CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications.

New methods of continuous glucose monitoring (CGM) data analysis are emerging that are valuable for interpreting CGM patterns and underlying metabolic physiology. These new methods use functional data analysis and artificial intelligence (AI), including machine learning (ML). Compared to traditional metrics for evaluating CGM tracing results (CGM Data Analysis 1.

Associations of CGM Metrics With Stimulated C-Peptide Measures in Youth With Recent-Onset Type 1 Diabetes.

Objective: Continuous glucose monitoring (CGM) measures could be a surrogate for stimulated C-peptide outcomes in type 1 diabetes trials.

Research Design And Methods: CGM and mixed-meal tolerance test-derived C-peptide measures at time points out to 52 weeks after diagnosis were compared in 103 children.

Results: At 52 weeks, CGM metrics moderately correlated with C-peptide area under the curve.

Diabetes-related distress over time and its associations with glucose levels in school-aged children.

Introduction: In a cohort of families of school-age children (8-12.99 years old) with type 1 diabetes, we examined the stability of parent and child diabetes-related distress (DRD) over 6 months and the associations between parent and child DRD and child glycated hemoglobin (HbA1c) over time.

Research Design And Methods: We recruited families from two large pediatric hospital systems in the USA and used validated measures of parent (Parent Problem Areas in Diabetes-Child, PPAID-C) and child (Problem Areas in Diabetes-Child, PAID-C) DRD and children's HbA1c.

Utilizing Crowdsourcing to Incorporate the Voice of the Family When Developing a Diabetes Distress Intervention.

Objective: Diabetes distress (DD) is the negative emotional toll of living with or caring for type 1 diabetes, encompassing the pervasiveness of its daily demands. We recently developed Remedy to Diabetes Distress (R2D2), a screen-to-treat intervention for families of school-aged children with type 1 diabetes. This article reports on the use of crowdsourcing methods to design the intervention and capture the voice of parent stakeholders.

Parent Feedback on the Reducing Emotional Distress for Childhood Hypoglycemia in Parents (REDCHiP) Intervention: A Qualitative Analysis.

Objectives: Severe hypoglycemia is more common among young children with type 1 diabetes mellitus (T1DM) than older youth, and parents report significant hypoglycemia fear (HF). Parents experiencing HF describe constant and extreme worry about the occurrence of hypoglycemia and may engage in potentially risky behaviors to avoid hypoglycemia. Our team developed and tested a behavioral intervention, Reducing Emotional Distress for Childhood Hypoglycemia in Parents (REDCHiP), to decrease HF in parents of young children with T1DM.

Predicting and Ranking Diabetic Ketoacidosis Risk Among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.

To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-related hospitalization or emergency care in youth with type 1 diabetes (T1D). To achieve a sharable predictive model, we engineered features using EHR data mapped to the T1D Exchange Quality Improvement Collaborative's (T1DX-QI) data schema used by 60+ U.S.

Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes.

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals who will progress faster to a clinical diagnosis of type 1 diabetes, and heterogeneous clinical responses in intervention trials. Classic placebo-controlled intervention trials often include monotherapies, broad participant populations and extended follow-up periods focused on clinical endpoints. While this approach remains the 'gold standard' of clinical research, efforts are underway to implement new approaches harnessing the power of artificial intelligence and machine learning to accelerate drug discovery and efficacy testing.

Fear of hypoglycemia relates to glycemic levels during and after real-world physical activity in adolescents with type 1 diabetes.

Background: We explore the association between hypoglycaemia fear (FH) and glycaemia during and after exercise sessions in a large sample of physically active youth with type 1 diabetes (T1D).

Methods: We used data from the Type 1 Diabetes Exercise Initiative Paediatric (T1DEXIP) Study. Youth self-reported on FH using the Hypoglycaemia Fear Survey-Child (HFS-C).

Early Results of an Innovative Scalable Digital Treatment for Diabetes Distress in Families of School-Age Children with Type 1 Diabetes.

Objective: This paper reports on the initial outcomes of a new mHealth intervention to reduce diabetes distress (DD) in families of school-age children living with type 1 diabetes (T1D) entitled, 'Remedy to Diabetes Distress' (R2D2).

Methods: We randomized 34 families (mean child age = 10 ± 1.4 years; 53% male, 85% White, mean HbA1c = 7.

Plastid phylogenomics reveals evolutionary relationships in the mycoheterotrophic orchid genus and provides insights into plastid gene degeneration.

The orchid genus R.Br. (Epidendroideae) comprises leafy autotrophic and leafless mycoheterotrophic species, with the latter confined to sect.

Digital Gaming and Exercise Among Youth With Type 1 Diabetes: Cross-Sectional Analysis of Data From the Type 1 Diabetes Exercise Initiative Pediatric Study.

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.

Plastid phylogenomics clarifies broad-level relationships in (Orchidaceae) and provides insights into range evolution of Australasian section .

The hyperdiverse orchid genus is the second largest genus of flowering plants and exhibits a pantropical distribution with a center of diversity in tropical Asia. The only section with a center of diversity in Australasia is sect. .

Correlation Between the Glycemia Risk Index and Longitudinal Hemoglobin A1c in Children and Young Adults With Type 1 Diabetes.

Background: The glycemia risk index (GRI) is a composite metric developed and used to estimate quality of glycemia in adults with diabetes who use continuous glucose monitor (CGM) devices. In a cohort of youth with type 1 diabetes (T1D), we examined the utility of the GRI for evaluating quality of glycemia between clinic visits by analyzing correlations between the GRI and longitudinal glycated hemoglobin A1c (HbA1c) measures.

Method: Using electronic health records and CGM data, we conducted a retrospective cohort study to analyze the relationship between the GRI and longitudinal HbA1c measures in youth (T1D duration ≥1 year; ≥50% CGM wear time) receiving care from a Midwest pediatric diabetes clinic network (March 2016 to May 2022).

Multicenter Evaluation of Ultra-Rapid Lispro Insulin with Control-IQ Technology in Adults, Adolescents, and Children with Type 1 Diabetes.

To evaluate the safety and explore the efficacy of use of ultra-rapid lispro (URLi, Lyumjev) insulin in the Tandem t:slim X2 insulin pump with Control-IQ 1.5 technology in children, teenagers, and adults living with type 1 diabetes (T1D). At 14 U.

Predicting Hypoglycemia and Hyperglycemia Risk During and After Activity for Adolescents with Type 1 Diabetes.

To predict hypoglycemia and hyperglycemia risk during and after activity for adolescents with type 1 diabetes (T1D) using real-world data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study. Adolescents with T1D ( = 225; [mean ± SD] age = 14 ± 2 years; HbA1c = 7.1 ± 1.

The Association Between Diet Quality and Glycemic Outcomes Among People with Type 1 Diabetes.

Background: The amount and type of food consumed impacts the glycemic response and insulin needs of people with type 1 diabetes mellitus (T1DM). Daily variability in consumption, reflected in diet quality, may acutely impact glycemic levels and insulin needs.

Objective: Type 1 Diabetes Exercise Initiative (T1DEXI) data were examined to evaluate the impact of daily diet quality on near-term glycemic control and interaction with exercise.

Associations between daily step count classifications and continuous glucose monitoring metrics in adults with type 1 diabetes: analysis of the Type 1 Diabetes Exercise Initiative (T1DEXI) cohort.

Aims/hypothesis: Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics.

Methods: In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.

Factors Affecting Reproducibility of Change in Glucose During Exercise: Results From the Type 1 Diabetes and EXercise Initiative.

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.

Exploring Factors That Influence Postexercise Glycemia in Youth With Type 1 Diabetes in the Real World: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study.

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.