Schneider Children''s Medical Center

Diabetes Technology & TherapeuticsVol. 26, No. S1 Original ArticlesFree AccessDiabetes Technology and Therapy in the Pediatric Age GroupDavid M. Maahs, Priya Prahalad, Darja Smigoc Schweiger, and Shlomit ShalitinDavid M. MaahsDepartment of Pediatrics, Division of Endocrinology and Diabetes, Stanford University, Stanford, CA.Stanford Diabetes Research Center, Stanford University, Stanford, CA.Department of Health Research and Policy (Epidemiology), Stanford University, Stanford, CA.Search for more papers by this author, Priya PrahaladDepartment of Pediatrics, Division of Endocrinology and Diabetes, Stanford University, Stanford, CA.Stanford Diabetes Research Center, Stanford University, Stanford, CA.Search for more papers by this author, Darja Smigoc SchweigerDepartment of Pediatric Endocrinology, Diabetes and Metabolic Diseases, University Children's Hospital, University Medical Centre Ljubljana, Ljubljana, Slovenia.Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.Search for more papers by this author, and Shlomit ShalitinJesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel.Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.Search for more papers by this authorPublished Online:1 Mar 2024https://doi.org/10.1089/dia.2024.2508AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail IntroductionIn this year's edition of the ATTD Yearbook, for the article focused on the pediatric age group we selected 20 articles from the numerous impactful publications in the past year. These articles have a common theme of advancement of diabetes technology in the pediatric age group in research with increasing translation to clinical practice. Diabetes technology has transformed pediatric diabetes care in a very positive way. However, challenges remain to further refine diabetes technology and very importantly to address barriers to increase access to the best possible care for all children, adolescents, and young adults with diabetes.Multiple studies were published on the development of automated insulin delivery systems in the pediatric population. These studies ranged from early safety studies performed as a necessary step before larger, pivotal trials for regulatory approval (which were also published this past year) with increasing "real-world" studies in which data describe approved closed-loop insulin delivery systems in use in pediatric diabetes clinics.The continued development of these systems and the transition from research to the clinic will continue to be highlighted in pediatric diabetes care in the years ahead. Common themes continue to be both the challenges and opportunities of diabetes technology in the pediatric population. Usability remains an important goal for translation from research to clinical implementation as does research in the youngest age groups. Broader access to diabetes technology will be an ongoing mission for all involved in pediatric diabetes care so that all children can benefit. In addition to closed-loop research, important articles in the pediatric age group were published on novel insulin formulations, national and individual clinical registry data to describe outcomes and best practices, and novel reports on the data generated from diabetes technology and their applications.To select these 20 articles focused on diabetes technology and therapeutics in the pediatric age group, we conducted a Medline search for articles dealing with the following topics: diabetes technology, insulin pump therapy (continuous subcutaneous insulin infusion, CSII), continuous glucose monitoring (CGM), closed-loop systems, and new therapies in type 1 diabetes (T1D) relating to the pediatric age group (0–18 years). We focused on key articles that offer some insight into these issues that were published between July 1, 2022, and June 30, 2023.Key Articles ReviewedCGM Metrics Identify Dysglycemic States in Participants from the TrialNet Pathway to Prevention StudyWilson DM, Pietropaolo SL, Acevedo-Calado M, Huang S, Anyaiwe D, Scheinker D, Steck AK, Vasudevan MM, McKay SV, Sherr JL, Herold KC, Dunne JL, Greenbaum CJ, Lord SM, Haller MJ, Schatz DA, Atkinson MA, Nelson PW, Pietropaolo M and the Type 1 Diabetes TrialNet Study GroupDiabetes Care2023; 46:526–534Glycemic Variability Patterns Strongly Correlate with Partial Remission Status in Children with