Closed-Loop Therapy and Preservation of C-Peptide Secretion in Type 1 Diabetes.

BACKGROUND: Whether improved glucose control with hybrid closed-loop therapy can preserve C-peptide secretion as compared with standard insulin therapy in persons with new-onset type 1 diabetes is unclear. METHODS: In a multicenter, open-label, parallel-group, randomized trial, we assigned youths 10.0 to 16.9 years of age within 21 days after a diagnosis of type 1 diabetes to receive hybrid closed-loop therapy or standard insulin therapy (control) for 24 months. The primary end point was the area under the curve (AUC) for the plasma C-peptide level (after a mixed-meal tolerance test) at 12 months after diagnosis. The analysis was performed on an intention-to-treat basis. RESULTS: A total of 97 participants (mean [±SD] age, 12±2 years) underwent randomization: 51 were assigned to receive closed-loop therapy and 46 to receive control therapy. The AUC for the C-peptide level at 12 months (primary end point) did not differ significantly between the two groups (geometric mean, 0.35 pmol per milliliter [interquartile range, 0.16 to 0.49] with closed-loop therapy and 0.46 pmol per milliliter [interquartile range, 0.22 to 0.69] with control therapy; mean adjusted difference, -0.06 pmol per milliliter [95% confidence interval {CI}, -0.14 to 0.03]). There was not a substantial between-group difference in the AUC for the C-peptide level at 24 months (geometric mean, 0.18 pmol per milliliter [interquartile range, 0.06 to 0.22] with closed-loop therapy and 0.24 pmol per milliliter [interquartile range, 0.05 to 0.30] with control therapy; mean adjusted difference, -0.04 pmol per milliliter [95% CI, -0.14 to 0.06]). The arithmetic mean glycated hemoglobin level was lower in the closed-loop group than in the control group by 4 mmol per mole (0.4 percentage points; 95% CI, 0 to 8 mmol per mole [0.0 to 0.7 percentage points]) at 12 months and by 11 mmol per mole (1.0 percentage points; 95% CI, 7 to 15 mmol per mole [0.5 to 1.5 percentage points]) at 24 months. Five cases of severe hypoglycemia occurred in the closed-loop group (in 3 participants), and one occurred in the control group; one case of diabetic ketoacidosis occurred in the closed-loop group. CONCLUSIONS: In youths with new-onset type 1 diabetes, intensive glucose control for 24 months did not appear to prevent the decline in residual C-peptide secretion. (Funded by the National Institute for Health and Care Research and others; CLOuD ClinicalTrials.gov number, NCT02871089.).

Multicenter, Randomized Trial of a Bionic Pancreas in Type 1 Diabetes.

BACKGROUND: Currently available semiautomated insulin-delivery systems require individualized insulin regimens for the initialization of therapy and meal doses based on carbohydrate counting for routine operation. In contrast, the bionic pancreas is initialized only on the basis of body weight, makes all dose decisions and delivers insulin autonomously, and uses meal announcements without carbohydrate counting. METHODS: In this 13-week, multicenter, randomized trial, we randomly assigned in a 2:1 ratio persons at least 6 years of age with type 1 diabetes either to receive bionic pancreas treatment with insulin aspart or insulin lispro or to receive standard care (defined as any insulin-delivery method with unblinded, real-time continuous glucose monitoring). The primary outcome was the glycated hemoglobin level at 13 weeks. The key secondary outcome was the percentage of time that the glucose level as assessed by continuous glucose monitoring was below 54 mg per deciliter; the prespecified noninferiority limit for this outcome was 1 percentage point. Safety was also assessed. RESULTS: A total of 219 participants 6 to 79 years of age were assigned to the bionic-pancreas group, and 107 to the standard-care group. The glycated hemoglobin level decreased from 7.9% to 7.3% in the bionic-pancreas group and did not change (was at 7.7% at both time points) in the standard-care group (mean adjusted difference at 13 weeks, -0.5 percentage points; 95% confidence interval [CI], -0.6 to -0.3; P<0.001). The percentage of time that the glucose level as assessed by continuous glucose monitoring was below 54 mg per deciliter did not differ significantly between the two groups (13-week adjusted difference, 0.0 percentage points; 95% CI, -0.1 to 0.04; P<0.001 for noninferiority). The rate of severe hypoglycemia was 17.7 events per 100 participant-years in the bionic-pancreas group and 10.8 events per 100 participant-years in the standard-care group (P = 0.39). No episodes of diabetic ketoacidosis occurred in either group. CONCLUSIONS: In this 13-week, randomized trial involving adults and children with type 1 diabetes, use of a bionic pancreas was associated with a greater reduction than standard care in the glycated hemoglobin level. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others; ClinicalTrials.gov number, NCT04200313.).

