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

Glycated haemoglobin variability and risk of renal function decline in type 2 diabetes mellitus: An updated systematic review and meta-analysis

OBJECTIVE

To assess the association between glycated haemoglobin (HbA1c) variability and risk of renal function decline in type 2 diabetes mellitus (T2DM).

RESEARCH DESIGN AND METHODS

A comprehensive search was carried out in PubMed, Embase, Web of Science and the Cochrane Library (until 12 March 2024). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines were followed for this meta-analysis. HbA1c variability was presented as indices of the standard deviation (SD), coefficient of variation (CV), HbA1c variability score (HVS) and haemoglobin glycation index (HGI). This meta-analysis was performed using random-effect models.

RESULTS

Eighteen studies met the objectives of this meta-analysis. The analyses showed positive associations between HbA1c variability and kidney function decline, with hazard ratio (HR) 1.26 (95% confidence interval [CI] 1.15-1.38) for high versus low SD groups, HR 1.47 (95% CI 1.30-1.65) for CV groups, HR 1.32 (95% CI 1.10-1.57) for HVS groups and HR 1.53 (95% CI 1.05-2.23) for HGI groups. In addition, each 1% increase in SD and CV was linked to kidney function decline, with HR 1.26 (95% CI 1.17-1.35), and 1.13 (95% CI 1.03-1.23), respectively. Also, each 1-SD increase in SD of HbA1c was associated with deterioration in renal function, with HR 1.17 (95% CI 1.07-1.29).

CONCLUSIONS

The four HbA1c variability indicators were all positively associated with renal function decline progression; therefore, HbA1c variability might play an important and promising role in guiding glycaemic control targets and predicting kidney function decline progression in T2DM.

Variability of urinary albumin to creatinine ratio and eGFR are independently associated with eGFR slope in Japanese with type 2 diabetes: a three-year, single-center, retrospective cohort study

BACKGROUND

To evaluate the seasonal variability of urinary albumin to creatinine ratio (UACR) and eGFR and these effects on three-year eGFR slope in persons with type 2 diabetes (T2D).

METHODS

A total of 1135 persons with T2D were analyzed in this single-center, retrospective cohort study in Japan. The standard deviation (SD) of UACR (SD [UACR]) and SD of eGFR (SD [eGFR]) were calculated for each person's 10-point data during the three years, and a multiple linear regression analysis was performed to evaluate associations with eGFR slope. A sensitivity analysis was performed in a group with no medication changes (n = 801).

RESULTS

UACR exhibited seasonal variability, being higher in winter and lower in spring, early summer, and autumn especially in the UACR ≥ 30 mg/g subgroup, while eGFR showed no seasonal variability. The eGFR slope was significantly associated with SD (eGFR) (regression coefficient -0.170 [95% CI -0.189--0.151]) and SD (UACR) (0.000 [-0.001-0.000]). SGLT-2 inhibitors, baseline eGFR, and baseline systolic blood pressure (SBP) were also significantly associated. These associated factors, except baseline SBP, were still significant in the sensitivity analysis.

CONCLUSIONS

The UACR showed clear seasonal variability. Moreover, SD (UACR) and SD (eGFR) were independently associated with a three-year eGFR slope in persons with T2D.

TRIAL REGISTRATION

This study was not registered for clinical trial registration because it was a retrospective observational study.

What is the best predictor of mortality in patients with type 2 diabetes and chronic kidney disease: mean, variability of HbA1c or HbA1c-Hemoglobin ratio?

AIM

Limitations in the measurement of glycated hemoglobin (HbA1c) in patients with type 2 diabetes (T2D) and chronic kidney disease (CKD) result in uncertainty about the best predictor of mortality among these patients. Our study aimed to determine the association between the mean and average real variability (ARV) of HbA1c, as well as HbA1c-hemoglobin (HH) ratio with mortality among patients with T2D and CKD.

