Shortfalls of the use of HbA1C-derived eAG in older adults with diabetes

Effectiveness of dapagliflozin as an insulin adjunct in type 1 diabetes: a semi-mechanistic exposure-response model

Introduction: Dapagliflozin-induced improvement of glycemic control in patients with inadequately controlled type 1 diabetes (T1D) is complicated by the delicate balance between blood glucose and exogenous insulin. In this work, we developed a semi-mechanistic population exposure-response model using pooled patient-level data to characterize the joint effect of dapagliflozin and insulin on average daily glucose concentrations and glycated hemoglobin (HbA1c) levels in patients with T1D. Methods: A non-linear mixed-effects model was developed in Monolix (Lixoft, France) and R software (R Project, www.r-project.org) using pooled patient-level data from phase 2 and phase 3 trials (NCT01498185, NCT02460978, NCT02268214). Results: Because of the apparent lack of association between bolus insulin dose and glucose concentrations measured by continuous glucose monitoring the model was able to capture the quantitative link between basal, but not bolus, insulin dose and plasma glucose. Even so, this association remained flat, with a 50% decrease in the basal insulin dose from pretreatment level, resulting in ∼5% increase in glucose exposure. Therefore, dapagliflozin efficacy was not significantly affected by the insulin dose adjustment, with 24-week HbA1c reduction on 10-mg dapagliflozin treatment changing from -0.5 [95% CI: -0.55, -0.45] to -0.42 [95%CI: -0.48, -0.36] after adjustment. At the same time, the analysis revealed ∼2-fold steeper slope of glucose-HbA1c relationship in dapagliflozin-treated patients vs. control group, suggesting the presence of additional dapagliflozin treatment-related benefits, not explained by the dapagliflozin-mediated ∼4% increase in plasma hemoglobin levels. Finally, the efficacy of 5 and 10-mg doses, represented by the mean HbA1c reduction at week 24 of dapagliflozin treatment, was shown to be notably greater than the 1- and 2.5-mg doses. Discussion: This research is an attempt to deconvolute and reconstruct dapagliflozin-HbA1c dose-response relationship in T1D by accounting for the drug's action on both daily insulin dose and plasma glucose on a subject-level. While the model is able to adequately capture the observed data, it also revealed that the variability in CGM is poorly approximated by the variability in insulin dose alone. Furthermore, the slope of CGM/HbA1c relationship may differ depending on the population and treatment scenarios. As such, a deeper dive into the physiological mechanisms is required to better quantify the intricate network of glycemic response under dapagliflozin treatment.

Racial and Ethnic Differences in the Association Between Mean Glucose and Hemoglobin A1c

Background: Studies have reported significantly higher hemoglobin A1c (A1C) in African American patients than in White patients with the same mean glucose, but less is known about other racial/ethnic groups. We evaluated racial/ethnic differences in the association between mean glucose, based on continuous glucose monitor (CGM) data, and A1C. Methods: Retrospective study among 1788 patients with diabetes from Kaiser Permanente Northern California (KPNC) who used CGM devices during 2016 to 2021. In this study population, there were 5264 A1C results; mean glucose was calculated from 124,388,901 CGM readings captured during the 90 days before each A1C result. Hierarchical mixed models were specified to estimate racial/ethnic differences in the association between mean glucose and A1C. Results: Mean A1C was 0.33 (95% confidence interval: 0.23-0.44; P < 0.0001) percentage points higher among African American patients relative to White patients for a given mean glucose. A1C results for Asians, Latinos, and multiethnic patients were not significantly different from those of White patients. The slope of the association between mean glucose and A1C did not differ significantly across racial/ethnic groups. Variance for the association between mean glucose and A1C was substantially greater within groups than between racial/ethnic groups (65% vs. 9%, respectively). Conclusions: For African American patients, A1C results may overestimate glycemia and could lead to premature diabetes diagnoses, overtreatment, or invalid assessments of health disparities. However, most of the variability in the mean glucose-A1C association was within racial/ethnic groups. Treatment decisions driven by guideline-based A1C targets should be individualized and supported by direct measurement of glycemia.

Performance of the Glucose Management Indicator (GMI) in Type 2 Diabetes

BACKGROUND

The glucose management indicator (GMI) is an estimated measure of hemoglobin A1c (HbA1c) recommended for the management of persons with diabetes using continuous glucose monitoring (CGM). However, GMI was derived primarily in young adults with type 1 diabetes, and its performance in patients with type 2 diabetes is poorly characterized.

