A1c Variability Research Articles

Hemoglobin A1c variability as an independent correlate of cardiovascular disease in patients with type 2 diabetes: a cross-sectional analysis of the Renal Insufficiency and Cardiovascular Events (RIACE) Italian Multicenter Study

BackgroundPrevious reports have clearly indicated a significant relationship between hemoglobin (Hb) A1c change from one visit to the next and microvascular complications, especially nephropathy (albuminuria and albuminuric chronic kidney disease, CKD). In contrast, data on macrovascular disease are less clear. This study was aimed at examining the association of HbA1c variability with cardiovascular disease (CVD) in the large cohort of subjects with type 2 diabetes from the Renal Insufficiency and Cardiovascular Events (RIACE) Italian Multicenter Study.MethodsSerial (3–5) HbA1c values obtained during the 2-year period preceding recruitment, including that obtained at the enrolment, were available from 8,290 subjects from 9 centers (out of 15,773 patients from 19 centers). Average HbA1c and HbA1c variability were calculated as the intra-individual mean (HbA1c-MEAN) and standard deviation (HbA1c-SD), respectively, of 4.52±0.76 values. Prevalent CVD, total and by vascular bed, was assessed from medical history by recording previous documented major acute events. Diabetic retinopathy (DR) was assessed by dilated fundoscopy. CKD was defined based on albuminuria, as measured by immunonephelometry or immunoturbidimetry, and estimated glomerular filtration rate, as calculated from serum creatinine.ResultsHbA1c-MEAN, but not HbA1c-SD, was significantly higher (P<0.0001) in subjects with history of any CVD (n. 2,133, 25.7%) than in those without CVD (n. 6,157, 74.3%). Median and interquartile range were 7.78 (7.04-8.56) and 7.49 (6.81-8.31), respectively, for HbA1c-MEAN, and 0.47 (0.29-0.75) and 0.46 (0.28-0.73), respectively, for HbA1c-SD. Logistic regression analyses showed that HbA1c-MEAN, but not HbA1c-SD (and independent of it), was a significant correlate of any CVD. Similar findings were observed in subjects with versus those without any coronary or cerebrovascular event or myocardial infarction. Conversely, none of these measures were associated with stroke, whereas both correlated with any lower limb vascular event and HbA1c-SD alone with ulceration/gangrene. All these associations were independent of known CVD risk factors and microvascular complications (DR and CKD).ConclusionsIn patients with type 2 diabetes, HbA1c variability has not a major impact on macrovascular complications, at variance with average HbA1c, an opposite finding as compared with microvascular disease, and particularly nephropathy.Trial registrationClinicalTrials.Gov NCT00715481

Changes in A1C Levels Are Significantly Associated With Changes in Levels of the Cardiovascular Risk Biomarker hs-CRP

OBJECTIVEThe effect of therapeutic strategies on cardiovascular (CV) disease can be evaluated by monitoring changes in CV risk biomarkers. This study investigated the effect of a structured self-monitoring of blood glucose (SMBG) protocol and the resulting improvements in glycemic control on changes in high-sensitivity C-reactive protein (hs-CRP) in insulin-naïve patients with type 2 diabetes.RESEARCH DESIGN AND METHODSThe Structured Testing Program (STeP) study was a prospective, cluster-randomized, multicenter trial in which 483 poorly controlled, insulin-naïve patients with type 2 diabetes were randomized to active control (ACG) or structured testing (STG) that included quarterly structured SMBG. Changes in A1C, hs-CRP, and glycemic variability (STG subjects only) were measured at baseline and quarterly.RESULTSReductions in geometric mean hs-CRP values were significantly greater in the STG group at months 3 (P = 0.005), 6 (P = 0.0003), and 12 (P = 0.04) than in the ACG group. STG patients at high CV risk (>3 mg/L) showed significantly greater reductions in hs-CRP levels than ACG patients at high CV risk: −3.64 mg/dL (95% CI −4.21 to −3.06) versus −2.18 mg/dL (−2.93 to −1.43), respectively (P = 0.002). There was a strong correlation between reductions in hs-CRP and A1C in both groups: standardized coefficient (β) was 0.25 for the entire cohort (P < 0.0001), 0.31 for STG (P < 0.0001), and 0.16 for ACG (P = 0.02).CONCLUSIONSReductions in hs-CRP level are associated with reductions in A1C but not reductions in lipids or glycemic variability. Comprehensive structured SMBG-based interventions that lower A1C may translate into improvements in CV risk, as evidenced by levels of the biomarker hs-CRP.

