Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aims: To identify dietary intake and eating patterns of people with type 1 diabetes from childhood to later adulthood in relation to HbA1c.Methods: Trained interviewers conducted 24-hour recalls via phone utilizing a multiple pass approach and administered two nutrition questionnaires; 463 participants (or parents of participants) within the T1D Exchange clinic registry were included. Participants were 5 to 81 years with 80-101 participants in five age groups; 56% were female, and 92% were white, with a median diabetes duration of 11.1 years and a median HbA1c of 7.4% [57 mmol/mol]).Results: Those with type 1 diabetes consumed less calories from carbohydrates and more from fats and protein than those in the general population, based on the National Health and Nutrition Examination Survey data. Carbohydrate intake was not correlated with HbA1c levels. Increased fiber intake, more eating occasions, higher Healthy Eating Index scores, and higher nutrition knowledge scores were each associated with lower HbA1c levels.Conclusions: Food intake, eating patterns and nutrition knowledge are associated with glycemic control across a registry-based cohort of adults and children with type 1 diabetes. Additionally, these data can inform the design of future studies to advance our understanding of nutritional influences on type 1 diabetes self-care and control. [ABSTRACT FROM AUTHOR]

Background: Debate exists as to whether the higher hemoglobin A1c (HbA1c) levels observed in black persons than in white persons are due to worse glycemic control or racial differences in the glycation of hemoglobin.Objective: To determine whether a racial difference exists in the relationship of mean glucose and HbA1c.Design: Prospective, 12-week observational study.Setting: 10 diabetes centers in the United States.Participants: 104 black persons and 104 white persons aged 8 years or older who had had type 1 diabetes for at least 2 years and had an HbA1c level of 6.0% to 12.0%.Measurements: Mean glucose concentration, measured by using continuous glucose monitoring and compared by race with HbA1c, glycated albumin, and fructosamine values.Results: The mean HbA1c level was 9.1% in black persons and 8.3% in white persons. For a given HbA1c level, the mean glucose concentration was significantly lower in black persons than in white persons (P = 0.013), which was reflected in mean HbA1c values in black persons being 0.4 percentage points (95% CI, 0.2 to 0.6 percentage points) higher than those in white persons for a given mean glucose concentration. In contrast, no significant racial differences were found in the relationship of glycated albumin and fructosamine levels with the mean glucose concentration (P > 0.20 for both comparisons).Limitation: There were too few participants with HbA1c levels less than 6.5% to generalize the results to such individuals.Conclusion: On average, HbA1c levels overestimate the mean glucose concentration in black persons compared with white persons, possibly owing to racial differences in the glycation of hemoglobin. However, because race only partially explains the observed HbA1c differences between black persons and white persons, future research should focus on identifying and modifying barriers impeding improved glycemic control in black persons with diabetes.Primary Funding Source: Helmsley Charitable Trust. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Maintenance of glycemic control during and after exercise remains a major challenge for individuals with type 1 diabetes. Glycemic responses to exercise may differ by exercise type (aerobic, interval, or resistance), and the effect of activity type on glycemic control after exercise remains unclear. RESEARCH DESIGN AND METHODS: The Type 1 Diabetes Exercise Initiative (T1DEXI) was a real-world study of at-home exercise. Adult participants were randomly assigned to complete six structured aerobic, interval, or resistance exercise sessions over 4 weeks. Participants self-reported study and nonstudy exercise, food intake, and insulin dosing (multiple daily injection [MDI] users) using a custom smart phone application and provided pump (pump users), heart rate, and continuous glucose monitoring data. RESULTS: A total of 497 adults with type 1 diabetes (mean age ± SD 37 ± 14 years; mean HbA1c ± SD 6.6 ± 0.8% [49 ± 8.7 mmol/mol]) assigned to structured aerobic (n = 162), interval (n = 165), or resistance (n = 170) exercise were analyzed. The mean (± SD) change in glucose during assigned exercise was −18 ± 39, −14 ± 32, and −9 ± 36 mg/dL for aerobic, interval, and resistance, respectively (P < 0.001), with similar results for closed-loop, standard pump, and MDI users. Time in range 70–180 mg/dL (3.9–10.0 mmol/L) was higher during the 24 h after study exercise when compared with days without exercise (mean ± SD 76 ± 20% vs. 70 ± 23%; P < 0.001). CONCLUSIONS: Adults with type 1 diabetes experienced the largest drop in glucose level with aerobic exercise, followed by interval and resistance exercise, regardless of insulin delivery modality. Even in adults with well-controlled type 1 diabetes, days with structured exercise sessions contributed to clinically meaningful improvement in glucose time in range but may have slightly increased time below range. [ABSTRACT FROM AUTHOR]

Purpose: To describe reliable methods for determining central corneal endothelial cell density (ECD) in a multicenter eye bank study. Methods: The Specular Microscopy Reading Center utilized a dual-grading procedure and adjudication process to classify image quality and determine ECD for a subset of donor endothelial images obtained in the Specular Microscopy Ancillary Study, which is part of the Cornea Donor Study. 1 Two certified readers classified images as analyzable (excellent, good, fair) or unanalyzable and determined the ECD using a variable frame technique. An adjudicator also evaluated the images if quality classifications by the two readers differed by one grade, if any reader found the image unanalyzable, and/or if the ECD determination between the two readers was = 5%. Results: Image quality categorization by the two readers was identical for 441 (64%) of 688 donor images. The ECD differed by [ABSTRACT FROM AUTHOR]

Purpose: To assess visual function more than 10 years after an episode of optic neuritis in patients enrolled in the Optic Neuritis Treatment Trial.Design: Longitudinal follow-up of a randomized clinical trial.Methods: Vision testing included measures of visual acuity, contrast sensitivity, and visual field. Quality of life was assessed with the National Eye Institute Visual Function Questionnaire.Results: Examinations were completed on 319 patients. In most patients, visual function test results in the eyes that experienced optic neuritis at study entry ("affected eyes") were normal or only slightly abnormal after 9.9 to 13.7 years. Visual acuity in the affected eyes was >or=20/20 in 74%, 20/25 to 20/40 in 18%, <20/40 to 20/200 in 5%, and <20/200 in 3%. On average, visual function was worse in patients with multiple sclerosis (MS) than in those without MS. Recurrent optic neuritis in either eye occurred in 35% of patients. Such attacks were more frequent in patients with MS (P <.001). The National Eye Institute Visual Function Questionnaire scores were lower when visual acuity was abnormal and when MS was present.Conclusions: Most patients retained good to excellent vision more than 10 years after an attack of optic neuritis. Recurrences were more frequent in patients with MS. [ABSTRACT FROM AUTHOR]

