diabetes Care, 24, 5–10. Polonsky, K. S. (2012). The past 200 years in diabetes. The New England Journal of Medicine, 367, 1332–1340. Prince, M. R., et al. (2008). Incidence of nephrogenic systemic fibrosis at two large medical centers. Radiology, 248, 807–816. Qiu, C., et al. (2008). Retinal and cerebral microvascular signs and diabetes: the age, gene/environment susceptibility-Reykjavik study. Diabetes, 57, 1645–1650. Ross, R. (1999). Atherosclerosis – an inflammatory disease. The New England Journal of Medicine, 340, 115–126.

242

K. Lin et al. / Journal of Diabetes and Its Complications 28 (2014) 234–242

Sachidanandam, K., et al. (2009). Glycemic control prevents microvascular remodeling and increased tone in type 2 diabetes: link to endothelin-1. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 296, R952–R959. Saydah, S. H., Fradkin, J., & Cowie, C. C. (2004). Poor control of risk factors for vascular disease among adults with previously diagnosed diabetes. JAMA, 291, 335–342. Schannwell, C. M., Schneppenheim, M., Perings, S., Plehn, G., & Strauer, B. E. (2002). Left ventricular diastolic dysfunction as an early manifestation of diabetic cardiomyopathy. Cardiology, 98, 33–39. Schiano, V., et al. (2012). Femoral plaque echogenicity and cardiovascular risk in claudicants. JACC. Cardiovascular Imaging, 5, 348–357. Schulte-Altedorneburg, G., et al. (2001). Accuracy of in vivo carotid B-mode ultrasound compared with pathological analysis: intima-media thickening, lumen diameter, and cross-sectional area. Stroke, 32, 1520–1524. Snell-Bergeon, J. K., et al. (2003). Progression of coronary artery calcification in type 1 diabetes: the importance of glycemic control. Diabetes Care, 26, 2923–2928. Sohn, D. W., et al. (1997). Assessment of mitral annulus velocity by Doppler tissue imaging in the evaluation of left ventricular diastolic function. Journal of the American College of Cardiology, 30, 474–480. Steeds, R. P. (2011). Echocardiography: frontier imaging in cardiology. The British Journal of Radiology, 84(Spec No 3), S237–S245. Stehouwer, C. D., Henry, R. M., & Ferreira, I. (2008). Arterial stiffness in diabetes and the metabolic syndrome: a pathway to cardiovascular disease. Diabetologia, 51, 527–539. Stolzmann, P., et al. (2008). Left ventricular and left atrial dimensions and volumes: comparison between dual-source CT and echocardiography. Investigative Radiology, 43, 284–289. Sun, J., et al. (2008). Identification and quantification of coronary atherosclerotic plaques: a comparison of 64-MDCT and intravascular ultrasound. AJR. American Journal of Roentgenology, 190, 748–754. Tahara, N., et al. (2007). Vascular inflammation evaluated by [18 F]-fluorodeoxyglucose positron emission tomography is associated with the metabolic syndrome. Journal of the American College of Cardiology, 49, 1533–1539. Taniwaki, H., et al. (1999). Correlation between the intima-media thickness of the carotid artery and aortic pulse-wave velocity in patients with type 2 diabetes. Vessel wall properties in type 2 diabetes. Diabetes Care, 22, 1851–1857. Tonelli, M., et al. (2012). Risk of coronary events in people with chronic kidney disease compared with those with diabetes: a population-level cohort study. Lancet, 380, 807–814. Tong, K. L., et al. (2005). Myocardial contrast echocardiography versus Thrombolysis In Myocardial Infarction score in patients presenting to the emergency department

with chest pain and a nondiagnostic electrocardiogram. Journal of the American College of Cardiology, 46, 920–927. Trick, G. L., et al. (2008). MRI retinovascular studies in humans: research in patients with diabetes. NMR in Biomedicine, 21, 1003–1012. Valabhji, J., et al. (2000). Prevalence of renal artery stenosis in subjects with type 2 diabetes and coexistent hypertension. Diabetes Care, 23, 539–543. Vintila, V. D., Roberts, A., Vinereanu, D., & Fraser, A. G. (2012). Progression of subclinical myocardial dysfunction in type 2 diabetes after 5 years despite improved glycemic control. Echocardiography, 29, 1045–1053. Virmani, R., Kolodgie, F. D., Burke, A. P., Farb, A., & Schwartz, S. M. (2000). Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. Arteriosclerosis, Thrombosis, and Vascular Biology, 20, 1262–1275. Wadwa, R. P. (2007). Noninvasive measures of cardiovascular changes in diabetes mellitus. Current Opinion in Endocrinology, Diabetes, and Obesity, 14, 263–268. Wang, Z. J., Kumar, R., Banerjee, S., & Hsu, C. Y. (2011). Blood oxygen level-dependent (BOLD) MRI of diabetic nephropathy: preliminary experience. Journal of Magnetic Resonance Imaging, 33, 655–660. Wang, Y., et al. (2011). Pilot study of optical coherence tomography measurement of retinal blood flow in retinal and optic nerve diseases. Investigative Ophthalmology & Visual Science, 52, 840–845. Wang, Y., et al. (2012). Adenosine-stress dynamic myocardial perfusion imaging with second-generation dual-source CT: comparison with conventional catheter coronary angiography and SPECT nuclear myocardial perfusion imaging. AJR. American Journal of Roentgenology, 198, 521–529. Weckbach, S., et al. (2009). Systemic cardiovascular complications in patients with long-standing diabetes mellitus: comprehensive assessment with whole-body magnetic resonance imaging/magnetic resonance angiography. Investigative Radiology, 44, 242–250. Weir, C. J., Murray, G. D., Dyker, A. G., & Lees, K. R. (1997). Is hyperglycaemia an independent predictor of poor outcome after acute stroke? Results of a long-term follow up study. BMJ, 314, 1303–1306. Yang, Q., et al. (2009). Contrast-enhanced whole-heart coronary magnetic resonance angiography at 3.0-T: a comparative study with X-ray angiography in a single center. Journal of the American College of Cardiology, 54, 69–76. Zarich, S. W., & Nesto, R. W. (1989). Diabetic cardiomyopathy. American Heart Journal, 118, 1000–1012. Zhang, X., et al. (2010). Prevalence of diabetic retinopathy in the United States, 2005– 2008. JAMA, 304, 649–656.