Associations between daily step count classifications and continuous glucose monitoring metrics in adults with type 1 diabetes: analysis of the Type 1 Diabetes Exercise Initiative (T1DEXI) cohort.

AIMS/HYPOTHESIS: Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics. METHODS: In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l). RESULTS: A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%). CONCLUSIONS/INTERPRETATION: Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes. DATA AVAILABILITY: The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).

A comparison of two hybrid closed-loop systems in adolescents and young adults with type 1 diabetes (FLAIR): a multicentre, randomised, crossover trial.

BACKGROUND: Management of type 1 diabetes is challenging. We compared outcomes using a commercially available hybrid closed-loop system versus a new investigational system with features potentially useful for adolescents and young adults with type 1 diabetes. METHODS: In this multinational, randomised, crossover trial (Fuzzy Logic Automated Insulin Regulation [FLAIR]), individuals aged 14-29 years old, with a clinical diagnosis of type 1 diabetes with a duration of at least 1 year, using either an insulin pump or multiple daily insulin injections, and glycated haemoglobin (HbA1c) levels of 7·0-11·0% (53-97 mmol/mol) were recruited from seven academic-based endocrinology practices, four in the USA, and one each in Germany, Israel, and Slovenia. After a run-in period to teach participants how to use the study pump and continuous glucose monitor, participants were randomly assigned (1:1) using a computer-generated sequence, with a permuted block design (block sizes of two and four), stratified by baseline HbA1c and use of a personal MiniMed 670G system (Medtronic) at enrolment, to either use of a MiniMed 670G hybrid closed-loop system (670G) or the investigational advanced hybrid closed-loop system (Medtronic) for the first 12-week period, and then participants were crossed over with no washout period, to the other group for use for another 12 weeks. Masking was not possible due to the nature of the systems used. The coprimary outcomes, measured with continuous glucose monitoring, were proportion of time that glucose levels were above 180 mg/dL (>10·0 mmol/L) during 0600 h to 2359 h (ie, daytime), tested for superiority, and proportion of time that glucose levels were below 54 mg/dL (<3·0 mmol/L) calculated over a full 24-h period, tested for non-inferiority (non-inferiority margin 2%). Analysis was by intention to treat. Safety was assessed in all participants randomly assigned to treatment. This trial is registered with ClinicalTrials.gov, NCT03040414, and is now complete. FINDINGS: Between June 3 and Aug 22, 2019, 113 individuals were enrolled into the trial. Mean age was 19 years (SD 4) and 70 (62%) of 113 participants were female. Mean proportion of time with daytime glucose levels above 180 mg/dL (>10·0 mmol/L) was 42% (SD 13) at baseline, 37% (9) during use of the 670G system, and 34% (9) during use of the advanced hybrid closed-loop system (mean difference [advanced hybrid closed-loop system minus 670G system] -3·00% [95% CI -3·97 to -2·04]; p<0·0001). Mean 24-h proportion of time with glucose levels below 54 mg/dL (<3·0 mmol/L) was 0·46% (SD 0·42) at baseline, 0·50% (0·35) during use of the 670G system, and 0·46% (0·33) during use of the advanced hybrid closed-loop system (mean difference [advanced hybrid closed-loop system minus 670G system] -0·06% [95% CI -0·11 to -0·02]; p<0·0001 for non-inferiority). One severe hypoglycaemic event occurred in the advanced hybrid closed-loop system group, determined to be unrelated to study treatment, and none occurred in the 670G group. INTERPRETATION: Hyperglycaemia was reduced without increasing hypoglycaemia in adolescents and young adults with type 1 diabetes using the investigational advanced hybrid closed-loop system compared with the commercially available MiniMed 670G system. Testing an advanced hybrid closed-loop system in populations that are underserved due to socioeconomic factors and testing during pregnancy and in individuals with impaired awareness of hypoglycaemia would advance the effective use of this technology FUNDING: National Institute of Diabetes and Digestive and Kidney Diseases.

