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Objective: To assess the performance of continuous glucose monitoring (CGM)-measured glycemic metrics in predicting development of gestational diabetes mellitus (GDM) and select perinatal complications. Research Methods: In a prospective observational study, CGM data were collected from 760 pregnant females throughout gestation after study enrollment. GDM was diagnosed using the oral glucose tolerance test (OGTT) at 24–34 weeks of gestation. Predictive models were built using logistic and elastic net regression. Predictive performance was assessed by the area under the receiver-operating characteristic (AUROC) curve. Results: The AUROCs of using second trimester percent time >140 mg/dL (TA140) and week 13–14 TA140 in predicting GDM were 0.81 and 0.74, respectively. The AUROCs for predicting large-for-gestational-age (LGA) births and hypertensive disorders of pregnancy (HDP) using second trimester TA140 were both 0.58. When matching the specificity of OGTT, a model using TA140 in weeks 13–14 achieved similar sensitivity to OGTT in predicting HDP (13% vs. 10%, respectively) and LGA (6% for both methods). Elastic net also demonstrated similar AUROC and diagnostic performance with no meaningful improvement by using multiple predictors. Conclusion: CGM-measured hyperglycemic metrics such as TA140 predicted GDM with high AUROCs as early as 13–14 weeks of gestation. These metrics were also similar statistically to the OGTT at 24–34 weeks in predicting perinatal complications, although sensitivity was low for both. CGM could potentially be used as an early screening tool for elevated hyperglycemia during gestation, which could be used in addition to or instead of the OGTT. [ABSTRACT FROM AUTHOR]

The article discusses the use of two metrics, time-in-range (TIR) and time-in-tight-range (TITR), for assessing glycemic control in diabetes. TIR measures the percentage of time that glucose values are in the range of 70-180 mg/dL, while TITR measures the percentage of time that glucose values are in the range of 70-140 mg/dL. The article presents data from various studies that compare TIR and TITR in different diabetic cohorts and insulin delivery methods. The author concludes that TIR is a better predictor of complications and suggests caution in switching from TIR to TITR as the primary glycemic control metric. [Extracted from the article]

Objective: To evaluate the relationship between continuous glucose monitoring (CGM)-measured time-in-range 70–180 mg/dL (TIR) and time-in-tight-range 70–140 mg/dL (TITR). Methods: TIR and TITR were calculated from CGM data collected using blinded or unblinded Dexcom sensors from 9 studies with 912 participants with type 1 diabetes (T1D) and 2 studies with 184 participants with type 2 diabetes (T2D). The TIR-TITR relationship was assessed overall and stratified by coefficient of variation (CV) and by time below range <70 mg/dL (TBR). Results: The correlation between TIR and TITR was 0.94. TITR was higher for a given TIR for T2D compared with T1D. However, after adjusting for the differences in CV or TBR, both of which were higher with T1D than T2D, the differences were minimized. The TIR-TITR relationship was nonlinear, with a higher ratio of TITR:TIR observed as TIR increased ranging from 0.42 when TIR was 20% to 0.66 when TIR was 80%. Similarly, as TITR increased, the ratio of TIR:TITR decreased, varying from 2.6 with TITR of 10% to 1.3 for TITR of 70%. The TIR-TITR relationship varied according to CV and TBR, such that the higher the CV or higher the amount of TBR the greater was TITR for a given TIR. Conclusions: TIR and TITR are highly correlated, although the relationship is nonlinear. With knowledge of TIR, TITR can be estimated with reasonable precision. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effect of hybrid-closed loop Control-IQ technology (Control-IQ) in randomized controlled trials (RCTs) in subgroups based on baseline characteristics such as race/ethnicity, socioeconomic status (SES), prestudy insulin delivery modality (pump or multiple daily injections), and baseline glycemic control. Methods: Data were pooled and analyzed from 3 RCTs comparing Control-IQ to a Control group using continuous glucose monitoring in 369 participants with type 1 diabetes (T1D) from age 2 to 72 years old. Results: Time in range 70–180 mg/dL (TIR) in the Control-IQ group (n = 256) increased from 57% ± 17% at baseline to 70% ± 11% during follow-up, and in the Control group (n = 113) was 56% ± 15% and 57% ± 14%, respectively (adjusted treatment group difference = 11.5%, 95% confidence interval +9.7% to +13.2%, P < 0.001), an increase of 2.8 h/day on average. Significant reductions in mean glucose, hyperglycemia metrics, hypoglycemic metrics, and HbA1c were also observed. A statistically similar beneficial treatment effect on time in range 70–180 mg/dL was observed across the full age range irrespective of race-ethnicity, household income, prestudy continuous glucose monitor use, or prestudy insulin delivery method. Participants with the highest baseline HbA1c levels showed the greatest improvements in TIR and HbA1c. Conclusion: This pooled analysis of Control-IQ RCTs demonstrates the beneficial effect of Control-IQ in T1D across a broad spectrum of participant characteristics, including racial-ethnic minority, lower SES, lack of prestudy insulin pump experience, and high HbA1c levels. The greatest benefit was observed in participants with the worst baseline glycemic control in whom the auto-bolus feature of the Control-IQ algorithm appears to have substantial impact. Since no subgroups were identified that did not benefit from Control-IQ, hybrid-closed loop technology should be strongly considered for all youth and adults with T1D. Clinical Trials Registry: clinicaltrials.gov; NCT03563313, NCT03844789, and NCT 04796779. [ABSTRACT FROM AUTHOR]

