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The performance of a diabetic retinopathy risk score for screening for diabetic retinopathy in Chinese overweight/obese patients with type 2 diabetes mellitus Jiao Wang, Hong Chen, Hua Zhang, Fan Yang, Rong-Ping Chen, Yan-Bing Li, Chuan Yang, Shao-Da Lin, Li-Shu Chen, Gan-Xiong Liang & De-Hong Cai To cite this article: Jiao Wang, Hong Chen, Hua Zhang, Fan Yang, Rong-Ping Chen, YanBing Li, Chuan Yang, Shao-Da Lin, Li-Shu Chen, Gan-Xiong Liang & De-Hong Cai (2014) The performance of a diabetic retinopathy risk score for screening for diabetic retinopathy in Chinese overweight/obese patients with type 2 diabetes mellitus, Annals of Medicine, 46:6, 417-423 To link to this article: http://dx.doi.org/10.3109/07853890.2013.878977
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Annals of Medicine, 2014; 46: 417–423 © 2014 Informa UK, Ltd. ISSN 0785-3890 print/ISSN 1365-2060 online DOI: 10.3109/07853890.2013.878977
Original article
The performance of a diabetic retinopathy risk score for screening for diabetic retinopathy in Chinese overweight/obese patients with type 2 diabetes mellitus Jiao Wang1,2*, Hong Chen1*, Hua Zhang1, Fan Yang1,3, Rong-Ping Chen1, Yan-Bing Li4, Chuan Yang5, Shao-Da Lin6, Li-Shu Chen7, Gan-Xiong Liang8 & De-Hong Cai1
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1Department of Endocrinology, Zhujiang Hospital, Southern Medical University, 510282, Guangzhou, China, 2Division of Endocrinology,
Department of Internal Medicine, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, China, 3Department of Endocrinology, Affiliated Hospital of Guilin Medical University, 541001, Guilin, China, 4Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, 510080, Guangzhou, China, 5Department of Endocrinology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 510120, Guangzhou, China, 6Department of Endocrinology, The First Affiliated Hospital of Shantou University Medical College, 515000, Shantou, China, 7Department of Endocrinology, The Second Affiliated Hospital of Shantou University Medical College, 515000, Shantou, China, and 8Department of Endocrinology, Zhongshan People’s Hospital, 528400, Zhongshan, China
Introduction. Diabetic retinopathy (DR) is a common chronic microvascular diabetic complication. The presence of DR may indicate microcirculatory dysfunction in other organ systems besides visual morbidity. The objective of this study was to develop a simple diabetic retinopathy risk score to identify DR in Chinese overweight/obese patients with type 2 diabetes mellitus (T2DM). Patients and methods. A multicentre hospital-based crosssectional study was carried out in Guangdong Province between August 2011 and March 2012. The evaluated 2699 patients included 1263 males and 1436 females, with an average age of 59.4 13.0 years. Results. The diabetic retinopathy risk score was conducted by age, duration of DM, history of antihypertensive drug treatment, and waist circumference. The area under the receiver operating characteristics curve for DR was 0.700 (95% CI 0.671–0.729). Comparing Youden’s index of different values, the optimal cut-off point was 20 to predict DR. The odds ratio for one unit increase in the diabetic retinopathy risk score associated with the risk of DR was 1.104 (95% CI 1.089–1.120). Conclusions. Our data suggest that the diabetic retinopathy risk score could be a reliable primary screening tool for the presence of DR in Chinese overweight/obese patients with T2DM. Key words: Diabetic retinopathy, obesity, overweight, risk factors, screening, type 2 diabetes mellitus
Introduction Diabetic retinopathy (DR) is a common chronic microvascular diabetic complication, and it is the leading cause of visual
Key messages ••The diabetic retinopathy risk score was conducted by age, duration of DM, history of antihypertensive drug treatment, and waist circumference. ••The diabetic retinopathy risk score could be a reliable primary screening tool for the presence of DR in Chinese overweight/obese patients with T2DM. ••The odds ratio for one unit increase in the diabetic retinopathy risk score associated with the risk of DR was 1.104 (95% CI 1.089–1.120).
impairment among working adults in the Western world (1). Apart from visual morbidity, the presence of DR may indicate microcirculatory dysfunction in other organ systems (2,3). To identify diabetic patients with high risk for DR early and diagnose DR timely are very important. In clinical practice, DR is diagnosed by non-mydriatic photographs at the posterior pole, which is less sensitive than using seven-field, stereoscopic fundus photography (4). Also, fundus photography is not cheap and requires skilled ophthalmologists to carry out the work. Therefore, it would be important to develop a simple and inexpensive DR screening tool for primary health care in order to detect subjects that will benefit from more specific/advanced tests. It is well known that obesity and weight gain are established causes of type 2 diabetes mellitus (T2DM); approximately 63% of patients with T2DM are overweight or obese in China (5). For China as the world’s most populous nation with approximately 114 million patients with diabetes (6), therefore, in the present
*Authors contributed equally to this work. Correspondence: Hong Chen, Department of Endocrinology, Southern Medical University, Zhujiang Hospital, 253# industry road, 510282, Guangzhou, Guangdong, China. E-mail: [email protected] (Received 23 September 2013; accepted 17 December 2013)
418 J. Wang et al. study, we aimed to report a simple diabetic retinopathy risk score and its performance for identifying DR in Chinese overweight/ obese patients with T2DM.
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Patients and methods This was a hospital-based multicentre cross-sectional study in Guangdong Province. The Guangdong Province is divided into four districts according to its administrative districts: Pearl River Delta with good economic conditions, East of Guangdong with medium economic conditions, West of Canton with relatively poor economic conditions, and North of Guangdong with poor economic conditions. A total of 60 hospitals were included in our study: 46 in Pearl River Delta, 6 in East of Guangdong, 3 in West of Canton, 5 in North of Guangdong. Comparing with the other three districts, Pearl River Delta has the largest population, and its economic conditions are better and allow a sufficiently high level of medical infrastructure to carry out the study. Therefore, the majority of hospitals included in the present study are located in Pearl River Delta. In each hospital, patients were enrolled according to all of the following criteria between August 2011 and March 2012: 1) Chinese T2DM patients aged over 20 years who had lived in Guangdong Province for 1 year, 2) body mass index (BMI) 25 kg/m2, 3) waist circumference was measured, 4) history of DR, history of antihypertensive drug treatment, physical activity, diet control, and date of diabetes diagnosis were recorded. Subjects with cancer, pregnancy, severe psychiatric disturbance, hepatic failure, and end-stage renal failure were excluded. The ethics committee at each participating institution approved the study protocol. Written informed consent was obtained from all participants before data collection. Because the aims of the present study were to produce a simple risk calculator that could be conveniently used in primary care and also by patients themselves, only parameters that are easy to assess without any clinical measurements requiring special skills or any laboratory tests were considered in the model. Longer duration of diabetes mellitus and hypertension are consistent risk factors in the pathogenesis and development of DR (7,8); older age, obesity, and physical inactivity are also risk factors reported in some studies (7–11). In addition, diet is an important factor associated with hyperglycaemia, and hyperglycaemia is a consistent risk factor of DR (7,8). Therefore, age, duration of DM, hypertensive drug treatment, BMI, waist circumference, physical inactivity, and diet control were considered as candidate risk factors. In order to obtain accurate measurement data, the nurses and doctors were uniformly trained. In each of the included patients, detailed demographic data including age, gender, address, education, and occupation were recorded. Histories of any chronic disease including DR, hypertension, and cardiovascular disease (CVD) were recorded. If the patient had a previous ophthalmologist diagnosis of DR, the grading of DR was already recorded by the ophthalmologist. Doctors in each hospital checked the medical record about DR and recorded the grading of DR. In addition, information regarding history of antihypertensive drug treatment (yes/no), diet control (yes/no), regular physical activity (yes/no), current smoking pattern (yes/no), and current drinking pattern (yes/no) were also recorded. All patients underwent a comprehensive medical examination including height, weight, waist circumference, and blood pressure measurements. After anthropometric and blood pressure measurements had been taken, fasting venous blood samples, which were drawn from the antecubital vein after at least an 8-hour overnight fast, were collected for use in measuring blood glucose, glycated hemoglobin (HbA1c), and serum lipids.