MATERIALS AND METHODS

We identified 16,868 T2D patients with stage 3 or above CKD from outpatient visits during 2003-2018. We ascertained all-cause and cardiovascular mortality through linkage to Taiwan's National Death Registry. Mortality rates were estimated using the Poisson distribution, and we conducted Cox proportional hazards regressions to assess relative risks of mortality corresponding to the mean HbA1c, ARV of HbA1c and HH ratio.

RESULTS

Compared to patients with a mean HbA1c of 7.0-7.9%, a mean HbA1c < 7.0% was persistently associated with highest risk of all-cause but not cardiovascular mortality after adjusting for confounders. On the contrary, patients with HbA1c-ARV in the second to fourth quartiles and HH ratios in the higher quartiles showed increased risk of all-cause and cardiovascular mortality compared to those in the first quartiles.

CONCLUSIONS

HbA1c-ARV was more effective than mean HbA1c or HH ratio in predicting mortality in T2D patients with CKD. Apart from optimal glucose control, multidisciplinary care focusing on glycemic variability is essential for reducing mortality in these patients.

Developing screening tools to estimate the risk of diabetic kidney disease in patients with type 2 diabetes mellitus

BACKGROUND

Diabetic kidney disease (DKD) is an important microvascular complication of diabetes mellitus (DM).

OBJECTIVE

This study aimed to develop predictive nomograms to estimate the risk of DKD in patients with type 2 diabetes mellitus (T2DM).

METHODS

The medical records of patients with T2DM in our hospital from March 2022 to March 2023 were retrospectively reviewed. The enrolled patients were randomly selected for training and validation sets in a 7:3 ratio. The models for predicting risk of DKD were virtualized by the nomograms using logistic regression analysis.

RESULTS

Among the enrolled 597 patients, 418 were assigned to the training set, while 179 were assigned to the validation set. Using the predictors included glycated hemoglobin A1c (HbA1c), high density lipoprotein cholesterol (HDL-C), presence of diabetic retinopathy (DR) and duration of diabetes (DD), we constructed a full model (model 1) for predicting DKD. And using the laboratory indexes of HbA1c, HDL-C, and cystatin C (Cys-C), we developed a laboratory-based model (model 2). The C-indexes were 0.897 for model 1 and 0.867 for model 2, respectively. The calibration curves demonstrated a good agreement between prediction and observation in the two models. The decision curve analysis (DCA) curves showed that the two models achieved a net benefit across all threshold probabilities.

CONCLUSION

We successfully constructed two prediction models to evaluate the risk of DKD in patients with T2DM. The two models exhibited good predictive performance and could be recommended for DKD screening and early detection.

[Validation and comparison of diabetic retinopathy-based diagnostic models for diabetic nephropathy]

OBJECTIVE

To validate and compare the efficacy of two noninvasive diagnostic models for diabetic nephropathy (DN) based on diabetic retinopathy (DR).

METHODS

A total of 565 patients with type 2 diabetes undergoing kidney biopsy in the Department of Nephrology, PLA General Hospital from January, 1993 to December, 2014 were studied. The patients were divided into DN group and non-diabetic nephropathy (NDRD) group according to renal pathological diagnosis. The data from the 22-year period were divided into 3 stages based on chronological order: early stage (from 1993 to 2003), middle stage (from 2004 to April, 2012), and late stage (from May, 2012 to December, 2014). The changes in clinical features and pathological diagnosis of the patients with renal biopsy over the 22 years were analyzed. The published DNT model and JDB model, both based on DR, were validated and compared for diagnostic effectiveness of DN, and the characteristics of the misdiagnosed cases were analyzed.

RESULTS

The incidences of hypertension and DR and levels of glycosylated hemoglobin (HbA1c), creatinine and 24-h urinary protein were all significantly higher, while hemoglobin and triglyceride levels were lower in DN group than in NDRD group (P<0.05). The proportion of NDRD cases increased gradually over time, with IgA nephropathy and membranous nephropathy as the main pathological types. The AUC of JDB model was 0.946, similar to that of NDT model (0.925; P=0.198). The disease course of diabetes, hematuria and incidence of DR were important clinical features affecting the diagnostic accuracy of the models.