Repeated measures random forests (RMRF): Identifying factors associated with nocturnal hypoglycemia.

Nocturnal hypoglycemia is a common phenomenon among patients with diabetes and can lead to a broad range of adverse events and complications. Identifying factors associated with hypoglycemia can improve glucose control and patient care. We propose a repeated measures random forest (RMRF) algorithm that can handle nonlinear relationships and interactions and the correlated responses from patients evaluated over several nights. Simulation results show that our proposed algorithm captures the informative variable more often than naïvely assuming independence. RMRF also outperforms standard random forest and extremely randomized trees algorithms. We demonstrate scenarios where RMRF attains greater prediction accuracy than generalized linear models. We apply the RMRF algorithm to analyze a diabetes study with 2524 nights from 127 patients with type 1 diabetes. We find that nocturnal hypoglycemia is associated with HbA1c, bedtime blood glucose (BG), insulin on board, time system activated, exercise intensity, and daytime hypoglycemia. The RMRF can accurately classify nights at high risk of nocturnal hypoglycemia.

Glycaemic profiles of diverse patients with type 2 diabetes using basal insulin: MOBILE study baseline data.

Basal insulin is often prescribed to patients with suboptimally controlled type 2 diabetes (T2D); however, its therapeutic efficacy is inadequate in many. During the MOBILE study's baseline phase, we evaluated 173 participants' continuous glucose monitoring (CGM) data (mean ± SD age 57 ± 9 years; 50% female; HbA1c 9.1% [range 7.1%-11.6%]; 40% using sulphonylureas; 19% using NPH; reported self-monitored blood glucose [SMBG] frequency median 1.0 checks/day) who were using basal, but not prandial insulin. Blinded CGM data were recorded for 10 days prior to randomization. The mean glucose value was 208 ± 47 mg/dL and it was lowest in the early morning. Mean time in the 70-180 mg/dL range was 9.6 ± 6.1 hours/day (40% ± 25%). Hyperglycaemia was extensive with medians of 14.7 (61%) and 5.0 (20.9%) hours/day with glucose greater than 180 and 250 mg/dL, respectively. Hypoglycaemia was infrequent (median [IQR] 0 [0, 4.3] minutes/day [0.0% {0.0%, 0.3%}] with glucose less than 70 mg/dL). Blinded CGM highlights the limitations of infrequent SMBG in basal insulin users with T2D and allows characterization of hyperglycaemia and hypoglycaemia in basal insulin users with suboptimal control. The MOBILE study randomized phase will define the benefits of using real-time CGM compared with SMBG in this population.

Hybrid closed-loop glucose control with faster insulin aspart compared with standard insulin aspart in adults with type 1 diabetes: A double-blind, multicentre, multinational, randomized, crossover study.