METHODS

We conducted a prospective cohort study in 144 adults with obstructive sleep apnea and type 2 diabetes not using insulin (mean age: 59.4 years; 45.1% female). HbA1c was measured at the study screening visit. Participants simultaneously wore 2 CGM sensors (Dexcom G4 and Abbott Libre Pro) for up to 4 weeks (2 weeks at baseline and 2 weeks at the 3-month follow-up visit). GMI was calculated using all available CGM data for each sensor.

RESULTS

Median wear time was 27 days (IQR: 23-29) for the Dexcom G4 and 28 days (IQR: 24-29) for the Libre Pro. The mean difference between HbA1c and GMI was small (0.12-0.14 percentage points) (approximately 2 mmol/mol). However, the 2 measures were only moderately correlated (r = 0.68-0.71), and there was substantial variability in GMI at any given value of HbA1c (root mean squared error: 0.66-0.69 percentage points [7 to 8 mmol/mol]). Between 36% and 43% of participants had an absolute difference between HbA1c and GMI ≥0.5 percentage points (≥5 mmol/mol), and 9% to 18% had an absolute difference >1 percentage points (>11 mmol/mol). Discordance was higher in the Libre Pro than the Dexcom G4.

CONCLUSIONS

GMI may be an unreliable measure of glycemic control for patients with type 2 diabetes and should be interpreted cautiously in clinical practice.Clinicaltrials.gov Registration Number: NCT02454153.

The Reproducibility and Usefulness of Estimated Average Glucose for Hyperglycemia Management during Health Checkups: A Retrospective Cross-Sectional Study

HbA1c reflects average glucose levels over 3 months, but it does not measure glycemic variability. This study aimed to determine the reproducibility and usefulness of HbA1c-derived estimated average glucose (eAG) and to analyze the factors associated with eAG during health checkups. This cross-sectional retrospective study consecutively selected subjects who had undergone health checkups at 16 health-promotion centers in 13 Korean cities in 2020. The subjects comprised 182,848 healthy subjects with normoglycemia, 109,555 with impaired fasting glucose (IFG), and 35,632 with diabetes. eAG was calculated using Nathan’s regression equation. In all subjects, fasting plasma glucose (FPG) was found to be fairly strongly correlated with eAG (r = 0.811). When the subjects were divided into FPG subgroups, the strength of the correlation decreased among those with normoglycemia and IFG (p < 0.001). Higher eAG levels were associated with older age, females, higher FPG, and lower HDL-C and triglycerides (p < 0.05). The proportion of subjects with a higher value of FPG than eAG was 46.3% in poorly controlled diabetic patients, compared with only 1.5% in normoglycemic subjects. This suggests eAG could help patients to understand their glycemic variability intuitively and healthcare providers to identify patients who might worsen in hyperglycemia control through measuring the difference between eAG and FPG.

The Relationship Between CGM-Derived Metrics, A1C, and Risk of Hypoglycemia in Older Adults With Type 1 Diabetes

OBJECTIVE

Continuous glucose monitoring (CGM) is now commonly used in the management of type 1 diabetes (T1D). The CGM-derived coefficient of variation (CV) measures glucose variability, and the glucose management indicator (GMI) measures mean glycemia (previously called estimated A1C). However, their relationship with laboratory-measured A1C and the risk of hypoglycemia in older adults with T1D is not well studied.

RESEARCH DESIGN AND METHODS

In a single-center study, older adults (age ≥65 years) with T1D wore a CGM device for 14 days. The CV (%) and GMI were calculated, and A1C and clinical and demographic information were collected.

RESULTS

We evaluated 130 older adults (age 71 ± 5 years), of whom 55% were women, 97% were White, diabetes duration was 39 ± 17 years, and A1C was 7.3 ± 0.6% (56 ± 15 mmol/mol). Participants were stratified by high CV (>36%; n = 77) and low CV (≤36%; n = 53). Although there was no difference in A1C levels between the groups with high and low CV (7.3% [56 mmol/mol] vs. 7.3% [53 mmol/mol], P = 0.4), the high CV group spent more time in hypoglycemia (<70 mg/dL and ≤54 mg/dL) compared with the group with low CV (median 31 vs. 84 min/day, P < 0.0001; 8 vs. 46 min/day, P < 0.001, respectively). An absolute difference between A1C and GMI of ≥0.5% was observed in 46% of the cohort. When the A1C was higher than the GMI by ≥0.5%, a higher duration of hypoglycemia was observed (P = 0.02).

CONCLUSIONS

In older adults with T1D, the use of CGM-derived CV and GMI can better identify individuals at higher risk for hypoglycemia compared with A1C alone. These measures should be combined with A1C for better diabetes management in older adults with T1D.

