HbA1c variability in type 2 diabetes is associated with the occurrence of new-onset albuminuria within three years

Association of fasting blood glucose variability with all-cause mortality in heart transplant recipients

BACKGROUND

Fasting blood glucose (FBG) variability, an emerging marker of glycemic control, has been shown to be related to the risk of cardiovascular events and all-cause mortality in subjects with or without diabetes. However, whether FBG variability is independently associated with a higher all-cause mortality in heart transplant recipients remains unknown.

METHODS

We performed a retrospective cohort study including 373 adult recipients who survived for at least 1 year after heart transplantation with a functioning graft and measured FBG more than three times within first year after transplantation. Multivariable adjusted Cox regression analyses were performed to assess the association between FBG variability and all-cause mortality.

RESULTS

Patients were categorized into three groups according to the coefficient of variation of FBG level: ≤7.0%, 7.0%-13.5%, and >13.5%. During a median follow-up of 44.4 months (interquartile range [IQR], 22.6-63.3 months), 31 (8.3%) participants died. In univariate analyses, FBG variability was associated with an increased all-cause mortality (hazard ratio [HR]: 3.00, 95% confidence interval [CI]: 1.67, 5.38; p < .001). This association remained materially unchanged in the multivariable model adjusted for components of demographics, cardiovascular history and lifestyle, hospital information, immunosuppressive therapy, and post-transplant renal function (HR: 2.75, 95% CI: 1.43, 5.28; p = .004).

CONCLUSIONS

After heart transplantation, high FBG variability is strongly and independently associated with an increased risk of all-cause mortality. Our findings suggest that FBG variability is a novel risk factor and prognostic marker for heart transplantation recipients in outpatient clinic.

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.

Prediction models of diabetes complications: a scoping review

BACKGROUND

Diabetes often places a large burden on people with diabetes (hereafter 'patients') and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes.

METHODS

Building on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards.

RESULTS

Overall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance.

CONCLUSION

This rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics. SCOPING REVIEW REGISTRATION: https://osf.io/fjubt/.

Prediction of the Short-Term Risk of New-Onset Renal Dysfunction in Patients with Type 2 Diabetes: A Longitudinal Observational Study

Background

Studies in the past decade have reported many novel biomarkers for predicting the new-onset or progression risk of renal dysfunction in patients with type 2 diabetes (T2D) based on the genomic, metabolomic, and proteomic technologies. These novel predictive markers, however, are difficult to be widely used in clinical practice over the short term due to their high technology content, instability, and high cost. This study was aimed at evaluating the associations of clinical features and six traditional renal markers with the short-term risk of new-onset renal dysfunction in patients with T2D.

Methods

This study involved 213 participants with T2D and normal renal function at baseline. The baseline levels of the albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), alpha-1-microglobulin-to-creatinine ratio (A1MCR), neutrophil gelatinase-associated lipocalin-to-creatinine ratio, transferrin-to-creatinine ratio (UTRF/Cr), and retinol-binding protein-to-creatinine ratio (URBP/Cr) were analyzed. Multivariate logistic models were established and validated.

Results

During the two-year follow-up period, 23.01% participants progressed to renal dysfunction. The basal levels of ACR, A1MCR, UTRF/Cr, and URBP/Cr were the independent risk factors of new-onset renal dysfunction (P < 0.05). Several logistic models incorporating clinical characteristics and these renal markers were constructed for predicting the short-term risk of new-onset renal dysfunction. Comparatively, the model including age, glycated hemoglobin (HbA1c), hypertension, ACR, A1MCR, UTRF/Cr, and URBP/Cr levels at baseline had the highest potential (C − index = 0.785, P < 0.001). This model was validated using the K-fold cross-validation method; the accuracy was 0.815 ± 0.013 in training sets and 0.784 ± 0.019 in validation sets, indicating a good consistency for predicting the new-onset renal dysfunction risk. Finally, a nomogram based on this model was constructed to provide a quantitative tool to assess the individualized risk of short-term new-onset renal dysfunction.

Conclusion

The model incorporating these markers and clinical features may have a high potential to predict the short-term risk of new-onset renal dysfunction.

A nomogram for predicting the risk of new-onset albuminuria based on baseline urinary ACR, orosomucoid, and HbA1c in patients with type 2 diabetes

OBJECTIVES

Numerous biomarkers have been shown to be associated with albuminuria. However, few of them are valuable separate predictors of albuminuria development. This study aimed to develop a model for predicting the short-term risk of new-onset albuminuria in normoalbuminuric patients with type 2 diabetes (T2D).