Background: Managing exercise in type 1 diabetes is challenging, in part, because different types of exercises can have diverging effects on glycemia. The aim of this work was to develop a classification model that can classify an exercise event (structured or unstructured) as aerobic, interval, or resistance for the purpose of incorporation into an automated insulin delivery (AID) system. Methods: A long short-term memory network model was developed with real-world data from 30-min structured sessions of at-home exercise (aerobic, resistance, or mixed) using triaxial accelerometer, heart rate, and activity duration information. The detection algorithm was used to classify 15 common free-living and unstructured activities and relate each to exercise-associated change in glucose. Results: A total of 1610 structured exercise sessions were used to train, validate, and test the model. The accuracy for the structured exercise sessions in the testing set was 72% for aerobic, 65% for interval, and 77% for resistance. In addition, we tested the classifier on 3328 unstructured sessions. We validated the session-associated change in glucose against the expected change during exercise for each type. Mean and standard deviation of the change in glucose of −20.8 (40.3) mg/dL were achieved for sessions classified as aerobic, −16.2 (39.0) mg/dL for sessions classified as interval, and −11.6 (38.8) mg/dL for sessions classified as resistance. Conclusions: The proposed algorithm reliably identified physical activity associated with expected change in glucose, which could be integrated into an AID system to manage the exercise disturbance in glycemia according to the predicted class. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes in the U.S. is more prevalent in youth of minority racial-ethnic background, but disparities in health outcomes have not been examined in this population.Research Design and Methods: We examined racial-ethnic differences in the initial presentation and subsequent comorbidities in 1,217 youth with type 2 diabetes (63% girls) enrolled in the Pediatric Diabetes Consortium (PDC) Registry from February 2012 to June 2018. Demographic and clinical data were collected from medical records and participant self-report.Results: Overall, the mean age at presentation was 13.4 ± 2.4 years, and BMI was 35.0 ± 9.4 kg/m2. HbA1c was higher and C-peptide was lower in non-Hispanic Black (NHB) and Hispanic (H) youth compared with non-Hispanic White (NHW) youth. NHB were three times as likely to present in diabetic ketoacidosis (19%) versus NHW (6.3%) and H (7.5%), and NHB and H both had a worse HbA1c trajectory compared with NHW peers. Microalbuminuria was documented in 11%, hypertension in 34%, and dyslipidemia in 42% of Registry participants, with no significant difference among racial-ethnic groups. Nonalcoholic fatty liver disease (NAFLD) was diagnosed in 9% and 11% of H and NHW, respectively, versus 2% in NHB.Conclusions: NHB and H youth with type 2 diabetes presented with worse metabolic control and had persistently worse HbA1c trajectories compared with NHW. Comorbidities exist in a large percentage of these youth independent of race-ethnicity, except for NAFLD being less prevalent in NHB. Greater efforts are needed to mitigate racial-ethnic disparities at diagnosis and in the management of youth with type 2 diabetes. [ABSTRACT FROM AUTHOR]

Background: The amount and type of food consumed impacts the glycemic response and insulin needs of people with type 1 diabetes mellitus (T1DM). Daily variability in consumption, reflected in diet quality, may acutely impact glycemic levels and insulin needs. Objective: Type 1 Diabetes Exercise Initiative (T1DEXI) data were examined to evaluate the impact of daily diet quality on near-term glycemic control and interaction with exercise. Methods: Using the Remote Food Photography Method, <8 d of dietary intake data were analyzed per participant. Diet quality was quantified with the Healthy Eating Index-2015 (HEI), where a score of 100 indicates the highest-quality diet. Each participant day was classified as low HEI (<57) or high HEI (>57) based on the mean of nationally reported HEI data. Within participants, the relationship between diet quality and subsequent glycemia measured by continuous glucose monitoring (CGM) and total insulin dose usage was evaluated using a paired t-test and robust regression models. Results: Two hundred twenty-three adults (76% female) with mean ± SD age, HbA1c, and body mass index (BMI) of 37 ± 14 y, 6.6% ± 0.7%, and 25.1 ± 3.6 kg/m², respectively, were included in these analyses. The mean HEI score was 56 across all participant days. On high HEI days (mean, 66 ± 4) compared with low HEI days (mean, 47 ± 5), total time in range (70-180 mg/dL) was greater (77.2% ± 14% compared with 75.7% ± 14%, respectively, P = 0.01), whereas time above 180 mg/dL (19% ± 14% compared with 21% ± 15%, respectively, P = 0.004), mean glucose (143 ± 22 compared with 145 ± 22 mg/dL, respectively, P = 0.02), and total daily insulin dose (0.52 ± 0.18 compared with 0.54 ± 0.18 U/kg/d, respectively, P = 0.009) were lower. The interaction between diet quality and exercise on glycemia was not significant. Conclusions: Higher HEI scores correlated with improved glycemia and lower insulin needs, although the impact of diet quality was modest and smaller than the previously reported impact of exercise. [ABSTRACT FROM AUTHOR]

Objective: This study analysis was designed to examine the 24-h effects of exercise on glycemic control as measured by continuous glucose monitoring (CGM). Methods: Individuals with type 1 diabetes (ages: 15-68 years; hemoglobin A1c: 7.5% ± 1.5% [mean ± standard deviation (SD)]) were randomly assigned to complete twice-weekly aerobic, high-intensity interval, or resistance-based exercise sessions in addition to their personal exercise sessions for a period of 4 weeks. Exercise was tracked with wearables and glucose concentrations assessed using CGM. An exercise day was defined as a 24-h period after the end of exercise, while a sedentary day was defined as any 24-h period with no recorded exercise ≥10 min long. Sedentary days start at least 24 h after the end of exercise. Results: Mean glucose was lower (150 ± 45 vs. 166 ± 49 mg/dL, P = 0.01), % time in range [70-180 mg/dL] higher (62% ± 23% vs. 56% ± 25%, P = 0.03), % time >180 mg/dL lower (28% ± 23% vs. 37% ± 26%, P = 0.01), and % time <70 mg/dL higher (9.3% ± 11.0% vs. 7.1% ± 9.1%, P = 0.04) on exercise days compared with sedentary days. Glucose variability and % time <54 mg/dL did not differ significantly between exercise and sedentary days. No significant differences in glucose control by exercise type were observed. Conclusion: Participants had lower 24-h mean glucose levels and a greater time in range on exercise days compared with sedentary days, with mode of exercise affecting glycemia similarly. In summary, this study offers data supporting frequency of exercise as a method of facilitating glucose control but does not suggest an effect for mode of exercise. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background/Aims: Many adolescents with type 2 diabetes (T2D) have rapid deterioration of glycemic control on metformin monotherapy within 2 years of diagnosis. Methods: Enrollment data from the Pediatric Diabetes Consortium T2D Registry were used to categorize 276 youth with a T2D duration ≥2 years into two groups: (1) participants with HbA1c <7.5% on metformin monotherapy (group 1, n = 75) and (2) participants treated with insulin ± metformin (group 2, n = 201). The characteristics of the groups were compared. Results: At enrollment, groups 1 and 2 did not differ in age (16.2 vs. 16.8 years) or BMI percentile (99 vs. 98%); group 2 had higher HbA1c (9.9% [85 mmol/mol] vs. 5.9% [41 mmol/mol], p < 0.001). Lower HbA1c and metformin monotherapy at diagnosis were associated with a greater likelihood of adequate control with metformin alone (p < 0.001). In multivariable analysis, HbA1c at diagnosis (p = 0.001) and diabetes duration (p = 0.009) were associated with adequate control on metformin. The HbA1c trajectory after diagnosis was worse in group 2. Conclusion: Durable metabolic control of T2D with metformin monotherapy is most likely in youth presenting with lower HbA1c and with shorter diabetes duration, independent of age, race-ethnicity, and BMI. Elevated HbA1c levels in those on insulin therapy highlight the importance of early diagnosis and a better understanding of glycemic control barriers. [ABSTRACT FROM AUTHOR]

