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PurposeThe aim of this study was to assess the effect of donor and recipient factors on corneal allograft rejection and evaluate whether a rejection event was associated with graft failure.MethodsOne thousand ninety subjects undergoing penetrating keratoplasty for a moderate risk condition (principally Fuchs dystrophy or pseudophakic corneal edema) were followed for up to 12 years. Associations of baseline recipient and donor factors with the occurrence of a rejection event were assessed in univariate and multivariate proportional hazards models.ResultsAmong 651 eyes with a surviving graft at 5 years, the 10-year graft failure (±99% confidence interval) rates were 12% ± 4% among eyes with no rejection events in the first 5 years, 17% ± 12% in eyes with at least 1 probable, but no definite rejection event, and 22% ± 20% in eyes with at least 1 definite rejection event. The only baseline factor significantly associated with a higher risk of definite graft rejection was a preoperative history of glaucoma, particularly when previous glaucoma surgery had been performed and glaucoma medications were being used at the time of transplant (10-year incidence 35% ± 23% compared with 14% ± 4% in eyes with no history of glaucoma/intraocular pressure treatment, P = 0.008).ConclusionsPatients who experienced a definite rejection event frequently developed graft failure raising important questions as to how we might change acute and long-term corneal graft management. Multivariate analysis indicated that previous use of glaucoma medications and glaucoma filtering surgery was a significant risk factor related to a definite rejection event.

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]

ObjectiveTo examine the effect of donor age and other perioperative factors on long-term endothelial cell loss after penetrating keratoplasty (PKP).DesignMulticenter, prospective, double-masked clinical trial.ParticipantsWe 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).MethodsCorneas 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 measuresEndothelial cell density at 10 years.ResultsAmong 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/mm(2) (interquartile range [IQR], 522-850 cells/mm(2)), 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/mm(2) (IQR, 483-694 cells/mm(2); P adjusted for baseline ECD = 0.03). In addition to younger donor age, higher ECD values were significantly associated with higher baseline ECD (P1000 cells/mm(2).ConclusionsSubstantial 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.

ObjectiveTo determine whether the 10-year success rate of penetrating keratoplasty for corneal endothelial disorders is associated with donor age.DesignMulticenter, prospective, double-masked clinical trial.ParticipantsA 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.MethodsForty-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 measuresGraft 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.ResultsIn 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

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 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]

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: 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]

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]

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]

