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OBJECTIVE To assess the relationship between the glucose management indicator (GMI) and HbA1c in non-White individuals with diabetes. RESEARCH DESIGN AND METHODS We performed a retrospective analysis of continuous glucose monitoring metrics in individuals with diabetes divided by race into non-White and White cohorts. RESULTS We evaluated 316 individuals (non-White n = 68; White n = 248). Although GMI was not different (7.6 vs. 7.7; P = not significant) between the cohorts, HbA1c was higher in the non-White cohort (8.7% vs. 8.1%; P = 0.004). HbA1c higher than GMI by ≥0.5% was more frequently observed in the non-White cohort (90% vs. 75%; P = 0.02). In the non-White cohort only, duration of hypoglycemia was longer among those with HbA1c higher than GMI by ≥0.5% compared with those with HbA1c and GMI within 0.5%. CONCLUSIONS A differential relationship between HbA1c and GMI in non-White versus White individuals with diabetes was observed. In non-White individuals, a greater difference between HbA1c and GMI was associated with higher risk of hypoglycemia.

Background The COVID-19 lockdown imposed a sudden change in lifestyle with self-isolation and a rapid shift to the use of technology to maintain clinical care and social connections. Objective In this mixed methods study, we explored the impact of isolation during the lockdown on the use of technology in older adults with type 1 diabetes (T1D). Methods Older adults (aged ≥65 years) with T1D using continuous glucose monitoring (CGM) participated in semistructured interviews during the COVID-19 lockdown. A multidisciplinary team coded the interviews. In addition, CGM metrics from a subgroup of participants were collected before and during the lockdown. Results We evaluated 34 participants (mean age 71, SD 5 years). Three themes related to technology use emerged from the thematic analysis regarding the impact of isolation on (1) insulin pump and CGM use to manage diabetes, including timely access to supplies, and changing Medicare eligibility regulations; (2) technology use for social interaction; and (3) telehealth use to maintain medical care. The CGM data from a subgroup (19/34, 56%; mean age 74, SD 5 years) showed an increase in time in range (mean 57%, SD 17% vs mean 63%, SD 15%; P=.001), a decrease in hyperglycemia (>180 mg/dL; mean 41%, SD 19% vs mean 35%, SD 17%; P<.001), and no change in hypoglycemia (<70 mg/dL; median 0.7%, IQR 0%-2% vs median 1.1%, IQR 0%-4%; P=.40) during the lockdown compared to before the lockdown. Conclusions These findings show that our cohort of older adults successfully used technology during isolation. Participants provided the positive and negative perceptions of technology use. Clinicians can benefit from our findings by identifying barriers to technology use during times of isolation and developing strategies to overcome these barriers.

Introduction: Glucose management indicator (GMI), a CGM-based metric, of mean glucose is thought to give more information on glycemic excursion than laboratory measured A1c. We have shown differences in relationship between A1c and GMI in different age groups, however it is not known whether racial differences affect the relationship between A1c and GMI. Methods: We analyzed data from patients with diabetes undergoing professional CGM (proCGM) at a tertiary care diabetes center between year 2017 and 2020, who had at least 10 days of CGM data. Medical records were queried to obtain demographic information and laboratory A1c results from within 1 month of CGM wear. GMI was calculated as Formula =3.31 + (0.02392 × mean glucose in mg/dL). Results: We evaluated 316 patients (mean age 60 ±18, 51% female, duration of diabetes 24±15 years, T1D 45% and T2D 55%, 78% white and 22% non-white). Subjects were identified by race as non-white (T1D 22%; T2D 78%) or white (T1D 45%; T2D 55%). CGM metrics in the non-white and white cohort were similar; time in range (728 ± 342 vs. 722 ± 307 min/day; p=ns), time &lt;70 mg/dL (90 ± 98 vs. 97 ± 108 min/day; p=ns ), and time &gt;180 mg/dL (568 ± 324 vs. 569 ± 346 min/day; p=ns). While GMI was not different in the nonwhite vs. white cohort (7.6 ± 1.3 vs. 7.7 ± 1.5; p=ns), A1c was higher in non-whites (8.7 ± 1.6 vs. 8.1 ±1.3; p=0.004). An absolute difference between A1c and GMI was higher in nonwhite vs. white (1.5 ± 0.88 vs. 1.1 ± 0.82 vs; p=0.006) and the absolute difference of 0.5% was observed in higher proportion of nonwhite patients (50 [75%] vs. 165 [68%]). Conclusions: In our study cohort, the relationship between GMI and A1c is different in non-white versus white population despite similar CGM metrics. This difference might be important to consider when assessing glycemic control and goals in diverse patient population. Disclosure E. Toschi: Consultant; Self; Medtronic. C. Slyne: None. A. Michals: None. K. Sifre: None. R. Dewar: None. A. Atakov-castillo: None. D. J. Davis: None. M. Munshi: Consultant; Self; Sanofi. Funding National Institutes of Health (1DP3DK112214-01)

Background: In recent years, professional continuous glucose monitoring (CGM) has become a beneficial tool in pattern management in older adults with type 2 diabetes (T2D) on insulin, however the need for use of professional CGM in this population had not been shown. Methods: We evaluated electronic health record (EHR) data of a tertiary diabetes center to identify the use of professional CGM in patients over the age of 65 years with T2D using insulin. Professional CGM data from this cohort were analyzed when a minimum of 70% of data was available (≥10 days). Results: A total of 2,481 patients over the age of 65 years with type 2 diabetes using insulin were seen at the Joslin Diabetes Center from January 2017-March 2020. Average age of the total cohort was 72±7 yrs, A1c was 8.2±1.5% and diabetes duration was 21±10 years. Professional CGM was used in 169 older adults with T2D using insulin (7% of the total patients seen). CGM data from 139/169 was sufficient for analysis. The average age of this cohort (139 patients) was 77±8 yrs, A1c 8.0±1.5%, duration of diabetes 21±12 yrs. The mean duration of hypoglycemia was 80±92 min/day &lt;70 mg/dL and 32±51 min/day ≤54 mg/dL. A total of 86% of the cohort had ≥1 episode of hypoglycemia. The measure of high glycemic variability (CV &gt;36%) was seen in 59 (42%) of the cohort, with a high A1c (8.2±1.2%). When comparing those people with and without hypoglycemia on CGM, there was no difference in age, A1c, or duration of diabetes. Conclusion: In the real world, a small percentage of older adults with T2D on insulin therapy received professional CGM. Our data shows a high burden of hypoglycemia in this population, despite suboptimal A1c. Broader use of professional CGM would be beneficial in recognizing hypoglycemia in this vulnerable population. Disclosure C. Slyne: None. E. Toschi: Consultant; Self; Medtronic. A. Michals: None. A. Atakov-castillo: None. K. Sifre: None. R. Dewar: None. D. J. Davis: None. M. Munshi: Consultant; Self; Sanofi. Funding National Institutes of Health (1DP3DK112214-01)