Newly Diagnosed Type 1 DiabetesPollé OG, Delfosse A, Martin M, Louis J, Gies I, den Brinker M, Seret N, Lebrethon MC, Mouraux T, Gatto L, Lysy PA on behalf of the DIATAG Working GroupDiabetes Care2022; 45:2360–2368Disparities in Hemoglobin A1c Levels in the First Year after Diagnosis among Youths with Type 1 Diabetes Offered Continuous Glucose MonitoringAddala A, Ding V, Zaharieva DP, Bishop FK, Adams AS, King AC, Johari R, Scheinker D, Hood KK, Desai M, Maahs DM, Prahalad P for the Teamwork, Targets, Technology, and Tight Control (4T) Study GroupJAMA Netw Open2023; 6:e238881Continuous Glucose Monitoring versus Blood Glucose Monitoring for Risk of Severe Hypoglycaemia and Diabetic Ketoacidosis in Children, Adolescents, and Young Adults with Type 1 Diabetes: A Population-based StudyKarges B, Tittel SR, Bey A, Freiberg C, Klinkert C, Kordonouri O, Thiele-Schmitz S, Schröder C, Steigleder-Schweiger C, Holl RWLancet Diabetes Endocrinol2023; 11:314–323A Longitudinal View of Disparities in Insulin Pump Use among Youth with Type 1 Diabetes: The SEARCH for Diabetes in Youth StudyEverett EM, Wright D, Williams A, Divers J, Pihoker C, Liese AD, Bellatorre A, Kahkoska AR, Bell R, Mendoza J, Mayer-Davis E, Wisk LEDiabetes Technol Ther2023; 25:131–139Trial of Hybrid Closed-Loop Control in Young Children with Type 1 DiabetesWadwa RP, Reed ZW, Buckingham BA, DeBoer MD, Ekhlaspour L, Forlenza GP, Schoelwer M, Lum J, Kollman C, Beck RW, Breton MD for the PEDAP Trial Study GroupN Engl J Med2023; 388:991–1001MiniMed 780G Six-Month Use in Children and Adolescents with Type 1 Diabetes: Clinical Targets and Predictors of Optimal Glucose ControlLombardo F, Passanisi S, Alibrandi A, Bombaci B, Bonfanti R, Delvecchio M, Di Candia F, Mozzillo E, Piccinno E, Piona CA, Rigamonti A, Scialabba F, Maffeis C, Salzano GDiabetes Technol Ther2023; 25:404–413Simplified Meal Announcement versus Precise Carbohydrate Counting in Adolescents with Type 1 Diabetes Using the MiniMed 780G Advanced Hybrid Closed Loop System: A Randomized Controlled Trial Comparing Glucose ControlPetrovski G, Campbell J, Pasha M, Day E, Hussain K, Khalifa A, van den Heuvel TDiabetes Care2023; 46:544–550Increased Technology Use Associated with Lower A1C in a Large Pediatric Clinical PopulationAlonso GT, Triolo TM, Akturk HK, Pauley ME, Sobczak M, Forlenza GP, Sakamoto C, Pyle L, Frohnert BIDiabetes Care2023; 46:1218–1222A Meta-analysis of Randomized Trial Outcomes for the t:slim X2 Insulin Pump with Control-IQ Technology in Youth and Adults from Age 2 to 72Beck RW, Kanapka LG, Breton MD, Brown SA, Wadwa RP, Buckingham BA, Kollman C, Kovatchev BDiabetes Technol Ther2023; 25:329–342Open-Source Automated Insulin Delivery in Type 1 DiabetesBurnside MJ, Lewis DM, Crocket HR, Meier RA, Williman JA, Sanders OJ, Jefferies CA, Faherty AM, Paul RG, Lever CS, Price SKJ, Frewen CM, Jones SD, Gunn TC, Lampey C, Wheeler BJ, de Bock MIN Engl J Med2022; 387:869–881First Use of Open-Source Automated Insulin Delivery AndroidAPS in Full Closed-Loop Scenario: Pancreas4ALL Randomized Pilot StudyPetruzelkova L, Neuman V, Plachy L, Kozak M, Obermannova B, Kolouskova S, Pruhova S, Sumnik ZDiabetes Technol Ther2023; 25:315–323Extended Use of an Open-Source Automated Insulin Delivery System in Children and Adults with Type 1 Diabetes: The 24-Week Continuation Phase Following the CREATE Randomized Controlled TrialBurnside MJ, Lewis DM, Crocket HR, Meier RA, Williman JA, Sanders OJ, Jefferies CA, Faherty AM, Paul RG, Lever CS, Price SKJ, Frewen CM, Jones SD, Gunn TC, Lampey C, Wheeler BJ, de Bock MIDiabetes Technol Ther2023; 25:250–259Multicenter, Randomized Trial of a Bionic Pancreas in Type 1 DiabetesBionic Pancreas Research Group; Russell SJ, Beck RW, Damiano ER, El-Khatib FH, Ruedy KJ, Balliro CA, Li Z, Calhoun P, Wadwa RP, Buckingham B, Zhou K, Daniels M, Raskin P, White PC, Lynch J, Pettus J, Hirsch IB, Goland R, Buse JB, Kruger D, Mauras N, Muir A, McGill JB, Cogen F, Weissberg-Benchell J, Sherwood JS, Castellanos LE, Hillard MA, Tuffaha M, Putman MS, Sands MY, Forlenza G, Slover R, Messer LH, Cobry E, Shah VN, Polsky S, Lal R, Ekhlaspour L, Hughes MS, Basina M, Hatipoglu B, Olansky L, Bhangoo A, Forghani N, Kashmiri H, Sutton F, Choudhary A, Penn J, Jafri R, Rayas M, Escaname E, Kerr C, Favela-Prezas R, Boeder S, Trikudanathan S, Williams KM, Leibel N, Kirkman MS, Bergamo K, Klein