Background/Aims: Youth with type 2 diabetes (T2D) have poor compliance with medical care. This study aimed to determine which demographic and clinical factors differ between youth with T2D who receive care in a pediatric diabetes center versus youth lost to follow-up for >18 months. Methods: Data were analyzed from 496 subjects in the Pediatric Diabetes Consortium registry. Enrollment variables were selected a priori and analyzed with univariable and multivariable logistic regression models. Results: After a median of 1.3 years from enrollment, 55% of patients were lost to follow-up. The final model included age, race/ethnicity, parent education, and estimated distance to study site. The odds ratio (99% confidence interval) of loss to follow-up was 2.87 (1.34, 6.16) for those aged 15 to <18 years versus those aged 10 to <13 years and 6.57 (2.67, 16.15) for those aged =18 years versus those aged 10 to <13 years. Among patients living more than 50 miles from the clinic, the odds ratio of loss to follow-up was 3.11 (1.14, 8.49) versus those living within 5 miles of the site. Conclusion: Older adolescents with T2D are more likely to be lost to follow-up, but other socioeconomic factors were not significant predictors of clinic follow-up. [ABSTRACT FROM AUTHOR]

[Display omitted] Position statement guidelines should help people with type 1 diabetes (T1D) improve glucose outcomes during exercise. In a 4-week observational study, continuous glucose, insulin, and nutrient data were collected from 561 adults with T1D. Glucose outcomes were calculated during exercise, post-exercise, and overnight, and were compared for sessions when participants used versus did not use exercise guidelines for open-loop (OL) and automated insulin delivery (AID) therapy. Guidelines requiring behaviour modification were rarely used while guidelines not requiring modification were often used. The guideline recommending reduced meal insulin before exercise was associated with lower time <3.9 mmol/L during exercise (−2.2 %, P=0.02) for OL but not significant for AID (−1.4 %, P=0.27). Compared to exercise with low glucose (<3.9 mmol/L) in prior 24-hours, sessions without recent low glucose had lower time <3.9 mmol/L during exercise (−1.2 %, P<0.001). The AID guideline for no carbohydrates before exercise when CGM is flat, or increasing, was not associated with improved glycaemia. Free-living datasets may be used to evaluate usage and benefit of position statement guidelines. Evidence suggests OL participants who reduced meal insulin prior to exercise and did not have low glucose in the prior 24 h had less time below range. [ABSTRACT FROM AUTHOR]

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]

Objective To examine the long-term effect of donor diabetes history on graft failure and endothelial cell density (ECD) after penetrating keratoplasty (PK) in the Cornea Donor Study. Design Multicenter, prospective, double-masked, controlled clinical trial. Participants One thousand ninety subjects undergoing PK for a moderate risk condition, principally Fuchs' dystrophy or pseudophakic or aphakic corneal edema, were enrolled by 105 surgeons from 80 clinical sites in the United States. Methods Corneas from donors 12 to 75 years of age were assigned by 43 eye banks to participants without respect to recipient factors. Donor and recipient diabetes status was determined from existing medical records. Images of the central endothelium were obtained before surgery (baseline) and at intervals for 10 years after surgery and were analyzed by a central image analysis reading center to determine ECD. Main Outcome Measures Time to graft failure (regraft or cloudy cornea for 3 consecutive months) and ECD. Results There was no statistically significant association of donor diabetes history with 10-year graft failure, baseline ECD, 10-year ECD, or ECD values longitudinally over time in unadjusted analyses, nor after adjusting for donor age and other significant covariates. The 10-year graft failure rate was 23% in the 199 patients receiving a cornea from a donor with diabetes versus 26% in the 891 patients receiving a cornea from a donor without diabetes (95% confidence interval for the difference, −10% to 6%; unadjusted P = 0.60). Baseline ECD ( P = 0.71), 10-year ECD ( P > 0.99), and changes in ECD over 10 years ( P = 0.86) were similar comparing donor groups with and without diabetes. Conclusions The study results do not suggest an association between donor diabetes and PK outcome. However, the assessment of donor diabetes was imprecise and based on historical data only. The increasing frequency of diabetes in the aging population in the United States affects the donor pool. Thus, the impact of donor diabetes on long-term endothelial health after PK or endothelial keratoplasty, or both, warrants further study with more precise measures of diabetes and its complications. [ABSTRACT FROM AUTHOR]

Objective: To determine whether the 10-year success rate of penetrating keratoplasty for corneal endothelial disorders is associated with donor age. Design: Multicenter, prospective, double-masked clinical trial. Participants: A total of 1090 participants undergoing penetrating keratoplasty at 80 sites for Fuchs' dystrophy (62%), pseudophakic/aphakic corneal edema (34%), or another corneal endothelial disorder (4%) and followed for up to 12 years. Methods: Forty-three eye banks provided corneas from donors aged 12 to 75 years, using a randomized approach to assign donor corneas to study participants without respect to recipient factors. Surgery and postoperative care were performed according to the surgeons' usual routines. Main Outcome Measures: Graft failure defined as a regraft or, in the absence of a regraft, a cloudy cornea that was sufficiently opaque to compromise vision for 3 consecutive months. Results: In the primary analysis, the 10-year success rate was 77% for 707 corneas from donors aged 12 to 65 years compared with 71% for 383 donors aged 66 to 75 years (difference, +6%; 95% confidence interval, −1 to +12; P = 0.11). When analyzed as a continuous variable, higher donor age was associated with lower graft success beyond the first 5 years (P < 0.001). Exploring this association further, we observed that the 10-year success rate was relatively constant for donors aged 34 to 71 years (75%). The success rate was higher for 80 donors aged 12 to 33 years (96%) and lower for 130 donors aged 72 to 75 years (62%). The relative decrease in the success rate with donor ages 72 to 75 years was not observed until after year 6. Conclusions: Although the primary analysis did not show a significant difference in 10-year success rates comparing donor ages 12 to 65 years and 66 to 75 years, there was evidence of a donor age effect at the extremes of the age range. Because we observed a fairly constant 10-year success rate for donors aged 34 to 71 years, which account for approximately 75% of corneas in the United States available for transplant, the Cornea Donor Study results indicate that donor age is not an important factor in most penetrating keratoplasties for endothelial disease. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references. [Copyright &y& Elsevier]

Objective: To examine the effect of donor age and other perioperative factors on long-term endothelial cell loss after penetrating keratoplasty (PKP). Design: Multicenter, prospective, double-masked clinical trial. Participants: We included 176 participants from the Cornea Donor Study cohort who had not experienced graft failure ≥10 years after PKP for a moderate risk condition (principally Fuchs' dystrophy or pseudophakic/aphakic corneal edema). Methods: Corneas from donors 12 to 75 years old were assigned to participants using a randomized approach, without respect to recipient factors. Surgery and postoperative care were performed according to the surgeons' usual routines. Images of the central endothelium were obtained preoperatively and at intervals for 10 years postoperatively. Images were analyzed by a central image analysis reading center to determine endothelial cell density (ECD). Main Outcome Measures: Endothelial cell density at 10 years. Results: Among study participants with a clear graft at 10 years, the 125 who received a cornea from a donor 12 to 65 years old experienced a median cell loss of 76%, resulting in a 10-year median ECD of 628 cells/mm2 (interquartile range [IQR], 522–850 cells/mm2), whereas the 51 who received a cornea from a donor 66 to 75 years old experienced a cell loss of 79%, resulting in a median 10-year ECD of 550 cells/mm2 (IQR, 483–694 cells/mm2; P adjusted for baseline ECD = 0.03). In addition to younger donor age, higher ECD values were significantly associated with higher baseline ECD (P < 0.001) and larger donor tissue size (P < 0.001). Forty-two of the 176 participants (24%) had an ECD of <500 cells/mm2 at 10 years and only 24 (14%) had an ECD of >1000 cells/mm2. Conclusions: Substantial cell loss occurs in eyes with a clear graft 10 years after PKP, with the rate of cell loss being slightly greater with older donor age. Greater preoperative ECD and larger donor tissue size are associated with higher ECD at 10 years. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references. [Copyright &y& Elsevier]

