The Role of Whole Food Plant-Based Food Intake on Postprandial Glycemia in Type 1 Diabetes.

BACKGROUND: Whole food plant-based diet (WFPBD), minimally processed foods with limited consumption of animal products, is associated with improved health outcomes. The benefits of WFPBD are underexplored in individuals with type 1 diabetes (T1D). The primary objective of this analysis is to evaluate the association between WFPBD on glycemia in individuals with T1D. RESEARCH DESIGN AND METHODS: Utilizing prospectively collected meal events from the Type 1 Diabetes Exercise Initiative, we examined the effect of WFPBD intake on glycemia, determined by the Plant-Based Diet Index (PDI). The PDI calculates overall, healthful (hPDI), and unhealthy PDI (uPDI) to evaluate for degree of processed foods and animal products (i.e. WFPBD). Mixed effects linear regression model assessed time-in-range (TIR), time-above-range, and time-below-range. RESULTS: We analyzed 7,938 meals from 367 participants. TIR improved with increasing hPDI scores, conferring a 4% improvement in TIR between highest and lowest hPDI scores (high hPDI:75%, low hPDI:71%; p<0.001). Compared to meals with low hPDI, meals with high hPDI had lower glucose excursion (high hPDI:53mg/dL, low hPDI:62mg/dL; p<0.001) and less time >250mg/dL (high hPDI:8%, low hPDI:14%; p<0.001). These effects were present but less pronounced by PDI (high PDI:74%, low PDI:71%; p=0.01). No differences in time below 70mg/dL and 54mg/dL were observed by PDI or hPDI. CONCLUSIONS: Meal events with higher hPDI were associated with 4% postprandial TIR improvement. These benefits were seen primarily in WFPBD meals (captured by hPDI) and less pronounced plant-based meals (captured by PDI), emphasizing the benefit of increasing unprocessed food intake over limiting animal products alone.

Safety of Options to "Boost" (Enhancing Insulin Infusion Rates) and "Ease-Off" (Reducing Insulin Infusion Rates) in CamAPS FX Hybrid Closed-Loop System: A Real-World Analysis.

The usage and safety of the Boost and Ease-off features in the CamAPS FX hybrid closed-loop system were analyzed in a retrospective analysis of real-world data from 7,464 users over a 12-month period. Boost was used more frequently than Ease-off, but for a shorter duration per use. Mean starting glucose was above range for Boost (229 ± 51 mg/dL), and within range for Ease-off (114 ± 29 mg/dL). Time spent below 70 mg/dL was low during Boost periods [median (interquartile range; IQR) 0.0% (0.0, 0.5%)], and lower than during no Boost periods [2.1% (1.2, 3.4%)], while time spent above 180 mg/dL was lower during Ease-off periods (15 ± 14%) compared with no Ease-off periods (25 ± 12%). There were no episodes of severe hypoglycemia or diabetic ketoacidosis attributed to Boost or Ease-off use. Boost and Ease-off allow users to engage safely with CamAPS FX to manage their glucose levels during periods of more-than-usual and less-than-usual insulin needs.

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.

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.

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.

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.

Automated Insulin Delivery in Women with Pregnancy Complicated by Type 1 Diabetes.

BACKGROUND: Hybrid closed-loop insulin therapy has shown promise for management of type 1 diabetes during pregnancy; however, its efficacy is unclear. METHODS: In this multicenter, controlled trial, we randomly assigned pregnant women with type 1 diabetes and a glycated hemoglobin level of at least 6.5% at nine sites in the United Kingdom to receive standard insulin therapy or hybrid closed-loop therapy, with both groups using continuous glucose monitoring. The primary outcome was the percentage of time in the pregnancy-specific target glucose range (63 to 140 mg per deciliter [3.5 to 7.8 mmol per liter]) as measured by continuous glucose monitoring from 16 weeks' gestation until delivery. Analyses were performed according to the intention-to-treat principle. Key secondary outcomes were the percentage of time spent in a hyperglycemic state (glucose level >140 mg per deciliter), overnight time in the target range, the glycated hemoglobin level, and safety events. RESULTS: A total of 124 participants with a mean (±SD) age of 31.1±5.3 years and a mean baseline glycated hemoglobin level of 7.7±1.2% underwent randomization. The mean percentage of time that the maternal glucose level was in the target range was 68.2±10.5% in the closed-loop group and 55.6±12.5% in the standard-care group (mean adjusted difference, 10.5 percentage points; 95% confidence interval [CI], 7.0 to 14.0; P<0.001). Results for the secondary outcomes were consistent with those of the primary outcome; participants in the closed-loop group spent less time in a hyperglycemic state than those in the standard-care group (difference, -10.2 percentage points; 95% CI, -13.8 to -6.6); had more overnight time in the target range (difference, 12.3 percentage points; 95% CI, 8.3 to 16.2), and had lower glycated hemoglobin levels (difference, -0.31 percentage points; 95% CI, -0.50 to -0.12). Little time was spent in a hypoglycemic state. No unanticipated safety problems associated with the use of closed-loop therapy during pregnancy occurred (6 instances of severe hypoglycemia, vs. 5 in the standard-care group; 1 instance of diabetic ketoacidosis in each group; and 12 device-related adverse events in the closed-loop group, 7 related to closed-loop therapy). CONCLUSIONS: Hybrid closed-loop therapy significantly improved maternal glycemic control during pregnancy complicated by type 1 diabetes. (Funded by the Efficacy and Mechanism Evaluation Program; AiDAPT ISRCTN Registry number, ISRCTN56898625.).