Evaluation of the relationship between glycated hemoglobin A1c and mean glucose levels derived from the professional continuous flash glucose monitoring system

Previously, we reported that short-term continuous glucose monitoring (CGM) with the professional iPro2^© CGM device is a good clinical indicator of glycated hemoglobin (HbA1c) levels. However, there was no significant correlation between CGM and HbA1c levels when HbA1c levels were >8.0%. To further investigate this issue, we performed a similar study using the FreeStyle Libre Pro^©, a newer device that does not require glucose calibration and allows patients to be examined for up to 14 days. Fifty-nine patients (68% women, 32% men) were examined. Twenty-eight and 31 patients presented with type 1 and type 2 diabetes, respectively. Clinically assessed HbA1c levels were compared to blood glucose levels determined by the FreeStyle Libre Pro^© for up to 14 days (10.7 ± 3.7 days). We found a significant correlation between HbA1c and CGM levels even when HbA1c levels were >8.0%. Additionally, the correlation between HbA1c and average glucose was identified with the modern CGM and was found to deviate substantially from the new suggested formula. More importantly, we found a more robust correlation between HbA1c and CGM levels in patients with type 2 diabetes. Overestimation or underestimation of blood glucose levels through CGM might increase the risks of inappropriate clinical treatment of diabetes patients. Our results indicate the need for proper CGM data interpretation individualized for each patient to better assist the determination of customized treatments for patients.

The sweet spot: fasting glucose, cardiovascular disease, and mortality in older adults with diabetes: a nationwide population-based study

Background

Growing evidences shows that fasting glucose target should be different according to their health condition in older adults with diabetes. However, there are limited data regarding the relationship between fasting glucose level and health outcomes in Korean older people with diabetes. We aimed to examine the association of fasting glucose with mortality and cardiovascular events in Korean older adults with type 2 diabetes.

Methods

From the Korean National Health Insurance System, 227,938 subjects (aged ≥ 65 years) with type 2 diabetes but no history of cardiovascular events (myocardial infarction or stroke) who underwent ≥ 2 health examinations from 2009 to 2010 and who were followed up until 2017 were identified. The primary exposure variable was the mean fasting glucose level. We estimated the relationship between the baseline fasting glucose level and incidences of all-cause death and cardiovascular events. Comorbidity load was assessed using the Charlson comorbidity index.

Results

Fasting glucose levels and all-cause mortality risk showed a J-shaped relationship regardless of sex and number of comorbidities. Fasting glucose levels associated with the lowest mortality and cardiovascular events were 110–124 and 95–124 mg/dL, respectively. Stratified analysis by comorbidity load using the Charlson comorbidity index revealed higher optimal fasting glucose levels for the lowest cardiovascular events in subjects with Charlson comorbidity index ≥ 3 than in those with Charlson comorbidity index ≤ 2 (119 vs. 112 mg/dL, P = 0.04).

Conclusions

J-shaped relationship existed between fasting glucose and all-cause mortality and cardiovascular events in Korean older adults with diabetes. We identified that fasting glucose levels associated with the lowest mortality and cardiovascular events were 110–124 and 95–124 mg/dL respectively. Increased risk of cardiovascular events with low fasting glucose level (< 95 mg/dL) was noted, especially in patients with high comorbidity. These findings suggested that less stringent targets of fasting glucose may be beneficial especially in older adults with multiple comorbidities.

Association of Glucose Fluctuations with Sarcopenia in Older Adults with Type 2 Diabetes Mellitus

Type 2 diabetes mellitus accelerates loss of muscle mass and strength. Patients with Alzheimer’s disease (AD) also show these conditions, even in the early stages of AD. The mechanism linking glucose management with these muscle changes has not been elucidated but has implications for clarifying these associations and developing preventive strategies to maintain functional capacity. This study included 69 type 2 diabetes patients with a diagnosis of cognitive impairment (n = 32) and patients with normal cognition (n = 37). We investigated the prevalence of sarcopenia in diabetes patients with and without cognitive impairment and examined the association of glucose alterations with sarcopenia. Daily glucose levels were evaluated using self-monitoring of blood glucose, and we focused on the effects of glucose fluctuations, postprandial hyperglycemia, and the frequency of hypoglycemia on sarcopenia. Diabetes patients with cognitive impairment displayed a high prevalence of sarcopenia, and glucose fluctuations were independently associated with sarcopenia, even after adjusting for glycated hemoglobin A1c (HbA1c) levels and associated factors. In particular, glucose fluctuations were significantly associated with a low muscle mass, low grip strength, and slow walking speed. Our observation suggests the importance of glucose management by considering glucose fluctuations to prevent the development of disability.