Background: Older adults may be less comfortable with continuous glucose monitoring (CGM) technology or require additional education to support use. The Virtual Diabetes Specialty Clinic study provided the opportunity to understand glycemic outcomes and support needed for older versus younger adults living with diabetes and using CGM., Methods: Prospective, virtual study of adults with type 1 diabetes (T1D, N = 160) or type 2 diabetes (T2D, N = 74) using basal-bolus insulin injections or insulin pump therapy. Remote CGM diabetes education (3 scheduled visits over 1 month) was provided by Certified Diabetes Care and Education Specialists with additional visits as needed. CGM-measured glycemic metrics, HbA1c and visit duration were evaluated by age (<40, 40-64 and ≥65 years)., Results: Median CGM use was ≥95% in all age groups. From baseline to 6 months, time 70 to 180 mg/dL improved from 45% ± 22 to 57% ± 16%; 50 ± 25 to 65 ± 18%; and 60 ± 28 to 69% ± 18% in the <40, 40-64, and ≥65-year groups, respectively (<40 vs 40-64 years P = 0.006). Corresponding values for HbA1c were 8.0% ± 1.6 to 7.3% ± 1.0%; 7.9 ± 1.6 to 7.0 ± 1.0%; and 7.4 ± 1.4 to 7.1% ± 0.9% (all P > 0.05). Visit duration was 41 min longer for ages ≥65 versus <40 years ( P = 0.001)., Conclusions: Adults with diabetes experience glycemic benefit after remote CGM use training, but training time for those >65 years is longer compared with younger adults. Addressing individual training-related needs, including needs that may vary by age, should be considered., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RSW reported personal fees from The Leona M. and Harry B. Helmsley Charitable Trust and Jaeb Center for Health Research during the conduct of the study; grants for participation in multicenter clinical trial through her institution from Eli Lilly, Novo Nordisk, Insulet, Tandem, Amgen, and MannKind outside the submitted work; and discounted devices for clinical research from Dexcom outside the submitted work. RLG reported grants from The Leona M. and Harry B. Helmsley Charitable Trust during the conduct of the study. RMB reported grants from Abbott Diabetes Care, Eli Lilly, Hygieia, Dexcom, Sanofi, Tandem Diabetes Care, Insulet, and Medtronic and consulting and/or advisory board fees paid to his institution from Abbott Diabetes Care, Dexcom, Onduo, Sanofi, Roche, Embecta, and Medtronic during the conduct of the study. CK reported grants from The Leona M. and Harry B. Helmsley Charitable Trust and nonfinancial support from Dexcom during the conduct of the study; grants from JDRF, Diasome, and MannKind outside the submitted work; and nonfinancial support from Tandem, Dexcom, and Insulet outside the submitted work. DK reported grants from Abbott and advisor and/or speaking fees from Abbott, Dexcom, and Jaeb Center for Health Research outside the submitted work. MLJ reported grants from Jaeb Center for Health Research paid to HealthPartners Institute during the conduct of the study and grants from Dexcom given directly to HealthPartners Institute outside the submitted work. BAO reported personal fees from Lagoon Health during the conduct of the study and stock ownership in Abbott Laboratories. SMO reported grants from The Leona M. and Harry B. Helmsley Charitable Trust during the conduct of the study and consulting fees from Cecelia Health and advisory board fees from Dexcom outside the submitted work. TKO reported grants from The Leona M. and Harry B. Helmsley Charitable Trust during the conduct of the study and consulting fees from Cecelia Health, advisory board fees from Dexcom, and an investigator-initiated grant from Abbott Diabetes Care outside the submitted work. RWB reported grants from The Leona M. and Harry B. Helmsley Charitable Trust during the conduct of the study and grants from Insulet, Tandem Diabetes Care, Beta Bionics, Dexcom, and Bigfoot Biomedical; nonfinancial support from Insulet, Tandem Diabetes Care, Beta Bionics, Dexcom, Medtronic, Ascenia, Roche, Eli Lilly, and Novo Nordisk; and consulting fees paid to his institution from Insulet, Tandem Diabetes Care, Beta Bionics, Eli Lilly, Novo Nordisk, Embecta, Hagar, and Ypsomed outside the submitted work. KH reported behavioral expertise consulting fees from Cecelia Health during the conduct of the study. GA reported grants from The Leona M. and Harry B. Helmsley Charitable Trust during the conduct of the study and personal fees from Bayer, Dexcom, and Insulet; nonfinancial support from Eli Lilly; and grants from Dexcom, Eli Lilly, Fractyl Health, Emmes, MannKind, Tandem Diabetes Care, and Welldoc outside the submitted work. No other disclosures were reported.

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

Aims: To evaluate factors affecting within-participant reproducibility in glycemic response to different forms of exercise., Methods: Structured exercise sessions ~30 minutes in length from the Type 1 Diabetes Exercise Initiative (T1DEXI) study were used to assess within-participant glycemic variability during and after exercise. The effect of several pre-exercise factors on the within-participant glycemic variability was evaluated., Results: Data from 476 adults with type 1 diabetes were analyzed. A participant's change in glucose during exercise was reproducible within 15 mg/dL of the participant's other exercise sessions only 32% of the time. Participants who exercised with lower and more consistent glucose level, insulin on board (IOB), and carbohydrate intake at exercise start had less variability in glycemic change during exercise. Participants with lower mean glucose ( P < .001), lower glucose coefficient of variation (CV) ( P < .001), and lower % time <70 mg/dL ( P = .005) on sedentary days had less variable 24-hour post-exercise mean glucose., Conclusions: Reproducibility of change in glucose during exercise was low in this cohort of adults with T1D, but more consistency in pre-exercise glucose levels, IOB, and carbohydrates may increase this reproducibility. Mean glucose variability in the 24 hours after exercise is influenced more by the participant's overall glycemic control than other modifiable factors., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Z.L. reports no conflict of interests. M.R.R. reports consultancy fees from Zealand Pharma. R.L.G. reports no conflict of interests. P.C. reports no conflict of interests. P.G.J. reports receiving grants from the National Institutes of Health, The Leona M. and Harry B. Charitable Trust, the Juvenile Diabetes Research Foundation, Dexcom, and the Oregon Health & Science University Foundation; consultancy fees from CDISC; US patents 62/352,939, 63/269,094, 62/944,287, 8810388, 9,480,418, 8,317,700, 61/570382, 8,810,388, 7,976,466, and 6,558,321; and reports stock options from Pacific Diabetes Technologies, outside submitted work. M.A.C. is Chief Medical Officer of Glooko, Inc and has received grants or contracts from Dexcom, Abbott Diabetes Care, National Institutes of Health, the Juvenile Diabetes Research Foundation, the Emily Rosebud Foundation, Eli Lilly, Tolerion, and Garmin. F.J.D. reports no conflict of interests. S.R.P. reports receiving grants from The Leona M. and Harry B. Helmsley Charitable Trust, the National Institutes of Health, and the Jaeb Center for Health Research and honorarium from the American Diabetes Association, outside the submitted work. J.R.C. reports receiving grants from the Juvenile Diabetes Research Foundation, the National Institutes of Health, Dexcom, and Medtronic and consultancy fees from Novo Nordisk, Insulet, and Zealand, outside the submitted work. M.B.G. reports no conflict of interest. R.W.B. reports receiving consulting fees, paid to his institution, from Insulet, Bigfoot Biomedical, vTv Therapeutics, and Eli Lilly, grant support and supplies, provided to his institution, from Tandem and Dexcom, and supplies from Ascenia and Roche. C.K.M. reports no conflict of interests. M.C.R. reports receiving consulting fees from the Jaeb Center for Health Research, Eli Lilly, Zealand Pharma, and Zucara Therapuetics; speaker fees from Sanofi Diabetes, Eli Lilly, Dexcom Canada, and Novo Nordisk; and stock options from Supersapiens and Zucara Therapeutics.