KR, Dostou JM, Machineni S, Young LA, Diner JC, Bhan A, Jones JK, Benson M, Bird K, Englert K, Permuy J, Cossen K, Felner E, Salam M, Silverstein JM, Adamson S, Cedeno A, Meighan S, Dauber AN Engl J Med2022; 387:1161–1172The Insulin-Only Bionic Pancreas Improves Glycemic Control in non-Hispanic White and Minority Adults and Children with Type 1 DiabetesCastellanos LE, Russell SJ, Damiano ER, Beck RW, Shah VN, Bailey R, Calhoun P, Bird K, Mauras N; Bionic Pancreas Research GroupDiabetes Care2023; 46:1185–1190Association of Achieving Time in Range Clinical Targets with Treatment Modality among Youths with Type 1 DiabetesDovc K, Lanzinger S, Cardona-Hernandez R, Tauschmann M, Marigliano M, Cherubini V, Preikša R, Schierloh U, Clapin H, AlJaser F, Pelicand J, Shukla R, Biester TJAMA Netw Open2023; 6:e230077Temporal Changes in Hemoglobin A1c and Diabetes Technology Use in DPV, NPDA, and T1DX Pediatric Cohorts from 2010 to 2018Lal RA, Robinson H, Lanzinger S, Miller KM, Pons Perez S, Kovacic R, Calhoun P, Campbell F, Naeke A, Maahs DM, Holl RW, Warner JDiabetes Technol Ther2022; 24:628–634Transatlantic Comparison of Pediatric Continuous Glucose Monitoring Use in the Diabetes-Patienten-Verlaufsdokumentation Initiative and Type 1 Diabetes Exchange Quality Improvement CollaborativeDeSalvo DJ, Lanzinger S, Noor N, Steigleder-Schweiger C, Ebekozien O, Sengbusch SV, Yayah Jones NH, Laubner K, Maahs DM, Holl RWDiabetes Technol Ther2022; 24:920–924Effect of Tight Glycemic Control on Pancreatic Beta Cell Function in Newly Diagnosed Pediatric Type 1 Diabetes: A Randomized Clinical TrialMcVean J, Forlenza GP, Beck RW, Bauza C, Bailey R, Buckingham B, DiMeglio LA, Sherr JL, Clements M, Neyman A, Evans-Molina C, Sims EK, Messer LH, Ekhlaspour L, McDonough R, Van Name M, Rojas D, Beasley S, DuBose S, Kollman C, Moran A for the CLVer Study GroupJAMA2023; 329:980–989Effect of Verapamil on Pancreatic Beta Cell Function in Newly Diagnosed Pediatric Type 1 Diabetes: A Randomized Clinical TrialForlenza GP, McVean J, Beck RW, Bauza C, Bailey R, Buckingham B, DiMeglio LA, Sherr JL, Clements M, Neyman A, Evans-Molina C, Sims EK, Messer LH, Ekhlaspour L, McDonough R, Van Name M, Rojas D, Beasley S, DuBose S, Kollman C, Moran A; for the CLVer Study GroupJAMA2023; 329:990–999Continuous Glucose MonitoringCGM Metrics Identify Dysglycemic States in Participants from the TrialNet Pathway to Prevention StudyWilson DM1, Pietropaolo SL2, Acevedo-Calado M2, Huang S3, Anyaiwe D4, Scheinker D1, Steck AK5, Vasudevan MM2, McKay SV2,6, Sherr JL7, Herold KC8, Dunne JL9, Greenbaum CJ10, Lord SM10, Haller MJ11, Schatz DA11, Atkinson MA11, Nelson PW4, Pietropaolo M2, and the Type 1 Diabetes TrialNet Study Group1Division of Pediatric Endocrinology, Stanford University School of Medicine, Palo Alto, CA; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 2Division of Endocrinology, Diabetes, and Metabolism, Diabetes Research Center, Department of Medicine, Baylor College of Medicine, Houston, TX; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 3Department of Industrial & Systems Engineering, University of Washington, Seattle, WA; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 4Department of Mathematics & Computer Science, Lawrence Technological University, Southfield, MI; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 5Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 6Department of Pediatrics, Baylor College of Medicine, Houston, TX; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 7Division of Pediatric Endocrinology, Yale University School of Medicine, New Haven, CT; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 8Departments of Immunobiology and Internal Medicine, Yale University, New Haven, CT; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 9JDRF, New York, NY; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 10Center for Interventional Immunology and Diabetes Program, Benaroya Research Institute, Seattle, WA; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 11Department of Pediatrics, University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FLDiabetes Care 2023;46:526–534This study is also discussed in DIA-2024-2502, page S-14.Because continuous glucose monitoring (CGM) parameters may identify individuals at risk for progression to overt type 1 diabetes (T1D), this study examined whether CGM