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]

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]

Understanding glucodynamics in youth with T1D during a variety of physical activities (PA) may help guide management customized to type of activity. The present study profiled PA, heart rate (HR) and glycemia in youth participating in 5-day hockey camp. Youth attending the Dskate hockey camp, which includes daily hockey drills (D), a tournament (T), mixed martial arts (MMA) and off-ice activities (O), were invited to participate. Data were collected from wrist-based HR (Garmin vívosmart® 3) and Dexcom G5. Thirty-one youth (age 13±2 yrs; 94% on CSII and all using CGM; BMI percentile 69±21%; insulin dose 42±18U/d; HbA1c 7.7±0.8%; duration of T1D 6±3yrs) participated in this observational study. During the day camp, time in range (70-180mg/dL) was 57±15% (mean±SD), time >180mg/dL was 41±16%, and the median time <70mg/dL was 1.7% (IQR: 1.0-3.6%). Mean glucose levels during the activity fell from onset in D (from 183±79 to 170±74 mg/dL) and MMA (201±77 to 194±67 mg/dL), but rose in T (174±53 to 188±50 mg/dL) and O (172±63 to 178±60 mg/dL). HR was similar across activities (110±12 bpm). In a cohort of motivated youth attending a hockey camp, glucose levels tend to drop during practice and MMA but tend to rise in tournaments and in off-ice activities. Future studies will use similar wearable technologies to examine glucodynamics in a larger observational study of a more diverse sample of youth living with T1D. Disclosure: M. Riddell: Advisory Panel; Self; Zucara Therapeutics Inc. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation. Speaker's Bureau; Self; Medtronic, Novo Nordisk A/S. Z. Li: None. R.L. Gal: None. A. Mazzuoccolo: None. E. Greene: None. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. L.C. Beaulieu: None. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc. J. Sherr: Advisory Panel; Self; Bigfoot Biomedical, Cecelia Health. Consultant; Self; Eli Lilly and Company, Lexicon Pharmaceuticals, Inc., Medtronic, Sanofi, T1D Exchange. S.R. Patton: None. R. Beck: None. Funding: The Leona M. and Harry B. Helmsley Charitable Trust [ABSTRACT FROM AUTHOR]

Background: Given the paucity of treatment options for youth with type 2 diabetes (T2D), insulin is commonly used to rapidly reverse gluco-toxicity. Subsequently, some youth with short duration T2D can be weaned off insulin soon after diagnosis. We analyzed data from the Pediatric Diabetes Consortium (PDC) Registry to determine how long glycemic control is maintained off insulin. Methods: Youth with T2D in the PDC Registry on metformin alone or lifestyle therapy alone upon registry enrollment but had previously been on insulin were included in this study (N=183). The primary outcome was time to treatment failure, defined by need to restart insulin or other diabetes medication (including metformin). Clinical data at enrollment and follow-up were analyzed using logistic regression to assess risk factors for treatment failure. Results: Of participants who had previously been on insulin therapy (58% female, 53% Hispanic, mean age 15 ± 2 years, median diabetes duration 1.7 years), 54% (98/183) experienced treatment failure. In the subgroup on metformin alone at enrollment (N=140), 45% restarted insulin, while in the lifestyle alone subgroup (N=43), 81% restarted insulin or other diabetes medication. Time to treatment failure was a median of 1.2 years. High HbA1c (mean 7.7% vs. 6.1% in the treatment failure group vs. non-failure group; p<0.001) and lifestyle alone treatment regimen (p=0.003) at enrollment were associated with treatment failure. At time of treatment failure, HbA1c had increased (mean: 1.2%) and BMI z-score decreased (mean: 0.07 kg/m2) from time of enrollment. HbA1c at diagnosis was not associated with treatment failure (mean 10.8 vs. 10.6% in the treatment failure group vs. non-failure group). Conclusion: Youth with T2D previously treated with insulin have a high treatment failure rate on lifestyle or metformin therapy alone and are likely to restart insulin therapy. Youth with T2D should be monitored closely for worsening glycemic control. Disclosure: R. Wolf: None. P. Cheng: None. R.L. Gal: None. L.C. Beaulieu: None. C. Kollman: Other Relationship; Self; Dexcom, Inc., Tandem Diabetes Care. E.M. Isganaitis: None. S.N. Magge: None. L.D. Mastrandrea: Board Member; Self; American Academy of Pediatrics. Research Support; Self; AstraZeneca, JDRF, Novo Nordisk Inc., Sanofi-Aventis. G.J. Klingensmith: Research Support; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Novo Nordisk Inc., Takeda Pharmaceutical Company Limited. W.V. Tamborlane: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Novo Nordisk Inc., Sanofi US. Other Relationship; Self; Eisai Inc., MannKind Corporation. M.A. Van Name: None. Funding: Boehringer Ingelheim; Novo Nordisk; Takeda Pharmaceutical Company Limited [ABSTRACT FROM AUTHOR]

Background: Youth with T2D experience barriers to obtaining diabetes care. We evaluated whether healthcare utilization (HCU) was associated with glycemic outcomes in youth with T2D. Methods: We evaluated annual HCU in 1,324 T2D PDC youth by diabetes duration up to 13 years post diagnosis. Medical record and self-reported HCU data were collected at enrollment and annually. Frequency of diabetes related inpatient admissions, Emergency Department (ED), urgent care, Diabetes Management (DM), Registered Dietician (RD) visits, and 911 calls were collected. Results: After adjusting for age, gender, diabetes duration, and annual visits, those with ≥1 admission/year, regardless of admission reason, (16%) had a higher A1c vs. those without admission, 8.8 vs. 8.4% (p=0.002). Those with ≥2 DM office visits/year (75%) had lower A1c vs. those with 0-1 visits, 8.4 vs. 8.8% (p=0.001). BMI and A1c did not differ by number of RD visits (p=0.63 and 0.39 respectively); RD visits/year decreased over time. Conclusions: In T2D youth, hospitalizations and <2 DM office visits/year are associated with higher A1c. Frequencies of ED and inpatient admissions for T2D are stable following the first year of T2D diagnosis. Only 24-38% of T2D youth had 4 DM visits/year while ∼50% have ≥3 visits a year, even with long T2D duration. Methods to improve follow-up care are needed to improve outcomes in youth with T2D. Disclosure: G.J. Klingensmith: Consultant; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Takeda Pharmaceutical Company Limited. Research Support; Self; Novo Nordisk Foundation. Stock/Shareholder; Spouse/Partner; Dexcom, Inc. P. Cheng: None. R.L. Gal: None. L.C. Beaulieu: None. C. Kollman: None. R.L. Adams: None. T.S. Hannon: Advisory Panel; Self; Eli Lilly and Company. L.M. Looper: None. W.V. Tamborlane: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic MiniMed, Inc., Novo Nordisk Inc., Sanofi, Takeda Pharmaceutical Company Limited. Funding: Boehringer Ingelheim; Novo Nordisk; Takeda Pharmaceutical Company Limited [ABSTRACT FROM AUTHOR]

