A1C Variability and the Risk of Microvascular Complications in Type 1 Diabetes

Kilpatrick, Eric S.; Rigby, Alan S.; Atkin, Stephen L.

doi:10.2337/dc08-0864

Cited by 377 publications

(403 citation statements)

References 26 publications

Supporting

Mentioning

363

Contrasting

Unclassified

Order By: Relevance

“…intensive treatment group over time in patients with similar average HbA 1c values in the two groups (5), suggesting that additional factors other than mean HbA 1c may be responsible for this increased retinopathy risk (5)(6)(7). Glycemic variability is now emerging as a possible additional measure of glycemic control, which may be a better predictor of complications than average glycemic measures.…”

mentioning

confidence: 99%

“…A recent meta-analysis concluded that HbA 1c variability, assessed by the SD, is associated with renal disease in type 1 and type 2 diabetes (9). However, no systematic reviews or meta-analyses have evaluated the relationship between long-term glycemic variability and other complications in diabetes, despite contradictory literature providing evidence in support (6,(10)(11)(12)(13)(14)(15) and against (16)(17)(18)(19)(20) such a relationship.…”

mentioning

confidence: 99%

“…First, unlike short-term glycemic variability, longterm glycemic variability may predict complications in both type 1 and type 2 diabetes (6,(10)(11)(12)(13)(14)(15)(21)(22)(23)(24)(25)(26)(27)(28)(29). Second, HbA 1c is routinely recorded in primary care for both types of diabetes, whereas measures of short-term variability are not (30,31).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Long-term Glycemic Variability and Risk of Adverse Outcomes: A Systematic Review and Meta-analysis

et al. 2015

View full text Add to dashboard Cite

OBJECTIVEGlycemic variability is emerging as a measure of glycemic control, which may be a reliable predictor of complications. This systematic review and meta-analysis evaluates the association between HbA 1c variability and micro-and macrovascular complications and mortality in type 1 and type 2 diabetes. RESEARCH DESIGN AND METHODSMedline and Embase were searched (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) for studies describing associations between HbA 1c variability and adverse outcomes in patients with type 1 and type 2 diabetes. Data extraction was performed independently by two reviewers. Random-effects meta-analysis was performed with stratification according to the measure of HbA 1c variability, method of analysis, and diabetes type. RESULTS

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Long-term Glycemic Variability and Risk of Adverse Outcomes: A Systematic Review and Meta-analysis

et al. 2015

View full text Add to dashboard Cite

show abstract

“…This conclusion is derived essentially from retrospective epidemiological analyses with hard (microvascular) endpoints [2] and from observational and interventional studies with surrogate endpoints (oxidative stress and endothelial function, assessed with flow-mediated dilation) [3,4]. Clearly, the correlation found between glycaemic variability and microvascular complications/oxidative stress does not necessarily indicate a cause-effect relationship; furthermore, the effect of glycaemic instability on macrovascular disease is not known.…”

mentioning

confidence: 99%

A rat model of glycaemic variability

2009

View full text Add to dashboard Cite

There is a growing body of scientific evidence that glycaemic variability is an HbA 1c -independent predictor of diabetic vascular complications [1]. This conclusion is derived essentially from retrospective epidemiological analyses with hard (microvascular) endpoints [2] and from observational and interventional studies with surrogate endpoints (oxidative stress and endothelial function, assessed with flow-mediated dilation) [3,4]. Clearly, the correlation found between glycaemic variability and microvascular complications/oxidative stress does not necessarily indicate a cause-effect relationship; furthermore, the effect of glycaemic instability on macrovascular disease is not known. As a consequence, whether glycaemic variability is causally linked to diabetic complications and, in particular, to macrovascular dysfunction, remains an open question.In their elegant paper, Horváth et al.[5] investigated both the effect of glucose fluctuation on endothelial function (a surrogate marker of macrovascular complications) and the possible mechanisms linking glucose 'swings' to vascular damage. In particular, they compared the effect of glycaemic variability on endothelial dysfunction in four rat models: poor glycaemic control (PGC), good glycaemic control, untreated diabetic (UD) and nondiabetic. Interestingly, although the 'global glycaemic exposition', mathematically expressed by the AUC of their 48 h glycaemic profiles and correlated with 48 h mean glucose level [6], was significantly higher in UD rats than in PGC rats, endothelial dysfunction was greater in the PGC rats. This finding supported their conclusion that 'the oscillation of blood glucose level itself is a more important contributor to the loss of endothelial function in diabetes than the average level of mean blood glucose level per se'. In our opinion, this conclusion is not completely supported by the data presented. In fact, the different variability of the glycaemic profiles of PGC versus UD rats is only presumptive and is not supported by an objective mathematical analysis, i.e. comparison of one or more indices of variability (mean amplitude of glycaemic excursions [MAGE], standard deviation, continuous overall net glycaemic action [CONGAn] and others) [6] between the two groups. If the indices of variability for PGC and UD rats were similar, then this would indicate that the better endothelial function of UD rats might be related to an unexplored variable. Therefore, the conclusion should state only that mean blood glucose is not enough to completely explain vascular dysfunction.Duality of interest The authors declare that there is no duality of interest associated with this manuscript.