Background: Optic neuritis is an inflammatory disease of the optic nerve. It usually presents with an abrupt loss of vision and recovery of vision is almost never complete. It occurs more commonly in women than in men. Closely linked in pathogenesis, optic neuritis may be the initial manifestation for multiple sclerosis. In some people, no underlying cause can be found., Objectives: The objective of this review was to assess the effects of corticosteroids on visual recovery in eyes with acute optic neuritis., Search Methods: We searched CENTRAL (which contains the Cochrane Eyes and Vision Group Trials Register) (The Cochrane Library 2015, Issue 4), MEDLINE (January 1950 to April 2015), EMBASE (January 1980 to April 2015), Latin American and Caribbean Health Sciences Literature (LILACS) (January 1982 to April 2015), PubMed (January 1946 to April 2015), the metaRegister of Controlled Trials (mRCT) (www.controlled-trials.com), ClinicalTrials.gov (www.clinicaltrials.gov), and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) (www.who.int/ictrp/search/en). There were no date or language restrictions in the electronic searches for trials. The metaRegister of Controlled Trials (mRCT) was last searched on 6 March 2014. The electronic databases were last searched on 7 April 2015. We also searched reference lists of identified trial reports for additional trials., Selection Criteria: We included randomized controlled trials (RCTs) that evaluated systemic corticosteroids, in any form, dose or route of administration, in people with acute optic neuritis., Data Collection and Analysis: We used standard methodological procedures expected by Cochrane., Main Results: We included six RCTs with a total of 750 participants. Each trial was conducted in a different country: Denmark, Germany, India, Japan, UK, and United States. Additionally, we identified two ongoing trials not due to be completed until 2016. Among the six trials included in this review, we judged one to be at high risk of bias. The remaining five trials were judged to be at either low or uncertain risk of biases.Five trials compared only two intervention groups and one trial had a three-arm comparison of oral corticosteroids or intravenous corticosteroids with placebo. Of the five trials with only two intervention groups, two trials compared oral corticosteroids versus placebo, two trials compared intravenous corticosteroids with placebo, and one trial compared intravenous dexamethasone with intravenous methylprednisolone plus oral prednisolone.Three trials evaluating oral corticosteroids used varying doses of corticosteroids versus placebo. In the meta-analyses to assess visual acuity, the risk ratio (RR) was 1.00 (95% confidence interval (CI) 0.82 to 1.23; participants = 398) at one month; 0.92 (95% CI 0.77 to 1.11; participants = 355) at six months; and 0.93 (95% CI 0.70 to 1.24; participants = 368) at one year. In the meta-analyses of two trials evaluating corticosteroids with total dose greater than 3000 mg administered intravenously, the RR of normal visual acuity (defined as 20/20 Snellen fraction or equivalent) in the intravenous corticosteroids group compared with the placebo group was 1.05 (95% CI 0.88 to 1.26; participants = 346) at six months. The RR of contrast sensitivity in the normal range for the same comparison was 1.11 (95% CI 0.92 to 1.33; participants = 346) at six months follow-up. The RR of normal visual field for this comparison was 1.08 (95% CI 0.96 to 1.21; 346 participants) at six months; and 1.01 (95% CI 0.86 to 1.19; participants = 316) at one year. Four trials reported adverse events primarily related to gastrointestinal symptoms and sleep disturbance; one trial reported minor adverse event of acne., Authors' Conclusions: There is no conclusive evidence of benefit in terms of recovery to normal visual acuity, visual field or contrast sensitivity six months after initiation with either intravenous or oral corticosteroids at the doses evaluated in trials included in this review.