Anthropometric and laboratory measurements Weight was measured, with clothing, using a balanced-beam scale. Height was measured using the clinic stadiometer, with the Frankfort plane held horizontal. The BMI was calculated as weight (kg) divided by squared height (m2). Waist circumference was measured at the midpoint between the lowest rib margin and the iliac crest. Blood pressure was the average of three measurements obtained by a sphygmomanometer at 5-minute intervals. The blood samples were measured at the department of clinical laboratory of each hospital, respectively. Serum uric acid levels were measured by the uricase method. Fasting plasma glucose (FPG) and HbA1c levels were measured by the glucose oxidase method and high-performance liquid chromatography, respectively. Serum creatinine, total cholesterol (TC), triglycerides (TG), HDL-cholesterol, and LDL-cholesterol, which were measured by the enzymic method, were measured on the Abbott Laboratories full automatic biochemical instrument (IL, USA).
Definitions and preferred cut-off values The diagnostic criteria of DM were based on the 1999 WHO diagnostic criteria (12). Overweight and obesity were diagnosed as 25 kg/m2 BMI 30 kg/m2 and BMI 30 kg/m2, respectively (13). The minimum criterion for diagnosis of DR was the presence of at least one microaneurysm (9). Retinopathy grading was based on the results of the worst eye, and the retinopathy severity score was assigned according to the International Clinical DR Disease Severity Scale (14) as follows: Grade 0, no abnormalities; Grade 1, mild non-proliferative retinopathy (microaneurysm only); Grade 2, moderate non-proliferative retinopathy (more than just microaneurysms, but less than Grade 3); Grade 3, severe non-proliferative retinopathy; Grade 4, proliferative retinopathy. Hypertension was defined as systolic blood pressure (SBP) 140 mmHg and/or diastolic blood pressure (DBP) 90 mmHg at examinations (15), and also defined if the participant had a previous physician diagnosis.
Statistical analysis All analyses were performed using SPSS software version 13.0. Normally distributed and continuous variables were presented as mean standard deviation, and non-normally distributed variables were presented as medians (quartiles 25% and 75%). The independent-samples t test was used to examine differences in normally distributed and continuous variables, and the Mann– Whitney U was used to examine differences in non-normally distributed variables. Categorical variables were expressed as percentages, and the chi-square test was used for comparisons of proportions. Bivariate correlations were measured by means of Spearman’s rank correlation coefficient. A value of P 0.05 was considered statistically significant (two-tailed). Binary logistic regression was used to assess the associations between DR and associated risk factors. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated. Candidate risk factors which were statistically significant (P 0.05) in the concise model were further fitted into the full logistic regression model using the enter method. Beta-coefficients derived from the full logistic regression were used to calculate the diabetic retinopathy risk score. A score for each variable in the full model was calculated by multiplying the b-coefficient by 10 (16). Crossvalidation was used to validate the method for establishment of the diabetic retinopathy risk score. Altogether, 1869 patients (approximately 70%) were selected by using systematic random sampling method to form training samples, and the remaining 830 patients (approximately 30%) were used as testing samples.
A risk score for screening for diabetic retinopathy 419
Development of the diabetic retinopathy risk score
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Binary logistic regression analysis was performed to estimate the strength of the association of candidate factors to the presence of DR. Age, waist circumference, history of antihypertensive drug treatment, and duration of DM, which were significantly (P 0.05) associated with the presence of DR in the concise model, were further fitted into the full logistic regression model using the enter method (Table II). Based on the b-coefficient of the full model, the risk score was constructed by multiplying the b-coefficients by 10 and rounding to the nearest integer (16). The detailed value of the risk score in training samples are shown in Table II. A sum score was calculated for every patient by adding the score for each variable. In addition, the questionnaire of the diabetic retinopathy risk score is shown in the Supplementary material Table I available online at http://informahealthcare.com/doi/abs/ 10.3109/07853890.2013.878977. Figure 1. The distribution of the study patients according to the diabetic retinopathy risk score.
Performance of diabetic retinopathy risk score and the risk score cut-off value of DR
Performance of the diabetic retinopathy risk score was tested using receiver operating characteristic (ROC) curve analysis. Based on the ROC analysis, the best cut-off value of the diabetic retinopathy risk score was determined from the highest Youden index, which is defined as follows: (sensitivity specificity – 1) (17).
The ROC curve shown in Figure 2 represents the diagnostic accuracy of the diabetic retinopathy risk score for DR. The area under the ROC curve for DR in training samples was 0.700 (95% CI 0.671–0.729). Table III shows Youden’s index of different values. The optimal cut-off point to predict DR in training samples was 20. Using the diabetic retinopathy risk score cut-off value of 20 points to identify DR, the sensitivity and specificity in testing samples (73.1% and 52.2%) were similar to those in the training samples (77.2% and 55.1%). Among 364 patients with DR deriving from training samples, 285 (78.3%) patients with diabetic retinopathy risk score 20 were diagnosed with DR. And among 167 patients with DR deriving from testing samples, 125 (74.9%) patients with diabetic retinopathy risk score 20 were diagnosed with DR.
Results Clinical characteristics of the study population The evaluated 2699 patients included 1263 males and 1436 females, with an average age of 59.4 13.0 years (20–90 years). Figure 1 shows the distribution of the study patients according to the diabetic retinopathy risk score. These patients were classified into patients without DR and patients with DR. Compared with patients who did not develop DR (Table I), patients with DR were older and had higher waist circumference, higher HbA1c, longer duration of DM, higher systolic blood pressure, and higher TC. The frequencies of females, hypertension, hypertensive drug treatment, and family history of diabetes were significantly higher in patients with DR than in patients without DR. BMI, TG, HDL-cholesterol, LDL-cholesterol, percentage of regular physical exercise, and diet control, however, were similar among patients without and with DR.