Letter: A1c Variability Can Predict Coronary Artery Disease in Patients with Type 2 Diabetes with Mean A1c Level Greater than 7 (Endocrinol Metab 2013;28:125-32, Eun Ju Lee et al.)

Effects of hemoglobin A1c (HbA1c) variability on the microvascular and cardiovascular complications in type 1 diabetes have been reported. However, effect of HbA1c variability on coronary artery disease in type 2 diabetes was not described, especially when using such a sensitive diagnostic method like coronary angiography, as in the article by Lee et al. [1]. Therefore, although this is a retrospective observation study, we find that A1c variability as an independent predictor of coronary artery disease in type 2 diabetic patients with A1c level greater than 7% has great diagnostic value. However, the authors did not present renal function or urine albumin excretion of the subjects, which can be important confounders in the study outcome. Not only advanced renal dysfunction, but also early change like microalbuminuria is well known as an independent factor of cardiovascular disease outcome [2]. In addition, it was recently reported that A1c variability was independently associated with the development of microalbuminuria in patients with type 2 diabetes [3]. Therefore, additional analysis including renal status might demonstrate a more clear relationship between A1c variability and coronary artery disease. Soon after the publication by Lee et al. [1], a contrary result was released that A1c variability had no major impact on macrovascular complications in patients with type 2 diabetes, which is in opposition to nephropathy [4].

Response: A1c Variability Can Predict Coronary Artery Disease in Patients with Type 2 Diabetes with Mean A1c Level Greater than 7 (Endocrinol Metab 2013;28:125-32, Eun Ju Lee et al.)

The authors would like to thank the reviewers for their kind comments concerning our manuscript. We sought to determine the effect of long-term A1c variability on the prediction of coronary artery disease (CAD) in type 2 diabetes mellitus (T2DM) in this study. We concluded that A1c variability determined by standard deviation (SD) of serial A1c can be an independent predictor for angiography-proven CAD in T2DM with mean A1c levels over 7%. We agree with the reviewer's comments. Renal function or urine albumin excretion of the subjects can be independent factors of cardiovascular disease (CVD) outcome [1]. Recently, there have been studies about the relationship between A1c variability and microvascular complications, including microalbuminuria, in patients with T2DM [2,3]. We also have investigated the relationships between A1c variability and diabetic nephropathy using the data of other subjects. This study, however, was a retrospective observational study and there were many limitations, despite our efforts to minimize them. We included subjects who were admitted to the cardiology department due to chest pain. Thus, we did not have enough data including microalbuminuria. We feel that additional analysis including microalbuminuria is needed. As mentioned in the letter, another study has been released with contrary results suggesting that A1c variability does not have a major impact on macrovascular complications in patients with T2DM [4]. In that study, risk factors of the subjects, including renal function or urine albumin excretion, were included in multiple regression analysis, but prevalent CVD at baseline included acute myocardial infarction (AMI), stroke, foot ulcer or gangrene, amputation, coronary, carotid, lower limb revascularization, and surgery for aortic aneurysm. Intra-individual standard deviation of HbA1c (HbA1c-SD) and adj-HbA1c-SD (HbA1c-SD adjusted by number of HbA1c assessment) were not higher in patients with a history of any CVD, but HbA1c-SD and adj-HbA1c-SD were significantly higher in subjects with AMI. Our study included only subjects with CAD according to coronary angiography. To investigate whether A1c SD was an independent predictor of CAD, we analyzed the incidence of CAD according to tertiles of A1c SD. In contrast, Penno et al. [4] used the CAD as dependent variable. We surmise that our results differed from those of Penno et al. [4] because of these factors. We feel that a prospective study is needed to further investigate the relationship between of A1c variability and diabetic nephropathy. Thank you again for your interest and comprehensive review of our paper.

