Effect of Glomerular Filtration Rate by Different Equations on Prediction Models for End-Stage Renal Disease in Diabetes

Disease areas:

• nutrition and metabolism
• reproductive and urinary health

Last updated:

19 November 2025

Author(s):

Liangjing Lv, Xiangjun Chen, Jinbo Hu, Jinshan Wu, Wenjin Luo, Yan Shen, Rui Lan, Xue Li, Yue Wang, Ting Luo, Shumin Yang, Qifu Li, Zhihong Wang

Publish date:

3 June 2022

Journal:

Frontiers in Endocrinology

PubMed ID:

35721706

DOI:

10.3389/fendo.2022.873318

Abstract

Background and Objectives: The study aimed to evaluate the performance of a predictive model using the kidney failure risk equation (KFRE) for end-stage renal disease (ESRD) in diabetes and to investigate the impact of glomerular filtration rate (GFR) as estimated by different equations on the performance of the KFRE model in diabetes.

Design Setting Participants and Measurements: A total of 18,928 individuals with diabetes without ESRD history from the UK Biobank, a prospective cohort study initiated in 2006-2010, were included in this study. Modification of diet in renal disease (MDRD), chronic kidney disease epidemiology collaboration (CKD-EPI) or revised Lund-Malmö (r-LM) were used to estimate GFR in the KFRE model. Cox proportional risk regression was used to determine the correlation coefficients between each variable and ESRD risk in each model. Harrell’s C-index and net reclassification improvement (NRI) index were used to evaluate the differentiation of the models. Analysis was repeated in subgroups based on albuminuria and hemoglobin A1C (HbA1c) levels.

Results: Overall, 132 of the 18,928 patients developed ESRD after a median follow-up of 12 years. The Harrell’s C-index based on GFR estimated by CKD-EPI, MDRD, and r-LM was 0.914 (95% CI = 0.8812-0.9459), 0.908 (95% CI = 0.8727-0.9423), and 0.917 (95% CI = 0.8837-0.9496), respectively. Subgroup analysis revealed that in diabetic patients with macroalbuminuria, the KFRE model based on GFR estimated by r-LM (KFRE-eGFR_r-LM) had better differentiation compared to the KFRE model based on GFR estimated by CKD-EPI (KFRE-eGFR_CKD-EPI) with a KFRE-eGFR_r-LM C-index of 0.846 (95% CI = 0.797-0.894, p = 0.025), while the KFRE model based on GFR estimated by MDRD (KFRE-eGFR_MDRD) showed no significant difference compared to the KFRE-eGFR_CKD-EPI (KFRE-eGFR_MDRD C-index of 0.837, 95% CI = 0.785-0.889, p = 0.765). Subgroup analysis of poor glycemic control (HbA1c >8.5%) demonstrated the same trend. Compared to KFRE-eGFR_CKD-EPI (C-index = 0.925, 95% CI = 0.874-0.976), KFRE-eGFR_r-LM had a C-index of 0.935 (95% CI = 0.888-0.982, p = 0.071), and KFRE-eGFR_MDRD had a C-index of 0.925 (95% CI = 0.874-0.976, p = 0.498).

Conclusions: In adults with diabetes, the r-LM equation performs better than the CKD-EPI and MDRD equations in the KFRE model for predicting ESRD, especially for those with macroalbuminuria and poor glycemic control (HbA1c >8.5%).