metrics could provide additional insight into the progression to clinical stage 3 T1D.MethodsFrom the TrialNet Pathway to Prevention study, 105 relatives of individuals in T1D probands (median age 16.8 years; 89% non-Hispanic White; 43.8% female) underwent 7-day CGM assessments and oral glucose tolerance tests (OGTTs) at 6-month intervals. The three groups evaluated were individuals with (1) stage 2 T1D (n = 42) with two or more diabetes-related autoantibodies and abnormal OGTT; (2) stage 1 T1D (n = 53) with two or more diabetes-related autoantibodies and normal OGTT; and (3) negative test for all diabetes-related autoantibodies and normal OGTT (n = 10).ResultsAfter the baseline data assessment, multiple CGM metrics were found to be associated with progression to stage 3 T1D: spending ≥ 5% time with glucose levels ≥ 140 mg/dL (P = 0.01), ≥ 8% time with glucose levels ≥ 140 mg/dL (P = 0.02), ≥ 5% time with glucose levels ≥ 160 mg/dL (P = 0.0001), and ≥ 8% time with glucose levels ≥ 160 mg/dL (P = 0.02). The stage 2 participants and those who progressed to stage 3 also exhibited higher mean daytime glucose values; they spent more time with glucose values over 120, 140, and 160 mg/dL, and they had greater variability.ConclusionsCGM could aid in the identification of individuals, including those with a normal OGTT, who are likely to rapidly progress to stage 3 T1D.CommentsRecent approval by the U.S. Food and Drug Association (FDA) of teplizumab for stage 2 T1D finally provides diabetes clinicians with an immunomodulatory treatment option to delay the development of stage 3 T1D. Now that such a treatment exists—and other immunotherapies are certain to follow—the case for increasing efforts for screening for T1D goes beyond prevention of diabetic ketoacidosis (DKA) to include therapies to delay initiation of insulin therapy. With the likelihood of increased screening efforts in the future, clinicians will be faced with questions of how to monitor progression of T1D and when to recommend immunotherapy. The data from CGM wearers provided by Wilson and TrialNet colleagues indicate clear thresholds at which rapid progression to stage 3 T1D occurs.As CGM has become more user friendly and more accepted by clinicians, it may well replace burdensome oral glucose tolerance tests as a more effective method to monitor for progression of T1D. This study and others will strengthen the evidence for the use of CGM in early stages of T1D. As diabetes technology becomes more effective in supporting care for T1D—and, we hope, more accessible to all people with T1D—we will see the role of CGM in ascertaining preclinical stages of T1D and timing of immunotherapies.Glycemic Variability Patterns Strongly Correlate with Partial Remission Status in Children with Newly Diagnosed Type 1 DiabetesPollé OG1,2, Delfosse A1,2, Martin M3, Louis J4, Gies I5,6, den Brinker M7,8, Seret N9, Lebrethon MC10, Mouraux T11, Gatto L3, Lysy PA1,2, on behalf of the DIATAG Working Group1Pôle de PEDI, Institut de Recherche Experimentale et Clinique, UCLouvain, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 2Specialized Pediatrics Service, Cliniques Universitaires Saint-Luc, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 3Computational Biology and Bioinformatics Unit, de Duve Institute, UCLouvain, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 4Division of Pediatric Endocrinology, Department of Pediatrics, Grand Hôpital de Charleroi, Charleroi, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 5Division of Pediatric Endocrinology, Department of Pediatrics, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 6Research Group GRON, Vrije Universiteit Brussel, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 7Laboratory of Experimental Medicine and Pediatrics and member of the Infla-Med Centre of Excellence, University of Antwerp, Faculty of Medicine and Health Sciences, Antwerp, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 8Division of Pediatric Endocrinology, Department of Pediatrics, Antwerp University Hospital, Antwerp, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 9Division of Pediatric Endocrinology, Department of Pediatrics, Centre Hospitalier Chrétien MontLégia, Liège, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 10Division of Pediatric Endocrinology, Department of Pediatrics, CHU