A1c Variability Can Predict Coronary Artery Disease in Patients with Type 2 Diabetes with Mean A1c Levels Greater than 7

BackgroundRecent studies suggested that the association of acute glucose variability and diabetic complications was not consistent, and that A1c variability representing long term glucose fluctuation may be related to coronary atherosclerosis in patients with type 1 diabetes. In this study, we attempt to determine whether or not A1c variability can predict coronary artery disease (CAD) in patients with type 2 diabetes.MethodsWe reviewed data of patients with type 2 diabetes who had undergone coronary angiography (CAG) and had been followed up with for 5 years. The intrapersonal standard deviation (SD) of serially-measured A1c levels adjusted by the different number of assessments among patients (adj-A1c-SD) was considered to be a measure of the variability of A1c.ResultsAmong the 269 patients, 121 of them had type 2 diabetes with CAD. In patients with A1c ≥7%, the mean A1c levels and A1c levels at the time of CAG among the three groups were significantly different. The ratio of patients with CAD was the highest in the high adj-A1c-SD group and the lowest in the low adj-A1c-SD group (P=0.017). In multiple regression analysis, adj-A1c-SD was an independent predictor for CAD in subjects with A1c ≥7% (odds ratio, 2.140; P=0.036).ConclusionPatients with higher A1c variability for several years showed higher mean A1c levels. A1c variability can be an independent predictor for CAD as seen in angiographs of patients with type 2 diabetes with mean A1c levels over 7%.

PDB15 A1C Variability is Associated With the Risk of Developing New Diabetes for the Healthy Adults

PDB15 A1C Variability is Associated With the Risk of Developing New Diabetes for the Healthy Adults

Variability in hemoglobin A1c predicts all-cause mortality in patients with type 2 diabetes

BackgroundTo evaluate the relationship between hemoglobin A1c variability and all-cause mortality in type 2 diabetic patients. MethodsThis was a retrospective cohort study in type 2 diabetic patients followed for at least 2years between 2003 and 2009. A1C variability was determined from the standard deviation or coefficient of variation of serial A1C values (A1CSD or A1CCV). Subjects were categorized into either the high or low A1C variability group according to their A1CCV median. Hazard ratios (HRs) of various factors for all-cause mortality were determined from Cox's proportional hazard models. ResultsA total of 881 subjects (422 men, 459 women) were included and 73 (8.3%) died during follow-up. The follow-up period was 4.7±2.3years. All-cause mortality was higher in subjects with high A1CCV (11.0% vs. 5.4%, p=0.002). In the Kaplan–Meier failure curve, subjects with higher A1CCV demonstrated a trend of higher mortality (p=0.1). In multivariate Cox's proportional hazards models, A1CSD and A1CCV significantly predicted all-cause mortality with an HR of 1.987 (p=0.02) and 1.062 (p=0.013), respectively, after adjusting for age, gender, body mass index, duration of diabetes, mean systolic blood pressure, use of antihypertensives and statins, mean LDL-cholesterol, smoking status, chronic kidney disease, and mean A1C values (A1CMEAN). The ability of A1CSD and A1CCV to predict all-cause mortality was more evident in subjects with relatively low A1CMEAN. ConclusionsA1C variability is an important risk factor for all-cause mortality in type 2 diabetic patients.