METHODS

213 patients with T2D who were normoalbuminuric at the baseline were enrolled in this study. Basal levels of clinical characteristics and renal biomarkers including urinary orosomucoid (alpha-1-acid-glycoprotein, UORM), neutrophil gelatinase-associated lipocalin, retinol-binding protein, alpha-1-microglobulin, transferrin, and albumin-to-creatinine ratio (ACR) were utilized to analyze the association with the short-term risk of new-onset albuminuria.

RESULTS

19.72% of normoalbuminuric subjects at baseline progressed to albuminuria over the 2-year follow-up period. Except for NGAL, the basal levels of the other five renal biomarkers were significantly associated with new-onset albuminuria risk in the univariate analysis. In the multivariate logistic regression analysis using Forward: LR method, a model incorporating UORM/Cr, ACR, and HbA1c was established. Comparatively, this model had a higher potential to predict new-onset albuminuria risk compared with the single use of renal markers. In the validation of this model performed by 5-fold cross-validation method, the accuracy of this model was 0.818 ± 0.008 in the training sets, 0.827 ± 0.062 in the test sets, indicating a good capability for assessing albuminuria risk. Finally, a nomogram based on this model was constructed to facilitate its use in clinical practice.

CONCLUSION

The combined analysis of UORM/Cr, ACR and HbA1c may be of potential value for predicting the short-term risk of new-onset albuminuria in such patients.

The Association Between Mean Glycated Haemoglobin or Glycaemic Variability and the Development of Retinopathy in People with Diabetes: A Retrospective Observational Cohort Study

INTRODUCTION

To determine the association between mean glycated haemoglobin (HbA_1c) or glycaemic variability and the development of diabetic retinopathy (DR) in people with diabetes.

METHODS

An observational cohort study with people registered with a DR eye screening service between October 2012 and October 2017. Those who had no DR at the start of the study were followed for a maximum of 5 years. HbA_1c measures were used to calculate HbA_1c variability and mean HbA_1c to assess any relationship between these and the risk of developing new onset DR.

RESULTS

A total of 2511 individuals were followed up for up to 5 years. Of these, 542 (21.6%) developed DR. After adjustment, HbA_1c variability was not significantly associated with the development of DR (p = 0.3435). However, the mean HbA_1c was (p < 0.0001). Those with type 1 diabetes had an odds of 1.63 (95% CI 1.11-2.40) of a retinopathy diagnosis compared to those with type 2 diabetes.

CONCLUSIONS

We have shown that mean HbA_1c is associated with an increased risk of developing diabetic retinopathy. However, after adjustment for sex, age, diabetes type and the mean, the HbA_1c variability no longer remained significant. Our data suggest that optimizing long-term glycaemic control remains paramount.

HbA1C variability among type 2 diabetic patients: a retrospective cohort study

BACKGROUND

Studies have found that HbA1C variability is an independent risk factor for diabetic complications in type 2 diabetic patients. This study aims to find factors contributing to higher HbA1C variability in the community.

METHODS

The study was conducted in the southern district of Israel, in Clalit Health Services (CHS). The study population was type 2 diabetic individuals aged 40-70 years in 2005, with a follow-up period of 11 years, until 2015. The definition of HbA1C variability was done by the standard deviation from the average HbA1C value of the entire study period, which was calculated for each participant. The study population was divided into two groups, "variability group" with HbA1C SD > 1.2, and "comparison group" of participants with HbA1C SD ≤ 1.2. In the univariate analysis we used X² or Fisher test for categorical variables and independent t-test for numeric continuous variables. In the multivariate analysis we used logistic regression as well as assessing for possible interactions. Statistical analysis was ascribed for p < 0.05. All the data was drawn from the computerized medical system used by all primary care physicians and nurses in CHS working in the community.

RESULTS

The study population included 2866 participants, the average age was 58.6 years, 43.3% men and 56.7% women. Each participant had an average of 20.9 HbA1C measures in their computerized medical record during the 11 years of follow up. The mean HbA1C value was 7.8%. We found 632 patients (22%) with a high variability, whereas 2234 (78%) had a low variability of HbA1C. In the "variability group" there was a higher percentage of smokers, BMI ≥ 30 and a higher rate of visits to diabetic clinics compared to the "no variability" group. In the "variability group" we found a much higher use of insulin and ACE inhibitors. The highest frequency of variability was between HbA1c values of 8.1-8.5. The multivariate analysis showed that HbA1C variability was associated with insulin use (OR = 4.1, p < 0.001), with age (OR = 0.939, p < 0.001), and Ischemic heart disease (OR = 1.258, p = 0.03). BMI ≥ 30 was almost statistically significant (OR = 1.206, p = 0.063). Gender was statistically insignificant.