Liège, Liège, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 11Division of Pediatric Endocrinology, Department of Pediatrics, CHU Namur, Namur, BelgiumDiabetes Care 2022;45:2360–2368Glucose variability parameters (also called CGM metrics) measured by continuous glucose monitoring (CGM) systems may strongly correlate with features of diabetes control related to β-cell function. CGM metrics improve the estimation of glucose control provided by HbA1c measurement and may help to better stratify existing phenotypes among patients with type 1 diabetes (T1D). This study evaluated whether indexes of glycemic variability may overcome residual β-cell secretion estimates in the longitudinal evaluation of partial remission (PR) in pediatric patients with new-onset T1D.MethodsIn this multicenter, prospective trial researchers tried to identify biomarkers of PR in children and adolescents (n = 78) with new-onset T1D. Values of residual β-cell secretion estimates, clinical parameters (e.g., HbA1c or insulin daily dose), and CGM data were longitudinally collected during 1 year and underwent cross-sectional comparison. Circadian patterns of CGM metrics were characterized and correlated to PR status using an adjusted mixed-effects model. Patients were clustered based on 46 CGM metrics and clinical parameters and were compared using nonparametric analysis of variance.ResultsThe mean age of the participants was 10.4 ± 3.6 years at diabetes onset; 65% of them underwent PR at 3 months. β-Cell residual secretion estimates demonstrated weak-to-moderate correlations with clinical parameters and CGM metrics. CGM metrics strongly correlated with clinical parameters (P < 0.05) and were satisfactory to distinguish those with PR from those without PR. Also, CGM metrics from those with PR showed specific early morning circadian patterns characterized by increased glycemic stability across days (within 63–140 mg/dL range) and decreased rate of grade II hypoglycemia (P < 0.0001) compared with those without PR. CGM analysis allowed the identification of four novel glucotypes (P < 0.001) that segregate patients into subgroups and reflect the evolution of PR after diabetes onset.ConclusionsCGM metrics (e.g., hyperglycemia and time in range) demonstrated a strong correlation with routine clinical parameters and demonstrated, for most of them, a specific circadian pattern that distinguished both remission groups.CommentsPartial remission (PR) is a state of low glycemic variability, low daily insulin needs, and lower HbA1c levels. Studies in young adults also have suggested that patients entering PR after diabetes onset were less at risk of vascular complications (1). Currently, little is known about the influence of PR and its duration on short-term glucose homeostasis outcomes, especially in children.PR can be evaluated by C-peptide levels and is commonly defined as the persistence of C-peptide secretion above a certain threshold (peak C-peptide > 200 pmol/L) (2). However, assays lack the power to discriminate residual β-cell mass from β-cell function. Therefore, new tools are needed that may both reflect the presence and predict the evolution of significant residual β-cell function, which qualifies PR.With the use of CGM systems, it seems that glucose variability parameters (CGM metrics) may strongly correlate with features of diabetes control related to β-cell function. Previous studies (3,4) showed that CGM metrics improve the estimation of glucose control provided by HbA1c measurement and may help to better stratify existing phenotypes among patients with T1D.In the study by Pollé and colleagues, patients without PR had a significantly higher prevalence of DKA at the onset of diabetes which may reflect a lower β-cell function or mass. Residual C-peptide secretion estimates, evaluated using either a single blood test or stimulation testing, were only weakly correlated with glucose homeostasis evaluated by CGM metrics and with clinical parameters (HbA1c, total daily dose of insulin, and insulin dose-adjusted A1c [IDAA1c]) without significant difference between the patients with and without PR. However, the CGM metrics showed strong correlations with the clinical parameters and allowed deeper characterization of glucose homeostasis (i.e., hypoglycemia episodes and glucose variability). As expected, the patients with PR spent more time in the target