Cardiovascular disease (CVD) is a major cause of mortality and morbidity in patients with type 2 diabetes mellitus (DM), making early diagnosis and treatment of atherosclerosis extremely important [1]. However, most patients with diabetes with subclinical atherosclerosis are asymptomatic [2]. In addition, the prevalence of silent myocardial ischemia (MI) is much higher in patients with diabetes compared to the general population [3]. Thus, in order to provide optimal medical therapy to prevent future cardiac events, identification of patients who are at high risk for CVD is of prime importance. The current guidelines on CVD prevention recommend targeted management of CV risk factors after assessment using one of the many available methods, even in asymptomatic patients.

Cardiovascular disease risk analysis can be performed using well-known risk-stratification approaches, including the Framingham Risk Score (FRS) algorithm [4] and the United Kingdom Prospective Diabetes Study Risk Engine (UKPDSRE) calculator [5]. The results of the FRS and UKPDSRE approaches, which include traditional CV risk factors, generally correlate with coronary heart disease risk [4]. However, a substantial number of people with low (<10 %) to intermediate (10–20 %) FRS scores go on to develop atherosclerosis [6]. Previous reports have also demonstrated that the UKPDSRE lacks adequate sensitivity and specificity for detection of subclinical atherosclerosis [7]. Therefore, additional tools are needed to improve CV risk assessment.

Recent investigations have shown that noninvasive techniques, such as carotid intima media thickness (CIMT), presence of plaque, coronary artery calcium score (CACS), ankle-brachial index (ABI), and aortic pulse wave velocity may accurately detect subclinical atherosclerosis that is associated with the development of cardiovascular or cerebrovascular diseases [8]. These studies have shown that imaging modalities are the best method for detecting the presence and extent of atherosclerosis. As such, it is important to conduct imaging studies in all patients regardless of the presence of traditional CV risk factors, such as hypertension, dyslipidemia, and diabetes mellitus, in order to comprehensively identify patients who are at risk for developing CVD. We chose to focus our study on CIMT because CUS is feasible in all individuals, dose not involve exposure to radiation, and is relatively inexpensive. When using imaging studies, a CACS >0, stenosis >50th percentile, or the presence of plaque are considered to be positive findings. These findings suggest a high risk of developing CVD according to the guidelines from the Screening for Heart Attack Prevention and Education (SHAPE), published by the Association for Eradication of Heart Attack (AEHA) [9].

However, the outcomes of this guideline have not yet been compared to those of the traditional guideline. Therefore, we analyzed the prevalences of abnormal carotid ultrasound (CUS) findings and compared them to traditional risk stratification results obtained using the FRS and UKPDSRE approaches.

Although physicians provide comprehensive treatment for diabetes, hypertension, and dyslipidemia, patient drug compliance is critical for optimal outcomes. If a physician assesses CVD risk and uses these results to educate the patient about ways to prevent CVD, the patient may implement lifestyle modifications or improve his/her drug compliance. However, no studies have yet investigated the effect of assessing subclinical atherosclerosis on patient behavior, or whether awareness of subclinical atherosclerosis alters physician treatment patterns.

Here we explored how two distinct assessment methods varied in their estimation of CV risk, a non-invasive imaging test (CUS) and traditional risk calculators (UKPDSRE, FRS). We also examined how awareness of being at high risk for CVD affected physician treatment patterns as well as patient behavior with respect to risk management. Our hypothesis was that receiving an explanation of CUS results, along with proper education about mitigating risk factors, would have a favorable effect on patient behavior and physician treatment plans.

This prospective, observational, multicenter study included 797 patients with type 2 DM aged >40 years who had never undergone a carotid ultrasound examination. Participants were recruited from 24 hospitals in Korea. We excluded patients who had previously undergone carotid artery ultrasound, or who had a history of coronary artery disease, symptomatic congestive heart failure, coronary revascularization, cerebrovascular disease, stroke, transient ischemic attack, or documented peripheral vascular disease (e.g. peripheral artery disease, abdominal aneurysm, or carotid artery stenosis). The investigation protocol was approved by the institutional review boards of each institution involved in the study. After obtaining informed written consent, the height, weight, and body mass index (BMI) (weight/height², kg/m²) of each patient were measured. Blood pressure was measured using a standard mercury sphygmomanometer. All patients were interviewed prior to CUS examination. Questionnaires were administered using one-on-one interviews and self-reporting techniques to collect data on smoking; alcohol use; stress; dietary habits; physical activity; past history of hypertension, dyslipidemia, and atrial fibrillation; medication compliance; and family history of CVD. The validated Korean version [10] of Morsky’s self-reported questionnaire [11] was used to assess medication compliance. Levels of fasting plasma glucose, HbA_1C, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C); current medications; and the microalbumin-to-creatinine ratio within the past 1 month were collected by reviewing patient medical records.

