Dataset Information

Predictive Ability of Visit-to-Visit Variability of HbA1c Measurements for the Development of Diabetic Kidney Disease: A Retrospective Longitudinal Observational Study.

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.

SUBMITTER: Yan Y

PROVIDER: S-EPMC8800606 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Predictive Ability of Visit-to-Visit Variability of HbA1c Measurements for the Development of Diabetic Kidney Disease: A Retrospective Longitudinal Observational Study.

Yan Yunyi Y Kondo Nozomi N Oniki Kentaro K Watanabe Hiroshi H Imafuku Tadashi T Sakamoto Yuki Y Shigaki Takuro T Maruyama Akari A Nakazawa Hitomi H Kaneko Tetsuya T Morita Ayami A Yoshida Akira A Maeda Hitoshi H Maruyama Toru T Jinnouchi Hideaki H Saruwatari Junji J

Journal of diabetes research 20220122

<h4>Aims</h4>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.<h4>Methods</h4>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 coefficie ...[more]

PMID: 35103243

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Project description:BackgroundIdentification of prognostic factors for diabetes complications are crucial. Glucose variability (GV) and its association with diabetes have been studied extensively but the inclusion of measures of glucose variability (GVs) in prognostic models is largely lacking. This study aims to assess which GVs (i.e., coefficient of variation (CV), standard deviation (SD), and time-varying) are better in predicting diabetic complications, including cardiovascular disease (CVD), diabetic retinopathy (DR), and chronic kidney disease (CKD). The model performance between traditional statistical models (adjusting for covariates) and machine learning (ML) models were compared.MethodsA retrospective cohort of type 2 diabetes (T2D) patients between 2010 and 2019 in Ramathibodi Hospital was created. Complete case analyses were used. Three GVs using HbA1c and fasting plasma glucose (FPG) were considered including CV, SD, and time-varying. Cox proportional hazard regression, ML random survival forest (RSF) and left-truncated, right-censored (LTRC) survival forest were compared in two different data formats (baseline and longitudinal datasets). Adjusted hazard ratios with 95% confidence intervals were used to report the association between three GVs and diabetes complications. Model performance was evaluated using C-statistics along with feature importance in ML models.ResultsA total of 40,662 T2D patients, mostly female (61.7%), with mean age of 57.2 years were included. After adjusting for covariates, HbA1c-CV, HbA1c-SD, FPG-CV and FPG-SD were all associated with CVD, DR and CKD, whereas time-varying HbA1c and FPG were associated with DR and CKD only. The CPH and RSF for DR (C-indices: 0.748-0.758 and 0.774-0.787) and CKD models (C-indices: 0.734-0.750 and 0.724-0.740) had modestly better performance than CVD models (C-indices: 0.703-0.730 and 0.698-0.727). Based on RSF feature importance, FPG GV measures ranked higher than HbA1c GV, and both GVs were the most important for DR prediction. Both traditional and ML models had similar performance.ConclusionsWe found that GVs based on HbA1c and FPG had comparable performance. Thus, FPG GV may be used as a potential monitoring parameter when HbA1c is unavailable or less accessible.

Project description:BackgroundGlycemic variability plays an important role in the development of cardiovascular disease (CVD). This study aims to determine whether long-term visit-to-visit glycemic variability is associated with aortic stiffness progression in participants with type 2 diabetes (T2D).MethodsProspective data were obtained from 2115 T2D participants in the National Metabolic Management Center (MMC) from June 2017 to December 2022. Two brachial-ankle pulse wave velocity (ba-PWV) measurements were performed to assess aortic stiffness over a mean follow-up period of 2.6 years. A multivariate latent class growth mixed model was applied to identify trajectories of blood glucose. Logistic regression models were used to determine the odds ratio (OR) for aortic stiffness associated with glycemic variability evaluated by the coefficient of variation (CV), variability independent of the mean (VIM), average real variability (ARV), and successive variation (SV) of blood glucose.ResultsFour distinct trajectories of glycated hemoglobin (HbA1c) or fasting blood glucose (FBG) were identified. In the U-shape class of HbA1c and FBG, the adjusted ORs were 2.17 and 1.21 for having increased/persistently high ba-PWV, respectively. Additionally, HbA1c variability (CV, VIM, SV) was significantly associated with aortic stiffness progression, with ORs ranging from 1.20 to 1.24. Cross-tabulation analysis indicated that the third tertile of the HbA1c mean and VIM conferred a 78% (95% confidence interval [CI] 1.23-2.58) higher odds of aortic stiffness progression. Sensitivity analysis demonstrated that the SD of HbA1c and the highest HbA1c variability score (HVS) were significantly associated with the adverse outcomes independent of the mean of HbA1c during the follow-up.ConclusionsLong-term visit-to-visit HbA1c variability was independently associated with aortic stiffness progression, suggesting that HbA1c variability was a strong predictor of subclinical atherosclerosis in T2D participants.

Dataset Information

Predictive Ability of Visit-to-Visit Variability of HbA1c Measurements for the Development of Diabetic Kidney Disease: A Retrospective Longitudinal Observational Study.

Aims

Methods

Results

Conclusions

Publications

Predictive Ability of Visit-to-Visit Variability of HbA1c Measurements for the Development of Diabetic Kidney Disease: A Retrospective Longitudinal Observational Study.

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