Background: Racial differences in diabetes control and cardiovascular disease risk factors have been reported in youth with diabetes, with minority youth having worse glycemic control and comorbidities compared with non-Hispanic white (NHW) peers. Methods: We examined racial differences in comorbidities in youth (<21 years at enrollment) with T2D (N=1217, 63% females) enrolled in the PDC Registry from February 2012 to June 2018. Demographic and clinical data collected from medical records and participant self-report were compared using logistic and linear regression models adjusted for relevant confounders. Results: The mean age at presentation was 13.4 ± 2.4 years, and mean BMI Z-score was 2.30 ± 0.45. African-American (AA) and Hispanic (H) youth had higher HbA1c and lower C-peptide at diagnosis compared to NHW, with AA 3 times as likely to present in DKA compared with NHW. Microalbuminuria was present in 11%, hypertension in 34% and dyslipidemia in 42% with no significant differences in these comorbidities among racial groups. Nonalcoholic fatty liver disease (NAFLD) was diagnosed in 9% and 11% of H and NHW, respectively vs. 2% in AA (Table). Conclusion: Minority youth with T2D presented with worse metabolic control compared with NHW, with higher rates of DKA in AA youth. Comorbidities exist in a large percentage of youth with T2D independent of ethnicity, except for NAFLD being less prevalent in AA. Disclosure: F. Bacha: Research Support; Self; National Institute of Diabetes and Digestive and Kidney Diseases, Pediatric Diabetes Consortium. Other Relationship; Self; AstraZeneca, Jaeb Center for Health Research. P. Cheng: None. R.L. Gal: None. L.C. Beaulieu: None. C. Kollman: None. A. Adolph: None. I. Jindal: None. A.H. Shoemaker: Advisory Panel; Self; Rhythm Pharmaceuticals, Inc. Research Support; Self; AstraZeneca, GLWL Research Inc., Novo Nordisk Inc., Rhythm Pharmaceuticals, Inc., Soleno Therapeutics. R.M. Wolf: None. G.J. Klingensmith: Consultant; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Takeda Pharmaceutical Company Limited. Research Support; Self; Novo Nordisk Foundation. Stock/Shareholder; Spouse/Partner; Dexcom, Inc. W.V. Tamborlane: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic MiniMed, Inc., Novo Nordisk Inc., Sanofi, Takeda Pharmaceutical Company Limited. Funding: Boehringer Ingelheim; Novo Nordisk; Takeda Pharmaceutical Company Limited [ABSTRACT FROM AUTHOR]

Background: While consensus guidelines indicate that all youth with T2D should be treated with metformin ± insulin at diagnosis, >10% of patients were treated solely with diet/lifestyle on enrollment in the Pediatric Diabetes Consortium (PDC) T2D Registry. Methods: Clinical data was collected on 1,324 Registry patients (age 6 to <21 years) from medical records and interviews. Data in patients not receiving drug treatment were compared to those receiving antidiabetic drug treatment using logistic regression models. Results: Of the 1,324 Registry patients, 163 (12%) were treated with diet/lifestyle alone on enrollment. Of these, 73% of were initially treated with metformin ± insulin at diagnosis. The diet/lifestyle and drug-treated groups did not differ by age, gender, diabetes duration, or BMI (Table). However, mean A1c was lower and the proportion of patients with A1c <7.5% was higher in the diet/lifestyle alone group compared to the drug-treated group at enrollment and diagnosis (p<0.001, Table). There were site differences in prescribing drug-treatment (p<0.001) and a greater proportion of patients in the diet/lifestyle alone group were without health insurance. Conclusion: These data indicate that a substantial proportion of youth with T2D can maintain target A1c levels <7.5% despite being weaned off glucose lowering drugs during the first 1-2 years after diagnosis Disclosure: W.V. Tamborlane: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic MiniMed, Inc., Novo Nordisk Inc., Sanofi, Takeda Pharmaceutical Company Limited. P. Cheng: None. R.L. Gal: None. L.C. Beaulieu: None. C. Kollman: None. M.A. Van Name: Research Support; Self; Novo Nordisk Inc. E.M. Isganaitis: None. L.D. Mastrandrea: Advisory Panel; Self; Pediatric Diabetes Consortium. Research Support; Self; AstraZeneca, JDRF, Novo Nordisk Inc., Sanofi-Aventis, T1D Exchange. Other Relationship; Self; Novo Nordisk Inc. G.J. Klingensmith: Consultant; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Takeda Pharmaceutical Company Limited. Research Support; Self; Novo Nordisk Foundation. Stock/Shareholder; Spouse/Partner; Dexcom, Inc. Funding: Boehringer Ingelheim; Novo Nordisk; Takeda Pharmaceutical Company Limited [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: To compare races/ethnicities for characteristics, at type 1 diabetes diagnosis and during the first 3 years postdiagnosis, known to influence long-term health outcomes.Research Design and Methods: We analyzed 927 Pediatric Diabetes Consortium (PDC) participants <19 years old (631 non-Hispanic white [NHW], 216 Hispanic, and 80 African American [AA]) diagnosed with type 1 diabetes and followed for a median of 3.0 years (interquartile range 2.2-3.6). Demographic and clinical data were collected from medical records and patient/parent interviews. Partial remission period or "honeymoon" was defined as insulin dose-adjusted hemoglobin A1c (IDAA1c) ≤9.0%. We used logistic, linear, and multinomial regression models, as well as repeated-measures logistic and linear regression models. Models were adjusted for known confounders.Results: AA subjects, compared with NHW, at diagnosis, were in a higher age- and sex-adjusted BMI percentile (BMI%), had more advanced pubertal development, and had higher frequency of presentation in diabetic ketoacidosis, largely explained by socioeconomic factors. During the first 3 years, AA subjects were more likely to have hypertension and severe hypoglycemia events; had trajectories with higher hemoglobin A1c, BMI%, insulin doses, and IDAA1c; and were less likely to enter the partial remission period. Hispanics, compared with NHWs, had higher BMI% at diagnosis and over the three subsequent years. During the 3 years postdiagnosis, Hispanics had higher prevalence of dyslipidemia and maintained trajectories of higher insulin doses and IDAA1c.Conclusions: Youth of minority race/ethnicity have increased markers of poor prognosis of type 1 diabetes at diagnosis and 3 years postdiagnosis, possibly contributing to higher risk of long-term diabetes complications compared with NHWs. [ABSTRACT FROM AUTHOR]

Objective Current data are limited on the course of type 1 diabetes ( T1D) in children and adolescents through the first few years of diabetes. The Pediatric Diabetes Consortium T1D new onset ( NeOn) Study was undertaken to prospectively assess natural history and clinical outcomes in children treated at 7 US diabetes centers from the time of diagnosis. This paper describes clinical outcomes in the T1D NeOn cohort during the first 3 years postdiagnosis. Results A total of 1048 participants (mean age 9.2 years, 49% female, 65% non-Hispanic White) were enrolled between July 2009 and April 2011. Mean glycated hemoglobin ( HbA1c) (± SD) was 7.2% (55 mmol/mol) at 3 months, followed by a progressive rise to 8.4% (68 mmol/mol) at 36 months postdiagnosis, with only 30% of participants achieving target HbA1c<7.5% (58 mmol/mol). The percentage of participants in partial remission estimated by insulin dose adjusted HbA1c [ HbA1c % + (4×insulin dose unit/kg/24 h)] ≤9 sharply declined from 23% at 12 months to 7% at 36 months. The percentage of participants developing diabetic ketoacidosis ( DKA) was 1% in the first year after diagnosis, increasing to 6% in years 2 and 3. Conclusions These results demonstrate the gradual decline in glycemic control due to waning residual endogenous insulin secretion with increasing duration of T1D in children and adolescents. These data indicate the need to translate recent advances in automated insulin delivery, new insulin analogs, and adjunctive pharmacologic agents into novel treatment strategies to maintain optimal glycemic control even early in the course of T1D. [ABSTRACT FROM AUTHOR]