Diabetes Management in the Elderly

IN BRIEF Older adults with diabetes present unique challenges and require considerations that are not traditionally associated with diabetes management. In this review, we focus on issues that are unique to the older population and provide practical guidance for clincians who care for them.

The Effects of Storage and Additives on Postmortem HbA1c Measurements

HbA1c is used in forensic toxicology to identify undiagnosed diabetes mellitus (DM) and those with poor glycemic control prior to death. HbA1c is typically measured in whole blood collected in tubes containing ethylenediaminetetraacetic acid (EDTA). The effect of other additives, including sodium fluoride (NaF), is unclear. Furthermore, the assessment of short- and long-term stability of HbA1c has produced conflicting results. In this study, we collected paired postmortem blood samples in EDTA and NaF tubes (n = 142) to assess their comparability for HbA1c measurement. Stability was assessed by measuring HbA1c at baseline, 2, 3, and 4 weeks postcollection (stored at 4°C) and at 2, 4, 6, and 12 months postcollection (stored at -20°C). We found no significant difference in HbA1c between the two preservatives at any of the time points indicating NaF is a suitable preservative for HbA1c measurement. We also determined that DM status, postmortem interval, and decomposition had no effect on stability.

Translating HbA1c measurements into estimated average glucose values in pregnant women with diabetes

AIMS/HYPOTHESIS

This study aimed to examine the relationship between average glucose levels, assessed by continuous glucose monitoring (CGM), and HbA_1c levels in pregnant women with diabetes to determine whether calculations of standard estimated average glucose (eAG) levels from HbA_1c measurements are applicable to pregnant women with diabetes.

METHODS

CGM data from 117 pregnant women (89 women with type 1 diabetes; 28 women with type 2 diabetes) were analysed. Average glucose levels were calculated from 5-7 day CGM profiles (mean 1275 glucose values per profile) and paired with a corresponding (±1 week) HbA_1c measure. In total, 688 average glucose-HbA_1c pairs were obtained across pregnancy (mean six pairs per participant). Average glucose level was used as the dependent variable in a regression model. Covariates were gestational week, study centre and HbA_1c.

RESULTS

There was a strong association between HbA_1c and average glucose values in pregnancy (coefficient 0.67 [95% CI 0.57, 0.78]), i.e. a 1% (11 mmol/mol) difference in HbA_1c corresponded to a 0.67 mmol/l difference in average glucose. The random effects model that included gestational week as a curvilinear (quadratic) covariate fitted best, allowing calculation of a pregnancy-specific eAG (PeAG). This showed that an HbA_1c of 8.0% (64 mmol/mol) gave a PeAG of 7.4-7.7 mmol/l (depending on gestational week), compared with a standard eAG of 10.2 mmol/l. The PeAG associated with maintaining an HbA_1c level of 6.0% (42 mmol/mol) during pregnancy was between 6.4 and 6.7 mmol/l, depending on gestational week.

CONCLUSIONS/INTERPRETATION

The HbA_1c-average glucose relationship is altered by pregnancy. Routinely generated standard eAG values do not account for this difference between pregnant and non-pregnant individuals and, thus, should not be used during pregnancy. Instead, the PeAG values deduced in the current study are recommended for antenatal clinical care.

Cognitive Dysfunction in Older Adults With Diabetes: What a Clinician Needs to Know

One of the challenges of managing older adults with diabetes is the individualization of care in people with multiple comorbid conditions. Although macrovascular and microvascular complications of diabetes are well recognized, there is a lack of awareness regarding other conditions such as cognitive dysfunction, depression, and physical disabilities. Cognitive dysfunction is of particular importance because of its impact on self-care and quality of life. In this Perspective, I discuss common and practical questions faced by clinicians managing diabetes in older adults who also have cognitive dysfunction.

HYPERGLYCEMIA AND HYPOGLYCEMIA IN PATIENTS WITH DIABETES IN SKILLED NURSING FACILITIES

OBJECTIVE

Endocrinologists are faced with a growing elderly patient population with diabetes mellitus (DM), some of whom are in skilled nursing facilities (SNFs). Efforts at managing their DM is hampered by concerns for hypoglycemia. This study aimed to determine the frequency of hypo- and hyperglycemia in SNFs, and associated factors.

METHODS

We reviewed medical records of 200 consecutive residents admitted to two SNFs in the Cleveland area in 2014 with documented DM, aged ≥65 years. Data collected included blood glucose (BG) levels and DM regimens. Frequency of hyper- and hypoglycemic events was noted. Since patients had different frequencies of BG checks, event-days were calculated.

