A1c Variability Research Articles

Assessment of Diabetes Distress in Appalachian Ohio

Diabetes rates in southeastern Ohio are more than double the national average (19.9% vs. 9.4%). Here, people battle a poverty rate 1.5 times that of the U.S. average, and suffer from higher unemployment, lower educational achievement, poorer health, and lower access to care. Despite the high presence of social determinants of health, the impact of diabetes is understudied and one critical outcome that has not been addressed is diabetes distress. We conducted a cross-sectional survey study with 121 students, faculty, and staff at a large university in Appalachian Ohio (A1C=7.3±1.4%, age=36±17years; female=60%; white=86%; type 2 diabetes=54%; duration=10±9 years, 48%=college degree or more). Diabetes distress was assessed using the Diabetes Distress Scale for type 2 diabetes and for type 1 diabetes; depressive symptoms were assessed using the Patient Health Questionnaire-9. Of the 65 type 2 diabetes participants, 34% (n=19) reported moderate to severe diabetes distress, with 48% (n=31) experiencing moderate to severe distress on the Regimen subscale. Among the 56 type 1 participants, 26% (n=15) reported moderate to severe diabetes distress, with 51% (n=28) experiencing moderate to severe distress on the Powerlessness subscale. Multiple regression analyses found high scores of diabetes distress (standardized b= 0.62, p=0.03) were independently associated with higher A1C levels after controlling for depressive symptoms (standardized b=0.01, p=79), age (standardized b= -0.03, p=0.14) and gender (standardized b=0.75, p<=.08). This model accounted for 29% of the variation in A1C. Multiple regression analyses using the same model found no association between type 2 diabetes, distress, and A1C; the lack of significance may be explained by well-educated, working adults with private insurance. Next steps include recruiting type 1 and type 2 patients from local specialty and primary care clinics to enhance representativeness. These data will help providers individualize clinical approaches for patient’s diabetes distress level. Disclosure E.A. Beverly: None. E.H. Guseman: None. K.A. Cook: None. A.M. Healy: None.

Hemoglobin A1c (A1c) Genetics Contributes to A1c/Glucose Mismatches in the Multiethnic VA Million Veteran Program (MVP)

A1c accuracy is important for diabetes management but mismatches in A1c relative to underlying glucose can impede care. Since the basis for mismatches is poorly understood, we assessed the contributions of 22 A1c variants identified in GWAS that were associated with erythrocytic traits but not glycemia. 73,128 MVP patients had continuity of care and monitoring: >1 PCP visit, >1 outpatient random plasma glucose (RPG) and >1 A1c within each of 3 consecutive two-year periods. They had mean age 67 years and BMI 31 kgm-2; 71% were white and 21% were black; 51% had diabetes (ICD 9 codes and diabetes Rx). Mismatches were defined by the hemoglobin glycation index (HGI) calculated from regression residuals of mean A1c on mean RPG within each period, averaged over the 3 periods. We evaluated the associations with HGI of each A1c variant and a genetic risk score (GRS; sum of alleles weighted by their reported A1c effect sizes) by race. Mean HGI was -0.07% A1c units in whites and 0.2% in blacks. Of the 22 variants, 15 at 13 loci were associated with HGI (P<5 x 10-8, ANK1, CNTN5, ERAL1, G6PD, HBS1L/MYB, HK1, HIST1H4A/HFE, MYO9B, SENP1, SPTA1, SYN2, TMPRSS6 and PEIZO1). GRS was associated with HGI (whites P=2 x 10-189, blacks P=1 x 10-233) and explained HGI variance (whites 1.7%, blacks 5.1%), especially in those without diabetes (whites 3.5%, blacks 11.4%). People in the lowest 10% of GRS had a glucose-independent fall in A1c (mean HGI, whites -0.19%, blacks -0.31% A1c units) while those in the highest 10% of GRS had the opposite (mean HGI, whites 0.06%, blacks 0.29%). The variants with the largest effects on HGI were rs1800562/HIST1H4A/HFE [mean HGI, AA: -0.37%, GG: -0.06% A1c units] in whites and rs1050828/G6PD [mean HGI, T: -0.31%; C: 0.29% A1c units] in black men. Conclusions: HGI is higher in blacks than whites - a glucose-independent higher A1c. Common genetic variation explains 2-11% of the variance in HGI. Accounting for genetic variation in A1c may improve its accuracy in guiding diabetes care. Disclosure A. Leong: None. D. Posner: None. B.R. Charest: None. J.B. Meigs: None. P.W. Wilson: None. R.M. Cohen: Stock/Shareholder; Self; Bristol-Myers Squibb Company. D.R. Miller: None. Y.V. Sun: None. K. Cho: None. L.S. Phillips: Other Relationship; Self; DIASYST Inc.. Research Support; Self; Amylin Pharmaceuticals, Eli Lilly and Company, Novo Nordisk Inc., Sanofi-Aventis, PhaseBio Pharmaceuticals, Inc., Roche Diabetes Care Health and Digital Solutions, AbbVie Inc., Vascular Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., GlaxoSmithKline plc., Pfizer Inc.. Other Relationship; Self; Novartis Pharmaceuticals Corporation, Merck & Co., Inc..

