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J Diabetes Res ; 2022: 6934188, 2022.
Article in English | MEDLINE | ID: mdl-35103243

ABSTRACT

AIMS: This study is aimed at clarifying the relationship between visit-to-visit variability of glycated hemoglobin (HbA1c) and the risk of diabetic kidney disease (DKD) and to identifying the most useful index of visit-to-visit variability of HbA1c. METHODS: This clinic-based retrospective longitudinal study included 699 Japanese type 2 diabetes mellitus patients. Visit-to-visit variability of HbA1c was calculated as the internal standard deviation of HbA1c (HbA1c-SD), the coefficient of variation of HbA1c (HbA1c-CV), the HbA1c change score (HbA1c-HVS), and the area under the HbA1c curve (HbA1c-AUC) with 3-year serial HbA1c measurement data, and the associations between these indices and the development/progression of DKD were examined. RESULTS: Cox proportional hazards models showed that the HbA1c-SD and HbA1c-AUC were associated with the incidence of microalbuminuria, independently of the HbA1c level. These results were verified and replicated in propensity score (PS) matching and bootstrap analyses. Moreover, the HbA1c-SD and HbA1c-AUC were also associated with oxidized human serum albumin (HSA), an oxidative stress marker. CONCLUSIONS: Visit-to-visit variability of HbA1c was an independent risk factor of microalbuminuria in association with oxidative stress among type 2 diabetes mellitus patients. HbA1c-AUC, a novel index of HbA1c variability, may be a potent prognostic indicator in predicting the risk of microalbuminuria.


Subject(s)
Diabetic Nephropathies/diagnosis , Glycated Hemoglobin/analysis , Risk Assessment/standards , Aged , Analysis of Variance , Biomarkers/analysis , Biomarkers/blood , Diabetic Nephropathies/blood , Diabetic Nephropathies/epidemiology , Female , Humans , Incidence , Japan/epidemiology , Kaplan-Meier Estimate , Longitudinal Studies , Male , Middle Aged , Proportional Hazards Models , Reproducibility of Results , Retrospective Studies , Risk Assessment/methods , Risk Assessment/statistics & numerical data , Risk Factors
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