AIM: To evaluate the use of hybrid closed-loop glucose control with faster-acting insulin aspart (Fiasp) in adults with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: In a double-blind, multinational, randomized, crossover study, 25 adults with T1D using insulin pump therapy (mean ± SD, age 38 ± 9 years, HbA1c 7.4% ± 0.8% [57 ± 8 mmol/mol]) underwent two 8-week periods of unrestricted living comparing hybrid closed-loop with Fiasp and hybrid closed-loop with standard insulin aspart in random order. During both interventions the CamAPS FX closed-loop system incorporating the Cambridge model predictive control algorithm was used. RESULTS: In an intention-to-treat analysis, the proportion of time sensor glucose was in the target range (3.9-10.0 mmol/L; primary endpoint) was not different between interventions (75% ± 8% vs. 75% ± 8% for hybrid closed-loop with Fiasp vs. hybrid closed-loop with standard insulin aspart; mean-adjusted difference -0.6% [95% CI -1.8% to 0.7%]; p < .001 for non-inferiority [non-inferiority margin 5%]). The proportion of time with sensor glucose less than 3.9 mmol/L (median [IQR] 2.4% [1.2%-3.2%] vs. 2.9% [1.7%-4.0%]; p = .01) and less than 3.0 mmol/L (median [IQR] 0.4% [0.2%-0.7%] vs. 0.7% [0.2%-0.9%]; p = .03) was reduced with Fiasp versus standard insulin aspart. There was no difference in mean glucose (8.1 ± 0.8 vs. 8.0 ± 0.8 mmol/L; p = .13) or glucose variability (SD of sensor glucose 2.9 ± 0.5 vs. 2.9 ± 0.5 mmol/L; p = .90). Total daily insulin requirements did not differ (49 ± 15 vs. 49 ± 15 units/day; p = .45). No severe hypoglycaemia or ketoacidosis occurred. CONCLUSIONS: The use of Fiasp in the CamAPS FX closed-loop system may reduce hypoglycaemia without compromising glucose control compared with standard insulin aspart in adults with T1D.

Effect of Continuous Glucose Monitoring on Glycemic Control in Patients With Type 2 Diabetes Treated With Basal Insulin: A Randomized Clinical Trial.

Importance: Continuous glucose monitoring (CGM) has been shown to be beneficial for adults with type 2 diabetes using intensive insulin therapy, but its use in type 2 diabetes treated with basal insulin without prandial insulin has not been well studied. Objective: To determine the effectiveness of CGM in adults with type 2 diabetes treated with basal insulin without prandial insulin in primary care practices. Design, Setting, and Participants: This randomized clinical trial was conducted at 15 centers in the US (enrollment from July 30, 2018, to October 30, 2019; follow-up completed July 7, 2020) and included adults with type 2 diabetes receiving their diabetes care from a primary care clinician and treated with 1 or 2 daily injections of long- or intermediate-acting basal insulin without prandial insulin, with or without noninsulin glucose-lowering medications. Interventions: Random assignment 2:1 to CGM (n = 116) or traditional blood glucose meter (BGM) monitoring (n = 59). Main Outcomes and Measures: The primary outcome was hemoglobin A1c (HbA1c) level at 8 months. Key secondary outcomes were CGM-measured time in target glucose range of 70 to 180 mg/dL, time with glucose level at greater than 250 mg/dL, and mean glucose level at 8 months. Results: Among 175 randomized participants (mean [SD] age, 57 [9] years; 88 women [50%]; 92 racial/ethnic minority individuals [53%]; mean [SD] baseline HbA1c level, 9.1% [0.9%]), 165 (94%) completed the trial. Mean HbA1c level decreased from 9.1% at baseline to 8.0% at 8 months in the CGM group and from 9.0% to 8.4% in the BGM group (adjusted difference, -0.4% [95% CI, -0.8% to -0.1%]; P = .02). In the CGM group, compared with the BGM group, the mean percentage of CGM-measured time in the target glucose range of 70 to 180 mg/dL was 59% vs 43% (adjusted difference, 15% [95% CI, 8% to 23%]; P < .001), the mean percentage of time at greater than 250 mg/dL was 11% vs 27% (adjusted difference, -16% [95% CI, -21% to -11%]; P < .001), and the means of the mean glucose values were 179 mg/dL vs 206 mg/dL (adjusted difference, -26 mg/dL [95% CI, -41 to -12]; P < .001). Severe hypoglycemic events occurred in 1 participant (1%) in the CGM group and in 1 (2%) in the BGM group. Conclusions and Relevance: Among adults with poorly controlled type 2 diabetes treated with basal insulin without prandial insulin, continuous glucose monitoring, as compared with blood glucose meter monitoring, resulted in significantly lower HbA1c levels at 8 months. Trial Registration: ClinicalTrials.gov Identifier: NCT03566693.

Postmarket surveillance of arthroplasty device components using machine learning methods.