Prevalence of DR A total of 531 (19.7%) of the 2699 patients were diagnosed with DR. As shown in Table IV, the prevalence of total DR, mild non-proliferative DR, moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR all increased with the diabetic retinopathy risk score. The prevalence was significantly higher in females than in males (22.8% versus 16.2%, P 0.001), and the prevalence of DR was comparable in overweight patients and obese patients (19.4% versus 21.1%, P 0.392). As shown in Table V, the prevalence of
Table I. Clinical characteristics of the study population. Variables Age (years) Gender: males Family history of diabetes mellitus Body mass index (kg/m2) Waist circumference (cm) History of antihypertensive drug treatment (%) Regular physical activity (%) Diet control (%) FPG (mmol/L) HbA1c (%) Duration of diabetes mellitus (years) Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Hypertension Total cholesterol (mmol/L) Triglycerides (mmol/L) HDL-cholesterol (mmol/L) LDL-cholesterol (mmol/L)
With DR (n 2168)
Without DR (n 531)
P value
58.5 13.2 48.8% (1059) 25.8% (512) 28.00 2.75 96.20 9.10 53.0% (1149) 46.0% (998) 66.8% (1448) 8.74 3.68 8.57 2.25 4.5 (1.2–9.9) 135.9 18.8 80.4 11.0 64.5% (1398) 5.29 1.61 1.78 (1.26–2.75) 1.21 0.69 3.05 1.18
60.0 11.4 38.4% (204) 31.1% (148) 28.06 2.59 97.29 8.77 74.2% (394) 46.7% (248) 70.1% (372) 9.08 3.58 8.80 2.23 9.9 (3.9–14.9) 142.3 20.2 80.9 11.1 80.2% (426) 5.45 1.51 1.92 (1.33–2.80) 1.27 0.61 3.15 1.19
0.001 0.001 0.018 0.619 0.013 0.001 0.781 0.150 0.062 0.041 0.001 0.001 0.375 0.001 0.045 0.189 0.095 0.104
420 J. Wang et al. Table II. Univariate and multivariate analyses of risk factors for diabetic retinopathy. Concise model with all samples (n 2699) Variable
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Age (years) 45–64 versus 20–44 65 versus 20–44 BMI (kg/m2) 30 versus 25–29.9 Central obesity (yes versus no) DM duration (years) 1–5 versus 1 5–10 versus 1 10–15 versus 1 15 versus 1 History of hypertensive treatment (yes versus no) Regular physical activity (yes versus no) Diet control (yes versus no)
OR (95% CI) 2.483 (1.693–3.640) 3.507 (2.387–5.153) 1.067 (0.838–1.359) 1.013 (1.003–1.023) 2.858 (1.933–4.226) 3.568 (2.393–5.319) 5.331 (3.578–7.944) 10.453 (6.940–15.744) 2.551 (2.063–3.153) 1.027 (0.849–1.243) 1.163 (0.947–1.430)
P 0.001
0.598 0.013 0.001
0.001 0.781 0.150
Full model with training samples (n 1869) Βeta-coefficient
OR (95% CI)
Score
0.490 0.449
1.633 (0.995–2.678) 1.567 (0.936–2.623)
5 4
0.213
1.237 (0.826–1.854)
2
0.891 1.039 1.383 1.982 0.698
2.438 (1.525–3.897) 2.825 (1.737–4.594) 3.988 (2.439–6.521) 7.261 (4.352–12.114) 2.010 (1.522–2.653)
9 10 14 20 7
entral obesity was defined as waist circumference 90 cm in men and 85 cm in women. Regular physical activity was defined as participation in 30 or C more minutes of moderate or vigorous activity per day at least 3 days per week. Diet control was defined as deliberately eating less after diagnosing of type 2 diabetes mellitus than before, especially eating less fat meat, fried food, dessert, and fruit with high sugar. Variables which were statistically significant (P 0.05) in the concise model were further fitted into the final multivariate logistic regression model. A score for each variable in the full model was calculated by multiplying the b-coefficient by 10.
DR increased with increasing age, the prevalence of DR in patients with history of antihypertensive drug treatment was dramatically higher than in patients without history of antihypertensive drug treatment, and the prevalence of DR increased with increasing duration of DM. However, the prevalence of DR was similar in patients with and without central obesity (P 0.187).
Risk factors for DR Table VI shows the odds ratio of DR for the different covariates alone (concise model) and controlled for (full model) in all study patients. The association of diabetic retinopathy risk score with DR remained statistically significant even after adjusting for blood glucose and serum lipid, and the odds ratio (OR) for one unit increase in the diabetic retinopathy risk score associated with the risk of DR was 1.102 (95% CI 1.084–1.119). HbA1c (OR 1.078; 95% CI 1.019–1.141) remained an independent risk factor of DR even after adjusting for serum lipids and diabetic retinopathy risk
score. In addition, the diabetic retinopathy risk score was more significantly associated with the presence of DR than duration of DM, hypertensive drug treatment, and HbA1c (rp 0.277, P 0.001 for diabetic retinopathy risk score; rp 0.248, P 0.001 for duration of DM; rp 0.170, P 0.001 for hypertensive drug treatment; rp 0.055, P 0.008 for HbA1c).
Discussion According to national data, non-communicable diseases accounted for an estimated 70% of the total disease burden in China in 2005 (18). The prevalence of diabetes, a major non-communicable disease, increased from 0.67% in 1980 (19) to 11.6% in 2010 in the Chinese population (6)—approximately 113.9 million Chinese adults have diabetes. Considering the large number of individuals with diabetes and the high medical cost of diabetes care in China, and as DR is a typical microvascular complication of diabetes, it is important to develop a simple diabetic retinopathy risk score to identify DR. In the current study, the diabetic retinopathy risk score was constructed with age, duration of DM, waist circumference, and history of antihypertensive drug treatment. Using the diabetic retinopathy risk score cut-off value of 20 points to identify DR, the similar sensitivity and specificity in testing samples (73.1% and 52.2%) and training samples (77.2% and 55.1%) indicated that the method for establishment of the diabetic retinopathy risk score had a good applicability. The area under the ROC curve for DR in training samples (0.700, 95% CI 0.671–0.729) showed that the diabetic retinopathy risk score had adequate performances for screening for DR in overweight/obese patient with T2DM. Comparing the Youden index of different values, the optimal Table III. The sensitivity, specificity, and Youden’s index for identifying diabetic retinopathy at different cut-off values of the risk score. Cut-off value
Figure 2. Receiver operating characteristics (ROC) curves showing the performance of the diabetic retinopathy risk score in detecting diabetic retinopathy.
Sensitivity
Specificity
Results based on training samples (n 1869) 0.786 Cut-off value 18 0.783 Cut-off value 19 0.772 Cut-off value 20 0.717 Cut-off value 21 0.651 Cut-off value 22 Results based on testing samples (n 830) 0.731 Cut-off value 20
Youden’s index
0.518 0.532 0.551 0.581 0.636
0.303 0.315 0.323 0.298 0.287
0522
0.253
A risk score for screening for diabetic retinopathy 421 Table IV. The prevalence of diabetic retinopathy (DR) according to the diabetic retinopathy risk score. Patients with diabetic retinopathy risk score 14 14–21 21–24 24 (n 739) (n 668) (n 660) (n 632)
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Diabetic retinopathy Mild non-proliferative DR Moderate non-proliferative DR Severe non-proliferative DR Proliferative DR
53 (7.2) 32 (4.3) 15 (2.0) 5 (0.7) 1 (0.1)
99 (14.8) 154 (23.3) 225 (35.6) 59 (8.8) 70 (10.6) 88 (13.9) 18 (2.7) 33 (5.0) 51 (8.1) 13 (1.9) 36 (5.5) 48 (7.6) 9 (1.3) 15 (2.3) 38 (6.0)
cut-off point to predict DR was 20. Therefore, using the diabetic retinopathy risk score as a primary screening tool, it is possible to detect diabetic patients with high risk for DR, without any complex clinical testing. The diabetic retinopathy risk score (20 points or above) will identify the overweight/obesity patients with T2DM who should be referred for further testing with more expensive and sophisticated methods. Another diabetic retinopathy risk score has been developed in Iran (20). The performance of the Iranian risk score (0.704, 95% CI 0.685–0.723) is similar to the risk score in the current study (0.700, 95% CI 0.671–0.729), although there are minor differences in sensitivity and specificity. The advantage of the our diabetic retinopathy risk score is that this questionnaire can be completed without any clinical measurements requiring special skills or any laboratory tests; therefore the questionnaire can be answered at home, in contrast to the Iranian diabetic retinopathy risk score (20), which is necessarily based on measurement of HbA1c. Among candidate risk factors, duration of DM and hyper tension are consistent risk factors in the pathogenesis and development of diabetic retinopathy (7,8). Hypertension causes the increase of retinal blood flow, mechanical damage, and stretching of vascular endothelial cells, stimulating the release of vascular endothelial growth factor which facilitates the progression of DR (21,22). In agreement with above studies, the current study showed that hypertension and duration of DM were strong independent risk factors. In addition, the present study showed that age was an important risk factor associated with DR, and the result was consistent with previous studies (9–11). BMI and waist circumference were also considered as candidate risk factors in this study. Although some studies reported that BMI was not a risk factor for DR (23–25), other studies revealed that BMI was a risk factor associated with the presence of DR (26–30). The current study showed that BMI (OR 1.067; 95% CI 0.838–1.359) was not a risk factor associated with DR. Table V. Prevalence of diabetic retinopathy for subjects in subgroups. Prevalence of DR (%) Age (years) 20–44 years 45–64 years 65 years Central obesity Yes No History of hypertensive drug treatment Yes No Duration of diabetes mellitus (years) 1 year 1–5 years 5–10 years 10–15 years 15 years
8.6% (33/383) 19.0% (250/1318) 24.8% (248/998) 20.0% (480/2396) 16.8% (51/303) 25.5% (394/1543) 11.9% (137/1156) 6.4% (35/550) 16.3% (128/787) 19.5% (113/579) 26.6% (125/470) 41.5% (130/313)
P value 0.001
0.187 0.001 0.001
Table VI. Risk factors for diabetic retinopathy.
