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Abstract Context Minority young adults (YA) currently represent the largest growing population with type 1 diabetes (T1D) and experience very poor outcomes. Modifiable drivers of disparities need to be identified, but are not well-studied. Objective To describe racial-ethnic disparities among YA with T1D and identify drivers of glycemic disparity other than socioeconomic status (SES). Design Cross-sectional multicenter collection of patient and chart-reported variables, including SES, social determinants of health, and diabetes-specific factors, with comparison between non-Hispanic White, non-Hispanic Black, and Hispanic YA and multilevel modeling to identify variables that account for glycemic disparity apart from SES. Setting Six diabetes centers across the United States. Participants A total of 300 YA with T1D (18-28 years: 33% non-Hispanic White, 32% non-Hispanic Black, and 34% Hispanic). Main Outcome Racial-ethnic disparity in HbA1c levels. Results Non-Hispanic Black and Hispanic YA had lower SES, higher HbA1c levels, and much lower diabetes technology use than non-Hispanic White YA (P < 0.001). Non-Hispanic Black YA differed from Hispanic, reporting higher diabetes distress and lower self-management (P < 0.001). After accounting for SES, differences in HbA1c levels disappeared between non-Hispanic White and Hispanic YA, whereas they remained for non-Hispanic Black YA (+ 2.26% [24 mmol/mol], P < 0.001). Diabetes technology use, diabetes distress, and disease self-management accounted for a significant portion of the remaining non-Hispanic Black–White glycemic disparity. Conclusion This study demonstrated large racial-ethnic inequity in YA with T1D, especially among non-Hispanic Black participants. Our findings reveal key opportunities for clinicians to potentially mitigate glycemic disparity in minority YA by promoting diabetes technology use, connecting with social programs, and tailoring support for disease self-management and diabetes distress to account for social contextual factors.

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. 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. 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). 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). 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.

Abstract Context A 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). Objective The primary objective of this analysis is to evaluate the association between WFPBD on glycemia in individuals with T1D. 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 (ie, WFPBD). Mixed effects linear regression model assessed time in range (TIR), time above range, and time below range. Results We analyzed 7938 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 < .001). Compared with meals with low hPDI, meals with high hPDI had lower glucose excursion (high hPDI: 53 mg/dL, low hPDI: 62 mg/dL; P < .001) and less time >250 mg/dL (high hPDI: 8%, low hPDI: 14%; P < .001). These effects were present but less pronounced by PDI (high PDI: 74%, low PDI: 71%; P = .01). No differences in time below 70 mg/dL and 54 mg/dL were observed by PDI or hPDI. Conclusion 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.

Abstract Context Adults with type 1 diabetes (T1D) face the necessity of balancing the benefits of exercise with the potential hazards of hypoglycemia. Objective This work aimed to assess whether impaired awareness of hypoglycemia (IAH) affects exercise-associated hypoglycemia in adults with T1D. Methods We compared continuous glucose monitoring (CGM)-measured glucose during exercise and for 24 hours following exercise from 95 adults with T1D and IAH (Clarke score ≥4 or ≥1 severe hypoglycemic event within the past year) to 95 “aware” adults (Clarke score ≤2 and no severe hypoglycemic event within the past year) matched on sex, age, insulin delivery modality, and glycated hemoglobin A1c. A total of 4236 exercise sessions, and 1794 exercise days and 839 sedentary days, defined as 24 hours following exercise or a day without exercise, respectively, were available for analysis. Results Participants with IAH exhibited a nonsignificant trend toward greater decline in glucose during exercise compared to “aware” (−21 ± 44 vs −19 ± 43 mg/dL [−1.17 ± 2.44 vs −1.05 ± 2.39 mmol/L], adjusted group difference of −4.2 [95% CI, −8.4 to 0.05] mg/dL [−0.23 95% CI, −.47 to 0.003 mmol/L]; P = .051). Individuals with IAH had a higher proportion of days with hypoglycemic events below 70 mg/dL [3.89 mmol/L] (≥15 minutes <70 mg/dL [<3.89 mmol/L]) both on exercise days (51% vs 43%; P = .006) and sedentary days (48% vs 30%; P = .001). The increased odds of experiencing a hypoglycemic event below 70 mg/dL (<3.89 mmol/L) for individuals with IAH compared to “aware” did not differ significantly between exercise and sedentary days (interaction P = .36). Conclusion Individuals with IAH have a higher underlying risk of hypoglycemia than “aware” individuals. Exercise does not appear to differentially increase risk for hypoglycemia during the activity, or in the subsequent 24 hours for IAH compared to aware individuals with T1D.