All subjects were assessed by CUS. Carotid intimal-media thickness (IMT) was measured with the patient in the supine position. A high-resolution B-mode ultrasound machine with a 7.5-MHz transducer was used on the bilateral segments of the carotid arteries. The carotid IMT was measured on the posterior far wall of the left carotid artery. At least 4 measurements were taken, each about 1 cm proximal to the bifurcation. Positive criteria for carotid atherosclerosis were defined as ≥1 mm of intima medial thickness or the presence of plaque.

Although CUS was performed separately in the 24 different hospitals, each used a standardized protocol recommended by the Mannheim carotid IMT consensus report [12]. In addition, to adjust for potential intercenter variations due to different sonographers, every hospital used Intimascope software (Media Cross Co, Ltd, Tokyo, Japan) for measurement. This software performs automated IMT measurements based on an algorithm that delineates the lumen-intima and media adventitial interfaces [13].

Patients were stratified by risk using the UKPDSRE and FRS assessments. A total of 622 patients provided all required information to be assessed by the UKPDSRE calculator and 648 patients provided sufficient information to be assessed by the FRS algorithm. A total of 622 patients were assessed by CUS, UKPDSRE, and FRS. The UKPDS calculator classified subjects into low (<15 %), intermediate (15–30 %), or high (>30 %) 10-year risk levels for CVD based on age, sex, duration of diabetes, smoking, systolic blood pressure, total cholesterol, HDL, ethnicity, and HbA_1C [4]. The FRS algorithm categorized subjects into low (<10 %), intermediate (10–19 %), or high (≤20 %) 10-year risk levels for symptomatic CVD according to age, sex, lipid levels, blood pressure, smoking, and presence of diabetes [5].

Blood samples were collected six months after carotid IMT assessment to measure levels of TC, HDL-C, TG, and LDL-C. Patients were also re-examined for changes in responses to interview questions, physician prescriptions, and patient behaviors.

All statistical analyses were completed using SAS (version 9.2, USA). All data are presented as means ± standard deviations (SDs) or as numbers (percentages). To compare clinical characteristics between the two groups, an independent t-test was used for continuous variables and a chi-squared test was used for categorical variables. Multiple logistic regression analysis was used to analyze the association between carotid IMT and CVD risk factors. A paired t-test was used to measure changes in patient behavior before and after they were informed about their subclinical carotid atherosclerosis risk. Differences with a p-level <0.05 were considered statistically significant.

Our results suggest that CUS can identify CVD-vulnerable patients out of the population of patients with type 2 DM with low-risk or intermediate-risk stratification scores. In addition, improved awareness of CVD risk based on carotid IMT results can improve CV risk management by increasing the prevention efforts of both physicians and patients.

According to CUS, 271 (43.5 %) patients were at high risk for CVD. In contrast, only 66 (10.6 %) and 28 (4.3 %) patients were at high risk for CVD according to the UKPDSRE calculator and the FRS algorithm, respectively. We also found that more high-risk patients were identified using the UKPDSRE calculator compared to the FRS algorithm (10.6 vs. 4.5 %, p < 0.0001). This finding was not surprising, since the UKPDS risk engine was developed especially for use with patients with diabetes [5]. As such, this method has a higher prognostic value for coronary heart disease in patients newly diagnosed with type 2 diabetes [14]. Also, the UKPDSRE calculator provided the highest odds ratios for predicting carotid atherosclerosis in Korean patients with type 2 diabetes compared to the FRS and the SCORE methods [15]. However, the prevalences of positive CUS findings were very similar in the low-risk (31.5 % vs. 35.8 %), intermediate-risk (52.6 % vs. 54.9 %), and high-risk (77.3 % vs. 78.6 %) groups for both the UKPDS risk engine and the FRS algorithm, respectively (Fig. 1).

The FRS and UKPDSRE approaches, both of which include traditional CV risk factors, have been validated for predicting CV risk in Asian populations [15‐17]. Although the results of these approaches are generally correlated with subclinical atherosclerosis, the majority of CVD events occur in patients with low or intermediate risk of CVD [4]. To our surprise, one-third of the low-risk patients according to both the UKPDSRE and FRS classifications had a positive CUS finding. Of the patients classified as low-risk based on their UKPDSRE score, 39.9 % had a positive CUS finding. One previous study found that 32.8 % of all women and 40.5 % of all men with low (<10 %) to intermediate (10–20 %) risk of CVD according to the FRS algorithm had subclinical atherosclerosis [6]. Analysis of factors related to atherosclerosis in the low-risk group indicated that positive CUS findings were significantly associated with older age, higher prevalences of hypertension and dyslipidemia, and a higher prevalence of antihypertensive medication history.