PURPOSE: While joint arthroplasty is generally a safe and effective procedure, there are concerns that some devices are at increased risk of failure. Early identification of total hip arthroplasty devices with increased risk of failure can be challenging because devices consist of multiple components, hundreds of distinct components are currently used in surgery, and any estimated effect needs to address confounding due to device and patient factors. The purpose of this study was to assess the effectiveness of machine learning approaches at identifying recalled components listed by the US Food and Drug Administration using data from a US total joint arthroplasty registry. METHODS: An open cohort study was conducted using data (January 1, 2001, to December 31, 2015) from 74 520 implantations and 348 unique components in the Kaiser Permanente Total Joint Replacement Registry. Exposures of interest were device components used in elective primary total hip arthroplasty. The outcome was time to first revision surgery, defined as exchange, removal, or addition of any component. Machine learning methods included regularized/unregularized Cox models and random survival forest. RESULTS: Among the recalled components detected were ASR acetabular shell/large femoral head, Durom acetabular shell/Metasul large femoral head, and Rejuvenate modular neck stem. The three components not identified were characterized by small numbers of devices recorded in the registry. CONCLUSIONS: The novel approaches to signal detection may improve postmarket surveillance of frequently used arthroplasty devices, which in turn will improve public health.

In-home nighttime predictive low glucose suspend experience in children and adults with type 1 diabetes.

Overnight predictive low glucose suspend (PLGS) reduces hypoglycemia across all ages; however, there are no reports on behavior or experience differences across age groups, especially in pediatrics. As run-in for a subsequent randomized clinical trial (RCT), 127 subjects (50% male) ages 4-45 yr utilized the experimental PLGS system nightly for 5-10 nights (PLGS active phase). We analyzed the number of blood glucose (BG) checks and boluses given per age group. During the subsequent 42 night RCT phase, we analyzed sensor use, skin reactions, errors, and reasons why the experimental system was not used. In 821 nights of active PLGS, subjects ages 4-6 yr (and their parents) tested BG levels 75% of nights compared with 65% of nights (7-10 yr), 53% of nights (11-14 yr), 33% of nights (15-25 yr), and 28% of nights (26-45 yr), respectively (p < 0.001). Likewise, youngest subjects (and parents) administered insulin boluses 56% of nights during active PLGS use compared with 48%, 33%, 20%, and 25%, respectively (p < 0.001). This was unrelated to study requirements. During the RCT phase, subjects 4-6 yr experienced more frequent and severe skin reactions (p = 0.02), while adult subjects (26-45 yr) wore individual sensors a median of 26 h longer than the youngest subjects (p < 0.001). Technical problems with the sensor (errors, miscalibrations, etc.), traveling, and BG levels >270 at bedtime (study requirement) were primary contributors to non-system use. Understanding the different use patterns and challenges in pediatrics and adolescence is needed to direct patient education to optimize use of PLGS and future artificial pancreas systems.

Descemet Stripping Endothelial Keratoplasty: Ten-Year Endothelial Cell Loss Compared with Penetrating Keratoplasty.