Haemoglobin A1c variability as an independent correlate of atherosclerosis and cardiovascular disease in Chinese type 2 diabetes.

To examine the association between haemoglobin A1c variability and macrovascular complication in type 2 diabetes. We retrospectively enrolled 5278 diabetes patients with no history of cardiovascular disease and atherosclerosis by ultrasound at their first visit to the hospital from 1999 to 2010. Patients had a median of 4 haemoglobin A1c (range = 3-9) measurements during follow-up. Average haemoglobin A1c and haemoglobin A1c variability were calculated as intra-individual mean, standard deviation, coefficient of variation and adjusted standard deviation. Cardiovascular disease events and ultrasound results were re-evaluated from the medical history at the end of the study. A total of 972 patients had macrovascular complication. Compared to those without atherosclerosis/cardiovascular disease (n = 4306), haemoglobin A1c intra-individual mean and haemoglobin A1c variability levels were significantly higher in patients with macrovascular complication ( p < 0.001). Multivariable logistic regression analysis showed that haemoglobin A1c variability was associated with macrovascular complication. Moreover, 488 patients with only atherosclerosis had significantly higher haemoglobin A1c intra-individual mean and haemoglobin A1c variability values than those without atherosclerosis/cardiovascular disease ( p < 0.001), but in 484 patients with cardiovascular disease incidents, only higher haemoglobin A1c intra-individual mean level was found ( p = 0.004). In Chinese type 2 diabetes, haemoglobin A1c variability was associated with macrovascular complication. Long-term stabilization of glucose is important in diabetes management, especially in the early stage of atherosclerosis.

Haemoglobin A1c variability is a strong, independent predictor of all-cause mortality in patients with type 2 diabetes.

To evaluate various measures of haemoglobin (Hb) A1c variability, compared with average HbA1c, as independent predictors of mortality. The Renal Insufficiency And Cardiovascular Events Italian multicentre study enroled 15 733 patients with type 2 diabetes from 19 diabetes clinics during 2006-2008. A total of 3 to 5 HbA1c measures, obtained during the 2-year period before enrolment, were available from 9 centres (8290 patients) and were used to calculate average HbA1c (HbA1c -MEAN) and HbA1c variability, measured as intra-individual standard deviation (HbA1c-SD), SD adjusted for the number of HbA1c assessments (HbA1c-AdjSD) and coefficient of variation (HbA1c-CV), that is, the HbA1c-SD to HbA1c-MEAN ratio. Vital status on October 31, 2015 was retrieved for 8252 patients (99.5%). The measures of HbA1c variability increased according to quartiles of HbA1c-MEAN and vice versa. HbA1c-MEAN and measures of HbA1c variability were associated with all-cause mortality; however, the strength of association of HbA1c-MEAN was lower than that of HbA1c -SD, HbA1c-CV or HbA1c-AdjSD, and disappeared after adjusting for confounders and any of the measures of HbA1c variability. Mortality increased with quartiles of HbA1c-MEAN, HbA1c -SD, HbA1c-CV and HbA1c-AdjSD, but only the association with HbA1c variability measures remained after adjustment for confounders and/or each other measure. In the fully adjusted model, mortality risk was lower for HbA1c-SD below the median and higher for HbA1c-SD above the median, regardless of whether HbA1c-MEAN was below or above the median. Conclusions HbA1c variability is a strong, independent predictor of all-cause mortality in type 2 diabetes and appears to be even more powerful than average HbA1c in predicting mortality.