A Comparison of Faster Insulin Aspart with Standard Insulin Aspart Using Hybrid Automated Insulin Delivery System in Active Children and Adolescents with Type 1 Diabetes: A Randomized Double-Blind Crossover Trial.

Objective: To evaluate the use of faster acting (FIA) and standard insulin aspart (SIA) with hybrid automated insulin delivery (AID) in active youth with type 1 diabetes. Research Design and Methods: In this double-blind multinational randomized crossover trial, 30 children and adolescents with type 1 diabetes (16 females; aged 15.0 ± 1.7 years; baseline HbA1c 7.5% ± 0.9% [58 ± 9.8 mmol/mol]) underwent two unrestricted 4-week periods using hybrid AID with either FIA or SIA in random order. During both interventions, participants were using the hybrid AID (investigational version of MiniMed™ 780G; Medtronic). Participants were encouraged to exercise as frequently as possible, capturing physical activity with an activity monitor. The primary outcome was the percentage of sensor glucose time above range (180 mg/dL [10.0 mmol/L]) measured by continuous glucose monitoring. Results: In an intention-to-treat analysis, mean time above range was 31% ± 15% at baseline, 19% ± 6% during FIA use, and 20% ± 6% during SIA use with no difference between treatments: mean difference = -0.9%; 95% CI: -2.4% to 0.6%; P = 0.23. Similarly, there was no difference in mean time in range (TIR) (78% and 77%) or median time below range (2.5% and 2.8%). Glycemic outcomes during exercise or postprandial periods were comparable for the two treatment arms. No severe hypoglycemia or diabetic ketoacidosis events occurred. Conclusions: FIA was not superior to SIA with hybrid AID system use in physically active children and adolescents with type 1 diabetes. Nonetheless, both insulin formulations enabled high overall TIR and low time above and below ranges, even during and after documented exercise. Trial Registration Clinicaltrials.gov: NCT04853030.

The Type 1 Diabetes and EXercise Initiative: Predicting Hypoglycemia Risk During Exercise for Participants with Type 1 Diabetes Using Repeated Measures Random Forest.

Objective: Exercise is known to increase the risk for hypoglycemia in type 1 diabetes (T1D) but predicting when it may occur remains a major challenge. The objective of this study was to develop a hypoglycemia prediction model based on a large real-world study of exercise in T1D. Research Design and Methods: Structured study-specified exercise (aerobic, interval, and resistance training videos) and free-living exercise sessions from the T1D Exercise Initiative study were used to build a model for predicting hypoglycemia, a continuous glucose monitoring value <70 mg/dL, during exercise. Repeated measures random forest (RMRF) and repeated measures logistic regression (RMLR) models were constructed to predict hypoglycemia using predictors at the start of exercise and baseline characteristics. Models were evaluated with area under the receiver operating characteristic curve (AUC) and balanced accuracy. Results: RMRF and RMLR had similar AUC (0.833 vs. 0.825, respectively) and both models had a balanced accuracy of 77%. The probability of hypoglycemia was higher for exercise sessions with lower pre-exercise glucose levels, negative pre-exercise glucose rates of change, greater percent time <70 mg/dL in the 24 h before exercise, and greater pre-exercise bolus insulin-on-board (IOB). Free-living aerobic exercises, walking/hiking, and physical labor had the highest probability of hypoglycemia, while structured exercises had the lowest probability of hypoglycemia. Conclusions: RMRF and RMLR accurately predict hypoglycemia during exercise and identify factors that increase the risk of hypoglycemia. Lower glucose, decreasing levels of glucose before exercise, and greater pre-exercise IOB largely predict hypoglycemia risk in adults with T1D.

Assessing non-adjunctive CGM safety at home and in new markets (ANSHIN).

INTRODUCTION: Continuous glucose monitoring (CGM) can guide treatment for people with type 1 (T1D) and type 2 diabetes (T2D). The ANSHIN study assessed the impact of non-adjunctive CGM use in adults with diabetes using intensive insulin therapy (IIT). MATERIALS AND METHODS: This single-arm, prospective, interventional study enrolled adults with T1D or T2D who had not used CGM in the prior 6 months. Participants wore blinded CGMs (Dexcom G6) during a 20-day run-in phase, with treatment based on fingerstick glucose values, followed by a 16-week intervention phase and then a randomized 12-week extension phase with treatment based on CGM values. The primary outcome was change in HbA1c. Secondary outcomes were CGM metrics. Safety endpoints were the number of severe hypoglycaemic (SH) and diabetic ketoacidosis (DKA) events. RESULTS: Of the 77 adults enrolled, 63 completed the study. Those enrolled had mean (SD) baseline HbA1c of 9.8% (1.9%), 36% had T1D, and 44% were ≥65 years old. Mean HbA1c decreased by 1.3, 1.0 and 1.0 percentage points for participants with T1D, T2D or age ≥65, respectively (p < .001 for each). CGM-based metrics including time in range also improved significantly. SH events decreased from the run-in period (67.3 per 100 person-years) to the intervention period (17.0 per 100 person-years). Three DKA events unrelated to CGM use occurred during the total intervention period. CONCLUSIONS: Non-adjunctive use of the Dexcom G6 CGM system improved glycaemic control and was safe for adults using IIT.