Fatemeh Azizi-Soleiman1, Motahar Heidari-Beni2, Gareth Ambler3, Rumana Omar3, Masoud Amini4, Sayed-Mohsen Hosseini5. 1. Food Security Research Center, Department of Clinical Nutrition, School of Nutrition and Food Sciences, Isfahan University of Medical Sciences, Isfahan, Iran. 2. Food Security Research Center, Department of Community Nutrition, School of Nutrition and Food Sciences, Isfahan University of Medical Sciences, Isfahan, Iran. 3. Department of Statistical Science, University College London, London, United Kingdom. 4. Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. 5. Skin Diseases and Leishmaniasis Research Center, Isfahan University of Medical Sciences, Isfahan; Department of Biostatistics and Epidemiology, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran. Electronic address: hosseini@hlth.mui.ac.ir.
Abstract
OBJECTIVE: Diabetic retinopathy (DR) is the leading cause of blindness in patients with type 1 or type 2 diabetes. The gold standard for the detection of DR requires expensive equipment. This study was undertaken to develop a simple and practical scoring system to predict the probability of DR. METHODS: A total of 1782 patients who had first-degree relatives with type II diabetes were selected. Eye examinations were performed by an expert ophthalmologist. Biochemical and anthropometric predictors of DR were measured. Logistic regression was used to develop a statistical model that can be used to predict DR. Goodness of fit was examined using the Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve. RESULTS: The risk model demonstrated good calibration and discrimination (ROC area=0.76) in the validation sample. Factors associated with DR in our model were duration of diabetes (odds ratio [OR]=2.14, confidence interval [CI] 95%=1.87 to 2.45); glycated hemoglobin (A1C) (OR=1.21, CI 95%=1.13 to 1.30); fasting plasma glucose (OR=1.83, CI 95%=1.28 to 2.62); systolic blood pressure (OR=1.01, CI 95%= 1.00 to 1.02); and proteinuria (OR=1.37, CI 95%=1.01 to 1.85). The only factor that had a protective effect against DR were body mass index and education level (OR=0.95, CI 95%=0.92 to 0.98). CONCLUSIONS: The good performance of our risk model suggests that it may be a useful risk-prediction tool for DR. It consisted of the positive predictors like A1C, diabetes duration, sex (male), fasting plasma glucose, systolic blood pressure and proteinuria, as well as negative risk factors like body mass index and education level.
OBJECTIVE:Diabetic retinopathy (DR) is the leading cause of blindness in patients with type 1 or type 2 diabetes. The gold standard for the detection of DR requires expensive equipment. This study was undertaken to develop a simple and practical scoring system to predict the probability of DR. METHODS: A total of 1782 patients who had first-degree relatives with type II diabetes were selected. Eye examinations were performed by an expert ophthalmologist. Biochemical and anthropometric predictors of DR were measured. Logistic regression was used to develop a statistical model that can be used to predict DR. Goodness of fit was examined using the Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve. RESULTS: The risk model demonstrated good calibration and discrimination (ROC area=0.76) in the validation sample. Factors associated with DR in our model were duration of diabetes (odds ratio [OR]=2.14, confidence interval [CI] 95%=1.87 to 2.45); glycated hemoglobin (A1C) (OR=1.21, CI 95%=1.13 to 1.30); fasting plasma glucose (OR=1.83, CI 95%=1.28 to 2.62); systolic blood pressure (OR=1.01, CI 95%= 1.00 to 1.02); and proteinuria (OR=1.37, CI 95%=1.01 to 1.85). The only factor that had a protective effect against DR were body mass index and education level (OR=0.95, CI 95%=0.92 to 0.98). CONCLUSIONS: The good performance of our risk model suggests that it may be a useful risk-prediction tool for DR. It consisted of the positive predictors like A1C, diabetes duration, sex (male), fasting plasma glucose, systolic blood pressure and proteinuria, as well as negative risk factors like body mass index and education level.
Authors: Jian-Bo Zhou; Jing Yuan; Xing-Yao Tang; Wei Zhao; Fu-Qiang Luo; Lu Bai; Bei Li; Jia Cong; Lu Qi; Jin-Kui Yang Journal: J Int Med Res Date: 2019-09-23 Impact factor: 1.671