Diabetes, Metabolic Syndrome and Obesity (Sep 2019)
A Differential Diagnosis Model For Diabetic Nephropathy And Non-Diabetic Renal Disease In Patients With Type 2 Diabetes Complicated With Chronic Kidney Disease
Abstract
Zhenhua Yang,1 Luhuai Feng,2 Yu Huang,3 Ning Xia41Department of Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 2Department of General Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 3Department of Nephrology, The People’s Hospital of Wuzhou, Wuzhou, People’s Republic of China; 4Department of Endocrinology and Metabolism, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of ChinaCorrespondence: Ning XiaDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, People’s Republic of ChinaTel +8613707714058Fax +860771-5356707Email [email protected]: 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)=β0+β1X1+β2X2+ … +βmXm.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–.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.Keywords: type 2 diabetes mellitus, diabetic nephropathy, non-diabetic nephropathy, puncture biopsy, formula, logistic regression
