We aimed to determine the relationship between lower extremity peripheral arterial disease (PAD), 10-year coronary heart disease (CHD), and stroke risks in patients with type 2 diabetes (T2DM) using the UKPDS risk engine. We enrolled 1178 hospitalized T2DM patients. The patients were divided into a lower extremity PAD group (ankle-brachial
Diabetes patients with peripheral arterial disease (PAD) are at an increased risk for cardiovascular disease [
Currently, a number of methods are available for predicting the 10-year risk of cardiovascular disease in individual subjects, such as the Framingham Risk Score (FRS), the 2013 American College of Cardiology (ACC)/American Heart Association (AHA) risk assessment, and the United Kingdom Prospective Diabetes Study (UKPDS) risk engine. The FRS was derived from the Framingham Heart Study to assess the cardiovascular risk based on age, low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), smoking, hypertension, and other factors [
Some reports have indicated that the ABI abnormality was linked to cardiovascular events, cerebrovascular events, and risk factors in patients with diabetes or metabolic syndrome [
This study involved T2DM patients who were admitted to the Department of Endocrinology of the Second Affiliated Hospital Zhejiang University Medical College between April 2008 and April 2013. All participants had been diagnosed with diabetes according to the 1999 World Health Organization diagnostic criteria for the diagnosis and classification of diabetes. In our study, we involved only type 2 diabetes mellitus patients. Those with gestational diabetes, other types of diabetes mellitus, type 1 diabetes mellitus, and GAD antibody positivity were excluded. Further, patients with CHD and stroke were additionally excluded, leaving a total of 1178 cases that were included in the statistical analysis. This study was approved by the Ethics Committee of the Second Affiliated Hospital Zhejiang University School of Medicine, and all subjects gave informed consent for participation.
A detailed medical history was obtained from each patient, including the patient’s age, age at diagnosis of diabetes, smoking history, hypertension, and antihypertensive therapy. Each subject also underwent a detailed physical examination, including height, weight, blood pressure, and body mass index (BMI) measurements. Prior to the blood pressure measurements, the patients were asked to sit for 5 min. Subsequently, two consecutive blood pressure measurements were taken with an electronic blood pressure meter (Kenz BPM SP-1, Japan), and the mean of the two values was used.
Venous blood was collected in the morning (6:00–9:00 AM) after the patient had fasted for 8–12 hours. The fasting blood glucose, total cholesterol (TC), triglyceride (TG), LDL-c, and HDL-c levels were measured by an Olympus AU4500 automatic chemistry analyzer (Olympus Corporation, Tokyo, Japan). The level of HbA1c was determined by a TOSOH HLC-723G8 automatic glycohemoglobin analyzer (Tosoh Corporation, Yamaguchi 746-0042, Japan).
The ABI was measured by a technician who was blinded to the patient history and biochemical indices. The ABI was determined using Doppler ultrasound and a portable optical volume detector (Vista AVS, Summit Doppler, USA). The patients were asked to take off their shoes and lie in a supine position for 5 min. The upper arm and ankle systolic pressures were measured by slowly moving the ultrasonic probe along the arterial contorts until the strongest information was gotten. The ABI was calculated as the ratio of the ankle systolic blood pressure to the brachial arterial systolic pressure. Blood pressure was measured in both lower extremities and used to calculate the ABI. The lower of the two ABI values thus obtained was used in the subsequent analyses, unless one of the ABI values was greater than 1.4. The patients were divided into two groups based on the ABI value as follows: patients with an
The risks of CHD, fatal CHD, stroke, and fatal stroke were calculated by the UKPDS risk engine according to the patient’s sex, age at diagnosis of diabetes, smoking, systolic blood pressure, hemoglobin, TC, HDL-c, duration of diabetes, atrial fibrillation, and race [
The SPSS 20 statistical software was used for data analysis. Data were expressed as mean ± standard deviation or mean (95% confidence interval). Categorical variables were presented as frequencies, with percentages given in parentheses. The CHD and stroke risks were assessed after stratifying patients by PAD status and age. We used the Mann-Whitney test or independent
Of the 1178 T2DM patients included in this study, 621 were men and 557 were women. Their average age was 58.1 ± 12.7 years (range, 21–90 years), and the mean duration of diabetes was 7.6 ± 6.6 years (range, 0–36 years). In total, 88 (7.5%) patients were assigned to the PAD group, and 1090 (92.5%) patients were included in the non-PAD group based on their ABI values. Among the 88 patients in the PAD group, 81 (6.9%) had an
General characteristics of subjects.