Association between HOMA‐B and A1C levels in Haitian Americans with type 2 diabetes

This study examined the association of pancreatic β‐cell function assessed by the homeostasis model assessment (HOMA) and glycosylated hemoglobin (A1C), and determined whether this association varies by gender in Haitian Americans with type 2 diabetes (T2D). The cross‐sectional study included 77 Haitian Americans (males=32 &amp; females=45). Participants were recruited from multiple sources from Miami‐Dade and Broward Counties, Florida. Fasting plasma glucose, A1C, and insulin levels were measured in fasting blood. Analyses included t‐test, chi‐square, Pearson's correlations and linear regression. Males had significantly lower HOMA‐B function (P=.032) and BMI (P=.007) compared to females. Regression analyses showed that after controlling for covariates, the association between HOMA‐B with A1C was only significant for Haitian American males (β= −1.625, SE=−.070, P=.001). The model explained 27.9% of the variability in A1C. A one unit decrease in HOMA‐B increased the A1C levels 1.6%. Assessment of both HOMA‐B and A1C could be a better predictor for diabetes care among Haitian Americans. Funding for this research was provided through an NIH/NIDDK sponsored grant.

PDB20 A1C Variability and the Risk of Developing New Diabetes for the Healthy Adults in Japan

PDB20 A1C Variability and the Risk of Developing New Diabetes for the Healthy Adults in Japan

A1C Variability as an Independent Risk Factor for Microalbuminuria in Young People With Type 1 Diabetes

OBJECTIVETo assess the potential association between A1C variability (A1C-SD) and microalbuminuria in young people with type 1 diabetes.RESEARCH DESIGN AND METHODSSerially collected samples for A1C measurement were available for 1,232 subjects with childhood-onset type 1 diabetes recruited to the Oxford Regional Prospective Study and the Nephropathy Family Study.RESULTSThe median (range) number of A1C assessments was 4 (2–16). Mean intrapersonal A1C was 9.5% and A1C-SD was 0.91. Mean A1C and A1C-SD values were higher in subjects with microalbuminuria (n = 227) than in those with normoalbuminuria (10.3 vs. 9.4%; 1.12 vs. 0.86, P < 0.001). In a Cox regression model, A1C-SD was independently associated with microalbuminuria (hazard ratio 1.31 [95% CI 1.01–1.35]).CONCLUSIONSIn the current study, A1C variability was an independent variable that added to the effect of A1C on the risk for microalbuminuria in youth with type 1 diabetes, a population highly vulnerable to vascular complications.

A solution might be within people with diabetes themselves

In 10 minutes, by the time you finish reading this editorial, 60 people with diabetes will die and 120 people will be diagnosed with diabetes worldwide. As tragic as the death of so many people is, the doubling of people with the same illness, an illness we have not learned how to manage and one that can be prevented in the first place, represents a failure of the world health system of apocalyptic proportions. Two hundred and fifty million people diagnosed with diabetes today will grow to nearly 400 million by 2025. As dramatic as this quantitative analysis is, qualitative considerations make this global suffering unsustainable and unacceptable. The burden of diabetes is the greatest in the developing world, where some 80% of people with diabetes live. Unfortunately, more than 80% of expenditures for medical care for diabetes are made in the world’s economically richest countries. The world spends about USD 250 billion to treat and prevent diabetes and its complications. One country alone, the US, spends more than half of this amount (www. IDF.org). In spite of this robust expenditure, only 7.3% of people with diabetes in the US achieve recommended levels of glucose, cholesterol and blood pressure control simultaneously (www.cdc.gov). This cannot be explained only by health disparities, since far more than 7.3% of US adults with diabetes enjoy access to quality health care. Rather it is a reflection of the complexity of managing diabetes as well as other chronic diseases, of which diabetes is an important example. There is no doubt that more resources are needed in countries of the developing world for the care of people with diabetes, but it is also clear that expenditures and investments do not guarantee satisfactory outcomes. Alternative hypotheses for the care of people with diabetes must be put forward and tested if we are to modify the course or predicted prevalence, morbidity and burden of diabetes. One such hypothesis is the focus of this supplement of Family Medicine. A World Health Organization (WHO) consultation on peer support programmes in diabetes was convened in November 2007 and published in July 2008 at www.who.int/diabetes/publications/en/. Participants were experts in peer support frommore than 25 countries, representing all regions of the world. The proceedings of this conference and the original scholarly background papers supporting the conference are published herein. As background to all of these, this editorial answers three questions: Why was the WHO consultation convened? What were the conclusions and recommendations? and How will implementation of these recommendations provide part of a solution (not necessarily the solution) to the global burden of diabetes? The premise of the WHO consultation and the projects that will follow from it is that people living with diabetes have a great deal to offer one another in terms of behavioural and emotional support. People with diabetes need more thanmedical treatment from their health-care providers: they also need support in mastering and sustaining complex self-care behaviour that enables them to live healthy and satisfying lives while managing their diabetes 365 days a year for the rest of their lives. That people living with diabetes have much to offer each other is reflected in a wide range of research. A recent study of the determinants of medical outcomes in Type 2 diabetes demonstrated that patient-level factors accounted for the majority of variability in haemoglobin A1c (98%) while physician-related factors accounted for a very small portion of metabolic control. An earlier review of self-management programmes found that the only characteristic predictive of metabolic control was the amount of time spent between the health-care professional and the patient. These two findings suggest development of patientlevel interventions that entail ongoing support and follow-up for those with diabetes to support their efforts at disease management. Peer mentors may be identified and trained to provide this kind of patientlevel support to assist others in need. If effective, the peer support model would be a promising addition to public health systems that face severe resource constraints and increasing needs among patients living with diabetes and/or other chronic diseases. Reflecting the hope that peer support might be part of the solution to worldwide diabetes, the Lilly