CONCLUSION

The clinical features and pathological diagnosis of DR change over time. The non-invasive diagnostic models based on DR have good diagnostic efficacy for DN.

Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study

Objective

Diabetic kidney disease (DKD) has been reported as a main microvascular complication of diabetes mellitus. Although renal biopsy is capable of distinguishing DKD from Non Diabetic kidney disease(NDKD), no gold standard has been validated to assess the development of DKD.This study aimed to build an auxiliary diagnosis model for type 2 Diabetic kidney disease (T2DKD) based on machine learning algorithms.

Methods

Clinical data on 3624 individuals with type 2 diabetes (T2DM) was gathered from January 1, 2019 to December 31, 2019 using a multi-center retrospective database. The data fell into a training set and a validation set at random at a ratio of 8:2. To identify critical clinical variables, the absolute shrinkage and selection operator with the lowest number was employed. Fifteen machine learning models were built to support the diagnosis of T2DKD, and the optimal model was selected in accordance with the area under the receiver operating characteristic curve (AUC) and accuracy. The model was improved with the use of Bayesian Optimization methods. The Shapley Additive explanations (SHAP) approach was used to illustrate prediction findings.

Results

DKD was diagnosed in 1856 (51.2 percent) of the 3624 individuals within the final cohort. As revealed by the SHAP findings, the Categorical Boosting (CatBoost) model achieved the optimal performance 1in the prediction of the risk of T2DKD, with an AUC of 0.86 based on the top 38 characteristics. The SHAP findings suggested that a simplified CatBoost model with an AUC of 0.84 was built in accordance with the top 12 characteristics. The more basic model features consisted of systolic blood pressure (SBP), creatinine (CREA), length of stay (LOS), thrombin time (TT), Age, prothrombin time (PT), platelet large cell ratio (P-LCR), albumin (ALB), glucose (GLU), fibrinogen (FIB-C), red blood cell distribution width-standard deviation (RDW-SD), as well as hemoglobin A1C(HbA1C).

Conclusion

A machine learning-based model for the prediction of the risk of developing T2DKD was built, and its effectiveness was verified. The CatBoost model can contribute to the diagnosis of T2DKD. Clinicians could gain more insights into the outcomes if the ML model is made interpretable.

Effects of finerenone in people with chronic kidney disease and type 2 diabetes are independent of HbA1c at baseline, HbA1c variability, diabetes duration and insulin use at baseline

AIM

To evaluate the effect of finerenone by baseline HbA1c, HbA1c variability, diabetes duration and baseline insulin use on cardiorenal outcomes and diabetes progression.

MATERIALS AND METHODS

Composite efficacy outcomes included cardiovascular (cardiovascular death, non-fatal myocardial infarction, non-fatal stroke or hospitalization for heart failure), kidney (kidney failure, sustained ≥ 57% estimated glomerular filtration rate decline or renal death) and diabetes progression (new insulin initiation, increase in antidiabetic medication, 1.0% increase in HbA1c from baseline, new diabetic ketoacidosis diagnosis or uncontrolled diabetes).

RESULTS

In 13 026 participants, risk reductions in the cardiovascular and kidney composite outcomes with finerenone versus placebo were consistent across HbA1c quartiles (P interaction .52 and .09, respectively), HbA1c variability (P interaction .48 and .10), diabetes duration (P interaction .12 and .75) and insulin use (P interaction .16 and .52). HbA1c variability in the first year of treatment was associated with a higher risk of cardiovascular and kidney events (hazard ratio [HR] 1.20; 95% confidence interval [CI] 1.07-1.35; P = .0016 and HR 1.36; 95% CI 1.21-1.52; P < .0001, respectively). There was no effect on diabetes progression with finerenone or placebo (HR 1.00; 95% CI 0.95-1.04). Finerenone was well-tolerated across subgroups; discontinuation and hospitalization because of hyperkalaemia were low.