range and less time in hyperglycemia during the whole day.By using CGM data, the investigators identified specific circadian patterns among remission groups for most CGM metrics, which peaked in their discriminative features in the early morning period. By integrating CGM metrics and clinical parameters, they identified four clinically meaningful clusters that exhibit specific glucotypes and reflect the progressive loss of glucose homeostasis during the first year after T1D onset. Therefore, CGM metrics provided additional information to segregate patients.The study's limitations included its relatively small number of patients and the cross-sectional analysis of parameters (i.e., clinic, secretion, and CGM data) that were only available for a subset of patients. The study's strength is the novelty that integrates CGM, clinical parameters, and residual β-cell secretion data to find the characteristics of PR and identify new glucotypes during the first year of T1D.The implementation of various CGM metrics as end points in trials of residual β-cell function preservation may provide applicable and more noninvasive precise clues to select the subgroup of patients who are better candidates for intervention and to evaluate the patient response to treatment. Another implication of more comprehensive sampling as obtained with CGM metrics may improve diagnostic accuracy of the transition from health to prediabetes or stage 2 T1D. New insights may offer earlier therapeutic options for reversing dysglycemia more successfully.Disparities in Hemoglobin A1c Levels in the First Year after Diagnosis among Youths with Type 1 Diabetes Offered Continuous Glucose MonitoringAddala A1, Ding V2, Zaharieva DP1, Bishop FK1, Adams AS1,3,4,5, King AC3,6, Johari R7, Scheinker D1,5,7,8, Hood KK1,5, Desai M2, Maahs DM1,3,5, Prahalad P1,5 for the Teamwork, Targets, Technology, and Tight Control (4T) Study Group1Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA; Stanford University, Stanford, CA; 2Division of Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA; Stanford University, Stanford, CA; 3Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA; Stanford University, Stanford, CA; 4Department of Health Policy, Stanford University School of Medicine, Stanford, CA; Stanford University, Stanford, CA; 5Stanford Diabetes Research Center, Stanford University, Stanford, CA; Stanford University, Stanford, CA; 6Stanford Prevention Research Center Division, Department of Medicine, Stanford University School of Medicine, Stanford, CA; Stanford University, Stanford, CA; 7Clinical Excellence Research Center, Stanford University, Stanford, CA; Stanford University, Stanford, CA; 8Department of Management Science and Engineering, Stanford University, Stanford, CAJAMA Netw Open 2023;6:e238881This study is also discussed in DIA-2024-2502, page S-14, and DIA-2024-2512, page S-187.Although continuous glucose monitoring (CGM) is associated with improvements in hemoglobin A1c (HbA1c) in youths with type 1 diabetes (T1D), youths from minoritized racial and ethnic groups and those with public insurance face greater challenges accessing this treatment. This study examined whether HbA1c decreases differed by ethnicity and insurance status among the youths in the Teamwork, Targets, Technology, and Tight Control (4T) study with newly diagnosed T1D on CGM.MethodsThe 4T study was a clinical research program that aimed to initiate CGM within 1 month of T1D diagnosis for all youths with new-onset T1D diagnosed between July 25, 2018, and June 15, 2020. For the Pilot-4T study cohort, youths at Stanford Children's Hospital were followed for 12 months and compared with a historical cohort of 272 youths diagnosed with T1D between June 1, 2014, and December 28, 2016. HbA1c change over the study period was assessed, and the analyses were stratified by ethnicity (Hispanic vs non-Hispanic) or insurance status (public vs private).ResultsThe Pilot-4T cohort comprised 135 youths, 71 male (52.6%), with a median age of 9.7 years (IQR, 6.8–12.7 years) at diagnosis. Participants' race was based on self-report and was categorized as 19 Asian or Pacific Islander (14.1%), 62 White (45.9%), and 39 other race (28.9%); 15 participants (11.1%) did not supply race information. The participants also self-reported their ethnicity: 29 Hispanic (21.5%) and 92 non-Hispanic (68.1%). A total of 104 participants (77.0%) had private insurance, and 31 (23.0%) had public insurance. Compared with the historical cohort, the Pilot-4T cohort had similar reductions in HbA1c at 6, 9, and 12 months after diagnosis observed for both the Hispanic individuals: estimated difference, −0.26% (95% CI, −1.05% to 0.43%), −0.60% (95% CI, −1.46% to 0.21%), and −0.15% (95% CI, −1.48% to 0.80%), respectively; and the non-Hispanic individuals: estimated difference, −0.27% (95% CI, −0.62% to 0.10%), −0.50% (95% CI, −0.81% to −0.11%), and −0.47% (95% CI, −0.91% to 0.06%), respectively. Similar reductions in HbA1c at 6, 9, and 12 months after diagnosis were also observed in the Pilot-4T cohort for both the publicly insured individuals: estimated difference, −0.52% (95% CI, −1.22% to 0.15%), −0.38% (95% CI, −1.26% to 0.33%), and −0.57% (95% CI, −2.08% to 0.74%, respectively; and the privately insured individuals: estimated difference, −0.34% (95% CI, −0.67% to 0.03%), −0.57% (95% CI, −0.85% to −0.26%), and −0.43% (−0.85% to 0.01%), respectively. Hispanic youths in the Pilot-4T cohort had higher HbA1c at 6, 9, and 12 months after diagnosis than the non-Hispanic youths (estimated difference, 0.28% [95% CI, −0.46% to 0.86%], 0.63% [95% CI, 0.02%–1.20%], and 1.39% [95% CI, 0.37%–1.96%]), as did the publicly insured youths compared with the privately insured youths (estimated difference, 0.39% [95% CI, −0.23% to 0.99%], 0.95%[95% CI, 0.28%–1.45%], and 1.16% [95% CI, −0.09% to 2.13%]).ConclusionsFor Hispanic and non-Hispanic youths as well as for publicly and privately insured youths, CGM initiation soon after diagnosis is associated with similar improvements in HbA1c. Equitable access to CGM soon after T1D diagnosis may be a first step to improve HbA1c for all youths but is unlikely to eliminate disparities entirely.CommentsCGM initiation early in the course of T1D is associated with improved clinical outcomes (5,6). The Pilot 4T study showed that a team-based approach to CGM initiation within the first month of diagnosis, supported by remote patient monitoring, can improve HbA1c at 1 year after diabetes diagnosis in a general diabetes clinic population (5).Addala and colleagues performed further analysis on the Pilot 4T population to determine whether disparities in clinical outcomes persisted among those from minoritized communities when receiving a standardized new onset protocol that removed provider-level bias. The results from their analysis demonstrated that the 4T intervention improved outcomes

ABSTRACT:We evaluated long-term outcomes of 288 children with refractory-Langerhans cell histiocytosis (R-LCH) from 26 countries, who were prescribed off-label MAPK inhibitors (MAPKis) according to clinical indications. MAPKi indications included 148 R-risk-organ-positive (R-RO+), 67 R-risk-organ-negative (R-RO-), 13 lung destruction (lung), 9 sclerosing cholangitis (SC), 49 neurodegeneration (ND), and 2 diabetes insipidus (DI) cases. Median ages at diagnosis and MAPKi onset were 1.3 and 2.3 years, respectively, with median follow-up of 3.7 years (1166 person-years). Agents mostly prescribed as monotherapies were 184 prescriptions of vemurafenib, 115 of dabrafenib, 3 of encorafenib, 42 of cobimetinib, 45 of trametinib, and/or 1 prescription of binimetinib; followed 51 times by various chemotherapies or hematopoietic stem-cell transplantation, or 28 times by combined anti-BRAF-anti-MEK. Short-term responses (<8 weeks) ranged from 98% (R-RO+ and R-RO-), to 30% (lung) to none (ND, DI, and SC); although long-term lung and ND responses could be observed. Skin rash was the most frequent adverse event (∼55%), and 7 others included 1 case of cardiomyopathy and 6 of retinitis. Five developed MAPKi-unrelated tumors and 9 patients died. Five-year survival was 98%. After 113 patients with R-LCH discontinued MAPKi, 69 experienced disease reactivation. None of the various empirical maintenance therapies were able to prevent secondary reactivation. Among the 143 assessable patients without ND-LCH at MAPKi onset, 60 developed ND (45%, 5-year risk). MAPKis appeared to be safe and effective in children with R-RO+/RO-LCH, whereas other indications' responses were less frequent or occurred later. Further studies are needed to find effective maintenance-therapy approaches, particularly to prevent frequently observed secondary ND.