Cardiovascular Protection in People With Diabetes.

Cardiovascular Protection in People With Diabetes.

Telemedicine in the Management of Type 1 Diabetes.

BackgroundVeterans with type 1 diabetes who live in rural Alabama and Georgia face barriers to receiving specialty diabetes care because of a lack of endocrinologists in the Central Alabama Veterans Health Care System. Telemedicine is a promising solution to help increase access to needed health care. We evaluated telemedicine’s effectiveness in delivering endocrinology care from Atlanta-based endocrinologists.MethodsWe conducted a retrospective chart review of patients who were enrolled in the Atlanta VAMC Endocrinology Telehealth Clinic from June 2014 to October 2016. Outcomes of interest were hemoglobin A1c levels, changes in glycemic control, time savings for patients, cost savings for the US Veterans Health Administration, appointment adherence rates, and patient satisfaction with telehealth.ResultsThirty-two patients with type 1 diabetes received telehealth care and in general received the recommended processes of diabetes care. Patients trended toward a decrease in mean hemoglobin A1c and glucose variability and a nonsignificant increase in hypoglycemic episodes. Patients saved 78 minutes of travel time (one way), and the VA saved $72.94 in travel reimbursements per patient visit. Patients adhered to 88% of scheduled telehealth appointments on average, and 100% of surveyed patients stated they would recommend telehealth to other veterans.ConclusionsSpecialty diabetes care delivered via telemedicine was safe and was associated with time savings, cost savings, high appointment adherence rates, and high patient satisfaction. Our findings support growing evidence that telemedicine is an effective alternative method of health care delivery.

Association of hemoglobin A1c variability and the incidence of heart failure with preserved ejection fraction in patients with type 2 diabetes mellitus and arterial hypertension

BackgroundThis study aimed to investigate the association between variability in hemoglobin A1c (HbA1c) and left ventricular (LV) diastolic function and incidence of symptomatic heart failure with preserved ejection fraction (HFpEF) in patients with type 2 diabetes mellitus (T2DM) and arterial hypertension. MethodsA retrospective cohort study was conducted on eligible patients with T2DM and hypertension and without clinical signs or symptoms of heart failure in our hospital medical record database. Variability in HbA1c was measured by standard deviation (SD) and coefficient of variation (CV). Risk of new onset of symptomatic HFpEF was evaluated by multivariable cox regression analysis. ResultsA total of 201 subjects were categorized according to the median value of HbA1c variability indicators. Over a median follow-up of 7.3 years, the percentage of subjects who had new incidence of symptomatic HFpEF was higher in those with higher HbA1c-SD level (5/105 vs. 13/96, P=0.029) and higher HbA1c-CV level (5/101 vs. 13/100, P=0.046). Multivariable cox regression analysis also showed that higher HbA1c variability [HbA1c-SD (HR 1.754, 95% CI: 1.003-3.104, P=0.049) or HbA1c-CV (HR 1.604 95% CI: 1.064-2.419, P=0.024)] was associated with the presence of new-onset symptomatic HFpEF, independent of HbA1c-mean level, in T2DM and hypertensive patients. Moreover, lower HbA1c variability generated more prominent improvement in LV diastolic function (E/E′) during the follow-up. ConclusionsHbA1c variability may provide additional valuable information as a potential predictor of the progression of HFpEF in T2DM and hypertensive patients.

Hemoglobin A1c Variability Predicts Symptoms of Depression in Elderly Individuals With Type 2 Diabetes.