Non-PAD group | PAD group | |
---|---|---|
Number | 1090 | 88 |
Age (years) | 57.2 ± 12.3 | 69.8 ± 11.8# |
Gender (men/women) | 586/504 | 35/53 |
YSDD (years) | 7.3 ± 6.4 | 11.0 ± 7.8# |
WC (cm) | 87.9 ± 10.1 | 88.0 ± 10.2 |
BMI (kg/m2) | 24.0 ± 3.6 | 23.3 ± 3.2 |
SBP (mmHg) | 135.6 ± 19.5 | 145.4 ± 20.3# |
DBP (mmHg) | 81.8 ± 11.1 | 79.5 ± 12.8 |
HbA1c (%) | 9.6 ± 2.4 | 9.5 ± 2.4 |
FBS (mmol/L) | 9.2 ± 3.7 | 8.8 ± 4.1 |
Total cholesterol (mmol/L) | 4.6 (4.5, 4.7) | 4.5 (4.2, 4.8) |
Triglycerides (mmol/L) | 1.9 (1.9, 2.0) | 1.9 (1.6, 2.2) |
HDL-c (mmol/L) | 1.2 (1.2, 1.3) | 1.2 (1.1, 1.3) |
LDL-c (mmol/L) | 2.9 (2.9, 3.0) | 2.9 (2.7, 3.2) |
Hypertension ( |
(514, 47.2%) | (65, 73.9%)# |
Smoker ( |
(368, 33.8%) | (26, 29.5%) |
Nonantidiabetic drugs ( |
(184, 16.9%) | (6, 6.8%) |
Only OAD ( |
(471, 43.2%) | (41, 46.6%) |
Insulin + OAD ( |
(435, 39.9%) | (41, 46.6%) |
Hypertension drugs ( |
(456, 41.9%) | (63, 71.6%)# |
ARB/ACEI ( |
(227, 20.8%) | (38, 43.2%)# |
Lipid-lowering drugs ( |
(157, 14.4%) | (18, 20.5%) |
Statins ( |
(135, 12.4%) | (18, 20.5%) |
Fibrates ( |
(20, 1.8%) | (0, 0%) |
CHD risk, fatal CHD risk, stroke risk, and fatal stroke risk were significantly higher in the PAD group than in the non-PAD group (
Comparison of CHD and stroke risks.
Non-PAD group | PAD group | |
---|---|---|
|
1090 | 88 |
CHD risk (%) | 20.5 (19.6–21.4) | 35.1 (30.7–39.5)# |
Fatal CHD risk (%) | 15.1 (14.3–16.0) | 29.7 (25.6–33.8)# |
Stroke risk (%) | 9.3 (8.6–10.0) | 26.3 (21.7–30.9)# |
Fatal stroke risk (%) | 1.5 (1.3–1.6) | 4.4 (3.5–5.4)# |
#
Considering that age is the most important factor affecting the ABI and CHD and stroke risks [
Age-related prevalence of CHD risk and stroke risk in diabetes patients. CHD: coronary heart disease; PAD: peripheral arterial disease; ABI: ankle-brachial index. PAD group:
The UKPDS CHD risk, fatal CHD risk, stroke risk, and fatal stroke risk were used as the dependent variables, and age, diabetes duration, PAD, HbA1c, TC, TG, HDL, LDL, BMI, systolic blood pressure, diastolic blood pressure, smoking, and sex were used as independent variables in a linear regression analysis. The results showed that age, diabetes duration, PAD, and sex were included in the linear regression equation (Table
Multivariate linear regression analysis of risk factors for CHD and stroke as estimated using the UKPDS risk engine.
Variables | UKPDS CVD risk | UKPDS stroke risk | UKPDS fatal CVD risk | UKPDS fatal stroke risk | ||||
---|---|---|---|---|---|---|---|---|
Beta |
|
Beta |
|
Beta |
|
Beta |
|
|
Male | −0.355 | 0.000 | −0.168 | 0.000 | −0.304 | 0.000 | −0.142 | 0.000 |
Age | 0.726 | 0.000 | 0.602 | 0.000 | 0.711 | 0.000 | 0.507 | 0.000 |
BMI | −0.023 | 0.040 | 0.004 | NS | −0.023 | NS | −0.004 | NS |
Duration | 0.081 | 0.000 | 0.270 | 0.000 | 0.148 | 0.000 | 0.248 | 0.000 |
SBP | 0.082 | 0.000 | 0.044 | NS | 0.101 | 0.000 | 0.232 | 0.000 |
DBP | −0.020 | NS | 0.000 | NS | −0.025 | NS | −0.022 | NS |
HbA1c | 0.352 | 0.000 | 0.031 | NS | 0.363 | 0.000 | 0.041 | 0.025 |
LDL−c | −0.024 | NS | −0.022 | NS | −0.016 | NS | −0.022 | NS |
HDL−c | −0.285 | 0.000 | −0.045 | 0.050 | −0.233 | 0.000 | −0.043 | NS |
TC | 0.302 | 0.000 | 0.061 | NS | 0.237 | 0.000 | 0.050 | NS |
TG | 0.000 | NS | 0.032 | NS | −0.004 | NS | 0.024 | NS |
Smoking | 0.051 | 0.000 | 0.019 | NS | 0.014 | NS | 0.018 | NS |
PAD | 0.055 | 0.000 | 0.140 | 0.000 | 0.066 | 0.019 | 0.140 | 0.000 |
NS: not significant; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; HDL-c: high-density lipoprotein cholesterol; LDL-c: low-density lipoprotein cholesterol; PAD: peripheral arterial disease; ABI: ankle-brachial index; TG: triglyceride; TC: total cholesterol; HbA1c: glycosylated hemoglobin; UKPDS: United Kingdom Prospective Diabetes Study; CHD: coronary heart disease.