A Genome-Wide Association Study of Treated A1C

Glycated hemoglobin (A1C), expressed as a percentage of total hemoglobin, results from the nonenzymatic glycation of hemoglobin molecules. Because this irreversible process is directly proportional to intracellular glucose concentrations (which are generally correlated with extracellular glucose levels), A1C can be used as a marker of average ambient glycemia over the mean life span of erythrocytes. Absent alterations in hemoglobin turnover or erythrocyte physiology, A1C adequately captures glucose homeostasis in the organism. Therefore, it has been widely adopted as an indicator of glycemic control in the treatment of diabetic patients (1), and it has recently been proposed as a diagnostic test for diabetes (2). Variation in A1C is subject to environmental and genetic determinants. Its heritability (the proportion of the variance explained by the familial contribution to the trait, integrating both genetic and early shared environmental components) nears 40% in the general population (3) and 60% in twin studies (4). The distribution of A1C displays a long tail to the right, populated by levels of people whose hyperglycemia signals diabetes. Pharmacological treatment can significantly reduce A1C, providing the rationale for near normalization of A1C as a treatment goal (1). This is particularly true for insulin as a therapeutic modality: barring hypoglycemia and other practical limitations, insulin can be gradually raised until the target A1C has been reached. Thus, presumably higher insulin doses should be able to overcome most endogenous (i.e., genetic) obstacles to lowering A1C. In the setting of treated A1C, therefore, the environmental contribution to the trait acquires a much larger role than that in the native state, and one might suspect that it could overwhelm any genetic component (Fig. 1). FIG. 1. Simplified theoretical model illustrating the potential contributors to A1C levels in the native ( left ) or treated ( right ) states. The heritability of native A1C is estimated to be …

Hemoglobin glycation index: a robust measure of hemoglobin A1c bias in pediatric type 1 diabetes patients