OBJECTIVEThis study aimed to analyze the relationship of variability in hemoglobin A1c (HbA1c) over years with subsequent depressive symptoms.RESEARCH DESIGN AND METHODSSubjects (n = 837) were participants of the Israel Diabetes and Cognitive Decline (IDCD) study, which aimed to examine the relationship of characteristics of long-term type 2 diabetes with cognitive decline. All pertain to a diabetes registry established in 1998, which contains an average of 18 HbA1c measurements per subject. The results presented here are based on the IDCD baseline examination. Symptoms of depression were assessed using the 15-item version of the Geriatric Depression Scale (GDS). To quantify the association between variability in glycemic control (measured as the SD of HbA1c measurements [HbA1c-SD]) since 1998 with the number of depression symptoms at IDCD baseline, incidence rate ratios (IRRs) and corresponding 95% CIs were estimated via negative binomial regression modeling and used to account for the overdispersion in GDS scores.RESULTSSubjects’ ages averaged 72.74 years (SD 4.63 years), and the mean number of years in the diabetes registry was 8.7 (SD 2.64 years). The mean GDS score was 2.16 (SD 2.26); 10% of subjects had a GDS score ≥6, the cutoff for clinically significant depression. Mean HbA1c significantly correlated with HbA1c-SD (r = 0.6625; P < 0.0001). The SD, but not the mean, of HbA1c measurements was significantly associated with the number of subsequent depressive symptoms. For each additional 1% increase in HbA1c-SD, the number of depressive symptoms increased by a factor of 1.31 (IRR = 1.31 [95% CI 1.03–1.67]; P = 0.03).CONCLUSIONSVariability in glycemic control is associated with more depressive symptoms.

The Association Between Preoperative Hemoglobin A1C and Postoperative Glycemic Variability on 30-Day Major Adverse Outcomes Following Isolated Cardiac Valvular Surgery.

Preoperative hemoglobin A1c (HbA1c) and postoperative glycemic variability predict major adverse events (MAEs) after coronary artery bypass grafting in a protocolized glycemic control setting. However, the influence of preoperative HbA1c and postoperative glycemic variability in isolated cardiac valvular surgery is unknown. In this study, we sought to establish (a) whether preoperative HbA1c could identify patients at increased risk of MAEs and (b) whether postoperative glycemic variability was associated with MAEs after isolated cardiac valvular surgery. Patients >18 years of age undergoing isolated valve surgery from January 2008 to December 2013 were enrolled in this prospective, single-center, observational cohort study with IRB approval. Patient demographics, intraoperative data, and postoperative MAEs were extracted from the institutional Society of Thoracic Surgery (STS) database. The primary outcome, MAEs, was a composite of in-hospital death, myocardial infarction, reoperations, sternal infection, cardiac tamponade, pneumonia, stroke, or renal failure. Glycemic variability in the postoperative period was assessed by the coefficient of variation. Patents were stratified by HbA1c levels (<6.5% or ≥6.5%) and assessed using multivariable logistic regression. Of the enrolled 763 patients, 109 (14.3%) had a preoperative HbA1c level ≥6.5%. Patients with HbA1c ≥6.5% were older (70 [63-79] vs 66 [56-75], P < .001) and had a higher incidence of dyslipidemia (83.5% vs 57.0%, P < .001) and congestive heart failure (39.5% vs 27.8%, P = .01). The calculated STS risk score for morbidity and mortality was also statistically higher in this group (0.18 [0.13-0.27] vs 0.13 [0.09-0.21], P < .001). The occurrence of MAEs was similar between the 2 groups (13.8% in HbA1c ≥6.5% vs 11.0% in HbA1c <6.5%, P = .40). Multivariate logistic regression analysis revealed that neither preoperative HbA1c ≥ 6.5% (odds ratio [OR] 1.48, 95% confidence interval [CI]: 0.78-2.82; P = .23) nor postoperative glycemic variability (CV per quartile; OR 1.05, 95% CI: 0.85-1.30; P = .67) was found to be associated with MAEs. An HbA1c ≥ 6.5% was associated with the increased glycemic variability in the postoperative period (0.173 [0.129-0.217] vs 0.141 [0.106-0.178], P < .0001). This study did not show an association between preoperative HbA1c and postoperative glycemic variability with MAEs after isolated cardiac valvular surgery. Specifically, lack of association between postoperative glycemic variability and MAEs is noteworthy and is in contrast to our previous finding in CABG patients. Future studies should focus a targeted glycemic variability reduction in CABG patients and evaluate the reduction in MAEs, without risk of employing a one-size fits all approach when approaching other cardiac procedures.