Purpose: To identify recipient factors that may be related to risk of corneal graft failure. Design: Multicenter, prospective, double-masked, controlled clinical trial. Participants: One thousand ninety subjects undergoing corneal transplantation for a moderate-risk condition (principally Fuchs'' dystrophy or pseudophakic corneal edema). Methods: Donor corneas were assigned using a random approach without respect to recipient factors, and surgeons were masked to information about the donor cornea, including donor age. Surgery and postoperative care were performed according to the surgeons'' usual routines, and subjects were followed up for 5 years. Baseline factors were evaluated for their association with graft failure. Main Outcome Measures: Graft failure, defined as a regraft or a cloudy cornea that was sufficiently opaque to compromise vision for a minimum of 3 consecutive months. Results: Preoperative diagnosis of pseudophakic or aphakic corneal edema increased graft failure risk approximately 4-fold compared with Fuchs'' dystrophy (27% vs. 7%). Prior glaucoma surgery with preoperative glaucoma medication use substantially increased the graft failure rate. Factors not strongly associated with graft failure included age, gender, diabetes, smoking, and graft size. Conclusions: The risk of graft failure is significantly increased in eyes with pseudophakic or aphakic corneal edema compared with Fuchs'' dystrophy, independent of lens status, and in eyes with a history of glaucoma. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references. [Copyright &y& Elsevier]

Purpose: To determine whether corneal graft survival over a 5-year follow-up period was affected by ABO blood type compatibility in participants in the Cornea Donor Study undergoing corneal transplantation principally for Fuchs dystrophy or pseudophakic corneal edema, conditions at low-risk for graft rejection. Design: Multi-center prospective, double-masked, clinical trial. Methods: ABO blood group compatibility was determined for 1,002 donors and recipients. During a 5-year follow-up period, episodes of graft rejection were documented, and graft failures were classified as to whether or not they were attributable to immunologic rejection. Endothelial cell density was determined by a central reading center for a subset of subjects. Results: ABO donor-recipient incompatibility was not associated with graft failure attributable to any cause including graft failure because of rejection, or with the occurrence of a rejection episode. The 5-year cumulative incidence of graft failure attributable to rejection was 32 (6%) for recipients with ABO recipient-donor compatibility and 12 (4%) for those with ABO incompatibility (hazard ratio, 0.65; 95% confidence interval, 0.33 to 1.25; P = .20). The 5-year incidence for a definite rejection episode, irrespective of whether graft failure ultimately occurred, was 64 (12%) for ABO compatible compared with 25 (8%) for ABO incompatible cases (P = .09). Among clear grafts at 5 years, percent loss of endothelial cells was similar in ABO compatible and incompatible cases. Conclusions: In patients undergoing penetrating keratoplasty for Fuchs dystrophy or pseudophakic corneal edema, ABO matching is not indicated since ABO incompatibility does not increase the risk of transplant failure attributable to graft rejection. [Copyright &y& Elsevier]

Background: Using continuous glucose monitoring (CGM), we investigated potential sex-related differences in glycemia during exercise in persons living with type 1 diabetes (T1D) or without diabetes. Methods: Participants used CGM (Dexcom G6), exercise/meal diaries, or an activity monitor with a meal capture smart phone app, to help identify home based exercise and meal events over a 10 day to 4 wk period. Results: The analyses included 40 T1D (age: 36±15 yrs; BMI: 26.3±3.1 kg/m2; HbA1c: 7.5±1.5%; 35% female; mean±SD) and 120 persons without diabetes (age: 30±20 years; BMI: 24.4±3.2 kg/m2; HbA1c: 5.1±0.3; 64% female) and 283 and 619 exercise events, respectively. The frequency distribution of all CGM values were comparable between males and females overall, but values had a wider distribution among T1D as compared to non-T1D (Fig). Hyperglycemia (> 180 mg/dL) represented ~0.1% of the exercise time in participants without T1D vs. 31% and 24% of the time in the female and male participants without diabetes, respectively. Hypoglycemic values (<70 mg/dL) represented 7.1% and 3.0% of the time in the non-T1D females and males, respectively (P<0.001), vs. 4.4% and 5.0% in the T1D females and males (P=0.67), respectively. Conclusions: Based on CGM, mild hypoglycemic exposure during exercise is surprisingly common in individuals with and without diabetes and may be particularly prevalent in females without diabetes. Disclosure: M. Riddell: Advisory Panel; Self; Zealand Pharma A/S. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation. Speaker's Bureau; Self; Novo Nordisk Inc., Sanofi. Stock/Shareholder; Self; Zucara Therapeutics Inc. Z. Li: None. N. DSouza: None. C. Yeung: None. D. Kesibi: None. S. R. Patton: None. R. Beck: Consultant; Self; Bigfoot Biomedical, Inc., Diasome Pharmaceuticals, Inc., Insulet Corporation, Lilly Diabetes, vTv Therapeutics. Research Support; Self; Beta Bionics, Inc., Dexcom, Inc., Medtronic, Novo Nordisk, Tandem Diabetes Care. P. G. Jacobs: Board Member; Self; Pacific Diabetes Technologies. Research Support; Self; Dexcom, Inc. M. A. Clements: Consultant; Self; Eli Lilly and Company. Employee; Self; Glooko, Inc. Research Support; Self; Abbott Diabetes, Dexcom, Inc. R. L. Gal: None. F. J. Doyle: Advisory Panel; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. C. K. Martin: Advisory Panel; Self; EHE Health. Board Member; Self; NaturallySlim. Research Support; Self; American Society for Nutrition, Leona M. and Harry B. Helmsley Charitable Trust, Lilly, National Institutes of Health, Patient-Centered Outcomes Research Institute, U. S. Department of Agriculture, WW. Other Relationship; Self; ABGIL, Academy of Nutrition and Dietetics. P. Calhoun: None. J. Sherr: Advisory Panel; Self; Cecelia Health, Insulet Corporation, Medtronic. Consultant; Self; Insulet Corporation, Lexicon Pharmaceuticals, Inc., Lilly Diabetes, Medtronic. Research Support; Self; Dexcom, Inc., Insulet Corporation, Medtronic. Speaker's Bureau; Self; Lilly Diabetes. J. R. Castle: Advisory Panel; Self; Novo Nordisk, Zealand Pharma A/S. Consultant; Self; ADOCIA. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Spouse/Partner; Insulet Corporation. Stock/Shareholder; Self; Pacific Diabetes Technologies. M. R. Rickels: Advisory Panel; Self; Semma Therapeutics, Sernova, Corp. Research Support; Self; Xeris Pharmaceuticals, Inc. [ABSTRACT FROM AUTHOR]

