Characteristics of autoantibody-positive individuals without high-risk HLA-DR4-DQ8 or HLA-DR3-DQ2 haplotypes.

Aims/hypothesis: Many studies of type 1 diabetes pathogenesis focus on individuals with high-risk HLA haplotypes. We tested the hypothesis that, among islet autoantibody-positive individuals, lacking HLA-DRB1*04-DQA1*03-DQB1*0302 (HLA-DR4-DQ8) and/or HLA-DRB1*0301-DQA1*0501-DQB1*0201 (HLA-DR3-DQ2) is associated with phenotypic differences, compared with those who have these high-risk HLA haplotypes.

Methods: We classified autoantibody-positive relatives of individuals with type 1 diabetes into four groups based on having both HLA-DR4-DQ8 and HLA-DR3-DQ2 (DR3/DR4; n=1263), HLA-DR4-DQ8 but not HLA-DR3-DQ2 (DR4/non-DR3; n=2340), HLA-DR3-DQ2 but not HLA-DR4-DQ8 (DR3/non-DR4; n=1607) and neither HLA-DR3-DQ2 nor HLA-DR4-DQ8 (DRX/DRX; n=1294).

Age and sex mark clinical differences in the presentation of pediatric type 1 diabetes mellitus.

Objectives: Type 1 diabetes mellitus (T1D) is a heterogeneous condition. We aimed to study the associations between age and sex with clinical characteristics at the onset of pediatric T1D.

Methods: A secondary analysis was conducted on data collected retrospectively from 706 children newly diagnosed with T1D at a large tertiary hospital in southeastern USA.

Identification of atypical pediatric diabetes mellitus cases using electronic medical records.

Introduction: There are no established methods to identify children with atypical diabetes for further study. We aimed to develop strategies to systematically ascertain cases of atypical pediatric diabetes using electronic medical records (EMR).

Research Design And Methods: We tested two strategies in a large pediatric hospital in the USA.

The Maturity-Onset Diabetes of the Young (MODY) Calculator Overestimates MODY Probability in Hispanic Youth.

Context: The applicability of the MODY risk calculator (Shields et al) to non- White European populations remains unknown.

Objective: We aimed to test its real-world application in Hispanic youth.

Methods: We conducted a retrospective chart review of Hispanic youth (<23 years) with diabetes (n=2033) in a large pediatric tertiary care center in the U.

Random C-Peptide and Islet Antibodies at Onset Predict β Cell Function Trajectory and Insulin Dependence in Pediatric Diabetes.

Objective: Identification of prognostic biomarkers in pediatric diabetes is important for precision medicine. We assessed whether C-peptide and islet autoantibodies are useful to predict the natural history of children with new-onset diabetes.

Methods: We prospectively studied 72 children with new-onset diabetes (median follow-up: 8 months) by applying the Aβ classification system ("A+": islet autoantibody positive, "β+": random serum C-peptide ≥1.

Extended reality training for mass casualty incidents: a systematic review on effectiveness and experience of medical first responders.

Introduction: Mass casualty incidents (MCI) are unforeseeable and complex events that occur worldwide, therefore enhancing the training that medical first responders (MFRs) receive is fundamental to strengthening disaster preparedness and response. In recent years, extended reality (XR) technology has been introduced as a new approach and promising teaching technique for disaster medicine education.

Objective: To assess the effectiveness of XR simulation as a tool to train MFRs in MCIs, and to explore the perception and experience of participants to these new forms of training.

Identification of type 1 diabetes risk phenotypes using an outcome-guided clustering analysis.

Aims/hypothesis: Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk.

Methods: We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123).

Diabetic ketoacidosis in youth with diabetes mellitus during the COVID-19 pandemic.

Objectives: We sought to determine if the early months of the coronavirus disease 2019 (COVID-19) pandemic influenced pediatric diabetic ketoacidosis (DKA) hospitalization characteristics.

Methods: This is a cross-sectional study of youth with laboratory-confirmed DKA admitted to a large tertiary children's hospital in the USA. Data were collected from admissions in March through July 2019 and March through July 2020, respectively.

Variants of Unknown Significance in Maturity-Onset Diabetes of the Young: High Rate of Conundrum Resolution via Variants of Unknown Significance Reanalysis.

Introduction: In the era of next-generation sequencing, clinicians frequently encounter variants of unknown significance (VUS) in genetic testing. VUS may be reclassified over time as genetic knowledge grows. We know little about how best to approach VUS in the maturity-onset diabetes of the young (MODY).

High Prevalence of Ketosis-Prone Diabetes in Children with Type 2 Diabetes and Diabetic Ketoacidosis at Diagnosis: Evidence from the Rare and Atypical Diabetes Network (RADIANT).