The hemoglobin glycation index (HGI) assesses biological variation in A1c after accounting for the effect of mean blood glucose (MBG). Previous studies minimized analytical variation that could mask biological variation and showed that HGI was consistent within individuals over time and positively associated with risk for microvascular complications. We tested the hypothesis that biological variation in A1c can be assessed by HGI calculated using routine MBG and A1c data obtained from a typical diabetes clinic. Self-monitored MBG and A1c were collected from charts of 202 pediatric type 1 diabetes patients attending 1612 clinic visits over 6 yr. Predicted A1c was calculated from the linear regression equation of A1c on MBG in the study population. HGI was calculated by subtracting predicted A1c from observed A1c. Patients were divided into low, moderate, and high HGI tertile groups. Patients used 12 models of glucose meters. Download protocols varied with clinical practice over time. A1c was measured by multiple assays and laboratories. Despite this analytical heterogeneity, HGI was significantly different between individuals and correlated within individuals. MBG (mean ± SD, mg/dL) was similar in the low (186 ± 31), moderate (195 ± 28), and high (199 ± 42) HGI groups. A1c (%) was significantly different (p < 0.0001) in the low (7.6 ± 0.7), moderate (8.4 ± 0.7), and high (9.6 ± 1.1) HGI groups. Biological variation in A1c is a robust quantitative trait that can be assessed using HGI calculated from routine clinic data. This suggests that HGI could be used clinically for more personalized assessment of complications risk.

Is A1C Variability an Independent Predictor for the Progression of Atherosclerosis in Type 2 Diabetic Patients?

BackgroundLittle is known about the relative contribution of long-term glycemic variability to the risk of macrovascular complications in type 2 diabetes. This study was conducted to evaluate the effect of A1C variability on the progression of carotid artery intima-media thickness (IMT) in type 2 diabetic patients.MethodsAmong type 2 diabetic patients who visited Hallym University Sacred Heart Hospital from March 2007 to September 2009, 120 patients who had carotid artery IMT measured annually and A1C checked every three months for at least one year were analyzed. Individual A1C variability was defined as the standard deviation (SD) of five A1C levels taken every three months for approximately one year. Change in IMT was defined as an increase in IMT on follow-up measurement. The association between the SD of A1C and changes in IMT was evaluated.ResultsWith greater A1C variability, there was a greater increase in the mean IMT (r = 0.350, P < 0.001) of the carotid artery. After adjusting for confounding factors that may influence IMT, A1C variability was significantly associated with the progression of IMT (r = 0.222, P = 0.034). However, the SD of A1C was not a significant independent risk factor for the progression of IMT in multiple regression analysis (β = 0.158, P = 0.093).ConclusionHigher A1C variability is associated with IMT progression in type 2 diabetic patients; however, it is not an independent predictor of IMT progression. Overall glycemic control is the most important factor in the progression of IMT.

Incremental Value of Continuous Glucose Monitoring When Starting Pump Therapy in Patients With Poorly Controlled Type 1 Diabetes

OBJECTIVETo compare the improvements in glycemic control associated with transitioning to insulin pump therapy in patients using continuous glucose monitoring versus standard blood glucose self-monitoring.RESEARCH DESIGN AND METHODSThe RealTrend study was a 6-month, randomized, parallel-group, two-arm, open-label study of 132 adults and children with uncontrolled type 1 diabetes (A1C ≥8%) being treated with multiple daily injections. One group was fitted with the Medtronic MiniMed Paradigm REAL-Time system (PRT group), an insulin pump with integrated continuous subcutaneous glucose monitoring (CGM) capability, with instructions to wear CGM sensors at least 70% of the time. Conventional insulin pump therapy was initiated in the other group (continuous subcutaneous insulin infusion [CSII] group). Outcome measures included A1C and glycemic variability.RESULTSA total of 115 patients completed the study. Between baseline and trial end, A1C improved significantly in both groups (PRT group −0.81 ± 1.09%, P < 0.001; CSII group −0.57 ± 0.94%, P < 0.001), with no significant difference between groups. When the 91 patients who were fully protocol-compliant (including CGM sensor wear ≥70% of the time) were considered, A1C improvement was significantly greater in the PRT group (P = 0.004) (PRT group −0.96 ± 0.93%, P < 0.001; CSII group −0.55 ± 0.93%, P < 0.001). Hyperglycemia parameters decreased in line with improvements in A1C with no impact on hypoglycemia.CONCLUSIONSCGM-enabled insulin pump therapy improves glycemia more than conventional pump therapy during the first 6 months of pump use in patients who wear CGM sensors at least 70% of the time.