PURPOSE: To assess endothelial cell density (ECD) and model 10-year cell loss after Descemet stripping endothelial keratoplasty (DSEK), and to compare with results after penetrating keratoplasty (PK) performed for the same indications in the Cornea Donor Study (CDS). DESIGN: Retrospective, longitudinal study. PARTICIPANTS: Five hundred ninety DSEK recipients, 21 to 96 years of age, treated for Fuchs' dystrophy (n = 498; 84%), pseudophakic or aphakic corneal edema (n = 89; 15%), or other endothelial dysfunction (n = 3; <1%). METHODS: Review of 1005 consecutive primary DSEK procedures by 6 surgeons identified 752 grafts (75%) in 590 recipients with ECD measurements at 1 or more postoperative examinations between 6 months and 10 years. Four statistical models applied previously to the CDS PK data were considered. The preferred model (Bayesian Markov chain Monte Carlo) accounted for missing data, selective dropout from graft failure, correlations between fellow eyes, and correlations between longitudinal repeated measures. MAIN OUTCOME MEASURES: Central corneal ECD. RESULTS: The median donor corneal ECD (25th-75th percentiles) was 3005 cells/mm(2) (2852-3203 cells/mm(2)) at baseline, 2077 cells/mm(2) (1660-2411 cells/mm(2)) at 6 months, and 926 cells/mm(2) (617-1433 cells/mm(2)), with a range from 408 to 2538 cells/mm(2) at 10 years. After median cell loss of 32% in the perioperative period, ECD declined at a linear rate of approximately 110 cells/mm(2) per year between 6 months and 10 years after DSEK. At 10 years, the median cell loss was 71% (53%-80%) relative to the baseline donor ECD, comparable with the 76% (70%-82%) median cell loss after PK by 68 surgeons in the CDS. The 10-year ECD correlated with the 6-month postoperative ECD (r = 0.56; P < 0.001), but not with the baseline donor ECD (r = -0.04; P = 0.78). Selective dropout from graft failure did not affect the cell loss model significantly. CONCLUSIONS: The linear decline in ECD after DSEK was consistent with shorter-term endothelial keratoplasty studies and was distinct from the biexponential cell loss trend characteristic of PK. By 10 years, cell loss was comparable in surviving clear grafts for both DSEK and PK.

Weight Gain and Glycemic Control in Adults with Type 1 Diabetes in the T1D Exchange Registry.

Aim: The impact of weight gain on insulin dosage and glycemic control in adults with type 1 diabetes (T1D) aged 25 years and older was investigated in the T1D Exchange Registry participants. Methods: Participants were categorized into four groups based on their change in weight from T1D Exchange registry enrollment to year 5: stable weight (-5 to <5 lb), gained 5 to <10 lb, gained 10 to <20 lb, or gained ≥20 lb. Those who lost >5 lb were excluded. The primary outcomes were glucose control, as measured by glycosylated hemoglobin (HbA1c), and total daily insulin dose (TDD) at year 5. Linear regression models were used to evaluate the association between weight gain, HbA1c, and TDD. Results: There were 1969 participants included in the analyses. The mean ± standard deviation age was 45 ± 13 years, 57% were female, and 92% were White non-Hispanic. For those with an enrollment HbA1c <8.0%, the mean HbA1c at year 5 was higher for those who gained ≥20 lb compared to those with a stable weight of -5 to <5 lb (7.4% ± 1.1% vs. 7.2% ± 0.8%, respectively; P = 0.005). For this cohort, the mean TDD at year 5 increased from 49 ± 25 to 61 ± 29 U for those who gained ≥20 lb, while decreased from 45 ± 27 to 44 ± 25 U for those with stable weight of -5 to <5 lb (P < 0.001). Among participants with an enrollment HbA1c ≥9.0%, the mean HbA1c at year 5 was statistically insignificant at 8.4% ± 1.3% for those who gained ≥20 lb compared to 9.2% ± 1.7% for those with a stable weight of -5 to <5 lb (P = 0.09). Conclusion: Significant weight gain in adults with T1D who had good to adequate glycemic control was associated with modest deterioration in glucose control despite an increase in TDD. Worsening glucose control may indicate insulin resistance related to weight gain despite significantly increased insulin dosage which was insufficient to maintain adequate glycemic control.

A Comparison of Continuous Glucose Monitoring-Measured Time-in-Range 70-180 mg/dL Versus Time-in-Tight-Range 70-140 mg/dL.

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.

The Acute Effects of Real-World Physical Activity on Glycemia in Adolescents With Type 1 Diabetes: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study.

