Zhenhua Yang1, Luhuai Feng2, Yu Huang3, Ning Xia4. 1. Department of Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China. 2. Department of General Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, People's Republic of China. 3. Department of Nephrology, The People's Hospital of Wuzhou, Wuzhou, People's Republic of China. 4. Department of Endocrinology and Metabolism, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China.
Abstract
PURPOSE: Differentiating between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) is difficult and inefficient. The aim of the present study was to create a model for the differential diagnosis of DN and NDRD in patients with type 2 diabetes mellitus (T2DM). PATIENTS AND METHODS: We consecutively screened 213 patients with T2DM complicated with chronic kidney disease, who underwent renal biopsy at The First Affiliated Hospital of Guangxi Medical University (Nanning, China) between 2011 and 2017. According to the pathological results derived from the renal biopsy, the patients were divided into three groups (74, 130, and nine in the DN, NDRD, and NDRD superimposed with DN group, respectively). Clinical and laboratory data were compared and a diagnostic model was developed based on the following logistic regression model: logit(P)=+++ … +. RESULTS: We observed a high incidence of NDRD (61.0% of all patients), including various pathological types; the most common type was idiopathic membranous nephropathy. By comparing clinical variables, we identified a number of differences between DN and NDRD. Logistic regression analyses showed that the following variables were statistically significant: the absence of diabetic retinopathy (DR), proteinuria within the non-nephrotic range, the absence of anemia and an estimated glomerular filtration rate (eGFR) ≥90 mL/min/1.73 m2. We subsequently constructed a diagnostic model for predicting NDRD, as follows: PNDRD=1/[1+exp(-17.382-3.339×DR-1.274×Proteinuria-2.217×Anemia-1.853×eGFR-0.993×DM+20.892Bp)]. PNDRD refers to the probability of a diagnosis of NDRD (a PNDRD≥0.5 predicts NDRD while a PNDRD <0.5 predicts DN); while DM refers to the duration of diabetes. This model had a sensitivity of 95.4%, a specificity of 83.8%, and the area under the receiver operating characteristic curve was 0.925. CONCLUSION: Our diagnostic model may facilitate the clinical differentiation of DN and NDRD, and assist physicians in developing more effective and rational criteria for kidney biopsy in patients with T2DM complicated with chronic kidney disease.
PURPOSE: Differentiating between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) is difficult and inefficient. The aim of the present study was to create a model for the differential diagnosis of DN and NDRD in patients with type 2 diabetes mellitus (T2DM). PATIENTS AND METHODS: We consecutively screened 213 patients with T2DM complicated with chronic kidney disease, who underwent renal biopsy at The First Affiliated Hospital of Guangxi Medical University (Nanning, China) between 2011 and 2017. According to the pathological results derived from the renal biopsy, the patients were divided into three groups (74, 130, and nine in the DN, NDRD, and NDRD superimposed with DN group, respectively). Clinical and laboratory data were compared and a diagnostic model was developed based on the following logistic regression model: logit(P)=+++ … +. RESULTS: We observed a high incidence of NDRD (61.0% of all patients), including various pathological types; the most common type was idiopathic membranous nephropathy. By comparing clinical variables, we identified a number of differences between DN and NDRD. Logistic regression analyses showed that the following variables were statistically significant: the absence of diabetic retinopathy (DR), proteinuria within the non-nephrotic range, the absence of anemia and an estimated glomerular filtration rate (eGFR) ≥90 mL/min/1.73 m2. We subsequently constructed a diagnostic model for predicting NDRD, as follows: PNDRD=1/[1+exp(-17.382-3.339×DR-1.274×Proteinuria-2.217×Anemia-1.853×eGFR-0.993×DM+20.892Bp)]. PNDRD refers to the probability of a diagnosis of NDRD (a PNDRD≥0.5 predicts NDRD while a PNDRD <0.5 predicts DN); while DM refers to the duration of diabetes. This model had a sensitivity of 95.4%, a specificity of 83.8%, and the area under the receiver operating characteristic curve was 0.925. CONCLUSION: Our diagnostic model may facilitate the clinical differentiation of DN and NDRD, and assist physicians in developing more effective and rational criteria for kidney biopsy in patients with T2DM complicated with chronic kidney disease.
Authors: Teresa Yuk Hwa Wong; Paul Cheung Lung Choi; Chun Cheuk Szeto; Ka Fai To; Nelson Leung Sang Tang; Anthony Wing Hung Chan; Philip Kam Tao Li; Fernand Mac-Moune Lai Journal: Diabetes Care Date: 2002-05 Impact factor: 19.112
Authors: Ja Min Byun; Cheol Hyun Lee; Sul Ra Lee; Ju Young Moon; Sang Ho Lee; Tae Won Lee; Chun Gyoo Ihm; Kyung Hwan Jeong Journal: Korean J Intern Med Date: 2013-08-14 Impact factor: 2.884