Association of self-monitoring blood glucose profiles with glycosylated hemoglobin in patients with insulin-dependent diabetes

Patient and Healthcare Professionals Perspectives on the Delivery of Exercise Education for Patients With Type 1 Diabetes

Objective: One way of improving the prognosis for the growing numbers of people with type 1 diabetes (T1D) is to increase their frequency of exercise. One known barrier to this is the lack of cohesive support and information from care providers. To better understand the issues around existing support for patients wishing to exercise and inform the design of an education package specifically to facilitate safe exercise we interviewed care providers and patients about the existing provision of support. Research Design and Methods: The study was based within two large UK teaching hospitals where four focus groups were undertaken two consisting of patients diagnosed with T1D who undertook regular exercise, and two with health care providers (HCPs) that were part of the diabetes care team. In all 14 patients and 11 staff were involved. These were complemented by two 1:1 interviews with staff unable to attend group discussions. Results: We found the successful provision of education and advice was influenced by factors relating to the individual patient and their service provider. Patient factors included the type of activity and complexity of the exercise regime, the level of engagement with their condition and care and health literacy. Service-related factors included inconsistent training, a lack of capacity and continuity, and limited coherence of information from across their care team. Conclusions: Any education package developed to support exercise in patients with type 1 diabetes should be offered at a time following diagnosis in accordance with patients' preferences and priorities, contain information on how to manage regular and irregular bouts of exercise. Patients described how they related more closely to the stories of their peers than famous sports stars and one way this can be facilitated is by group delivery. The content and relevance of any supporting materials should be closely considered. Training in the delivery of a novel education package should be made available to staff across the care team to enable them to either deliver the course or increase their confidence in offering salient advice as part of routine care.

Steady-state relationship between average glucose, HbA1c and RBC lifespan

HbA1c is used to estimate average glucose. Previous studies showed linear relationship between average glucose and HbA1c. We made a new theoretical relationship using recently proposed Γ-like function model of erythrocyte lifespan. We showed the relationship between average glucose and HbA1c; we approximated it into a simple hyperbolic function: HbA1c=M_RBCk_gAG/(1+(2/3)M_RBCk_gAG), whose inverse function is easily obtained. Apparent linear relationship is an approximation of the curved relationship. Hyperbolic function would provide a more accurate approximation than a linear equation. Physicians should keep in mind the curved relationship and be aware that extremely high HbA1c indicates acceleratingly high glucose level.

Glycated Hemoglobin, Plasma Glucose, and Erythrocyte Aging

BACKGROUND

The relationship between HbA1c and blood glucose averages has been characterized many times, yet, a unifying, mechanistic description is still lacking.

METHODS

We calculated the level of HbA1c from plasma glucose averages based solely on the in vivo rate of hemoglobin glycation, and the different turnover rates for erythrocytes of different ages. These calculations were then compared to the measured change of HbA1c due to changes in mean blood glucose (MBG), to complex models in the literature, and our own experiments.

RESULTS

Analysis of data on erythrocyte ageing patterns revealed that 2 separate RBC turnover mechanisms seem to be present. We calculated the mean red blood cell (RBC) life span within individuals to lie between 60 and 95 days. Comparison of expected HbA1c levels to data taken from continuous glucose monitors and finger-stick MBG yielded good agreement (r = .87, P < .0001). Experiments on the change with time of HbA1c induced by a change of MBG were in excellent agreement with our calculations (r = .98, P < .0001).

CONCLUSIONS

RBC turnover seems to be dominated by a constant rate of cell loss, and a mechanism that targets cells of a specific age. Average RBC life span is 80 ± 10.9 days. Of HbA1c change toward treatment goal value, 50% is reached in about 30 days. Many factors contribute to the ratio of glycated hemoglobin, yet we can make accurate estimations considering only the in vivo glycation constant, MBG, and the age distribution of erythrocytes.

Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes

Glucose control, glucose variability (GV), and risk for hypoglycemia are intimately related, and it is now evident that GV is important in both the physiology and pathophysiology of diabetes. However, its quantitative assessment is complex because blood glucose (BG) fluctuations are characterized by both amplitude and timing. Additional numerical complications arise from the asymmetry of the BG scale. In this Perspective, we focus on the acute manifestations of GV, particularly on hypoglycemia, and review measures assessing the amplitude of GV from routine self-monitored BG data, as well as its timing from continuous glucose monitoring (CGM) data. With availability of CGM, the latter is not only possible but also a requirement-we can now assess rapid glucose fluctuations in real time and relate their speed and magnitude to clinically relevant outcomes. Our primary message is that diabetes control is all about optimization and balance between two key markers-frequency of hypoglycemia and HbA1c reflecting average BG and primarily driven by the extent of hyperglycemia. GV is a primary barrier to this optimization, including to automated technologies such as the "artificial pancreas." Thus, it is time to standardize GV measurement and thereby streamline the assessment of its two most important components-amplitude and timing.

Is there a relationship between mean blood glucose and glycated hemoglobin?

Measurement of hemoglobin A1c (HbA1c) is considered the gold standard for monitoring chronic glycemia of diabetes patients. Hemoglobin A1c indicates an average of blood glucose levels over the past 3 months. Its close association with the risk for the development of long-term complications is well established. However, HbA1c does not inform patients about blood glucose values on a daily basis; therefore, frequent measurements of blood glucose levels are necessary for the day-to-day management of diabetes. Clinicians understand what HbA1c means and how it relates to glucose, but this is not the case with patients. Therefore, the translation of the HbA1c results into something more familiar to patients seemed a necessity. The scope of this article is to review the literature to search for enough scientific evidence to support the idea of a close relationship between HbA1c and mean blood glucose (MBG), and to justify the translation of HbA1c into something that reflects the MBG. Most studies confirm a close relationship between HbA1c and MBG, although different studies result in different linear equations. Factors affecting this relationship may limit the usefulness and applicability of a unique mathematical equation to all diabetes populations.

Effect of automated bio-behavioral feedback on the control of type 1 diabetes

OBJECTIVE

To test the effect of an automated system providing real-time estimates of HbA(1c), glucose variability, and risk for hypoglycemia.

RESEARCH DESIGN AND METHODS

For 1 year, 120 adults with type 1 diabetes (69 female/51 male, age = 39.1 [14.3] years, duration of diabetes 20.3 [12.9] years, HbA(1c) = 8.0 [1.5]), performed self-monitoring of blood glucose (SMBG) and received feedback at three increasingly complex levels, each continuing for 3 months: level 1--routine SMBG; level 2--adding estimated HbA(1c), hypoglycemia risk, and glucose variability; and level 3--adding estimates of symptoms potentially related to hypoglycemia. The subjects were randomized to feedback sequences of either levels 1-2-3 or levels 2-3-1. HbA(1c), symptomatic hypoglycemia, and blood glucose awareness were evaluated at baseline and at the end of each level.

RESULTS

For all subjects, HbA(1c) was reduced from 8.0 to 7.6 from baseline to the end of study (P = 0.001). This effect was confined to subjects with baseline HbA(1c) >8.0 (from 9.3 to 8.5, P < 0.001). Incidence of symptomatic moderate/severe hypoglycemia was reduced from 5.72 to 3.74 episodes/person/month (P = 0.019), more prominently for subjects with a history of severe hypoglycemia (from 7.20 to 4.00 episodes, P = 0.008) and for those who were hypoglycemia unaware (from 6.44 to 3.71 episodes, P = 0.045). The subjects' ratings of the feedback were positive, with up to 89% approval of the provided features.

CONCLUSIONS

Feedback of SMBG data and summary SMBG-based measures resulted in improvement in average glycemic control and reduction in moderate/severe hypoglycemia. These effects were most prominent in subjects who were at highest risk at the baseline.