Objective: While it is recognized that there is a strong relationship between the amount of time glucose levels are <70 mg/dL (T<70) and the amount of time <54 mg/dL (T<54), the association has not been well quantified. Methods: Datasets with Dexcom continuous glucose monitoring (CGM) data from nine type 1 diabetes randomized trials were pooled to evaluate the relationship between CGM-measured T<70 and T<54. Penalized B-spline regression lines were fitted to assess the relationship between T<70 and T<54 for blinded CGM use, unblinded CGM use without an automated insulin delivery (AID) system, and unblinded CGM use with an AID system. Results: For blinded data, the T<54 : T<70 ratio varied from 19% when the amount of T<70 was <1% to 44% when the amount of T<70 was ≥7% whereas for unblinded data the ratio varied from 15% to 42%, respectively. When T<70 was 4%, the predicted T<54 was 1.18%, 0.94%, and 0.91% for the blinded, unblinded, and AID data, respectively (P<0.001 comparing blinded versus unblinded and AID). Conclusions: The T<54 : T<70 ratio increases with greater T<70, and the ratio generally is higher with blinded than unblinded CGM data, with the latter appearing to be similar to AID system data. The finding of greater T<54 for a given T<70 with blinded CGM data is presumed to be due to an action being taken by the unblinded CGM user and/or by the AID system to minimize hypoglycemia which will have the effect of reducing the amount of T<54. [ABSTRACT FROM AUTHOR]

Association of estimated time-in-range capillary glucose levels versus HbA1c with progression of microvascular complications in the Diabetes Control and Complications Trial. While none of the TIR studies come close to the rigor and breadth of the DCCT, collectively they indicate that the association of TIR with chronic complications is likely comparable with that of HbA1c. Hemoglobin A1c (HbA1c) became the gold standard for assessing glycemic management after the landmark Diabetes Control and Complications Trial (DCCT) demonstrated the strong association between HbA1c levels and the risk of chronic diabetic vascular complications.[1] However, as continuous glucose monitoring (CGM) use has become more widespread, the value of HbA1c when the actual glucose concentrations are available that contribute to the HbA1c level has been questioned. [Extracted from the article]

Objective: To evaluate the insulin-only configuration of the iLet® bionic pancreas (BP) using fast-acting insulin aspart (Fiasp®) in adults with type 1 diabetes (T1D). Research Design and Methods: In this multicenter, randomized trial, 275 adults with T1D (18–83 years old, baseline HbA1c 5.3%–14.9%) were randomly assigned 2:2:1 to use the BP with fast-acting insulin aspart (BP-F group, N = 114), BP with aspart or lispro (BP-A/L group, N = 107), or a control group using their standard-care insulin delivery (SC group, N = 54) plus real-time continuous glucose monitoring (CGM). The primary outcome was HbA1c at 13 weeks. The BP-F versus SC comparison was considered primary and BP-F versus BP-A/L secondary. Results: Mean ± standard deviation (SD) HbA1c decreased from 7.8% ± 1.2% at baseline to 7.1% ± 0.6% at 13 weeks with BP-F versus 7.6% ± 1.2% to 7.5% ± 0.9% with SC (adjusted difference = −0.5%, 95% CI −0.7 to −0.3, P < 0.001). CGM-measured percent time <54 mg/dL over 13 weeks with BP-F was noninferior to SC (adjusted difference = 0.00%, 95% CI −0.07 to 0.05, P < 0.001 for noninferiority based on a prespecified noninferiority limit of 1%). Over 13 weeks, mean time in range 70–180 mg/dL (TIR) increased by 14% (3.4 h/day) and mean CGM glucose was reduced by 18 mg/dL with BP-F compared with SC (P < 0.001). Analyses of time >180 mg/dL, time >250 mg/dL, and the SD of CGM glucose all favored BP-F compared with SC (P < 0.001). Differences between BP-F and BP-A/L were minimal, with no difference in HbA1c at 13 weeks (adjusted difference = −0.0%, 95% CI −0.2 to 0.1, P = 0.67) or mean glucose (adjusted difference = −2.0 mg/dL, 95% CI −4.3 to 0.4, P = 0.10). Mean TIR was 2% greater with BP-F than BP-A/L (95% CI 1 to 4, P = 0.005), but the percentages of participants improving TIR by ≥5% were not significantly different (P = 0.49) and there were no significant differences comparing BP-F versus BP-A/L across nine patient-reported outcome surveys. The rate of severe hypoglycemia events did not differ among the three groups. Conclusions: In adults with T1D, HbA1c was improved with the BP using fast-acting insulin aspart compared with standard care without increasing CGM-measured hypoglycemia. However, the effect was no better than the reduction observed with the BP using aspart or lispro. Clinical Trial Registry: clinicaltrials.gov; NCT04200313. [ABSTRACT FROM AUTHOR]