Background: ketosis-prone diabetes (KPD) in adults is characterized by presentation with diabetic ketoacidosis (DKA), negative islet autoantibodies, and preserved -cell function in persons with a phenotype of obesity-associated type 2 diabetes (T2D). The prevalence of KPD has not been evaluated in children. We investigated children with DKA at "T2D" onset and determined the prevalence and characteristics of pediatric KPD within this cohort.

Inaccurate diagnosis of diabetes type in youth: prevalence, characteristics, and implications.

Classifying diabetes at diagnosis is crucial for disease management but increasingly difficult due to overlaps in characteristics between the commonly encountered diabetes types. We evaluated the prevalence and characteristics of youth with diabetes type that was unknown at diagnosis or was revised over time. We studied 2073 youth with new-onset diabetes (median age [IQR] = 11.

Early Metabolic Endpoints Identify Persistent Treatment Efficacy in Recent-Onset Type 1 Diabetes Immunotherapy Trials.

Objective: Mixed-meal tolerance test-stimulated area under the curve (AUC) C-peptide at 12-24 months represents the primary end point for nearly all intervention trials seeking to preserve β-cell function in recent-onset type 1 diabetes. We hypothesized that participant benefit might be detected earlier and predict outcomes at 12 months posttherapy. Such findings would support shorter trials to establish initial efficacy.

Comparisons of Metabolic Measures to Predict T1D vs Detect a Preventive Treatment Effect in High-Risk Individuals.

Context: Metabolic measures are frequently used to predict type 1 diabetes (T1D) and to understand effects of disease-modifying therapies.

Objective: Compare metabolic endpoints for their ability to detect preventive treatment effects and predict T1D.

Methods: Six-month changes in metabolic endpoints were assessed for (1) detecting treatment effects by comparing placebo and treatment arms from the randomized controlled teplizumab prevention trial, a multicenter clinical trial investigating 14-day intravenous teplizumab infusion and (2) predicting T1D in the TrialNet Pathway to Prevention natural history study.

Clinical Prediction Models Combining Routine Clinical Measures Have High Accuracy in Identifying Youth-Onset Type 2 Diabetes Defined by Maintained Endogenous Insulin Secretion: The SEARCH for Diabetes in Youth Study.

Objective: With high prevalence of obesity and overlapping features between diabetes subtypes, accurately classifying youth-onset diabetes can be challenging. We aimed to develop prediction models that, using characteristics available at diabetes diagnosis, can identify youth who will retain endogenous insulin secretion at levels consistent with type 2 diabetes (T2D).

Research Design And Methods: We studied 2,966 youth with diabetes in the prospective SEARCH for Diabetes in Youth study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting C-peptide ≥250 pmol/L (≥0.

The impact of the COVID-19 pandemic on people living with HIV: a cross-sectional study in Caracas, Venezuela.

Background: The coronavirus disease 2019 (COVID-19) pandemic has disrupted multiple health services, including human immunodeficiency virus (HIV) testing, care, and treatment services, jeopardizing the achievement of the Joint United Nations Programme on HIV/AIDS 90-90-90 global target. While there are limited studies assessing the impact of the COVID-19 pandemic on people living with HIV (PLHIV) in Latin America, there are none, to our knowledge, in Venezuela. This study aims to assess the impact of the COVID-19 pandemic among PLHIV seen at the outpatient clinic of a reference hospital in Venezuela.

Diabetes Study of Children of Diverse Ethnicity and Race: Study design.

Aims: Determining diabetes type in children has become increasingly difficult due to an overlap in typical characteristics between type 1 diabetes (T1D) and type 2 diabetes (T2D). The Diabetes Study in Children of Diverse Ethnicity and Race (DISCOVER) programme is a National Institutes of Health (NIH)-supported multicenter, prospective, observational study that enrols children and adolescents with non-secondary diabetes. The primary aim of the study was to develop improved models to differentiate between T1D and T2D in diverse youth.

Type 1 Diabetes Risk Phenotypes Using Cluster Analysis.

Background: Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk.

Methods: We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention (PTP) study data (n=1127).

Clinical prediction models combining routine clinical measures identify participants with youth-onset diabetes who maintain insulin secretion in the range associated with type 2 diabetes: The SEARCH for Diabetes in Youth Study.

Objective: With the high prevalence of pediatric obesity and overlapping features between diabetes subtypes, accurately classifying youth-onset diabetes can be challenging. We aimed to develop prediction models that, using characteristics available at diabetes diagnosis, can identify youth who will retain endogenous insulin secretion at levels consistent with type 2 diabetes (T2D).

Methods: We studied 2,966 youth with diabetes in the prospective SEARCH study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting c-peptide ≥250 pmol/L (≥0.

Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes.

Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model.