The effect of a low-glycemic diet vs a standard diet on blood glucose levels and macronutrient intake in children with type 1 diabetes

A low-glycemic index (GI) diet may lower postprandial hyperglycemia and decrease the risk for postabsorptive hypoglycemia in people with type 1 diabetes. However, insufficient evidence exists on the efficacy of a low-GI diet to support practice recommendations. The goal of this study was to examine the blood glucose response to and the macronutrient composition of low-GI meals vs usual meals consumed ad libitum at home in children with type 1 diabetes. A within-subject, crossover design was employed. Twenty-three participants were recruited between June and August 2006. Participants wore a continuous blood glucose monitoring system and completed diet diaries on 2 days. On 1 day, participants consumed their usual meal; on another day, participants consumed low-GI meals ad libidum. Order of the 2 days was counterbalanced. The mean GI was 34+/-6 for the low-GI day and 57+/-6 for the usual meal day (P<0.0001). During the low-GI day, mean daytime blood glucose values (125+/-28 mg/dL [6.9+/-1.5 nmol/L] vs 185+/-58 mg/dL [10.3+/-3.2 nmol/L], P<0.001), blood glucose area above 180 mg/dL (4,486+/-6,138 vs 26,707+/-25,038, P<0.006), and high blood glucose index (5.1+/-5.1 vs 13.6+/-7.6, P<0.001) were lower compared to the usual mean day. During the low-GI day, subjects consumed more fiber (24.5+/-12.3 g vs 14.5+/-6.1 g, P<0.007) and less fat (45.7+/-12.2 g vs 76.8+/-32.4 g, P<0.005); however, there were no differences in energy, carbohydrate, or protein intake. In this pilot study, a low-GI diet was associated with improved diet quality and a reduction in hyperglycemia.

HbA1c during pregnancy: Its relationship to meal related glycaemia and neonatal birth weight in patients with diabetes

OBJECTIVE

Although home blood glucose (HBG) profiles correlate closely with HbA1c, the strength of the relationship during pregnancy is unclear due to physiological changes which can induce subnormal HbA1c levels. We therefore aimed to establish the strength of the association between mean HBG profiles and HbA1c in diabetic pregnancies and whether HbA1c levels and glycaemic variability affects neonatal birth weight (NBW).

STUDY DESIGN

7-point glycaemic profiles performed throughout pregnancy were obtained retrospectively in 94 consecutive patients attending the diabetes antenatal clinic and compared to the corresponding mean HbA1c levels.

RESULTS

There was a significant linear correlation between mean HBG and HbA1c (HbA1c=0.5HBG+3.1, r=0.71, p<0.0001). Multiple regression analysis demonstrated that both pre- and post-prandial HBG levels correlated significantly and independently with HbA1c, correlation coefficients (r) were 0.63 and 0.65, respectively both p<0.0001. Significant correlations were also observed in patients with gestational diabetes (n=67, mean HbA1c=6.11, r=0.67; p<0.0001) and type 1 diabetes (n=18, mean HbA1c=6.75, r=0.64; p=0.004). All meal related HBG measurements showed similar significant correlations with HbA1c (r values pre- and post-breakfast, pre- and post-lunch, pre- and post-tea and pre-bed are 0.56, 0.55, 0.59, 0.55, 0.56, 0.59, 0.51, respectively p<0.0001 for all time points). Post hoc analysis showed that NBW increased with higher levels of HbA1c; NBW (centiles)+/-S.D. for HbA1c <6.5% versus >6.5% was 78.9%+/-29.2 versus 90.2%+/-18.6, p=0.02.

CONCLUSION

Mean HbA1c levels are closely correlated to all meal related glucose measurements during pregnancy. It is therefore a reliable indicator of overall glycaemic control among patients with diabetes during pregnancy.

Effect of varying glycemic index meals on blood glucose control assessed with continuous glucose monitoring in youth with type 1 diabetes on basal-bolus insulin regimens

OBJECTIVE

The purpose of this study was to test the effect of high glycemic index (HGI) and low glycemic index (LGI) meals on blood glucose levels using continuous blood glucose monitoring in youths with type 1 diabetes.