Carotid IMT and the presence of carotid plaque are important markers of subclinical atherosclerosis that can be used to predict cardiovascular morbidity. Many epidemiology studies, such as the Atherosclerosis Risk in Communities (ARIC) study [18] and the Insulin Resistance Atherosclerosis Study (IRAS) [19], have demonstrated that age, male sex, smoking, hypertension, dyslipidemia, and postmenopausal status are independent correlates of carotid atherosclerosis. In particular, Chin et al. [20] showed that LDL-C levels in males and HDL-C levels in females were risk factors for IMT progression among patients newly diagnosed with type 2 diabetes. We found that old age, diabetes duration, percentage of antihypertensive medication use, antiplatelet agent use, and log hs-CRP level were significantly higher in patients with positive CUS findings. After adjustment for age and sex, LDL-C was an independent correlate of subclinical carotid atherosclerosis. These data suggest that LDL-C should be managed to protect or delay the progression of atherosclerosis. Therefore, comprehensive management of CV risk factors and patient adherence to the treatment plan may prevent or delay the progression of atherosclerosis.

We also assessed how patient lifestyle and physician prescriptions changed after receiving the CUS results, as well as how knowing these results affected the achievement of target lipid and BP levels after 6 months. Among the patients with positive CUS findings, significant increases in the use or dosage of anti-hypertensive drugs and antiplatelet agents were observed compared to those in patients with negative CUS findings. We speculate that learning that a patient had subclinical atherosclerosis of the carotid artery may have encouraged physicians to more intensively manage patient risk factors for atherosclerosis. In addition, this intervention was associated with a significant improvement in the achievement of target LDL-C levels, even though a change in medication prescriptions related to hypercholesterolemia was not observed. This finding implies that medication compliance for lipid-lowering drugs might increase after patients receive their CUS findings, regardless of whether these findings are negative or positive. Hong et al. [21] reported that among asymptomatic patients with hypertension, atherosclerosis detection by CUS significantly increased the proportion of patients who achieved their target LDL-C levels compared to patients who received a negative CUS finding. However, we found that patient knowledge of CUS findings improved outcomes, regardless of whether the results were positive or negative. As such, we propose that CUS is a beneficial tool for increasing adherence to lipid-lowering drug regimens as part of CV risk management in patients with type 2 diabetes.

Patient behaviors also changed significantly after the patients received their CUS results. Specifically, patients who underwent CUS examination subsequently reduced smoking and salt intake (for the latter, by reducing soup consumption). We infer that since consumption of Korean soup or stew has been shown to be associated with high salt intake [22], patients made an effort to reduce their salt intake by decreasing their soup consumption. Thus, our data indicate that knowledge of carotid US results and subsequent explanation of the relevant CVD risks is a useful approach for enabling patients to achieve the recommended lifestyle modifications. Furthermore, CUS is a very helpful tool that enables patients to better understand their atherosclerosis status, particularly when CUS imaging results are employed. Our results thus indicate that explanation of CUS results assists patients with diabetes and their physicians to achieve patient therapeutic targets through behavior changes and medication plan alterations.

This study did have some limitations. First, the study period was only 6 months, which is a relatively short period of time for full evaluation of CVD event outcomes. Second, although our results indicated that awareness of CUS results may positively influence physician management of CV risk factors and patient behavior, correlation does not imply causality. Third, we did not evaluate the quality or area of the carotid plaques found by CUS. Several studies have shown that the quality of plaque and the plaque area are more strongly predictive of CV events than IMT [23, 24]. Fourth, we defined a positive CUS finding as an IMT ≥ 1 mm or the presence of plaque. We chose these criteria based on several large clinical studies (e.g. the ARIC study and studies performed in Finland) that compared the hazard ratios between CIMT ≥ 1 mm and < l mm [25‐27]. Specifically, the ARIC study showed that the hazard ratio comparing extreme mean CIMT (≥1 mm) to not extreme CIMT (<1 mm) was 5.07 for women and 1.85 for men. Above 1 mm, the CV event rates were elevated [26]. However, this cutoff point was derived from a non-Asian population. To more accurately predict CV risk in future studies, a more comprehensive investigation of the optimal CIMT cutoff point to predict CV risk in an Asian population would be beneficial. Finally, all subjects in the present study were Asian, and thus our findings may not be applicable to other populations.

Our data indicate that CUS screening is an effective method for identifying patients with subclinical atherosclerosis, even among patients considered to be low-risk according to the UKPDRES or FRS approach. In addition, educating patients with type 2 DM about their atherosclerosis risk as determined by their CUS results may result in improved management of CV risk factors.