Based on the plethora of abstracts presented at this year's American Diabetes Association (ADA) meeting on the use of continuous glucose monitoring (CGM) in type 2 diabetes (T2D), it is apparent that CGM use by people with T2D is expanding rapidly - both real-time CGM and intermittent scanning (flash) CGM, which we will refer to collectively as CGM. Key HbA1c outcomes in the MOBILE randomized trial of adults with T2D treated with basal insulin without bolus insulin. About 30% of people with T2D use insulin,[21] which represents considerably more T2D insulin users than T1D insulin users. As shown in clinical trials and real-world experience, CGM is well accepted and improves HbA1c, increases TIR, and decreases hypoglycemia in most insulin-treated patients with T2D. [Extracted from the article]

Background: The COVID-19 pandemic has impacted the conduct of clinic visits. We conducted a study to evaluate two academic laboratories' fingerstick capillary blood collection kits suitable for home use for laboratory measurement of HbA1c. Methods: Four clinical sites recruited 240 participants (aged 4-80 years, HbA1c 5.1%-13.5%). Capillary blood samples were obtained by the participant or parent using collection kits from two laboratories (University of Minnesota Advanced Research and Diagnostic Laboratory (ARDL) and Children's Mercy Hospital Laboratory (CMH)) and mailed under varying shipping conditions by United States Postal Service to the laboratories. Comparisons were made between HbA1c measurements from capillary samples and contemporaneously obtained venous samples. The primary outcome was percentage of capillary HbA1c values within 5% of the corresponding venous values. Results: HbA1c values were within 5% of venous values for 96% of ARDL kit specimens shipped with a cold pack and 98% without a cold pack and 99% and 99%, respectively, for the CMH kits. R2 values were 0.98, 0.99, 0.99, and 0.99, respectively. Results appeared similar across HbA1c levels and for pediatric and adult participants. Usability survey scores were high. Conclusions: Capillary blood collection kits, suitable for home use, from two academic laboratories, were demonstrated to be easy to use and provided results that are comparable with those obtained from venous specimens. Based on these results, there is strong evidence that HbA1c measurements from capillary specimens obtained with these specific kits can be used interchangeably with HbA1c measurements from venous specimens for clinical research and clinical care. [ABSTRACT FROM AUTHOR]

Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method© (RFPM©). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15-68 years (34 ± 14 years); 40% were female. The phone app plus RFPM© analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with <30 g of carbohydrates were overestimated by an average of 25.7 ± 17.2 g. The majority (64%) of large carbohydrate meals (≥60 g) were underestimated by an average of 53.6 ± 33.8 g. Glycemic response to large carbohydrate meals was similar between participants who underestimated or overestimated carbohydrate content, suggesting that factors beyond carbohydrate counting may impact postprandial glycemic response. Conclusions: Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop insulin delivery systems; development of tools to improve macronutrient estimation skills should be considered. [ABSTRACT FROM AUTHOR]

Objective: The T1D Exchange Clinic Registry figure of HbA1c levels according to age has become a classic picture of how average HbA1c varies from childhood to elderly. To further assess the course of HbA1c across the life span in T1D, we created similar figures stratified by device use and socioeconomic status (SES). Methods: Mean HbA1c was plotted versus age for 21,253 T1D Exchange Clinic Registry participants with an HbA1c measurement between January 1, 2016 and March 31, 2018 according to device use, race/ethnicity, and measures of SES. Results: Across the age range from childhood to elderly, continuous glucose monitoring (CGM) use without an insulin pump had better average HbA1c than pump without CGM; and among CGM users, pump and injection users had similar HbA1c levels. Any device use (pump or CGM) was associated with better HbA1c levels than no device use across the age range. Lower SES and African American race were associated with higher HbA1c across the age range. Across all device use, SES, and race/ethnicity factors, average HbA1c levels were highest in adolescents and young adults. Conclusion: Although the plot of average HbA1c from early childhood to elderly shifts according to device use and SES factors, the shape of the plots remains reasonably constant with highest HbA1c levels in adolescents and young adults. These findings emphasize the importance of targeting adolescence and early adulthood as the ages with the greatest need for improving diabetes management irrespective of device use and SES. [ABSTRACT FROM AUTHOR]