RESEARCH DESIGN AND METHODS

A total of 20 youths on basal-bolus regimens consumed macronutrient-matched HGI and LGI meals 1 day each in a controlled setting in varying order following consumption of a standardized evening meal. Medtronic MiniMed Continuous Glucose Monitoring Systems were used to assess blood glucose (BG) profiles.

RESULTS

Participants demonstrated significantly lower daytime mean BG, BG area >180 mg/dl, and high BG index when consuming LGI meals but no differences for daytime BG area <70 mg/dl, daytime low BG index, or any nighttime values. Significantly more BG values <80 mg/dl were treated on LGI days.

CONCLUSIONS

Findings indicate that consumption of an LGI diet may reduce glucose excursions, improving glycemic control.

Is glycemic variability important to assessing antidiabetes therapies?

Traditionally, statistical estimation of glycemic variability includes computing standard deviation of glucose readings or related statistics (eg, M value, mean amplitude of glucose excursions, and so forth). We advocate an alternative approach using risk measures of variability, which have substantial clinical and numerical advantages. In addition, continuous glucose monitoring (CGM) data have clinically important inherent temporal structure that should be taken into consideration. Thus, temporal variability methods are discussed for the analysis and interpretation of CGM output.

Assessment of the relationship between glucose and A1c using kinetic modeling

Treatment goals for diabetic patients are directed towards lowering A1c values by controlling blood glucose concentrations (BGC), making it important to understand the relationship between the two parameters. Because findings from clinical trials about the relationship between BGC and A1c values show a profound variability around the obtained regression lines, they are difficult to apply to individual patients. Therefore, a model was developed and applied based on the kinetics of HbA1c formation and removal. It takes the instability of A1c and loss of hemoglobin into consideration. Data from clinical studies and hypothetical scenarios were used to test the model and to describe the relationship between A1c and BGC. A close agreement between experimental and calculated data was obtained in steady-state and non-steady-state conditions. Aside the erythrocyte life span, the chemical instability of A1c appears to affect A1c levels markedly and their changes due to therapy. A threefold increase in BGC over 30 days prior to A1c measurement can cause an increase in A1c value of about 120% as compared with 4% when it occurs 4 months prior to A1c measurement. Profound daily fluctuations in BGC result in minor changes in A1c. In conclusion, A1c provides information about a patient's glycemia, mainly over the past 2 months, and may not reflect well daily blood glucose fluctuations. This model might be suitable to identify individual differences in glycation rates.

Contributions of Weekly Mean Blood Glucose Values to Hemoglobin A1c in Insulin-Treated Type 2 Diabetes: The Diabetes Outcomes in Veterans Study (DOVES)

BACKGROUND

Daily self-monitored blood glucose testing is recommended for patients with insulin-treated type 2 diabetes. However, most patients do not test frequently enough for optimal glycemic control. Less frequent testing may be sufficient for assessing glycemic control among stable patients as well as improving patient compliance. The study objective was to evaluate the weekly contribution of glucose readings to hemoglobin (Hb)A1c during an 8-week period of intensified self-monitored blood glucose testing.

METHODS

The authors randomly selected stable, insulin-treated subjects with type 2 diabetes. Subjects monitored their blood glucose four times daily for 8 weeks; the authors then downloaded glucose meters and measured an HbA1c. Mean blood glucose values were calculated for each of the 8 weeks. Multiple linear regression analyses examined the contribution of these mean values to the HbA1c.

RESULTS

A total of 182 subjects completed the monitoring protocol; mean HbA1c was 7.63 +/- 1.42%, mean glucose was 9.78 +/- 2.27 mmol/L, the regression correlation was 0.77, P < 0.001. A fitted multiple linear model using all 8 weekly mean blood glucose values showed large variation in their independent contributions to the HbA1c. Mean blood glucose values from consecutive weeks were highly correlated and did not provide independent information about glycemic control. Stepwise regression showed that the mean blood glucose values from weeks 4, 6, and 8 significantly and equally influenced HbA1c.

CONCLUSIONS

Glycemic control can be efficiently assessed by reviewing at least 5 weeks' worth of monitoring results, focusing on alternate weeks and giving less weight to more remote readings.