Purpose: We constructed several mathematical models that predict endothelial cell density (ECD) for patients after penetrating keratoplasty (PK) for a moderate-risk condition (principally Fuchs' dystrophy or pseudophakic/aphakic corneal edema)., Methods: In a subset (n = 591) of Cornea Donor Study participants, postoperative ECD was determined by a central reading center. Various statistical models were considered to estimate the ECD trend longitudinally over 10 years of follow-up. A biexponential model with and without a logarithm transformation was fit using the Gauss-Newton nonlinear least squares algorithm. To account for correlated data, a log-polynomial model was fit using the restricted maximum likelihood method. A sensitivity analysis for the potential bias due to selective dropout was performed using Bayesian analysis techniques., Results: The three models using a logarithm transformation yield similar trends, whereas the model without the transform predicts higher ECD values. The adjustment for selective dropout turns out to be negligible. However, this is possibly due to the relatively low rate of graft failure in this cohort (19% at 10 years). Fuchs' dystrophy and pseudophakic/aphakic corneal edema (PACE) patients had similar ECD decay curves, with the PACE group having slightly higher cell densities by 10 years., Conclusions: Endothelial cell loss after PK can be modeled via a log-polynomial model, which accounts for the correlated data from repeated measures on the same subject. This model is not significantly affected by the selective dropout due to graft failure. Our findings warrant further study on how this may extend to ECD following endothelial keratoplasty., (© ARVO.)

Background: Optic neuritis is an inflammatory disease of the optic nerve. It occurs more commonly in women than in men. Usually presenting with an abrupt loss of vision, recovery of vision is almost never complete. Closely linked in pathogenesis to multiple sclerosis, it may be the initial manifestation for this condition. In certain patients, no underlying cause can be found., Objectives: To assess the effects of corticosteroids on visual recovery of patients with acute optic neuritis., Search Methods: We searched CENTRAL (which contains the Cochrane Eyes and Vision Group Trials Register) (The Cochrane Library 2012, Issue 1), MEDLINE (January 1950 to February 2012), EMBASE (January 1980 to February 2012), Latin American and Caribbean Literature on Health Sciences (LILACS) (January 1982 to February 2012), the metaRegister of Controlled Trials (mRCT) (www.controlled-trials.com), ClinicalTrials.gov (www.clinicaltrials.gov) and the WHO International Clinical Trials Registry Platform (ICTRP) (www.who.int/ictrp/search/en). There were no date or language restrictions in the electronic searches for trials. The electronic databases were last searched on 21 February 2012. We also searched reference lists of identified trial reports to find additional trials., Selection Criteria: We included randomized trials that evaluated corticosteroids, in any form, dose or route of administration, in people with acute optic neuritis., Data Collection and Analysis: Two authors independently extracted the data on methodological quality and outcomes for analysis., Main Results: We included six randomized trials which included a total of 750 participants. Two trials evaluated low dose oral corticosteroids while one trial evaluated low dose intravenous corticosteroids across two treatment arms and two trials evaluated a higher dose of intravenous corticosteroids. One three-arm trial evaluated low-dose oral corticosteroids and high-dose intravenous corticosteroids against placebo. Trials evaluating oral corticosteroids compared varying doses of corticosteroids with placebo. Hence, we did not conduct a meta-analysis of such trials. In a meta-analysis of trials evaluating corticosteroids with total dose greater than 3000 mg administered intravenously, the relative risk of normal visual acuity with intravenous corticosteroids compared with placebo was 1.06 (95% confidence interval (CI) 0.89 to 1.27) at six months and 1.06 (95% CI 0.92 to 1.22) at one year. The risk ratio of normal contrast sensitivity for the same comparison was 1.10 (95% CI 0.92 to 1.32) at six months follow up. We did not conduct a meta-analysis for this outcome at one year follow up since there was substantial statistical heterogeneity. The risk ratio of normal visual field for this comparison was 1.08 (95% CI 0.96 to 1.22) at six months and 1.02 (95% CI 0.86 to 1.20) at one year. Quality of life was assessed and reported in one trial., Authors' Conclusions: There is no conclusive evidence of benefit in terms of recovery to normal visual acuity, visual field or contrast sensitivity with either intravenous or oral corticosteroids at the doses evaluated in trials included in this review.