Search

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. [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]

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]

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. [ABSTRACT FROM AUTHOR]

Objective Nocturnal hypoglycemia is a known challenge for people with type 1 diabetes, especially for physically active individuals or those on multiple daily injections. We developed an evidential neural network (ENN) to predict at bedtime the probability and timing of nocturnal hypoglycemia (0-4 vs 4-8 h after bedtime) based on several glucose metrics and physical activity patterns. We utilized these predictions in silico to prescribe bedtime carbohydrates with a Smart Snack intervention specific to the predicted minimum nocturnal glucose and timing of nocturnal hypoglycemia. Materials and methods We leveraged free-living datasets collected from 366 individuals from the T1DEXI Study and Glooko. Inputs to the ENN used to model nocturnal hypoglycemia were derived from demographic information, continuous glucose monitoring, and physical activity data. We assessed the accuracy of the ENN using area under the receiver operating curve, and the clinical impact of the Smart Snack intervention through simulations. Results The ENN achieved an area under the receiver operating curve of 0.80 and 0.71 to predict nocturnal hypoglycemic events during 0-4 and 4-8 h after bedtime, respectively, outperforming all evaluated baseline methods. Use of the Smart Snack intervention reduced probability of nocturnal hypoglycemia from 23.9 ± 14.1% to 14.0 ± 13.3% and duration from 7.4 ± 7.0% to 2.4 ± 3.3% in silico. Discussion Our findings indicate that the ENN-based Smart Snack intervention has the potential to significantly reduce the frequency and duration of nocturnal hypoglycemic events. Conclusion A decision support system that combines prediction of minimum nocturnal glucose and proactive recommendations for bedtime carbohydrate intake might effectively prevent nocturnal hypoglycemia and reduce the burden of glycemic self-management. [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]

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: 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]

The purpose of this study was to evaluate feasibility of initiating continuous glucose monitoring (CGM) through telehealth as a means of expanding access. Adults with type 1 diabetes (N = 27) or type 2 diabetes using insulin (N = 7) and interest in starting CGM selected a CGM system (Dexcom G6 or Abbott FreeStyle Libre), which they received by mail. CGM was initiated with a certified diabetes care and education specialist providing instruction via videoconference or phone. The primary outcome was days per week of CGM use during the last 4 weeks. Hemoglobin A1c (HbA1c) was measured at baseline and 12 weeks. Participant self-reported outcome measures were also evaluated. All 34 participants (mean age, 46 ± 18 years; 53% female, 85% white) were using CGM at 12 weeks, with 94% using CGM at least 6 days per week during weeks 9 to 12. Mean HbA1c decreased from 8.3 ± 1.6 at baseline to 7.2 ± 1.3 at 12 weeks (P  < .001) and mean time in range (70-180 mg/dL, 3.9-10.0 mmol/L) increased from an estimated 48% ± 18% to 59% ± 20% (P  < .001), an increase of approximately 2.7 hours/day. Substantial benefits of CGM to quality of life were observed, with reduced diabetes distress, increased satisfaction with glucose monitoring, and fewer perceived technology barriers to management. Remote CGM initiation was successful in achieving sustained use and improving glycemic control after 12 weeks as well as improving quality-of-life indicators. If widely implemented, this telehealth approach could substantially increase the adoption of CGM and potentially improve glycemic control for people with diabetes using insulin. [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]

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: 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]

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]

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]