Objective: To provide a snapshot of the profile of adults and youth with type 1 diabetes (T1D) in the United States and assessment of longitudinal changes in T1D management and clinical outcomes in the T1D Exchange registry.Research Design and Methods: Data on diabetes management and outcomes from 22,697 registry participants (age 1-93 years) were collected between 2016 and 2018 and compared with data collected in 2010-2012 for 25,529 registry participants.Results: Mean HbA1c in 2016-2018 increased from 65 mmol/mol at the age of 5 years to 78 mmol/mol between ages 15 and 18, with a decrease to 64 mmol/mol by age 28 and 58-63 mmol/mol beyond age 30. The American Diabetes Association (ADA) HbA1c goal of <58 mmol/mol for youth was achieved by only 17% and the goal of <53 mmol/mol for adults by only 21%. Mean HbA1c levels changed little between 2010-2012 and 2016-2018, except in adolescents who had a higher mean HbA1c in 2016-2018. Insulin pump use increased from 57% in 2010-2012 to 63% in 2016-2018. Continuous glucose monitoring (CGM) increased from 7% in 2010-2012 to 30% in 2016-2018, rising >10-fold in children <12 years old. HbA1c levels were lower in CGM users than nonusers. Severe hypoglycemia was most frequent in participants ≥50 years old and diabetic ketoacidosis was most common in adolescents and young adults. Racial differences were evident in use of pumps and CGM and HbA1c levels.Conclusions: Data from the T1D Exchange registry demonstrate that only a minority of adults and youth with T1D in the United States achieve ADA goals for HbA1c. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the association of biochemical hypoglycemia with subsequent severe hypoglycemia (SH) events using the Diabetes Control and Complications Trial (DCCT) data set.Research Design and Methods: The frequency of biochemical hypoglycemia (percentage of values <70 and <54 mg/dL [3.9 and 3.0 mmol/L) was assessed using DCCT blood glucose concentrations measured at a central laboratory from seven finger-stick samples (7-point testing: pre- and 90-min postmeals and at bedtime) collected during 1 day every 3 months. SH events required a change in mental status necessitating the involvement of another individual to provide treatment. A Poisson model accounting for repeated measures from each participant was used to assess the association of biochemical hypoglycemia frequency, computed from the 7-point finger-stick data, with the development of SH events.Results: The risk of SH during a 3-month period was substantially higher (P < 0.001) when there was at least one hypoglycemic blood glucose value in the preceding 7-point profile, with similar results seen for both the 70 mg/dL (rate ratio = 3.0 [95% confidence interval: 2.6-3.3]) and 54 mg/dL (rate ratio = 2.7 [95% confidence interval: 2.4-3.1]) thresholds.Conclusions: The occurrence of biochemical hypoglycemia <70 or <54 mg/dL is associated with an increased risk of SH. For this reason as well as the deleterious effects of hypoglycemia on glucose counter-regulation and hypoglycemia awareness, cognition, quality of life, and arrhythmias, it is important in diabetes management to avoid hypoglycemic glucose levels as much as possible. [ABSTRACT FROM AUTHOR]

The article offers information on a transformative effect on the landscape of research in type 1 diabetes (T1D). It mentions in forming the T1D Exchange Clinic Network, the initial goal was to attract the interest of 25 diabetes centers in the U.S.; and also mentions the clinic registry has provided a rich data set to address important clinical and public health issues, including important observations regarding the state of T1D management and outcomes.

Objective: To determine the minimum sample of continuous glucose monitoring (CGM) data needed to accurately reflect 3 months of glycemic control.Research Design and Methods: Three months of CGM data were collected on 257 individuals (age 10-78 years) with type 1 diabetes in two studies (one using the Abbott FreeStyle Libre Pro™ and the other using the Dexcom™ G4). Correlations were calculated between the full 3 months and incremental sampling periods of CGM data.Results: Correlation to the full 3 months of data improved with an increasing number of days of data collection, plateauing by about 14 days, with an R2 value of 0.84-0.86 for mean glucose, time at 70-180 mg/dL, and time >180 mg/dL, with lower values for time <70 mg/dL (0.76) and coefficient of variation (0.70).Conclusions: Fourteen days of CGM data provide a good estimation of glucose metrics for a 3-month period. [ABSTRACT FROM AUTHOR]