CONCLUSIONS

In conclusion, HbA1C variability might be used as an additional marker in Diabetes Mellitus type 2, reflecting the disease complexity characteristics and the patient's lifestyle profile.

Variability in estimated glomerular filtration rate and the risk of major clinical outcomes in diabetes: Post hoc analysis from the ADVANCE trial

There are limited data on whether estimated glomerular filtration rate (eGFR) variability modifies the risk of future clinical outcomes in type 2 diabetes (T2D). We assessed the association between 20-month eGFR variability and the risk of major clinical outcomes in T2D among 8241 participants in the ADVANCE trial. Variability in eGFR (coefficient of variation [CV_eGFR ]) was calculated from three serum creatinine measurements over 20 months. Participants were classified into three groups by thirds of CV_eGFR : low (≤6.4; reference), moderate (>6.4 to ≤12.1) and high (>12.1). The primary outcome was the composite of major macrovascular events, new or worsening nephropathy and all-cause mortality. Cox regression models were used to estimate hazard ratios (HRs). Over a median follow-up of 2.9 years following the 20-month period, 932 (11.3%) primary outcomes were recorded. Compared with low variability, greater 20-month eGFR variability was independently associated with higher risk of the primary outcome (HR for moderate and high variability: 1.07, 95% CI: 0.91-1.27 and 1.22, 95% CI: 1.03-1.45, respectively) with evidence of a positive linear trend (p = .015). These data indicate that eGFR variability predict changes in the risk of major clinical outcomes in T2D.

Glycaemic variability in diabetes: clinical and therapeutic implications

Glycaemic variability is an integral component of glucose homoeostasis. Although it has not yet been definitively confirmed as an independent risk factor for diabetes complications, glycaemic variability can represent the presence of excess glycaemic excursions and, consequently, the risk of hyperglycaemia or hypoglycaemia. Glycaemic variability is currently defined by a large and increasing number of metrics, representing either short-term (within-day and between-day variability) or long-term glycaemic variability, which is usually based on serial measurements of HbA_1c or other measures of glycaemia over a longer period of time. In this Review, we discuss recent evidence examining the association between glycaemic variability and diabetes-related complications, as well as non-pharmacological and pharmacological strategies currently available to address this challenging aspect of diabetes management.

Long-term visit-to-visit glycemic variability as predictor of micro- and macrovascular complications in patients with type 2 diabetes: The Rio de Janeiro Type 2 Diabetes Cohort Study

BACKGROUND

Long-term visit-to-visit glycemic variability is an additional measure of glycemic control. We aimed to evaluate the prognostic value of several measures of glycemic variability for the occurrence of micro- and macrovascular complications, and all-cause mortality in patients with type 2 diabetes.

METHODS

654 individuals were followed-up over a median of 9.3 years. Glycemic variability (SDs and coefficients of variation of HbA1c and fasting glycaemia) was measured during the first 12- and 24-months. Multivariate Cox analysis, adjusted for risk factors and mean HbA1c and fasting glycaemia levels, examined the associations between glycemic variability and the occurrence of microvascular (retinopathy, microalbuminuria, renal function deterioration, peripheral neuropathy) and macrovascular complications [total cardiovascular events (CVE), major adverse CVEs (MACE) and cardiovascular mortality], and of all-cause mortality.

RESULTS

During follow-up, 128 patients had a CVE (96 MACE), and 158 patients died (67 from cardiovascular diseases); 152 newly-developed or worsened diabetic retinopathy, 183 achieved the renal composite outcome (89 newly developed microalbuminuria and 91 deteriorated renal function), and 96 newly-developed or worsened peripheral neuropathy. Glycemic variability, particularly the 24-month parameters either estimated by HbA1c or by fasting glycemia, predicted all endpoints, except for retinopathy and peripheral neuropathy development/progression, and was a better predictor than mean HbA1c. Glycemic variability predicted retinopathy development/progression in patients with good glycemic control (HbA1c ≤ 7.5%, 58 mmol/mol) and predicted new-incident peripheral neuropathy.

CONCLUSIONS

Long-term visit-to-visit glycemic variability is an additional and frequently a better glycemic parameter than mean HbA1c levels for assessing the risk of future development of micro- and macrovascular complications in patients with type 2 diabetes.