HbA1c (%) Fasting plasma glucose (mmol/L) Total cholesterol (mmol/L) Triglycerides (mmol/L) LDL-cholesterol (mmol/L) HDL-cholesterol (mmol/L) Diabetic retinopathy risk score (score)
Concise model OR (95% CI)
Full model OR (95% CI)
1.046 (1.002–1.092) 1.024 (0.999–1.050) 1.062 (1.001–1.126) 1.000 (0.960–1.042) 1.069 (0.986–1.160) 1.115 (0.972–1.279) 1.104 (1.089–1.120)
1.078 (1.019–1.141) 1.014 (0.980–1.049) 0.999 (0.877–1.139) 1.022 (0.960–1.088) 1.069 (0.922–1.239) 1.198 (0.979–1.466) 1.102 (1.084–1.119)
Studies have reported that waist circumference was a risk factor associated with DR (28,30). Our study revealed that central obesity (OR 1.013; 95% CI 1.003–1.023) was a risk factor associated with DR. However, the prevalence of DR was similar in patients with and without central obesity. The main reason for the similar prevalence of DR was that the patients included in the present study were overweight or obese, and 88.8% (2396/2699) of the patients had central obesity. Another two candidate risk factors were physical inactivity and diet control. Kriska et al. (31) reported that physical inactivity was a risk factor associated with the presence of DR. Inconsistent with the above study, the present study did not show a similar result. Although diet is an important factor associated with hyperglycaemia, and hyperglycaemia is a consistent risk factor for DR (7,8), this study showed that diet control (OR 1.163; 95% CI 0.947–1.430) was not a protective factor associated with DR. Observational studies support a role for dyslipidaemia in the pathogenesis of DR (7,32). However, epidemiological studies have not found a consistent association between serum lipid levels and DR (9,33–36). The present study did not reveal significant associations between serum lipid profiles and DR. In addition, this study indicated that HbA1c (OR 1.080; 95% CI 1.020–1.142) was an independent risk factor associated with the presence of DR, and the result was consistent with previous studies (7,8). The present study developed a simple diabetic retinopathy risk score to identify DR in overweight/obese patients with T2DM, and confirmed that it was a reliable tool. Moreover, the questionnaire can be completed without any clinical measurements requiring special skills or any laboratory tests. However, several limitations should be mentioned. First, because this was a crosssectional study, the current diabetic retinopathy risk score might not accurately predict the risk of future development of diabetic retinopathy. Second, the diagnosis of DR was according to the history of DR, and not all patients underwent retinal photography. If all patients included in this study had undergone fundus photography, the results would be more validated. Third, a large, prospective study with the general DM population undergoing retinal photography is required to develop a diabetic retinopathy risk score, and to determine whether the risk score can be used to identify DR and what the optimal score cut-off value for DR is. In conclusion, the diabetic retinopathy risk score, which was constructed by age, duration of DM, history of antihypertensive drug treatment, and waist circumference, had reasonable performance for screening for DR in Chinese overweight/obese patients with T2DM.
Acknowledgements The study was supported by the following hospitals: Zhujiang Hospital of Southern Medical University, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, The Second Affiliated Hospital of Shan-
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422 J. Wang et al. tou University Medical College, The First Affiliated Hospital of Shantou University Medical College, Wu Jing Zong Dui Hospital of Guangdong Province, The People’s Hospital of Jiangmen, Jiangmen Central Hospital, The People’s Hospital of Shenzhen, Zhongshan People’s Hospital, Guangdong General Hospital, Affiliated Hospital of Guangdong Medical College, The first Affiliated Hospital of Clinical Medicine of GDPU, The Second Affiliated Hospital of Guangzhou Medical University, The First Affiliated Hospital of Guangzhou Medical University, The Third Affiliated Hospital of Guangzhou Medical University, General Hospital of Guangzhou Military Command of People’s Liberation Army, Guangzhou First Municipal People’s Hospital, The First Affiliated Hospital of Jinan University, The Third Affiliated Hospital of Southern Medical University, Guangdong No. 2 Provincial People’s Hospital, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou Red Cross Hospital, The 458th Hospital of PLA, Clifford Hospital, Shantou Central Hospital, People’s Hospital of Boan District of Shenzhen City, Peking University Shenzhen Hospital, People’s Hospital of Futian District of Shenzhen City, The Second People’s Hospital of Shenzhen, People’s Hospital of Nanshan District of Shenzhen City, Longgang Central Hospital, Chaozhou Central Hospital, The People’s Hospital of Dongguan, Shenzhen Donghua Hospital, People’s Hospital of Nanhai District of Foshan City, The First People’s Hospital of Foshan, Foshan Hospital of Traditional Chinese Medicine, Mingjing Diabetic Hospital of Shunde, The People’s Hospital of Heshan, Huizhou Central Hospital, Wuyi Hospital of Traditional Chinese Medicine, The People’s Hospital of Xinhui, Xinhui Hospital of Traditional Chinese Medicine, The People’s Hospital of Jieyang, Kaiping Central Hospital, Kaiping Hospital of Traditional Chinese Medicine, The People’s Hospital of Qingyuan, The People’s Hospital of Shanwei, The People’s Hospital of Xinxing, The People’s Hospital of Yangjiang, The People’s Hospital of Yuebei, The People’s Hospital of Lechang, The First People’s Hospital of Zhaoqing, Chenxinghai Hospital Affiliated to Guangdong Medical College, The Second People’s Hospital of Zhuhai, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhongshan Hospital of Traditional Chinese Medicine, Sanxiang Hospital of Zhongshan, Maoming People’s Hospital, Meizhou People’s Hospital, and Zhongshan Hospital of Traditional Chinese Medicine. Jiao Wang and Hong Chen contributed equally to this work. Declaration of interest: The authors do not have any conflict of interest to declare.