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; HbA 1c 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 HbA 1c ≥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 ). Graphical Abstract

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.

Importance As the number of patients with diabetes continues to increase in the United States, novel approaches to clinical care access should be considered to meet the care needs for this population, including support for diabetes-related technology. Objective To evaluate a virtual clinic to facilitate comprehensive diabetes care, support continuous glucose monitoring (CGM) integration into diabetes self-management, and provide behavioral health support for diabetes-related issues. Design, Setting, and Participants This cohort study was a prospective, single-arm, remote study involving adult participants with type 1 or type 2 diabetes who were referred through community resources. The study was conducted virtually from August 24, 2020, to May 26, 2022; analysis was conducted at the clinical coordinating center. Intervention Training and education led by a Certified Diabetes Care and Education Specialist for CGM use through a virtual endocrinology clinic structure, which included endocrinologists and behavioral health team members. Main Outcomes and Measures Main outcomes included CGM-measured mean glucose level, coefficient of variation, and time in range (TIR) of 70 to 180 mg/dL, time with values greater than 180 mg/dL or 250 mg/dL, and time with values less than 70 mg/dL or 54 mg/dL. Hemoglobin A1cwas measured at baseline and at 12 and 24 weeks. Results Among the 234 participants, 160 had type 1 diabetes and 74 had type 2 diabetes. The mean (SD) age was 47 (14) years, 123 (53%) were female, and median diabetes duration was 20 years. Median (IQR) CGM use over 6 months was 96% (91%-98%) for participants with type 1 diabetes and 94% (85%-97%) for those with type 2 diabetes. Mean (SD) hemoglobin A1c(HbA1c) in those with type 1 diabetes decreased from 7.8% (1.6%) at baseline to 7.1% (1.0%) at 3 months and 7.1% (1.0%) at 6 months (mean change from baseline to 6 months, −0.6%, 95% CI, −0.8% to −0.5%;P < .001), with an 11% mean TIR increase over 6 months (95% CI, 9% to 14%;P < .001). Mean HbA1cin participants with type 2 diabetes decreased from 8.1% (1.7%) at baseline to 7.1% (1.0%) at 3 months and 7.1% (0.9%) at 6 months (mean change from baseline to 6 months, −1.0%; 95% CI, −1.4% to −0.7%;P < .001), with an 18% TIR increase over 6 months (95% CI, 13% to 24%;P < .001). In participants with type 1 diabetes, mean percentage of time with values less than 70 mg/dL and less than 54 mg/dL decreased over 6 months by 0.8% (95% CI, −1.2% to −0.4%;P = .001) and by 0.3% (95% CI, −0.5% to −0.2%,P < .001), respectively. In the type 2 diabetes group, hypoglycemia was rare (mean [SD] percentage of time <70 mg/dL, 0.5% [0.6%]; and <54 mg/dL, 0.07% [0.14%], over 6 months). Conclusions and Relevance Results from this cohort study demonstrated clinical benefits associated with implementation of a comprehensive care model that included diabetes education. This model of care has potential to reach a large portion of patients with diabetes, facilitate diabetes technology adoption, and improve glucose control.