RESULTS

Mean age, BG, and glycated hemoglobin (±SD) were as follows: 80.2 ± 8.2 years, 172.4 ± 40.3 mg/dL, and 7.5 ± 1.9% (59 mmol/mol), respectively. Seventy-one percent were on insulin alone, 15.5% on insulin and oral diabetes agents, and 13.5% on oral diabetes agent on admission. Patients with at least one event were as follows: 38% hypoglycemia, 3.5% severe hypoglycemia, 90.5% hyperglycemia, and 15% severe hyperglycemia. Event-days were: 3.4% hypoglycemia and 52.4% hyperglycemia. Risk of hypoglycemia was highest with concomitant sulfonylurea and prandial or sliding-scale insulin. Hyperglycemia risk was high in basal insulin-containing regimens.

CONCLUSION

Hypoglycemia was seen in one-third of patients, and hyperglycemia was common despite insulin use. Concomitant use of sulfonylurea and prandial or sliding-scale insulin is best avoided in this fragile population with hypo- and hyperglycemia.

ABBREVIATIONS

ADA = American Diabetes Association BG = blood glucose DM = diabetes mellitus GLP-1 = glucagon-like peptide 1 HBA1c = glycated hemoglobin LOS = length of stay NPH = neutral protamine Hagedorn SNF = skilled nursing facility SSI = sliding-scale insulin.

HBA1C AND MEAN GLUCOSE DERIVED FROM SHORT-TERM CONTINUOUS GLUCOSE MONITORING ASSESSMENT DO NOT CORRELATE IN PATIENTS WITH HBA1C >8

OBJECTIVE

Optimum therapy for patients with diabetes depends on both acute and long-term changes in plasma glucose, generally assessed by glycated hemoglobin (HbA1c) levels. However, the correlation between HbA1c and circulating glucose has not been fully determined. Therefore, we carefully examined this correlation when glucose levels were assessed by continuous glucose monitoring (CGM).

METHODS

Fifty-one patients (70% female, 30% male) were examined; among them were 28 with type 1 diabetes and 23 with type 2 diabetes. Clinically determined HbA1c levels were compared with blood glucose determined by CGM during a short time period.

RESULTS

Changes in HbA1c levels up to 8.0% showed a clear and statistically strong correlation (R = 0.6713; P<.0001) with mean blood glucose levels measured by CGM, similar to that observed in the A1c-derived Average Glucose study in which patients were monitored for a longer period. However, we found no statistical correlation (R = 0.0498; P = .83) between HbA1c and CGM-assessed glucose levels in our patient population when HbA1c was >8.0%.

CONCLUSION

Short-term CGM appears to be a good clinical indicator of long-term glucose control (HbA1c levels); however, cautions should be taken while interpreting CGM data from patients with HbA1c levels >8.0%. Over- or underestimation of the actual mean glucose from CGM data could potentially increase the risks of inappropriate treatment. As such, our results indicate that a more accurate analysis of CGM data might be useful to adequately tailor clinical treatments.

ABBREVIATIONS

ADAG = A1c-Derived Average Glucose CGM = continuous glucose monitoring %CV = percent coefficient of variation HbA1c = glycated hemoglobin.

Estimation of the Relative Contribution of Postprandial Glucose Exposure to Average Total Glucose Exposure in Subjects with Type 2 Diabetes

We hypothesized that the relative contribution of fasting plasma glucose (FPG) versus postprandial plasma glucose (PPG) to glycated haemoglobin (HbA1c) could be calculated using an algorithm developed by the A1c-Derived Average Glucose (ADAG) study group to make HbA1c values more clinically relevant to patients. The algorithm estimates average glucose (eAG) exposure, which can be used to calculate apparent PPG (aPPG) by subtracting FPG. The hypothesis was tested in a large dataset (comprising 17 studies) from the vildagliptin clinical trial programme. We found that 24 weeks of treatment with vildagliptin monotherapy (n = 2523) reduced the relative contribution of aPPG to eAG from 8.12% to 2.95% (by 64%, p < 0.001). In contrast, when vildagliptin was added to metformin (n = 2752), the relative contribution of aPPG to eAG insignificantly increased from 1.59% to 2.56%. In conclusion, glucose peaks, which are often prominent in patients with type 2 diabetes, provide a small contribution to the total glucose exposure assessed by HbA1c, and the ADAG algorithm is not robust enough to assess this small relative contribution in patients receiving combination therapy.