Exercise increases nocturnal hypoglycemia risk which may impact sleep in T1D. Adults with T1D (n=25; 19 CSII; age 33±14yrs; HbA1c 7.6±1.3%; T1D duration 15yrs [IQR: 10, 22]) were divided into 3 exercise groups: aerobic, interval, or resistance, performed twice/week using instructional videos for 4 weeks. Participants used CGM, and wore an activity/sleep monitor (Garmin Vivosmart 3). Sleep duration was lower on nights following interval exercise compared to nights following sedentary days (p=0.04, Figure 1a). Participants experienced hypoglycemia earlier in the night following interval or aerobic exercise, compared to sedentary or resistance exercise (Figure 1b). Exercise types differentially impact sleep loss and nocturnal hypoglycemia. Disclosure: P.G. Jacobs: Consultant; Self; SFC Fluidics. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Self; Pacific Diabetes Technologies. Other Relationship; Self; AgaMatrix. Z. Li: None. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. R.L. Gal: None. R. Beck: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; ADOCIA, Dexcom, Inc., Zealand Pharma A/S. Other Relationship; Self; Pacific Diabetes Technologies, Roche Diabetes Care. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker's Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. F.J. Doyle: Research Support; Self; DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Stock/Shareholder; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. M.B. Gillingham: None. C.K. Martin: Advisory Panel; Self; EHE Health, NaturallySlim. Consultant; Self; Florida Hospital, Gila Therapeutics, OpenFit, Zafgen, Inc. Research Support; Self; Egg Board, Helmsley Trust, IDEA Public Schools, National Institutes of Health, NIHR, Patient-Centered Outcomes Research Institute, USDA, Weight Watchers International, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics, Louisiana State Univ./Pennington Biomedical Research Center. M.R. Rickels: Consultant; Self; Semma Therapeutics, Inc. Research Support; Self; Xeris Pharmaceuticals, Inc. S.R. Patton: None. M. Riddell: Advisory Panel; Self; Zucara Therapeutics Inc. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation. Speaker's Bureau; Self; Medtronic, Novo Nordisk A/S. Funding: The Leona M. and Harry B. Helmsley Charitable Trust (T1-DEXI Main Study) [ABSTRACT FROM AUTHOR]

People with type 1 diabetes (T1D) have difficulty controlling glucose during exercise. Different types, intensities, and durations of exercise impact glucose differently. There is currently no algorithm that can accurately predict glucose changes during aerobic, resistance, interval, and free-living exercise. A support vector regression (SVR) model was designed to predict the maximum change in glucose during exercise. The model was trained and validated on 30-minute exercise sessions collected twice weekly over 4 weeks from 33 people with T1D (age 33±13 years, BMI 26.3±2.9 kg/m2, 18±12 years since diagnosis). Participants completed study assigned aerobic (n = 40), resistance (n= 33), or interval (n=20) exercise sessions in addition to their typical exercise regimen (n = 254). Participants wore a Dexcom (G5 or G6) or Medtronic CGM and used their own insulin pen (n = 10) or pump to administer insulin. The exercise information and food consumed was reported using a custom app. Heart rate, accelerometry, and sleep metrics were acquired using a Garmin vivosmart watch. Training was done on 90% of the observations while 10% of data were used for testing using 10-fold cross validation. The mean absolute error (MAE) of the model-predicted change in glucose during exercise was 23.59 mg/dL. The SVR algorithm may be useful within a decision support tool to help people with T1D better manage their glucose levels during exercise. Disclosure: G. Young: None. Z. Li: None. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. R.L. Gal: None. R. Beck: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; ADOCIA, Dexcom, Inc., Zealand Pharma A/S. Other Relationship; Self; Pacific Diabetes Technologies, Roche Diabetes Care. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker's Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. F.J. Doyle: Research Support; Self; DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Stock/Shareholder; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. M.B. Gillingham: None. C.K. Martin: Advisory Panel; Self; EHE Health, NaturallySlim. Consultant; Self; Florida Hospital, Gila Therapeutics, OpenFit, Zafgen, Inc. Research Support; Self; Egg Board, Helmsley Trust, IDEA Public Schools, National Institutes of Health, NIHR, Patient-Centered Outcomes Research Institute, USDA, Weight Watchers International, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics, Louisiana State Univ./Pennington Biomedical Research Center. M.R. Rickels: Consultant; Self; Semma Therapeutics, Inc. Research Support; Self; Xeris Pharmaceuticals, Inc. S.R. Patton: None. M. Riddell: Advisory Panel; Self; Zucara Therapeutics Inc. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation. Speaker's Bureau; Self; Medtronic, Novo Nordisk A/S. P.G. Jacobs: Consultant; Self; SFC Fluidics. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Self; Pacific Diabetes Technologies. Other Relationship; Self; AgaMatrix. Funding: Exercise in Diabetes Initiative (T1-DEXI Main Study) [ABSTRACT FROM AUTHOR]

Young adults (YA) with type 1 diabetes (T1D) are vulnerable to short and long-term complications as they incorporate disease self-management into the transition to adulthood. Relationships between race-ethnicity and socioeconomic status (SES) have not been examined in this population, despite poorer outcomes in minority and low SES subgroups. We administered a survey on demographics, clinical factors, self-management, and social determinants of health to non-Hispanic black (NHB), Hispanic, and non-Hispanic white (NHW) YA from 6 centers in the T1D Exchange. We compared survey responses and diabetes outcomes among the racial-ethnic groups using Fisher's exact test or Wilcoxon rank-sum test, as appropriate. Participants (97 NHB, 103 Hispanic, 100 NHW) had a mean age of 21±2 years and T1D duration of 11±5 years. Low SES was more common in NHB and Hispanic vs. NHW YA (Medicaid 57% and 76% vs. 22%; high school education or less 66% and 70% vs. 24%, respectively; p<0.001). NHB and Hispanic YA had lower use of continuous glucose monitors (28% and 37%) and insulin pumps (18% and 39%) vs. NHW (71% and 72%) (p<0.001). Non-Hispanic black, but not Hispanic, YA had higher food insecurity, poorer self-management, greater diabetes-related distress, and more adverse childhood experiences than NHW (all p<0.001). NHB also had higher A1c levels (NHB 10.5%, Hispanic 8.6%, NHW 8.1%) and higher diabetes-specific health care utilization, with 46% of NHB having ≥1 hospital or ER admission in the past year vs. 17% and 12% in Hispanic and NHW (p<0.001 for NHB vs. NHW). Non-Hispanic black and Hispanic YA with T1D face similar economic disadvantages, yet NHB YA exhibit poorer glycemic control and higher acute healthcare utilization than Hispanic YA. NHB differed from Hispanic YA on self-management, psychosocial factors, and adverse childhood experiences. Factors contributing to these disparities (e.g., healthcare system interactions, cultural differences) should be explored to design effective interventions. Disclosure: S. Agarwal: None. L. Kanapka: None. J. Raymond: Other Relationship; Self; Insulet Corporation. A.F. Walker: None. A. Gerard Gonzalez: None. D.F. Kruger: Advisory Panel; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc., Insulet Corporation. Consultant; Self; CeQur Corporation, Hygieia. Speaker's Bureau; Self; AstraZeneca, Dexcom, Inc., Eli Lilly and Company. Stock/Shareholder; Self; Dexcom, Inc. Other Relationship; Self; Novo Nordisk Inc. M.J. Redondo: None. M.R. Rickels: None. V.N. Shah: Advisory Panel; Self; Sanofi US. Consultant; Self; Dexcom, Inc. A. Butler: None. V. Miller: None. R.L. Gal: None. S.M. Willi: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Roche Diagnostic USA. Research Support; Self; Jaeb Center for Health Research, National Institute of Allergy and Infectious Diseases, Sanofi-Aventis. Other Relationship; Self; National Institute of Diabetes and Digestive and Kidney Diseases. J.A. Long: None. Funding: The Leona M. and Harry B. Helmsley Charitable Trust; National Institute of Diabetes and Digestive and Kidney Diseases [ABSTRACT FROM AUTHOR]