CONCLUSIONS

Finerenone efficacy was not modified by baseline HbA1c, HbA1c variability, diabetes duration or baseline insulin use. Greater HbA1c variability appeared to be associated with an increased risk of cardiorenal outcomes.

Long-term HbA1c variability and macro-/micro-vascular complications in type 2 diabetes mellitus: a meta-analysis update

AIMS

The aim of the present study was to evaluate, by means of a meta-analysis approach, whether new available data, appeared on qualified literature, can support the effectiveness of an association of HbA1c variability with the risk of macro- and/or micro-vascular complications in type 2 diabetes mellitus (T2DM).

METHODS

The meta-analysis was conducted according to PRISMA Statement guidelines and considered published studies on T2DM, presenting HbA1c variability as standard deviation (SD) or its derived coefficient of variation (CV). Literature search was performed on PubMed in the time range 2015-July 2022, with no restrictions of language.

RESULTS

Twenty-three selected studies fulfilled the aims of the present investigation. Overall, the analysis of the risk as hazard ratios (HR) indicated a significant association between the HbA1c variability, expressed either as SD or CV, and the complications, except for neuropathy. Macro-vascular complications were all significantly associated with HbA1c variability, with HR 1.40 (95%CI 1.31-1.50, p < 0.0001) for stroke, 1.30 (95%CI 1.25-1.36, p < 0.0001) for transient ischaemic attack/coronary heart disease/myocardial infarction, and 1.32 (95%CI 1.13-1.56, p = 0.0007) for peripheral arterial disease. Micro-vascular complications yielded HR 1.29 (95%CI 1.22-1.36, p < 0.0001) for nephropathy, 1.03 (95%CI 0.99-1.08, p = 0.14) for neuropathy, and 1.15 (95%CI 1.08-1.24, p < 0.0001) for retinopathy. For all-cause mortality, HR was 1.33 (95%CI 1.27-1.39, p < 0.0001), and for cardiovascular mortality 1.25 (95%CI 1.17-1.34, p < 0.0001).

CONCLUSIONS

Our meta-analysis on HbA1c variability performed on the most recent published data since 2015 indicates positive association between HbA1c variability and macro-/micro-vascular complications, as well as mortality events, in T2DM, suggesting that this long-term glycaemic parameter merits further attention as a predictive, independent risk factor for T2DM population.

Glycaemic variability and progression of chronic kidney disease in people with diabetes and comorbid kidney disease: Retrospective cohort study

AIM

To investigate the association between glycaemic variability and the development of End-Stage-Kidney-Disease (ESKD) among individuals with diabetes and chronic kidney disease.

METHODS

A cohort study using UK electronic primary care health records from the Clinical Practice Research Datalink. Glycaemic variability was assessed using a variability score and intra-individual coefficient of variation (CV) of HbA1c. We calculated sub-distribution hazard ratios (sHR) for developing ESKD using competing risk regression analysis.

RESULTS

There were 37,222 eligible participants (45.5 % male), with a mean age of 76.4 years (SD ± 9.2), and a mean baseline eGFR 40.7 (±10.7) ml/min/1.73 m². There were 5,086 incidents of ESKD in the follow-up period. The adjusted sHR (95 %CI) for each variability score group, were as follows: 21-40, 1.38 (1.27-1.50); 41-60, 1.54 (1.41-1.68); 61-80, 1.61 (1.45-1.79); and 81-100, 1.42 (1.19-1.68), compared with the group (score 0-20) with least variability. The adjusted sHR for CV were as follows: 6.7-9.9, 1.29 (1.15-1.45); 10.0-13.9, 1.55 (1.39-1.74); 14.0-20.1, 1.79 (1.60-2.01) and ≥20.2, 2.10 (1.88-2.34) compared to reference group 0-6.6.

CONCLUSIONS

Glycaemic variability was strongly associated with the development of ESKD in people with diabetes and CKD.