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Updated: Dec 23, 2025

A High-throughput Method for Measurement of Glomerular Filtration Rate in Conscious Mice
Published on: May 10, 2013
Variability in estimated glomerular filtration rate and the incidence of type 2 diabetes: a nationwide
You-Bin Lee1, Da Hye Kim2, Eun Roh1
1Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine and School of Medicine, Seoul, Republic of Korea.
Objective:
Variability in estimated glomerular filtration rate (eGFR) has been associated with adverse outcomes in patients with diabetes or chronic kidney disease (CKD). However, no previous study has explored the relationship between eGFR variability and type 2 diabetes incidence.
Research Design And Methods:
In this nationwide, longitudinal, cohort study, we investigated the association between eGFR variability and type 2 diabetes risk using the Korean National Health Insurance Service datasets from 2002 to 2017. eGFR variability was calculated using the variability independent of the mean (eGFR-VIM), coefficient of variation (eGFR-CV), standard deviation (eGFR-SD) and average real variability (eGFR-ARV).
Results:
Within 7 673 905.58 person-years of follow-up (mean follow-up: 3.19 years; n=2 402 668), 11 981 cases of incident type 2 diabetes were reported. The HRs and 95% CIs for incident type 2 diabetes increased according to advance in quartiles of eGFR-VIM (HR (95% CI): Q2, 1.068 (1.009 to 1.130); Q3, 1.077 (1.018 to 1.138); Q4, 1.203 (1.139 to 1.270)) even after adjusting for confounding factors including mean eGFR and mean fasting plasma glucose levels. The subgroup analyses according to risk factors as well as analyses using eGFR-CV, eGFR-SD and eGFR-ARV showed consistent results. The association between increased eGFR variability and type 2 diabetes risk was more prominent in men, individuals with dyslipidemia and those with CKD as shown in the subgroup analysis (p for interaction <0.001).
Conclusions:
Increased eGFR variability may be an independent predictor of type 2 diabetes and might be useful for risk stratification of individuals without diabetes.
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