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ISSN: 0785-3890 (Print) 1365-2060 (Online) Journal homepage: http://www.tandfonline.com/loi/iann20
The performance of a diabetic retinopathy risk score for screening for diabetic retinopathy in Chinese overweight/obese patients with type 2 diabetes mellitus Jiao Wang, Hong Chen, Hua Zhang, Fan Yang, Rong-Ping Chen, Yan-Bing Li, Chuan Yang, Shao-Da Lin, Li-Shu Chen, Gan-Xiong Liang & De-Hong Cai To cite this article: Jiao Wang, Hong Chen, Hua Zhang, Fan Yang, Rong-Ping Chen, YanBing Li, Chuan Yang, Shao-Da Lin, Li-Shu Chen, Gan-Xiong Liang & De-Hong Cai (2014) The performance of a diabetic retinopathy risk score for screening for diabetic retinopathy in Chinese overweight/obese patients with type 2 diabetes mellitus, Annals of Medicine, 46:6, 417-423 To link to this article: http://dx.doi.org/10.3109/07853890.2013.878977
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Annals of Medicine, 2014; 46: 417–423 © 2014 Informa UK, Ltd. ISSN 0785-3890 print/ISSN 1365-2060 online DOI: 10.3109/07853890.2013.878977
Original article
The performance of a diabetic retinopathy risk score for screening for diabetic retinopathy in Chinese overweight/obese patients with type 2 diabetes mellitus Jiao Wang1,2*, Hong Chen1*, Hua Zhang1, Fan Yang1,3, Rong-Ping Chen1, Yan-Bing Li4, Chuan Yang5, Shao-Da Lin6, Li-Shu Chen7, Gan-Xiong Liang8 & De-Hong Cai1
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1Department of Endocrinology, Zhujiang Hospital, Southern Medical University, 510282, Guangzhou, China, 2Division of Endocrinology,
Department of Internal Medicine, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, China, 3Department of Endocrinology, Affiliated Hospital of Guilin Medical University, 541001, Guilin, China, 4Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, 510080, Guangzhou, China, 5Department of Endocrinology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 510120, Guangzhou, China, 6Department of Endocrinology, The First Affiliated Hospital of Shantou University Medical College, 515000, Shantou, China, 7Department of Endocrinology, The Second Affiliated Hospital of Shantou University Medical College, 515000, Shantou, China, and 8Department of Endocrinology, Zhongshan People’s Hospital, 528400, Zhongshan, China
Introduction. Diabetic retinopathy (DR) is a common chronic microvascular diabetic complication. The presence of DR may indicate microcirculatory dysfunction in other organ systems besides visual morbidity. The objective of this study was to develop a simple diabetic retinopathy risk score to identify DR in Chinese overweight/obese patients with type 2 diabetes mellitus (T2DM). Patients and methods. A multicentre hospital-based crosssectional study was carried out in Guangdong Province between August 2011 and March 2012. The evaluated 2699 patients included 1263 males and 1436 females, with an average age of 59.4 13.0 years. Results. The diabetic retinopathy risk score was conducted by age, duration of DM, history of antihypertensive drug treatment, and waist circumference. The area under the receiver operating characteristics curve for DR was 0.700 (95% CI 0.671–0.729). Comparing Youden’s index of different values, the optimal cut-off point was 20 to predict DR. The odds ratio for one unit increase in the diabetic retinopathy risk score associated with the risk of DR was 1.104 (95% CI 1.089–1.120). Conclusions. Our data suggest that the diabetic retinopathy risk score could be a reliable primary screening tool for the presence of DR in Chinese overweight/obese patients with T2DM. Key words: Diabetic retinopathy, obesity, overweight, risk factors, screening, type 2 diabetes mellitus
Introduction Diabetic retinopathy (DR) is a common chronic microvascular diabetic complication, and it is the leading cause of visual
Key messages ••The diabetic retinopathy risk score was conducted by age, duration of DM, history of antihypertensive drug treatment, and waist circumference. ••The diabetic retinopathy risk score could be a reliable primary screening tool for the presence of DR in Chinese overweight/obese patients with T2DM. ••The odds ratio for one unit increase in the diabetic retinopathy risk score associated with the risk of DR was 1.104 (95% CI 1.089–1.120).
impairment among working adults in the Western world (1). Apart from visual morbidity, the presence of DR may indicate microcirculatory dysfunction in other organ systems (2,3). To identify diabetic patients with high risk for DR early and diagnose DR timely are very important. In clinical practice, DR is diagnosed by non-mydriatic photographs at the posterior pole, which is less sensitive than using seven-field, stereoscopic fundus photography (4). Also, fundus photography is not cheap and requires skilled ophthalmologists to carry out the work. Therefore, it would be important to develop a simple and inexpensive DR screening tool for primary health care in order to detect subjects that will benefit from more specific/advanced tests. It is well known that obesity and weight gain are established causes of type 2 diabetes mellitus (T2DM); approximately 63% of patients with T2DM are overweight or obese in China (5). For China as the world’s most populous nation with approximately 114 million patients with diabetes (6), therefore, in the present
*Authors contributed equally to this work. Correspondence: Hong Chen, Department of Endocrinology, Southern Medical University, Zhujiang Hospital, 253# industry road, 510282, Guangzhou, Guangdong, China. E-mail: [email protected] (Received 23 September 2013; accepted 17 December 2013)
418 J. Wang et al. study, we aimed to report a simple diabetic retinopathy risk score and its performance for identifying DR in Chinese overweight/ obese patients with T2DM.
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Patients and methods This was a hospital-based multicentre cross-sectional study in Guangdong Province. The Guangdong Province is divided into four districts according to its administrative districts: Pearl River Delta with good economic conditions, East of Guangdong with medium economic conditions, West of Canton with relatively poor economic conditions, and North of Guangdong with poor economic conditions. A total of 60 hospitals were included in our study: 46 in Pearl River Delta, 6 in East of Guangdong, 3 in West of Canton, 5 in North of Guangdong. Comparing with the other three districts, Pearl River Delta has the largest population, and its economic conditions are better and allow a sufficiently high level of medical infrastructure to carry out the study. Therefore, the majority of hospitals included in the present study are located in Pearl River Delta. In each hospital, patients were enrolled according to all of the following criteria between August 2011 and March 2012: 1) Chinese T2DM patients aged over 20 years who had lived in Guangdong Province for 1 year, 2) body mass index (BMI) 25 kg/m2, 3) waist circumference was measured, 4) history of DR, history of antihypertensive drug treatment, physical activity, diet control, and date of diabetes diagnosis were recorded. Subjects with cancer, pregnancy, severe psychiatric disturbance, hepatic failure, and end-stage renal failure were excluded. The ethics committee at each participating institution approved the study protocol. Written informed consent was obtained from all participants before data collection. Because the aims of the present study were to produce a simple risk calculator that could be conveniently used in primary care and also by patients themselves, only parameters that are easy to assess without any clinical measurements requiring special skills or any laboratory tests were considered in the model. Longer duration of diabetes mellitus and hypertension are consistent risk factors in the pathogenesis and development of DR (7,8); older age, obesity, and physical inactivity are also risk factors reported in some studies (7–11). In addition, diet is an important factor associated with hyperglycaemia, and hyperglycaemia is a consistent risk factor of DR (7,8). Therefore, age, duration of DM, hypertensive drug treatment, BMI, waist circumference, physical inactivity, and diet control were considered as candidate risk factors. In order to obtain accurate measurement data, the nurses and doctors were uniformly trained. In each of the included patients, detailed demographic data including age, gender, address, education, and occupation were recorded. Histories of any chronic disease including DR, hypertension, and cardiovascular disease (CVD) were recorded. If the patient had a previous ophthalmologist diagnosis of DR, the grading of DR was already recorded by the ophthalmologist. Doctors in each hospital checked the medical record about DR and recorded the grading of DR. In addition, information regarding history of antihypertensive drug treatment (yes/no), diet control (yes/no), regular physical activity (yes/no), current smoking pattern (yes/no), and current drinking pattern (yes/no) were also recorded. All patients underwent a comprehensive medical examination including height, weight, waist circumference, and blood pressure measurements. After anthropometric and blood pressure measurements had been taken, fasting venous blood samples, which were drawn from the antecubital vein after at least an 8-hour overnight fast, were collected for use in measuring blood glucose, glycated hemoglobin (HbA1c), and serum lipids.