OBJECTIVE: Data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were evaluated to understand glucose changes during activity and identify factors that may influence changes. RESEARCH DESIGN AND METHODS: In this real-world observational study, adolescents with type 1 diabetes self-reported physical activity, food intake, and insulin dosing (multiple-daily injection users) using a smartphone application. Heart rate and continuous glucose monitoring data were collected, as well as pump data downloads. RESULTS: Two hundred fifty-one adolescents (age 14 ± 2 years [mean ± SD]; HbA1c 7.1 ± 1.3% [54 ± 14.2 mmol/mol]; 42% female) logged 3,738 activities over ∼10 days of observation. Preactivity glucose was 163 ± 66 mg/dL (9.1 ± 3.7 mmol/L), dropping to 148 ± 66 mg/dL (8.2 ± 3.7 mmol/L) by end of activity; median duration of activity was 40 min (20, 75 [interquartile range]) with a mean and peak heart rate of 109 ± 16 bpm and 130 ± 21 bpm. Drops in glucose were greater in those with lower baseline HbA1c levels (P = 0.002), shorter disease duration (P = 0.02), less hypoglycemia fear (P = 0.04), and a lower BMI (P = 0.05). Event-level predictors of greater drops in glucose included self-classified "noncompetitive" activities, insulin on board >0.05 units/kg body mass, glucose already dropping prior to the activity, preactivity glucose >150 mg/dL (>8.3 mmol/L) and time 70-180 mg/dL >70% in the 24 h before the activity (all P < 0.001). CONCLUSIONS: Participant-level and activity event-level factors can help predict the magnitude of drop in glucose during real-world physical activity in youth with type 1 diabetes. A better appreciation of these factors may improve decision support tools and self-management strategies to reduce activity-induced dysglycemia in active adolescents living with the disease.

Postprandial Glucose Variability Following Typical Meals in Youth Living with Type 1 Diabetes.

We explored the association between macronutrient intake and postprandial glucose variability in a large sample of youth living with T1D and consuming free-living meals. In the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study, youth took photographs before and after their meals on 3 days during a 10 day observation period. We used the remote food photograph method to obtain the macronutrient content of youth's meals. We also collected physical activity, continuous glucose monitoring, and insulin use data. We measured glycemic variability using standard deviation (SD) and coefficient of variation (CV) of glucose for up to 3 h after meals. Our sample included 208 youth with T1D (mean age: 14 ± 2 years, mean HbA1c: 54 ± 14.2 mmol/mol [7.1 ± 1.3%]; 40% female). We observed greater postprandial glycemic variability (SD and CV) following meals with more carbohydrates. In contrast, we observed less postprandial variability following meals with more fat (SD and CV) and protein (SD only) after adjusting for carbohydrates. Insulin modality, exercise after meals, and exercise intensity did not influence associations between macronutrients and postprandial glycemic variability. To reduce postprandial glycemic variability in youth with T1D, clinicians should encourage diversified macronutrient meal content, with a goal to approximate dietary guidelines for suggested carbohydrate intake.

Exploring Factors That Influence Postexercise Glycemia in Youth With Type 1 Diabetes in the Real World: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study.

OBJECTIVE: To explore 24-h postexercise glycemia and hypoglycemia risk, data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were analyzed to examine factors that may influence glycemia. RESEARCH DESIGN AND METHODS: This was a real-world observational study with participant self-reported physical activity, food intake, and insulin dosing (multiple daily injection users). Heart rate, continuous glucose data, and available pump data were collected. RESULTS: A total of 251 adolescents (42% females), with a mean ± SD age of 14 ± 2 years, and hemoglobin A1c (HbA1c) of 7.1 ± 1.3% (54 ± 14.2 mmol/mol), recorded 3,319 activities over ∼10 days. Trends for lower mean glucose after exercise were observed in those with shorter disease duration and lower HbA1c; no difference by insulin delivery modality was identified. Larger glucose drops during exercise were associated with lower postexercise mean glucose levels, immediately after activity (P < 0.001) and 12 to <16 h later (P = 0.02). Hypoglycemia occurred on 14% of nights following exercise versus 12% after sedentary days. On nights following exercise, more hypoglycemia occurred when average total activity was ≥60 min/day (17% vs. 8% of nights, P = 0.01) and on days with longer individual exercise sessions. Higher nocturnal hypoglycemia rates were also observed in those with longer disease duration, lower HbA1c, conventional pump use, and if time below range was ≥4% in the previous 24 h. CONCLUSIONS: In this large real-world pediatric exercise study, nocturnal hypoglycemia was higher on nights when average activity duration was higher. Characterizing both participant- and event-level factors that impact glucose in the postexercise recovery period may support development of new guidelines, decision support tools, and refine insulin delivery algorithms to better support exercise in youth with diabetes.