Impact of long-term glycemic variability on development of atrial fibrillation in type 2 diabetic patients.

Objective:It is well known that patients with type 2 diabetes mellitus (T2DM) have a high risk of atrial fibrillation (AF). The current study was designed to determine the relationship between long-term glycemic variability and incidence of new-onset AF in T2DM patients.Methods:Between January 2008 and December 2009, we conducted a retrospective cohort study in patients with T2DM referred to our hospital. In 505 consecutive patients without any medical history of AF at baseline, the relationship between hemoglobin A1c (HbA1c) variability and future AF incidence was evaluated, with adjustments for other possible confounding factors. HbA1c variability was determined by standard deviation (SD) and coefficient of variation (CV).Results:Over a median of 6.9-year follow-up period, 48 patients (9.5%) developed incident AF. Multiple cox regression revealed that higher HbA1c-SD (HR: 1.726, 95% CI: 1.104–1.830, p=0.001) or HbA1c-CV (HR: 1.241, 95% CI: 1.029–1.497, p=0.024) remained the remarkable predictor of new-onset AF after adjusting for age, body mass index, left ventricular mass index, and left atrium diameter. Receiver operating curve analysis identified thresholds for HbA1c-SD (0.665%, sensitivity 71.4%, specificity 54.9%) and HbA1c-CV (8.970%, sensitivity 73.8%, specificity 47.1%) to detect new-onset AF development.Conclusion:In patients with T2DM, higher HbA1c variability is significantly associated with future AF development.

Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in Chinese patients with type 2 diabetes mellitus — A retrospective population-based cohort study

AimsThis study aimed to investigate the association between variability in HbA1c and incidence of cardiovascular disease (CVD) event and mortality among Chinese primary care patients with Type 2 diabetes mellitus (T2DM). MethodsA retrospective cohort study was conducted on 91,866 T2DM patients aged ≥18years without any history of CVD. Variability in HbA1c, was measured by standard deviation (SD), associated with the risks of CVD and all-cause mortality were evaluated using Cox proportional hazards regression analysis by age groups ( 65 and ≥ 65 years old)." to "Variability in HbA1c was measured by standard deviation (SD) The association between Variability in HbA1c and the incidence of CVD and all-cause mortality were evaluated using Cox proportional hazards regression analysis by age groups ( 65 and ≥ 65 years old). ResultsOver a median follow-up of 58.5months, our study identified a positive linear relationship between variability in HbA1c and incidence of CVD and all-cause mortality in the younger and older groups. For every 1-SD increase in HbA1c, the risk of CVD events in the older group only increased by 15.2% (95% CI: 1.026–1.293), and the risks of all-cause mortality in both age groups increased by 49.5% (95% CI: 1.154–1.936) and 77.8% (95% CI: 1.563–2.024), respectively. ConclusionsThe HbA1c variability independently of the mean HbA1c level may provide additional valuable information as a potential predictor for the development of CVD and all-cause mortality in diabetic patients, particularly for the elderly patients.

Abstracts from ATTD 2016 9th International Conference on Advanced Technologies & Treatments for Diabetes Milan, Italy-February 3-6, 2016.

Abstracts from ATTD 2016 9th International Conference on Advanced Technologies & Treatments for Diabetes Milan, Italy-February 3-6, 2016.

High hemoglobin A1c variability is associated with early risk of microalbuminuria in children with T1D.