Background Regular exercise is an important component of care for children with type 1 diabetes (T1D), supporting both physical and psychological health. However, many do not meet recommended activity levels, and exercise does not consistently improve glycemic control. Exercise management in T1D is complex and influenced by multiple factors, including child, activity and treatment characteristics. These challenges highlight the importance of comprehensive exercise-directed guidance (EDG). This study aimed to characterize EDG practices in pediatric T1D and identify key challenges faced by healthcare professionals. Methods Physicians, nurses and dietitians from 11 pediatric diabetes centers, participated. Participants completed anonymized electronic questionnaires assessing current practices, content, and perceived challenges related to EDG. Results One-hundred-and-thirty professionals participated, all reported providing EDG to patients. Considerable variability was observed in the timing, content, and educational resources used. While the majority (85.9%) reported offering guidance to all patients, others indicated guiding only when specific needs arose. The most frequently addressed topics included the immediate effects on blood-glucose, adjusting carbohydrate intake and general preparations for exercise. Less frequently discussed topics were planning exercise regimens and encouraging involvement of family or peers. Challenges by order of prevalence were insufficient teaching-aids (42.1%), EDG complexity (40.0%), visit duration (36.1%), and knowledge gaps (33.1%). Conclusions Although healthcare professionals acknowledge the importance of exercise in pediatric T1D and incorporate EDG into clinical care, substantial variability exists in its delivery. Improving the availability and usability of educational tools and strengthening social support components may enhance exercise education for children with type 1 diabetes.

Candida species are the leading cause of invasive fungal infections in children and are associated with substantial morbidity and mortality. We conducted a multicenter retrospective study across nine hospitals in Israel, including all candidemia episodes in patients aged 0–18 years between 2010 and 2022. Clinical, microbiological, and outcome data were collected, and multivariable logistic regression was used to identify predictors of 30-day mortality. A total of 498 candidemia episodes were identified in 482 patients. Most episodes occurred in immunocompromised children or those admitted to intensive care units. Non-albicans Candida species predominated (63.3%), although C. albicans remained the single most common species (36.7%). The most frequent non-albicans species were C. parapsilosis (25.5%), C. tropicalis (15.9%), and C. glabrata (9.6%). No cases of C. auris were identified. Fluconazole non-susceptibility was observed in 19.4% of isolates and increased from 15.5% during 2010–2016 to 23.3% during 2017–2022 (OR 2.89, 95% CI 1.45–5.11, p = 0.007). The 30-day mortality rate was 23.4%. Independent predictors of mortality included liver disease, neutropenia, total parenteral nutrition, urinary tract infection, skin lesions, and peritonitis. Pediatric candidemia remains associated with considerable mortality, while increasing fluconazole non-susceptibility among non-albicans Candida species highlights the need for ongoing surveillance and antifungal stewardship.

Pediatric-Onset Multiple Sclerosis (POMS) affects children’s physical, cognitive, and emotional state, with consequences for parents as well. Parents of children with chronic illnesses often report elevated stress and psychological distress, yet their role in shaping children’s psychological outcomes in POMS remains underexplored. This study examined whether parental emotional state mediates the association between children’s disease-related factors and self-esteem. Thirty children and adolescents with POMS (ages 10–18) and their parents participated. Measures included child fatigue, processing speed, disease severity (EDSS), self-esteem, and parental stress and well-being. All participants scored within the low self-esteem range of the Rosenberg Self-Esteem Scale (RSES; M = 15.93, SD = 2.21), indicating reduced global self-worth. Among parents, emotional distress was evident, with 43