Potential Role of the Renal Arterial Resistance Index in the Differential Diagnosis of Diabetic Kidney Disease

• Published in: Frontiers in endocrinology (Lausanne) 2022-01, Vol.12, p.731187-731187
• Main Authors: Li, Haiyang, Shen, Yunzhu, Yu, Zhikai, Huang, Yinghui, He, Ting, Xiao, Tangli, Li, Yan, Xiong, Jiachuan, Zhao, Jinghong
• Format: Article
• Language: English
• Subjects: Adult; Biopsy; Body Mass Index; Case-Control Studies; Diabetes Mellitus, Type 2 - complications; Diabetes Mellitus, Type 2 - metabolism; diabetic kidney disease; Diabetic Nephropathies - diagnosis; Diabetic Nephropathies - etiology; Diabetic Nephropathies - pathology; Diabetic Nephropathies - physiopathology; Diabetic Retinopathy - etiology; Diagnosis, Differential; differential diagnosis; Endocrinology; Female; Glycated Hemoglobin A - metabolism; Humans; Kidney - pathology; Logistic Models; Male; Middle Aged; Multivariate Analysis; non-diabetic kidney disease; Renal Artery - physiopathology; Renal Insufficiency, Chronic - diagnosis; Renal Insufficiency, Chronic - pathology; Renal Insufficiency, Chronic - physiopathology; resistance index; ROC Curve; Sensitivity and Specificity; Time Factors; type 2 diabetes mellitus; Vascular Resistance
• ISSN: 1664-2392; 1664-2392
• DOI: 10.3389/fendo.2021.731187

To investigate the potential role of renal arterial resistance index (RI) in the differential diagnosis between diabetic kidney disease (DKD) and non-diabetic kidney disease (NDKD) and establish a better-quantified differential diagnostic model. We consecutively reviewed 469 type 2 diabetes patients who underwent renal biopsy in our center. According to the renal biopsy results, eligible patients were classified into the DKD group and the NDKD group. The diagnostic significance of RI was evaluated by receiver operating characteristic (ROC) curve analysis. Logistic regression analysis was used to search for independent risk factors associated with DKD. Then a novel diagnostic model was established using multivariate logistic regression analysis. A total of 332 DKD and 137 NDKD patients were enrolled for analysis. RI was significantly higher in the DKD group compared with those in the NDKD group (0.70 vs. 0.63, < 0.001). The optimum cutoff value of RI for predicting DKD was 0.66 with sensitivity (69.2%) and specificity (80.9%). Diabetic retinopathy, diabetes duration ≥ 60 months, HbA1c ≥ 7.0(%), RI ≥ 0.66, and body mass index showed statistical significance in the multivariate logistic regression analysis. Then, we constructed a new diagnostic model based on these results. And the validation tests indicated that the new model had good sensitivity (81.5%) and specificity (78.6%). RI has a potential role in discriminating DKD from NDKD. The RI-based predicting model can be helpful for differential diagnosis of DKD and NDKD.