To test the hypothesis that HbA1c variability, as measured by standard deviation (SD), is associated with increased risk for incident microalbuminuria and persistent microalbuminuria in pediatric type 1 diabetes (T1D). A retrospective analysis using data from electronic health records was performed on 1195 patients from a pediatric diabetes clinic network in the Midwest USA from 1993 to 2009 with ≥1 yr of T1D, ≥4 total HbA1c values, ≥2 HbA1c values/yr, ≥1 urine microalbumin. Microalbuminuria, the main outcome was defined as albumin excretion rate ≥20 mcg/min or 2 of 3 consecutive urine microalbumin/creatinine ≥30 mg/gm. Patients who had persistently high microalbumin or who were treated with an angiotensin-converting-enzyme inhibitor within 1 yr were considered to have persistent microalbuminuria. Sex, race, age, diagnosis age, and duration were covariates. Median numbers of per-patient HbA1c and microalbumin results were 14 and 3, respectively. Median intrapersonal mean HbA1c and SD were 8.62% (70.72 mol/mol) and 1.47% (16.07 mmol/mol), respectively. The median interquartile range (IQR) of diagnosis age was 9.4 yr (6.26-12.02) and diabetes duration was 4.97 yr (2.93-7.64). A total of 172 patients (14.4%) developed microalbuminuria; 55 (4.6%) had persistent microalbuminuria. Patients with higher SD of HbA1c had shorter time to microalbuminuria. In time-dependent Cox Proportional Hazard models, updated SD of HbA1c was significantly associated with microalbuminuria [univariate hazard ratio (HR) 1.48 (1.25-1.76); multivariable HR 1.28 (1.04-1.58)], whereas updated mean HbA1c was not [univariate HR 1.08 (0.97-1.22); multivariable HR 1.05 (0.92-1.2)]. Patients with persistent microalbuminuria had similar HRs. HbA1c variability is independently associated with development of microalbuminuria in children with T1D, highlighting the importance of maintaining stable glycemic control in pediatric patients.

Association between hemoglobin A1c variability and subclinical coronary atherosclerosis in subjects with type 2 diabetes

AimsWe examined the association between hemoglobin A1c (HbA1c) variability and subclinical coronary atherosclerosis in subjects with type 2 diabetes. MethodsWe used the multidetector coronary computed tomography data collected from subjects with type 2 diabetes who did not have a history of cardiovascular disease or angina symptoms. HbA1c measurements preceding the date of cardiac imaging were retrospectively collected, and intraindividual SD (HbA1c-SD), CV and adjusted SD of HbA1c measurements were calculated. Subclinical coronary atherosclerosis was defined as calcium score >400 without any cardiac symptoms. ResultsA total of 595 subjects were categorized according to the median value of each HbA1c variability indicators. The prevalence of subclinical coronary atherosclerosis was higher in higher HbA1c variability group compared with lower HbA1c variability group. Multivariable logistic regression analysis showed that higher HbA1c-SD and -CV were associated with the presence of subclinical coronary atherosclerosis, independent of mean HbA1c level in subjects with diabetes duration ≤10years (OR [95% CI]; HbA1c-SD, 2.894 [1.105–7.584]; HbA1c-CV, 2.540 [1.022–6.316]). ConclusionsLong-term stabilization of blood glucose level might be important in preventing subclinical coronary atherosclerosis in subjects with earlier period of type 2 diabetes.

Association of visit-to-visit variability of hemoglobin A1c and medication adherence.