Anthropometric and laboratory measurements Weight was measured, with clothing, using a balanced-beam scale. Height was measured using the clinic stadiometer, with the Frankfort plane held horizontal. The BMI was calculated as weight (kg) divided by squared height (m2). Waist circumference was measured at the midpoint between the lowest rib margin and the iliac crest. Blood pressure was the average of three measurements obtained by a sphygmomanometer at 5-minute intervals. The blood samples were measured at the department of clinical laboratory of each hospital, respectively. Serum uric acid levels were measured by the uricase method. Fasting plasma glucose (FPG) and HbA1c levels were measured by the glucose oxidase method and high-performance liquid chromatography, respectively. Serum creatinine, total cholesterol (TC), triglycerides (TG), HDL-cholesterol, and LDL-cholesterol, which were measured by the enzymic method, were measured on the Abbott Laboratories full automatic biochemical instrument (IL, USA).
Definitions and preferred cut-off values The diagnostic criteria of DM were based on the 1999 WHO diagnostic criteria (12). Overweight and obesity were diagnosed as 25 kg/m2 BMI 30 kg/m2 and BMI 30 kg/m2, respectively (13). The minimum criterion for diagnosis of DR was the presence of at least one microaneurysm (9). Retinopathy grading was based on the results of the worst eye, and the retinopathy severity score was assigned according to the International Clinical DR Disease Severity Scale (14) as follows: Grade 0, no abnormalities; Grade 1, mild non-proliferative retinopathy (microaneurysm only); Grade 2, moderate non-proliferative retinopathy (more than just microaneurysms, but less than Grade 3); Grade 3, severe non-proliferative retinopathy; Grade 4, proliferative retinopathy. Hypertension was defined as systolic blood pressure (SBP) 140 mmHg and/or diastolic blood pressure (DBP) 90 mmHg at examinations (15), and also defined if the participant had a previous physician diagnosis.
Statistical analysis All analyses were performed using SPSS software version 13.0. Normally distributed and continuous variables were presented as mean standard deviation, and non-normally distributed variables were presented as medians (quartiles 25% and 75%). The independent-samples t test was used to examine differences in normally distributed and continuous variables, and the Mann– Whitney U was used to examine differences in non-normally distributed variables. Categorical variables were expressed as percentages, and the chi-square test was used for comparisons of proportions. Bivariate correlations were measured by means of Spearman’s rank correlation coefficient. A value of P 0.05 was considered statistically significant (two-tailed). Binary logistic regression was used to assess the associations between DR and associated risk factors. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated. Candidate risk factors which were statistically significant (P 0.05) in the concise model were further fitted into the full logistic regression model using the enter method. Beta-coefficients derived from the full logistic regression were used to calculate the diabetic retinopathy risk score. A score for each variable in the full model was calculated by multiplying the b-coefficient by 10 (16). Crossvalidation was used to validate the method for establishment of the diabetic retinopathy risk score. Altogether, 1869 patients (approximately 70%) were selected by using systematic random sampling method to form training samples, and the remaining 830 patients (approximately 30%) were used as testing samples.
A risk score for screening for diabetic retinopathy 419
Development of the diabetic retinopathy risk score
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Binary logistic regression analysis was performed to estimate the strength of the association of candidate factors to the presence of DR. Age, waist circumference, history of antihypertensive drug treatment, and duration of DM, which were significantly (P 0.05) associated with the presence of DR in the concise model, were further fitted into the full logistic regression model using the enter method (Table II). Based on the b-coefficient of the full model, the risk score was constructed by multiplying the b-coefficients by 10 and rounding to the nearest integer (16). The detailed value of the risk score in training samples are shown in Table II. A sum score was calculated for every patient by adding the score for each variable. In addition, the questionnaire of the diabetic retinopathy risk score is shown in the Supplementary material Table I available online at http://informahealthcare.com/doi/abs/ 10.3109/07853890.2013.878977. Figure 1. The distribution of the study patients according to the diabetic retinopathy risk score.
Performance of diabetic retinopathy risk score and the risk score cut-off value of DR
Performance of the diabetic retinopathy risk score was tested using receiver operating characteristic (ROC) curve analysis. Based on the ROC analysis, the best cut-off value of the diabetic retinopathy risk score was determined from the highest Youden index, which is defined as follows: (sensitivity specificity – 1) (17).
The ROC curve shown in Figure 2 represents the diagnostic accuracy of the diabetic retinopathy risk score for DR. The area under the ROC curve for DR in training samples was 0.700 (95% CI 0.671–0.729). Table III shows Youden’s index of different values. The optimal cut-off point to predict DR in training samples was 20. Using the diabetic retinopathy risk score cut-off value of 20 points to identify DR, the sensitivity and specificity in testing samples (73.1% and 52.2%) were similar to those in the training samples (77.2% and 55.1%). Among 364 patients with DR deriving from training samples, 285 (78.3%) patients with diabetic retinopathy risk score 20 were diagnosed with DR. And among 167 patients with DR deriving from testing samples, 125 (74.9%) patients with diabetic retinopathy risk score 20 were diagnosed with DR.
Results Clinical characteristics of the study population The evaluated 2699 patients included 1263 males and 1436 females, with an average age of 59.4 13.0 years (20–90 years). Figure 1 shows the distribution of the study patients according to the diabetic retinopathy risk score. These patients were classified into patients without DR and patients with DR. Compared with patients who did not develop DR (Table I), patients with DR were older and had higher waist circumference, higher HbA1c, longer duration of DM, higher systolic blood pressure, and higher TC. The frequencies of females, hypertension, hypertensive drug treatment, and family history of diabetes were significantly higher in patients with DR than in patients without DR. BMI, TG, HDL-cholesterol, LDL-cholesterol, percentage of regular physical exercise, and diet control, however, were similar among patients without and with DR.