Digital Gaming and Exercise Among Youth With Type 1 Diabetes: Cross-Sectional Analysis of Data From the Type 1 Diabetes Exercise Initiative Pediatric Study.

Background: Regular physical activity and exercise are fundamental components of a healthy lifestyle for youth living with type 1 diabetes (T1D). Yet, few youth living with T1D achieve the daily minimum recommended levels of physical activity. For all youth, regardless of their disease status, minutes of physical activity compete with other daily activities, including digital gaming. There is an emerging area of research exploring whether digital games could be displacing other physical activities and exercise among youth, though, to date, no studies have examined this question in the context of youth living with T1D. Objective: We examined characteristics of digital gaming versus nondigital gaming (other exercise) sessions and whether youth with T1D who play digital games (gamers) engaged in less other exercise than youth who do not (nongamers), using data from the Type 1 Diabetes Exercise Initiative Pediatric study. Methods: During a 10-day observation period, youth self-reported exercise sessions, digital gaming sessions, and insulin use. We also collected data from activity wearables, continuous glucose monitors, and insulin pumps (if available). Results: The sample included 251 youths with T1D (age: mean 14, SD 2 y; self-reported glycated hemoglobin A1c level: mean 7.1%, SD 1.3%), of whom 105 (41.8%) were female. Youth logged 123 digital gaming sessions and 3658 other exercise (nondigital gaming) sessions during the 10-day observation period. Digital gaming sessions lasted longer, and youth had less changes in glucose and lower mean heart rates during these sessions than during other exercise sessions. Youth described a greater percentage of digital gaming sessions as low intensity (82/123, 66.7%) when compared to other exercise sessions (1104/3658, 30.2%). We had 31 youths with T1D who reported at least 1 digital gaming session (gamers) and 220 youths who reported no digital gaming (nongamers). Notably, gamers engaged in a mean of 86 (SD 43) minutes of other exercise per day, which was similar to the minutes of other exercise per day reported by nongamers (mean 80, SD 47 min). Conclusions: Digital gaming sessions were longer in duration, and youth had less changes in glucose and lower mean heart rates during these sessions when compared to other exercise sessions. Nevertheless, gamers reported similar levels of other exercise per day as nongamers, suggesting that digital gaming may not fully displace other exercise among youth with T1D.

Predicting Hypoglycemia and Hyperglycemia Risk During and After Activity for Adolescents with Type 1 Diabetes.

Objective: To predict hypoglycemia and hyperglycemia risk during and after activity for adolescents with type 1 diabetes (T1D) using real-world data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study. Methods: Adolescents with T1D (n = 225; [mean ± SD] age = 14 ± 2 years; HbA1c = 7.1 ± 1.3%; T1D duration = 5 ± 4 years; 56% using hybrid closed loop), wearing continuous glucose monitors (CGMs), logged 3738 total activities over 10 days. Repeated Measures Random Forest (RMRF) and Repeated Measures Logistic Regression (RMLR) models were used to predict a composite risk of hypoglycemia (<70 mg/dL) and hyperglycemia (>250 mg/dL) within 2 h after starting exercise. Results: RMRF achieved high precision predicting composite risk and was more accurate than RMLR Area under the receiver operating characteristic curve (AUROC 0.737 vs. 0.661; P < 0.001). Activities with minimal composite risk had a starting glucose between 132 and 160 mg/dL and a glucose rate of change at activity start between -0.4 and -1.9 mg/dL/min. Time <70 mg/dL and time >250 mg/dL during the prior 24 h, HbA1c level, and insulin on board at activity start were also predictive. Separate models explored factors at the end of activity; activities with glucose between 128 and 133 mg/dL and glucose rate of change between 0.4 and -0.6 mg/dL/min had minimal composite risk. Conclusions: Physically active adolescents with T1D should aim to start exercise with an interstitial glucose between 130 and 160 mg/dL with a flat or slightly decreasing CGM trend to minimize risk for developing dysglycemia. Incorporating factors such as historical glucose and insulin can improve prediction modeling for the acute glucose responses to exercise.