Medication nonadherence is widespread, but there are few efficient means of detecting medication nonadherence at the point of care. Visit-to-visit variability in clinical biomarkers has shown inconsistent efficiency to predict medication adherence. To examine the performance of visit-to-visit variability (VVV) of hemoglobin A1c to predict nonadherence to antidiabetic medications. In this cross-sectional study using a clinical and administrative database, adult members of a managed care plan at a safety-net medical center from 2008 to 2012 were included if they had ≥ 3 noninsulin antidiabetic prescription fills within the same class and ≥ 3 A1c measurements between the first and last prescription fills. The independent variable was VVV of A1c (within-subject standard deviation of A1c), and the dependent variable was medication adherence (defined by medication possession ratio) determined from pharmacy claims. Unadjusted and adjusted multivariate logistic regression models were created to examine the relationship between VVV of A1c and medication nonadherence. Receiver-operating characteristic (ROC) curves assessed the performance of the adjusted model at discriminating adherence from nonadherence. Among 632 eligible subjects, mean A1c was 7.7% ± 1.3%, and 83% of the sample was nonadherent to antidiabetic medications. Increasing quintiles of VVV of A1c and medication nonadherence were both associated with increased within-subject mean A1c and younger subject age. The logistic regression model (adjusted for age, sex, race/ethnicity, within-subject mean A1c, number of A1c measurements, number of days between the first and last antidiabetic medication prescription fills, and rate of primary care visits during the study period) showed a nonsignificant association of VVV of A1c and medication nonadherence (OR = 1.19, 95% CI = 0.42-3.38 for the highest quintile of VVV). Adding VVV of A1c to a model including age, sex, and race only modestly improved the C-statistic of the ROC curve from 0.6786 to 0.7064. VVV of A1c is not a robust predictor of antidiabetic medication nonadherence. Further innovation is needed to develop novel methods of detecting nonadherence.

Clinical Implications of Glucose Variability: Chronic Complications of Diabetes.

Glucose variability has been identified as a potential risk factor for diabetic complications; oxidative stress is widely regarded as the mechanism by which glycemic variability induces diabetic complications. However, there remains no generally accepted gold standard for assessing glucose variability. Representative indices for measuring intraday variability include calculation of the standard deviation along with the mean amplitude of glycemic excursions (MAGE). MAGE is used to measure major intraday excursions and is easily measured using continuous glucose monitoring systems. Despite a lack of randomized controlled trials, recent clinical data suggest that long-term glycemic variability, as determined by variability in hemoglobin A1c, may contribute to the development of microvascular complications. Intraday glycemic variability is also suggested to accelerate coronary artery disease in high-risk patients.

¿Qué podemos esperar del control de la variabilidad glucémica?

Some aspects of glycemic variability are still not clear. Questions like the role that «short term glycemic variability» could play in the development of diabetic complications is still unanswered. On the other hand, A1C variability, or «long term glycemic variability», emerges as one of the most relevant actors in development and progression of the chronic vascular complications of diabetes. Furthermore, glycemic variability plays a central role in causing dangerous and unnoticed hypoglycemia, and it becomes the limiting factor to achieve an optimal regulation of diabetes. In this review, the most important aspects of glycemic variability are discussed, as well as clarifying, as far as is possible, what we should expect from its control. Some recommendations are also offered on how to evaluate it properly.

Hemoglobin A1c assay variations and implications for diabetes screening in obese youth

Standardization of the hemoglobin A1c (A1c) assay has led to its increasing utilization as a screening tool for the diagnosis of prediabetes and type 2 diabetes in youth. However, significant A1c assay variability remains and has implications for clinical management. To describe our center's experiences with A1c results in youth and to evaluate inter-method differences and their clinical implications. Seventy-five youth (aged 10-18 yr old), body mass index (BMI) ≥85th‰ participated. Seventy-two participants had two A1c values performed on the same sample, one via immunoassay (DCA Vantage Analyzer, A1c1 ) and the other via high performance liquid chromatography (Bio-Rad Variant II, A1c2 ). Nineteen had A1c run on two immunoassay devices (A1c1 and Dimensions Vista, A1c3 ). Mean age of participants was 13.9 years, BMI% 97.89%, 33% male, 16% white, 21% black, and 61% Hispanic (H). Mean A1c1 was 5.68% ± 0.38 vs. a mean A1c2 of 5.73% ± 0.39, p = 0.049. Concordance in diabetes status between methods was achieved in 79% of subjects. Nineteen subjects with A1c3 results had testing performed an average of 22 ± 9 days prior to A1c1 . Mean A1c3 was 6.24% ± 0.4, compared to a mean A1c1 of 5.74% ± 0.31, (p < 0.0001). A1c1 was on average systematically -0.5 ± 0.28 lower compared to A1c3 . There was poor agreement in diabetes classification between A1c1 and A1c3 , with a concordance in classification between methods of only 36.8%. Clinically significant inter-method A1c variability exists that impacts patient classification and treatment recommendations. In the screening of obese youth for diabetes, A1c results should be interpreted with caution.