Prevalence of DR A total of 531 (19.7%) of the 2699 patients were diagnosed with DR. As shown in Table IV, the prevalence of total DR, mild non-proliferative DR, moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR all increased with the diabetic retinopathy risk score. The prevalence was significantly higher in females than in males (22.8% versus 16.2%, P 0.001), and the prevalence of DR was comparable in overweight patients and obese patients (19.4% versus 21.1%, P 0.392). As shown in Table V, the prevalence of
Table I. Clinical characteristics of the study population. Variables Age (years) Gender: males Family history of diabetes mellitus Body mass index (kg/m2) Waist circumference (cm) History of antihypertensive drug treatment (%) Regular physical activity (%) Diet control (%) FPG (mmol/L) HbA1c (%) Duration of diabetes mellitus (years) Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Hypertension Total cholesterol (mmol/L) Triglycerides (mmol/L) HDL-cholesterol (mmol/L) LDL-cholesterol (mmol/L)
With DR (n 2168)
Without DR (n 531)
P value
58.5 13.2 48.8% (1059) 25.8% (512) 28.00 2.75 96.20 9.10 53.0% (1149) 46.0% (998) 66.8% (1448) 8.74 3.68 8.57 2.25 4.5 (1.2–9.9) 135.9 18.8 80.4 11.0 64.5% (1398) 5.29 1.61 1.78 (1.26–2.75) 1.21 0.69 3.05 1.18
60.0 11.4 38.4% (204) 31.1% (148) 28.06 2.59 97.29 8.77 74.2% (394) 46.7% (248) 70.1% (372) 9.08 3.58 8.80 2.23 9.9 (3.9–14.9) 142.3 20.2 80.9 11.1 80.2% (426) 5.45 1.51 1.92 (1.33–2.80) 1.27 0.61 3.15 1.19
0.001 0.001 0.018 0.619 0.013 0.001 0.781 0.150 0.062 0.041 0.001 0.001 0.375 0.001 0.045 0.189 0.095 0.104
420 J. Wang et al. Table II. Univariate and multivariate analyses of risk factors for diabetic retinopathy. Concise model with all samples (n 2699) Variable
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Age (years) 45–64 versus 20–44 65 versus 20–44 BMI (kg/m2) 30 versus 25–29.9 Central obesity (yes versus no) DM duration (years) 1–5 versus 1 5–10 versus 1 10–15 versus 1 15 versus 1 History of hypertensive treatment (yes versus no) Regular physical activity (yes versus no) Diet control (yes versus no)
OR (95% CI) 2.483 (1.693–3.640) 3.507 (2.387–5.153) 1.067 (0.838–1.359) 1.013 (1.003–1.023) 2.858 (1.933–4.226) 3.568 (2.393–5.319) 5.331 (3.578–7.944) 10.453 (6.940–15.744) 2.551 (2.063–3.153) 1.027 (0.849–1.243) 1.163 (0.947–1.430)
P 0.001
0.598 0.013 0.001
0.001 0.781 0.150
Full model with training samples (n 1869) Βeta-coefficient
OR (95% CI)
Score
0.490 0.449
1.633 (0.995–2.678) 1.567 (0.936–2.623)
5 4
0.213
1.237 (0.826–1.854)
2
0.891 1.039 1.383 1.982 0.698
2.438 (1.525–3.897) 2.825 (1.737–4.594) 3.988 (2.439–6.521) 7.261 (4.352–12.114) 2.010 (1.522–2.653)
9 10 14 20 7
entral obesity was defined as waist circumference 90 cm in men and 85 cm in women. Regular physical activity was defined as participation in 30 or C more minutes of moderate or vigorous activity per day at least 3 days per week. Diet control was defined as deliberately eating less after diagnosing of type 2 diabetes mellitus than before, especially eating less fat meat, fried food, dessert, and fruit with high sugar. Variables which were statistically significant (P 0.05) in the concise model were further fitted into the final multivariate logistic regression model. A score for each variable in the full model was calculated by multiplying the b-coefficient by 10.
DR increased with increasing age, the prevalence of DR in patients with history of antihypertensive drug treatment was dramatically higher than in patients without history of antihypertensive drug treatment, and the prevalence of DR increased with increasing duration of DM. However, the prevalence of DR was similar in patients with and without central obesity (P 0.187).
Risk factors for DR Table VI shows the odds ratio of DR for the different covariates alone (concise model) and controlled for (full model) in all study patients. The association of diabetic retinopathy risk score with DR remained statistically significant even after adjusting for blood glucose and serum lipid, and the odds ratio (OR) for one unit increase in the diabetic retinopathy risk score associated with the risk of DR was 1.102 (95% CI 1.084–1.119). HbA1c (OR 1.078; 95% CI 1.019–1.141) remained an independent risk factor of DR even after adjusting for serum lipids and diabetic retinopathy risk
score. In addition, the diabetic retinopathy risk score was more significantly associated with the presence of DR than duration of DM, hypertensive drug treatment, and HbA1c (rp 0.277, P 0.001 for diabetic retinopathy risk score; rp 0.248, P 0.001 for duration of DM; rp 0.170, P 0.001 for hypertensive drug treatment; rp 0.055, P 0.008 for HbA1c).
Discussion According to national data, non-communicable diseases accounted for an estimated 70% of the total disease burden in China in 2005 (18). The prevalence of diabetes, a major non-communicable disease, increased from 0.67% in 1980 (19) to 11.6% in 2010 in the Chinese population (6)—approximately 113.9 million Chinese adults have diabetes. Considering the large number of individuals with diabetes and the high medical cost of diabetes care in China, and as DR is a typical microvascular complication of diabetes, it is important to develop a simple diabetic retinopathy risk score to identify DR. In the current study, the diabetic retinopathy risk score was constructed with age, duration of DM, waist circumference, and history of antihypertensive drug treatment. Using the diabetic retinopathy risk score cut-off value of 20 points to identify DR, the similar sensitivity and specificity in testing samples (73.1% and 52.2%) and training samples (77.2% and 55.1%) indicated that the method for establishment of the diabetic retinopathy risk score had a good applicability. The area under the ROC curve for DR in training samples (0.700, 95% CI 0.671–0.729) showed that the diabetic retinopathy risk score had adequate performances for screening for DR in overweight/obese patient with T2DM. Comparing the Youden index of different values, the optimal Table III. The sensitivity, specificity, and Youden’s index for identifying diabetic retinopathy at different cut-off values of the risk score. Cut-off value
Figure 2. Receiver operating characteristics (ROC) curves showing the performance of the diabetic retinopathy risk score in detecting diabetic retinopathy.
Sensitivity
Specificity
Results based on training samples (n 1869) 0.786 Cut-off value 18 0.783 Cut-off value 19 0.772 Cut-off value 20 0.717 Cut-off value 21 0.651 Cut-off value 22 Results based on testing samples (n 830) 0.731 Cut-off value 20
Youden’s index
0.518 0.532 0.551 0.581 0.636
0.303 0.315 0.323 0.298 0.287
0522
0.253
A risk score for screening for diabetic retinopathy 421 Table IV. The prevalence of diabetic retinopathy (DR) according to the diabetic retinopathy risk score. Patients with diabetic retinopathy risk score 14 14–21 21–24 24 (n 739) (n 668) (n 660) (n 632)
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Diabetic retinopathy Mild non-proliferative DR Moderate non-proliferative DR Severe non-proliferative DR Proliferative DR
53 (7.2) 32 (4.3) 15 (2.0) 5 (0.7) 1 (0.1)
99 (14.8) 154 (23.3) 225 (35.6) 59 (8.8) 70 (10.6) 88 (13.9) 18 (2.7) 33 (5.0) 51 (8.1) 13 (1.9) 36 (5.5) 48 (7.6) 9 (1.3) 15 (2.3) 38 (6.0)
cut-off point to predict DR was 20. Therefore, using the diabetic retinopathy risk score as a primary screening tool, it is possible to detect diabetic patients with high risk for DR, without any complex clinical testing. The diabetic retinopathy risk score (20 points or above) will identify the overweight/obesity patients with T2DM who should be referred for further testing with more expensive and sophisticated methods. Another diabetic retinopathy risk score has been developed in Iran (20). The performance of the Iranian risk score (0.704, 95% CI 0.685–0.723) is similar to the risk score in the current study (0.700, 95% CI 0.671–0.729), although there are minor differences in sensitivity and specificity. The advantage of the our diabetic retinopathy risk score is that this questionnaire can be completed without any clinical measurements requiring special skills or any laboratory tests; therefore the questionnaire can be answered at home, in contrast to the Iranian diabetic retinopathy risk score (20), which is necessarily based on measurement of HbA1c. Among candidate risk factors, duration of DM and hyper tension are consistent risk factors in the pathogenesis and development of diabetic retinopathy (7,8). Hypertension causes the increase of retinal blood flow, mechanical damage, and stretching of vascular endothelial cells, stimulating the release of vascular endothelial growth factor which facilitates the progression of DR (21,22). In agreement with above studies, the current study showed that hypertension and duration of DM were strong independent risk factors. In addition, the present study showed that age was an important risk factor associated with DR, and the result was consistent with previous studies (9–11). BMI and waist circumference were also considered as candidate risk factors in this study. Although some studies reported that BMI was not a risk factor for DR (23–25), other studies revealed that BMI was a risk factor associated with the presence of DR (26–30). The current study showed that BMI (OR 1.067; 95% CI 0.838–1.359) was not a risk factor associated with DR. Table V. Prevalence of diabetic retinopathy for subjects in subgroups. Prevalence of DR (%) Age (years) 20–44 years 45–64 years 65 years Central obesity Yes No History of hypertensive drug treatment Yes No Duration of diabetes mellitus (years) 1 year 1–5 years 5–10 years 10–15 years 15 years
8.6% (33/383) 19.0% (250/1318) 24.8% (248/998) 20.0% (480/2396) 16.8% (51/303) 25.5% (394/1543) 11.9% (137/1156) 6.4% (35/550) 16.3% (128/787) 19.5% (113/579) 26.6% (125/470) 41.5% (130/313)
P value 0.001
0.187 0.001 0.001
Table VI. Risk factors for diabetic retinopathy.
