Rationale and Design for a GRADE Substudy of Continuous Glucose Monitoring

Estimating Glycemia From HbA1c and CGM: Analysis of Accuracy and Sources of Discrepancy

OBJECTIVE

To examine the accuracy of different periods of continuous glucose monitoring (CGM), hemoglobin A1c (HbA1c), and their combination for estimating mean glycemia over 90 days (AG90).

RESEARCH DESIGN AND METHODS

We retrospectively studied 985 CGM periods of 90 days with <10% missing data from 315 adults (86% of whom had type 1 diabetes) with paired HbA1c measurements. The impact of mean red blood cell age as a proxy for nonglycemic effects on HbA1c was estimated using published theoretical models and in comparison with empirical data. Given the lack of a gold standard measurement for AG90, we applied correction methods to generate a reference (eAG90) that we used to assess accuracy for HbA1c and CGM.

RESULTS

Using 14 days of CGM at the end of the 90-day period resulted in a mean absolute error (95th percentile) of 14 (34) mg/dL when compared with eAG90. Nonglycemic effects on HbA1c led to a mean absolute error for average glucose calculated from HbA1c of 12 (29) mg/dL. Combining 14 days of CGM with HbA1c reduced the error to 10 (26) mg/dL. Mismatches between CGM and HbA1c >40 mg/dL occurred more than 5% of the time.

CONCLUSIONS

The accuracy of estimates of eAG90 from limited periods of CGM can be improved by averaging with an HbA1c-based estimate or extending the monitoring period beyond ∼26 days. Large mismatches between eAG90 estimated from CGM and HbA1c are not unusual and may persist due to stable nonglycemic factors.

Determination of Glucose-Independent Racial Disparity in HbA1c for Youth With Type 1 Diabetes in the Era of Continuous Glucose Monitoring

BACKGROUND

The magnitude and importance of higher HbA1c levels not due to mean blood glucose (MBG) in non-Hispanic black (B) versus non-Hispanic white (W) individuals is controversial. We sought to clarify the relationship of HbA1c with glucose data from continuous glucose monitoring (CGM) in a young biracial population.

METHODS

Glycemic data of 33 B and 85 W, healthy youth with type 1 diabetes (age 14.7 ± 4.8 years, M/F = 51/67, duration of diabetes 5.4 ± 4.7 years) from a factory-calibrated CGM was compared with HbA1c. Hemoglobin glycation index (HGI) = assayed HbA1c - glucose management index (GMI).

RESULTS

B patients had higher unadjusted levels of HbA1c, MBG, MBGSD, GMI, and HGI than W patients. Percent glucose time in range (TIR) and percent sensor use (PSU) were lower for B patients. Average HbA1c in B patients 8.3% was higher than 7.7% for W (P < .0001) after statistical adjustment for MBG, age, gender, insulin delivery method, and accounting for a race by PSU interaction effect. Higher HbA1c persisted in B patients when TIR was substituted for MBG. Predicted MBG was higher in B patients at any level of PSU. The 95th percentile for HGI was 0.47 in W patients, and 52% of B patients had HGI ≥ 0.5. Time below range was similar for both.

CONCLUSIONS

Young B patients have clinically relevant higher average HbA1c at any given level of MBG or TIR than W patients, which may pose an additional risk for diabetes complications development. HGI ≥ 0.5 may be an easy way to identify high-risk patients.

Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis

Background

Hyperinsulinism (HI) due to excess and dysregulated insulin secretion is the most common cause of severe and recurrent hypoglycemia in childhood. High cerebral glucose use in the early hours results in a high risk of hypoglycemia in people with diabetes and carries a significant risk of brain injury. Prevention of hypoglycemia is the cornerstone of the management of HI, but the risk of hypoglycemia at night or the timing of hypoglycemia in children with HI has not been studied; thus, the digital phenotype remains incomplete and management suboptimal.

Objective

This study aims to quantify the timing of hypoglycemia in patients with HI to describe glycemic variability and to extend the digital phenotype. This will facilitate future work using computational modeling to enable behavior change and reduce exposure of patients with HI to injurious hypoglycemic events.

Methods

Patients underwent continuous glucose monitoring (CGM) with a Dexcom G4 or G6 CGM device as part of their clinical assessment for either HI (N=23) or idiopathic ketotic hypoglycemia (IKH; N=24). The CGM data were analyzed for temporal trends. Hypoglycemia was defined as glucose levels <3.5 mmol/L.

Results

A total of 449 hypoglycemic events totaling 15,610 minutes were captured over 237 days from 47 patients (29 males; mean age 70 months, SD 53). The mean length of hypoglycemic events was 35 minutes. There was a clear tendency for hypoglycemia in the early hours (3-7 AM), particularly for patients with HI older than 10 months who experienced hypoglycemia 7.6% (1480/19,370 minutes) of time in this period compared with 2.6% (2405/92,840 minutes) of time outside this period (P<.001). This tendency was less pronounced in patients with HI who were younger than 10 months, patients with a negative genetic test result, and patients with IKH. Despite real-time CGM, there were 42 hypoglycemic events from 13 separate patients with HI lasting >30 minutes.

Conclusions

This is the first study to have taken the first step in extending the digital phenotype of HI by describing the glycemic trends and identifying the timing of hypoglycemia measured by CGM. We have identified the early hours as a time of high hypoglycemia risk for patients with HI and demonstrated that simple provision of CGM data to patients is not sufficient to eliminate hypoglycemia. Future work in HI should concentrate on the early hours as a period of high risk for hypoglycemia and must target personalized hypoglycemia predictions. Focus must move to the human-computer interaction as an aspect of the digital phenotype that is susceptible to change rather than simple mathematical modeling to produce small improvements in hypoglycemia prediction accuracy.

Association of Baseline Characteristics With Insulin Sensitivity and β-Cell Function in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness (GRADE) Study Cohort

OBJECTIVE

We investigated sex and racial differences in insulin sensitivity, β-cell function, and glycated hemoglobin (HbA1c) and the associations with selected phenotypic characteristics.

RESEARCH DESIGN AND METHODS

This is a cross-sectional analysis of baseline data from 3,108 GRADE (Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study) participants. All had type 2 diabetes diagnosed <10 years earlier and were on metformin monotherapy. Insulin sensitivity and β-cell function were evaluated using the HOMA of insulin sensitivity and estimates from oral glucose tolerance tests, including the Matsuda Index, insulinogenic index, C-peptide index, and oral disposition index (DI).

RESULTS

The cohort was 56.6 ± 10 years of age (mean ± SD), 63.8% male, with BMI 34.2 ± 6.7 kg/m2, HbA1c 7.5 ± 0.5%, and type 2 diabetes duration 4.0 ± 2.8 years. Women had higher DI than men but similar insulin sensitivity. DI was the highest in Black/African Americans, followed by American Indians/Alaska Natives, Asians, and Whites in descending order. Compared with Whites, American Indians/Alaska Natives had significantly higher HbA1c, but Black/African Americans and Asians had lower HbA1c. However, when adjusted for glucose levels, Black/African Americans had higher HbA1c than Whites. Insulin sensitivity correlated inversely with BMI, waist-to-hip ratio, triglyceride-to-HDL-cholesterol ratio (TG/HDL-C), and the presence of metabolic syndrome, whereas DI was associated directly with age and inversely with BMI, HbA1c, and TG/HDL-C.

CONCLUSIONS

In the GRADE cohort, β-cell function differed by sex and race and was associated with the concurrent level of HbA1c. HbA1c also differed among the races, but not by sex. Age, BMI, and TG/HDL-C were associated with multiple measures of β-cell function and insulin sensitivity.