A1C Variability Predicts Incident Cardiovascular Events, Microalbuminuria, and Overt Diabetic Nephropathy in Patients With Type 1 Diabetes

OBJECTIVERecent data from the Diabetes Control and Complications Trial (DCCT) indicated that A1C variability is associated with the risk of diabetes microvascular complications. However, these results might have been influenced by the interventional study design. Therefore, we investigated the longitudinal associations between A1C variability and diabetes complications in patients with type 1 diabetes in the observational Finnish Diabetic Nephropathy (FinnDiane) Study.RESEARCH DESIGN AND METHODSA total of 2,107 patients in the FinnDiane Study had complete data on renal status and serial measurements of A1C from baseline to follow-up (median 5.7 years), and 1,845 patients had similar data on cardiovascular disease (CVD) events. Intrapersonal SD of serially measured A1C was considered a measure of variability.RESULTSDuring follow-up, 10.2% progressed to a higher albuminuria level or to end-stage renal disease, whereas 8.6% had a CVD event. The SD of serial A1C was 1.01 versus 0.75 (P < 0.001) for renal status and 0.87 versus 0.79 (P = 0.023) for CVD in progressors versus nonprogressors, respectively. In a Cox regression model, SD of serial A1C was independently associated with progression of renal disease (hazard ratio 1.92 [95% CI 1.49–2.47]) and of a CVD event (1.98 [1.39–2.82]) even when adjusting for mean A1C and traditional risk factors. Interestingly for CVD, mean serial A1C itself was not predictive even though SD of A1C was.CONCLUSIONSIn patients with type 1 diabetes, A1C variability was not only predictive of incident microalbuminuria and progression of renal disease but also of incident CVD events.

Biologic Variability in Plasma Glucose, Hemoglobin A1c, and Advanced Glycation End Products Associated with Diabetes Complications

Plasma glucose plays a key role in the complications of diabetes mellitus. Hemoglobin A1c (HbA1c) and circulating concentrations of advanced glycation end products (AGEs) are central to diabetes clinical care and pathophysiology. However, there is evidence for variation between individuals in the relationship of plasma glucose to both these measures and to specific complications. The glycation gap (GG) and hemoglobin glycation index represent tools for quantitating the variability in the relationship between plasma glucose and HbA1c useful for identification of underlying mechanisms. Recent evidence demonstrates the heritability of HbA1c, the GG, and AGEs, yet not of glycated serum proteins. There has been tremendous effort devoted to identifying the heritable basis of types 1 and 2 diabetes; however, studies on the heritable contributors to these mediators of glucose effect on complications are only beginning. New evidence for normal biologic variation in the distribution of glucose into the red blood cell (RBC) intracellular compartment and RBC lifespan in people with and without diabetes represent candidates for heritable mechanisms and contributors to the rise in HbA1c with age. Taken as a whole, genetic and mechanistic evidence suggests new potential targets for complications prevention and improvement in complications risk estimation. These observations could help tilt the risk-benefit balance in glycemic control toward a more beneficial outcome.

Cardiovascular Disease, Neuropathy, and Retinopathy

This is the sixth of a series of six articles based on presentations at the American Diabetes Association Scientific Sessions held 6–10 June 2008 in San Francisco, California. ### Role of glycemia in cardiovascular disease Jacqueline Dekker (Amsterdam, the Netherlands) discussed the association between hyperglycemia and cardiovascular disease (CVD), particularly addressing A1C measurement in nondiabetic individuals. A1C, she noted, is considered a gold-standard measure of chronic glycemia in diabetic patients. In nondiabetic populations, its implications are somewhat less clear. In a study of 648 apparently healthy individuals, 12% had an elevated total A 1 level (measured in that study) not explained by measurement error or glucose intolerance and remaining in the same range over 3.5 years in 90% of those initially with high and in 68% of those initially with low baseline levels (1). There was no correlation with glucose tolerance, with caloric intake, or with physical activity, but A 1 level was associated with cigarette use and with clinically overt atherosclerosis, leading Dekker to conclude that “factors unrelated to glucose metabolism are the main determinants of A 1” in nondiabetic individuals, perhaps with bearing on risk of what are considered complications of diabetes. A subsequent study of 3,240 nondiabetic individuals showed weak correlation of A1C with fasting and 2-h glucose, cholesterol, and insulin. Weak but significant correlations were also found with erythrocyte characteristics, and cigarette use was associated with higher A1C, again suggesting nonglycemic effects on hemoglobin glycation (2). In the Islington Diabetes Survey, 1,084 participants had glucose tolerance tests and four different A1C assays. The 2-h postload glucose explained 19–41% of the variation in A1C, depending on the assay, and 26–48% of the variation was explained by the fasting glucose, with low and high glycators remaining constant over 4 years (3). If 50–80% is not explained by glycemia, the implication is, again, that although …