HbA1c Variability as an Independent Correlate of Nephropathy, but Not Retinopathy, in Patients With Type 2 Diabetes

OBJECTIVETo examine the association of hemoglobin (Hb) A1c variability with microvascular complications in the large cohort of subjects with type 2 diabetes from the Renal Insufficiency And Cardiovascular Events (RIACE) Italian Multicenter Study.RESEARCH DESIGN AND METHODSSerial (3–5) HbA1c values collected in a 2-year period before enrollment were available from 8,260 subjects from 9 centers (of 15,773 patients from 19 centers). HbA1c variability was measured as the intraindividual SD of 4.52 ± 0.76 values. Diabetic retinopathy (DR) was assessed by dilated funduscopy. Chronic kidney disease (CKD) was defined based on albuminuria, as measured by immunonephelometry or immunoturbidimetry, and estimated glomerular filtration rate (eGFR) was calculated from serum creatinine.RESULTSMedian and interquartile range of average HbA1c (HbA1c-MEAN) and HbA1c-SD were 7.57% (6.86–8.38) and 0.46% (0.29–0.74), respectively. The highest prevalence of microalbuminuria, macroalbuminuria, reduced eGFR, albuminuric CKD phenotypes, and advanced DR was observed when both HbA1c parameters were above the median and the lowest when both were below the median. Logistic regression analyses showed that HbA1c-SD adds to HbA1c-MEAN as an independent correlate of microalbuminuria and stages 1–2 CKD and is an independent predictor of macroalbuminuria, reduced eGFR, and stages 3–5 albuminuric CKD, whereas HbA1c-MEAN is not. The opposite was found for DR, whereas neither HbA1c-MEAN nor HbA1c-SD affected nonalbuminuric CKD.CONCLUSIONSIn patients with type 2 diabetes, HbA1c variability affects (albuminuric) CKD more than average HbA1c, whereas only the latter parameter affects DR, thus suggesting a variable effect of these measures on microvascular complications.

Glucose Variability Assessed by Low Blood Glucose Index Is Predictive of Hypoglycemic Events in Patients With Type 1 Diabetes Switched to Pump Therapy

OBJECTIVETo determine whether subgroups of type 1 diabetic patients with different glucose variability indices respond differently to continuous subcutaneous insulin infusion (CSII) in terms of reduced hypoglycemic events.RESEARCH DESIGN AND METHODSWe studied 50 adults with long-standing type 1 diabetes switched to CSII because of persistently high A1C or frequent hypoglycemia despite well-managed intensive basal-bolus therapy. We compared A1C, hypoglycemic events, and glucose variability from self-monitoring of blood glucose profiles at baseline and after 6 months of CSII. Regression analysis was performed to identify predictors of response.RESULTSIn multivariate analysis, baseline low blood glucose index (LBGI) was the best independent predictor of hypoglycemia outcome on CSII (R2 = 0.195, P = 0.0013). An ROC curve analysis demonstrated a sensitivity of 70.8% (95% CI 48.9–87.4) and specificity of 73.1% (52.2–88.4) by using the LBGI cutoff of 3.34 as predictor of reduction of hypoglycemia on CSII. By grouping patients by LBGI tertiles, we found a 23.3% reduction in hypoglycemic events (<60 mg/dL [3.3 mmol/L]) in the third tertile (range 4.18–9.34) without change in A1C (P < 0.05). Conversely, the first tertile (range 0.62–2.05) demonstrated the greatest A1C reduction, −0.99% (P = 0.00001), but with increasing hypoglycemia.CONCLUSIONSBaseline LBGI predicts the outcome of type 1 diabetic patients who switch to CSII in terms of hypoglycemia.