HbA1c (%) Fasting plasma glucose (mmol/L) Total cholesterol (mmol/L) Triglycerides (mmol/L) LDL-cholesterol (mmol/L) HDL-cholesterol (mmol/L) Diabetic retinopathy risk score (score)
Concise model OR (95% CI)
Full model OR (95% CI)
1.046 (1.002–1.092) 1.024 (0.999–1.050) 1.062 (1.001–1.126) 1.000 (0.960–1.042) 1.069 (0.986–1.160) 1.115 (0.972–1.279) 1.104 (1.089–1.120)
1.078 (1.019–1.141) 1.014 (0.980–1.049) 0.999 (0.877–1.139) 1.022 (0.960–1.088) 1.069 (0.922–1.239) 1.198 (0.979–1.466) 1.102 (1.084–1.119)
Studies have reported that waist circumference was a risk factor associated with DR (28,30). Our study revealed that central obesity (OR 1.013; 95% CI 1.003–1.023) was a risk factor associated with DR. However, the prevalence of DR was similar in patients with and without central obesity. The main reason for the similar prevalence of DR was that the patients included in the present study were overweight or obese, and 88.8% (2396/2699) of the patients had central obesity. Another two candidate risk factors were physical inactivity and diet control. Kriska et al. (31) reported that physical inactivity was a risk factor associated with the presence of DR. Inconsistent with the above study, the present study did not show a similar result. Although diet is an important factor associated with hyperglycaemia, and hyperglycaemia is a consistent risk factor for DR (7,8), this study showed that diet control (OR 1.163; 95% CI 0.947–1.430) was not a protective factor associated with DR. Observational studies support a role for dyslipidaemia in the pathogenesis of DR (7,32). However, epidemiological studies have not found a consistent association between serum lipid levels and DR (9,33–36). The present study did not reveal significant associations between serum lipid profiles and DR. In addition, this study indicated that HbA1c (OR 1.080; 95% CI 1.020–1.142) was an independent risk factor associated with the presence of DR, and the result was consistent with previous studies (7,8). The present study developed a simple diabetic retinopathy risk score to identify DR in overweight/obese patients with T2DM, and confirmed that it was a reliable tool. Moreover, the questionnaire can be completed without any clinical measurements requiring special skills or any laboratory tests. However, several limitations should be mentioned. First, because this was a crosssectional study, the current diabetic retinopathy risk score might not accurately predict the risk of future development of diabetic retinopathy. Second, the diagnosis of DR was according to the history of DR, and not all patients underwent retinal photography. If all patients included in this study had undergone fundus photography, the results would be more validated. Third, a large, prospective study with the general DM population undergoing retinal photography is required to develop a diabetic retinopathy risk score, and to determine whether the risk score can be used to identify DR and what the optimal score cut-off value for DR is. In conclusion, the diabetic retinopathy risk score, which was constructed by age, duration of DM, history of antihypertensive drug treatment, and waist circumference, had reasonable performance for screening for DR in Chinese overweight/obese patients with T2DM.
Acknowledgements The study was supported by the following hospitals: Zhujiang Hospital of Southern Medical University, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, The Second Affiliated Hospital of Shan-
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422 J. Wang et al. tou University Medical College, The First Affiliated Hospital of Shantou University Medical College, Wu Jing Zong Dui Hospital of Guangdong Province, The People’s Hospital of Jiangmen, Jiangmen Central Hospital, The People’s Hospital of Shenzhen, Zhongshan People’s Hospital, Guangdong General Hospital, Affiliated Hospital of Guangdong Medical College, The first Affiliated Hospital of Clinical Medicine of GDPU, The Second Affiliated Hospital of Guangzhou Medical University, The First Affiliated Hospital of Guangzhou Medical University, The Third Affiliated Hospital of Guangzhou Medical University, General Hospital of Guangzhou Military Command of People’s Liberation Army, Guangzhou First Municipal People’s Hospital, The First Affiliated Hospital of Jinan University, The Third Affiliated Hospital of Southern Medical University, Guangdong No. 2 Provincial People’s Hospital, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou Red Cross Hospital, The 458th Hospital of PLA, Clifford Hospital, Shantou Central Hospital, People’s Hospital of Boan District of Shenzhen City, Peking University Shenzhen Hospital, People’s Hospital of Futian District of Shenzhen City, The Second People’s Hospital of Shenzhen, People’s Hospital of Nanshan District of Shenzhen City, Longgang Central Hospital, Chaozhou Central Hospital, The People’s Hospital of Dongguan, Shenzhen Donghua Hospital, People’s Hospital of Nanhai District of Foshan City, The First People’s Hospital of Foshan, Foshan Hospital of Traditional Chinese Medicine, Mingjing Diabetic Hospital of Shunde, The People’s Hospital of Heshan, Huizhou Central Hospital, Wuyi Hospital of Traditional Chinese Medicine, The People’s Hospital of Xinhui, Xinhui Hospital of Traditional Chinese Medicine, The People’s Hospital of Jieyang, Kaiping Central Hospital, Kaiping Hospital of Traditional Chinese Medicine, The People’s Hospital of Qingyuan, The People’s Hospital of Shanwei, The People’s Hospital of Xinxing, The People’s Hospital of Yangjiang, The People’s Hospital of Yuebei, The People’s Hospital of Lechang, The First People’s Hospital of Zhaoqing, Chenxinghai Hospital Affiliated to Guangdong Medical College, The Second People’s Hospital of Zhuhai, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhongshan Hospital of Traditional Chinese Medicine, Sanxiang Hospital of Zhongshan, Maoming People’s Hospital, Meizhou People’s Hospital, and Zhongshan Hospital of Traditional Chinese Medicine. Jiao Wang and Hong Chen contributed equally to this work. Declaration of interest: The authors do not have any conflict of interest to declare.
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