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

OBJECTIVE—Debate remains as to whether short- or long-term glycemic instability confers a risk of microvascular complications in addition to that predicted by mean glycemia alone. In this study, we analyzed data from the Diabetes Control and Complications Trial (DCCT) to assess the effect of A1C variability on the risk of retinopathy and nephropathy in patients with type 1 diabetes.RESEARCH DESIGN AND METHODS—A1C was collected quarterly during the DCCT in 1,441 individuals. The mean A1C and the SD of A1C variability after stabilization of glycemia (from 6 months onwards) were compared with the risk of retinopathy and nephropathy with adjustments for age, sex, disease duration, treatment group, and baseline A1C.RESULTS—Multivariate Cox regression showed that the variability in A1C added to mean A1C in predicting the risk of development or progression of both retinopathy (hazard ratio 2.26 for every 1% increase in A1C SD [95% CI 1.63–3.14], P < 0.0001) and nephropathy (1.80 [1.37–2.42], P < 0.0001), with the relationship a feature in conventionally treated patients in particular.CONCLUSIONS—This study has shown that variability in A1C adds to the mean value in predicting microvascular complications in type 1 diabetes. Thus, in contrast to analyses of DCCT data investigating the effect of short-term glucose instability on complication risk, longer-term fluctuations in glycemia seem to contribute to the development of retinopathy and nephropathy in type 1 diabetes.

Real-Time Continuous Glucose Monitoring Together with Telemedical Assistance Improves Glycemic Control and Glucose Stability in Pump-Treated Patients

Real-time continuous glucose monitoring (CGM) has recently been incorporated into routine diabetes management because of the potential advantages it offers for glycemic control. The aim of our study was to evaluate the impact of the use of real-time CGM together with a telemedicine system in hemoglobin A1c and glucose variability in patients with type 1 diabetes treated with insulin pumps. Ten patients (five women, 41.2 [range, 21-62] years old, duration of diabetes 14.9 [range, 3-52] years) were included in this randomized crossover study. Patients used the DIABTel telemedicine system throughout the study, and real-time CGM was used for 3 days every week during the intervention phase. At the end of the control phase, a blind 3-day CGM was performed. Glucose variability was evaluated using the Glucose Risk Index (GRI), a comparative analysis of continuous glucose values over two consecutive hours. Hemoglobin A1c decreased significantly (8.1 +/- 1.1% vs. 7.3 +/- 0.8%; P = 0.007) after the intervention phase, while no changes were observed during the control phase. The mean number of daily capillary glucose readings was higher during the intervention phase (4.7 +/- 1.1 vs. 3.8 +/- 1.0; P < 0.01), because of an increase in random analyses (1.22 +/- 0.3 vs. 0.58 +/- 0.1; P < 0.01), and there was also a significant increase in the mean number of bolus doses per day (5.23 +/- 1.1 vs. 4.4 +/- 0.8; P < 0.05). The GRI was higher during the control phase than during the experimental phase (9.6 vs. 6.25; P < 0.05). Real-time CGM in conjunction with the DIABTel system improves glycemic control and glucose stability in pump-treated patients with type 1 diabetes.