Multivariate binary logistic regression analysis of risk factors for CHD and stroke as estimated using the UKPDS risk engine.
Variables | CHD risk | Stroke risk | ||
---|---|---|---|---|
OR (95% CL) |
|
OR (95% CL) |
|
|
|
33.2 (20.2–54.4) | 0.000 | 255.1 (35.5–1832.4.5) | 0.000 |
Hypertension | 2.0 (1.5–2.8) | 0.000 | 2.1 (1.5–2.8) | 0.000 |
Smoking | 5.5 (3.9–7.6) | 0.000 | 1.5 (1.1−2.0) | 0.013 |
Elevated HbA1c | 4.5 (3.4–6.1) | 0.000 | 0.8 (0.6–1.1) | 0.182 |
Reduced HDL-c | 1.4 (1.1–1.9) | 0.012 | 0.9 (0.7–1.2) | 0.594 |
PAD | 3.6 (2.2–6.0) | 0.000 | 6.9 (4.0–11.8) | 0.000 |
UKPDS: United Kingdom Prospective Diabetes Study; CHD: coronary heart disease. Elevated HbA1c (≥the average value 9.61%); reduced HDL-c (<1.04 mmol/L (men) or <1.29 mmol/L (women)).
The ABI is a simple, inexpensive, and noninvasive method of detecting lower extremity PAD in diabetes patients. Various ABI cutoffs have been proposed for detecting PAD in different studies. The 2011 ACCF/AHA guidelines set the ABI cutoff at ≤0.9; in addition, they stated that an
This study showed that the proportion of women with a low ABI was significantly higher than that of men with a low ABI, which is consistent with previous literature [
Our study also demonstrated that the 10-year CHD and stroke risks were significantly greater in the PAD group than in the non-PAD group, and the ABI was an independent predictor of the 10-year CHD and stroke risks. In addition to the diagnosis of PAD, the ABI is associated with cardiovascular risk factors and cardiovascular events. A low ABI has been related to many known cardiovascular risk factors, including hypertension, diabetes, smoking, dyslipidemia, obesity, and C-reactive protein [
Considering that age is the most important factor affecting the ABI and CHD and stroke risks, we stratified the patients by age and recalculated the CHD and stroke risks. The results showed that the cardiovascular risk was higher in the PAD group than in the non-PAD group for every age group, which indicated that an abnormal ABI predicts CHD and stroke risks independent of age.
Furthermore, the combination of the ABI in cardiovascular risk stratification with the current methods for predicting the 10-year risk of cardiovascular disease would improve risk prediction. The ABI Collaboration conducted a meta-analysis of 16 cohort studies based on individuals, focusing on whether the ABI can predict the risk of cardiovascular events and death independently from the FRS and whether it can improve risk prediction when used in combination with the FRS [
This study has some limitations. First, the study involved only hospitalized patients, many of whom had poor glycemic control. However, at present, glycemic control is less than ideal all over the world. A cross-sectional study of 9065 T2DM outpatients from 26 medical centers in China found that blood glucose levels were controlled in only 32.6% of patients [
In conclusion, our study found that the 10-year CHD and stroke risks were higher in diabetes patients with lower extremity PAD than in diabetes patients without PAD, and lower extremity PAD was an independent risk factor for cardiovascular diseases in diabetes patients. Given that the ABI is a simple and easy method of detecting lower extremity PAD, ABI measurements will be beneficial for the estimation of cardiovascular disease and stroke risks in T2DM patients.
The authors declare that there is no conflict of interests regarding the publication of this paper.
Xiao-Hong Pang and Jue Han contributed equally.
This study was supported in part by funding from the Chinese Society of Endocrinology (13040620447), the National Natural Science Foundation of China (81370968, 81670744), and the Foundation of Education Department of Zhejiang Province of China (Y201328533).