Insulin is commonly used to reverse gluco‐toxicity in youth with newly diagnosed type 2 diabetes (T2D), but many are subsequently weaned off insulin. We analyzed Pediatric Diabetes Consortium (PDC) data to determine how long glycemic control is maintained after termination of initial insulin treatment. Youth with T2D who had previously been on insulin but were on either an intensive lifestyle intervention alone or metformin alone upon enrollment in the PDC T2D Registry were studied (N = 183). The primary outcome was time to treatment failure, defined by need to restart insulin or metformin or another diabetes medication. Data were analyzed using logistic regression to assess risk factors for treatment failure. Of the 183 participants studied (mean age 15 years, diabetes duration 1.7 years), 54% experienced treatment failure (median follow‐up time 1.7 years). In the subgroup on metformin monotherapy (N = 140), 45% subsequently required restart of insulin. Moreover, of participants in the subgroup treated with an intensive lifestyle intervention alone (N = 43), 81% restarted insulin or were treated with metformin or other diabetes medication. In both groups, median time to treatment failure was 1.2 years. Higher HbA1c at enrollment was significantly associated with treatment failure (p < 0.001). Youth with T2D who are initially treated with insulin have a high rate of treatment failure when switched to intensive lifestyle alone or metformin alone. Our data highlight the severe and progressive nature of youth onset T2D, hence patients should be monitored closely for deteriorating glycemic control after being weaned off insulin.

Objective To examine and compare the clinical characteristics and treatment of youth with type 2 diabetes (T2D) in two registries: one in Europe and one in the United States. Methods Youth with onset of T2D at 10 to 18 years of age with current age <20 years and an office visit after diabetes duration >1 year were identified in the European (Prospective Diabetes Follow‐up, DPV) and the United States (Pediatric Diabetes Consortium, PDC) databases. Demographic, physical and clinical characteristics and treatment at diagnosis as well as physical characteristics, treatment, laboratory data, and diabetes adverse events at most recent visit were analyzed from both registries. Results At diagnosis, the majority were female and obese; 70% of DPV vs 34% of PDC youth were diagnosed by targeted diabetes testing. PDC youth were younger, 12 vs 13 years (P < 0.001), had a greater body mass index‐SDS, 3.07 vs 2.74 (P < 0.001), a higher hemoglobin A1c (HbA1c), 9.9% vs 7.1% (P < 0.001), were more likely to present in DKA, 7.5% vs 1.3% (P < 0.001) and more likely to be treated with insulin, 62% vs 32% (P < 0.001); insulin treatment difference was not significant when adjusted for HbA1c. At follow‐up, DPV youth had shorter diabetes duration, 2.1 vs 3.2 years (P < 0.001), lower HbA1c, 6.5% vs 7.8% (P < 0.001), were less likely to be treated with insulin, 36% vs 56%, (P < 0.001), and were more likely to have dyslipidemia and hypertension than PDC youth. PDC youth had a higher rate of microalbuminuria. Conclusions Both DPV and PDC youth have multiple risks for diabetes complications. Understanding reasons for persistently higher HbA1c in PDC youth requires further study.

Background/Objective Restrictive eligibility criteria have hampered enrollment into trials for new drugs for youth with type 2 diabetes (T2D). We utilized Pediatric Diabetes Consortium (PDC) T2D Registry enrollment data to estimate the percentage of patients who would be excluded from current T2D trials based on out‐of‐range HbA1c levels. We also examined whether well‐controlled patients could be included because baseline HbA1c would rise during a 6 to 12‐month study if assigned to control group. Methods Clinical characteristics and HbA1c levels were collected from 956 T2D patients aged 10 to <18 years upon Registry enrollment. HbA1c levels were also analyzed in 6‐month intervals during the first 30 months of T2D duration. Results There was an approximately 2:1 ratio of females to males; the majority were obese and from economically disadvantaged minority families. On enrollment in the Registry, 53% of patients would be excluded from current trials because HbA1c levels were either <6.5% (<48 mmol/mol) (37%) or >10.5% (>91 mmol/mol) (16%). Furthermore, in patients with HbA1c levels <6.5% (<48 mmol/mol) and T2D duration between 6 and 30 months, mean HbA1c levels increased by 0.6% (6 mmol/mol) and 0.9% (10 mmol/mol) over the subsequent 6 and 12 months, respectively. Conclusions Eligibility criteria for current clinical trials still exclude a large proportion of pediatric T2D patients because of HbA1c levels. Including patients with HbA1c <6.5% (<48 mmol/mol) would enhance recruitment and allow comparisons of the investigational treatment with placebo‐assigned subjects in whom HbA1c levels would on average increase during the 6 to 12 months of the trial.