Minority and socioeconomic disparity in glycemic control exists in young adults (YA) with type 1 diabetes (T1D). The contribution of social determinants of health versus diabetes-specific clinical care factors to these disparities remains unclear. We administered a survey on demographics, clinical factors, self-management, and social determinants to non-Hispanic black (NHB), Hispanic, and non-Hispanic white (NHW) YA from 6 centers in the T1D Exchange. We examined bivariate associations of variables with A1c and added significant factors to a linear regression model in sequential groups. A1c disparity was the outcome. We adjusted models for race-ethnicity, age, gender, and T1D duration. We recruited 300 YA (97 NHB, 103 Hispanic, 100 NHW) with mean age of 21±2 years and T1D duration of 11±5 years. NHB but not Hispanic YA had higher A1c vs. NHW (p<0.001) (NHB 10.5%, Hispanic 8.6%, NHW 8.1%). Only lower education attainment, lower pump use, receiving care in an adult system, and worse self-management were associated with A1c in the final model (Table 1), explaining 31% of the variance in A1c. Few social determinants were associated with A1c once examined with diabetes care factors. While social factors should be considered in the care of vulnerable YA, clinicians should continue to emphasize diabetes self-management and technology treatment options. Disclosure: S. Agarwal: None. L. Kanapka: None. J. Raymond: Other Relationship; Self; Insulet Corporation. A.F. Walker: None. A. Gerard Gonzalez: None. D.F. Kruger: Advisory Panel; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc., Insulet Corporation. Consultant; Self; CeQur Corporation, Hygieia. Speaker's Bureau; Self; AstraZeneca, Dexcom, Inc., Eli Lilly and Company. Stock/Shareholder; Self; Dexcom, Inc. Other Relationship; Self; Novo Nordisk Inc. M.J. Redondo: None. M.R. Rickels: None. V.N. Shah: Advisory Panel; Self; Sanofi US. Consultant; Self; Dexcom, Inc. A. Butler: None. V. Miller: None. A. Verdejo: None. R.L. Gal: None. S.M. Willi: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Roche Diagnostic USA. Research Support; Self; Jaeb Center for Health Research, National Institute of Allergy and Infectious Diseases, Sanofi-Aventis. Other Relationship; Self; National Institute of Diabetes and Digestive and Kidney Diseases. J.A. Long: None. Funding: The Leona M. and Harry B. Helmsley Charitable Trust; National Institute of Diabetes and Digestive and Kidney Diseases [ABSTRACT FROM AUTHOR]

Structured exercise can help patients with type 1 diabetes (T1D) achieve physical activity targets, however the effects of exercise type on percent time in range (TIR) and glucose variability are unclear. The Type 1 Diabetes Exercise Initiative Pilot Study provided the opportunity to examine glycemic control on structured exercise days compared to sedentary days. Adults with T1D ([mean ± SD] age= 32 ± 14 years; HbA1C= 7.8 ± 1.6%), using a continuous glucose monitor, were assigned to complete twice weekly aerobic, interval or resistance exercise sessions, using study-developed instructional videos for a period of 4 weeks. TIR was higher (Figure 1) and mean glucose concentration was lower overall on exercise days compared with sedentary days (160 ± 52 vs. 176 ± 49 mg/dL, P<0.001). Glucose variability was similar between exercise and sedentary days (coefficient of variation: 36 ± 12% vs. 37% ± 12%). In summary, participants had lower 24-hour mean glucose levels and a greater percent time in range on active days compared with inactive days. Disclosure: M. Riddell: Advisory Panel; Self; Xeris Pharmaceuticals, Inc. Research Support; Self; Dexcom, Inc. Speaker's Bureau; Self; Insulet Corporation, Medtronic MiniMed, Inc. Stock/Shareholder; Self; Zucara Therapeutics Inc. Z. Li: None. R. Beck: Other Relationship; Self; Abbott Laboratories, Ascensia Diabetes Care, Bigfoot Biomedical, Dexcom, Inc., Insulet Corporation, Lilly Diabetes, Roche Diabetes Care, Tandem Diabetes Care. R.L. Gal: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc., Zealand Pharma A/S. Consultant; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. M.B. Gillingham: None. M.A. Clements: Advisory Panel; Self; Glooko, Inc. Consultant; Self; Eli Lilly and Company. Speaker's Bureau; Self; Medtronic. S.R. Patton: None. E. Dassau: Consultant; Self; Eli Lilly and Company, Insulet Corporation. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Ltd., Insulet Corporation, Roche Diabetes Care, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker's Bureau; Self; Roche Diabetes Care. Other Relationship; Self; ModAGC. F.J. Doyle: Consultant; Self; ModeAGC. Other Relationship; Self; Insulet Corporation. C.K. Martin: Advisory Panel; Self; EHE. Consultant; Self; ACAP Health, Florida Hospital, WW, Zafgen, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. M.R. Rickels: None. Funding: The Leona M. and Harry B. Helmsley Charitable Trust [ABSTRACT FROM AUTHOR]

People with type 1 diabetes (T1D) estimate the carbohydrate (CHO) content of meals to enable accurate insulin dosing, yet protein and fat content of meals also influences post-prandial glycemia. As part of an observational study examining the impact of exercise and nutrient consumption on glycemia, we examined accuracy in estimating macronutrient content of free-living meals via a novel phone app. Participant estimates of nutrient content were compared to expert nutrition analyses performed via the Remote Food Photography Method (RFPM). We report results from 30 of 33 randomized with analyzable food photos. Participants were 15-65 years (32±14 years); 27% identified as female. Participants were asked to take photos before and after meals/snacks on up to 16 days over a 28-day period, enter CHO estimates and estimate if meals were low (<13%), typical (13-<18%), or high (≥18%) protein and if meals were low (<26%), medium (26-<32%), or high (≥32%) fat. The phone app plus RFPM captured 92±27% of estimated energy needs. Of 1,292 food photos analyzed, 429 contained < 25 g (small), 641 contained 25-75 g (medium), and 222 contained > 75 g (large) amounts of CHO. Participants estimated CHO in small or medium meals within 10 g of the expert analyses. They were less accurate estimating CHO for larger meals (-56 ± 46 g). Likewise, most correctly categorized low and typical protein (63%, 50%) as well as low and typical fat (62%, 67%) meals. Few correctly categorized meals high in protein (17%) or fat (16%). Participants' estimation accuracy for larger meals did not differ by T1D duration. The phone app successfully collected individuals' food intake and estimated macronutrient intake, but showed that participants consistently underestimated nutrient intake for large meals. Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop systems; development of tools to improve macronutrient estimation skills should be considered. Disclosure: M.B. Gillingham: None. C.K. Martin: Advisory Panel; Self; EHE. Consultant; Self; ACAP Health, Florida Hospital, WW, Zafgen, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics. S.R. Patton: None. Z. Li: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. M. Riddell: Advisory Panel; Self; Xeris Pharmaceuticals, Inc. Research Support; Self; Dexcom, Inc. Speaker's Bureau; Self; Insulet Corporation, Medtronic MiniMed, Inc. Stock/Shareholder; Self; Zucara Therapeutics Inc. M.R. Rickels: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc., Zealand Pharma A/S. Consultant; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. M.A. Clements: Advisory Panel; Self; Glooko, Inc. Consultant; Self; Eli Lilly and Company. Speaker's Bureau; Self; Medtronic. E. Dassau: Consultant; Self; Eli Lilly and Company, Insulet Corporation. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Ltd., Insulet Corporation, Roche Diabetes Care, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker's Bureau; Self; Roche Diabetes Care. Other Relationship; Self; ModAGC. F.J. Doyle: Consultant; Self; ModeAGC. Other Relationship; Self; Insulet Corporation. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. R.L. Gal: None. R. Beck: Other Relationship; Self; Abbott Laboratories, Ascensia Diabetes Care, Bigfoot Biomedical, Dexcom, Inc., Insulet Corporation, Lilly Diabetes, Roche Diabetes Care, Tandem Diabetes Care. Funding: The Leona M. and Harry B. Helmsley Charitable Trust [ABSTRACT FROM AUTHOR]