show abstract

“…Diabetic nephropathy advances through a number of recognisable steps from subclinical disease to the first measurable stage of microalbuminuria (MIC), defined as persistent urinary albumin excretion rates (UAER) of 30-300 mg/24 h, to macroalbuminuria/diabetic nephropathy (DMN) with UAER > 300 mg/24 h. DMN is characterised by declining renal function and ultimately end-stage renal disease. Although positive effects on the development and progression of diabetic nephropathy through strict control of blood glucose [4], blood pressure [5] and, in particular, blockade of the renin-angiotensin system (6, 7) have been reported, it still has not been enough to prevent the high incidence of end-stage kidney damage caused by diabetes. The urinary albumin excretion rate is used to identify patients at risk, but only a fraction of all patients with microalbuminuria progress to DMN.…”

Section: Introductionmentioning

confidence: 99%

Quantitative iTRAQ-Based Proteomic Identification of Candidate Biomarkers for Diabetic Nephropathy in Plasma of Type 1 Diabetic Patients

et al. 2010

View full text Add to dashboard Cite

Introduction As part of a clinical proteomics programme focused on diabetes and its complications, it was our goal to investigate the proteome of plasma in order to find improved candidate biomarkers to predict diabetic nephropathy. Methods Proteins derived from plasma from a crosssectional cohort of 123 type 1 diabetic patients previously diagnosed as normoalbuminuric, microalbuminuric or macroalbuminuric were enriched with hexapeptide library beads and subsequently pooled within three groups. Proteins from the three groups were compared by online liquid chromatography and tandem mass spectrometry in three identical repetitions using isobaric mass tags (iTRAQ). The results were further analysed with ingenuity pathway analysis. Levels of apolipoprotein A1, A2, B, C3, E and J were analysed and validated by a multiplex immunoassay in 20 type 1 diabetic patients with macroalbuminuria and 10 with normoalbuminuria. Results A total of 112 proteins were identified in at least two out of three replicates. The global protein ratios were further evaluated by ingenuity pathway analysis, resulting in the recognition of apolipoprotein A2, B, C3, D and E as key nodes in the top-rated network. The multiplex immunoassay confirmed the overall protein expression patterns observed by the iTRAQ analysis. Conclusion The candidate biomarkers discovered in this cross-sectional cohort may turn out to be progression biomarkers and might have several clinical applications in the treatment and monitoring of diabetic nephropathy; however, they need to be confirmed in a longitudinal cohort.

show abstract

A1C Variability and the Risk of Microvascular Complications in Type 1 Diabetes

Cited by 377 publications

References 26 publications

Long-term Glycemic Variability and Risk of Adverse Outcomes: A Systematic Review and Meta-analysis

Long-term Glycemic Variability and Risk of Adverse Outcomes: A Systematic Review and Meta-analysis

A rat model of glycaemic variability

Quantitative iTRAQ-Based Proteomic Identification of Candidate Biomarkers for Diabetic Nephropathy in Plasma of Type 1 Diabetic Patients

Contact Info

Product

Resources

About