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  1. ; ; ; ; ; ( , Journal of Diabetes Science and Technology)
    Background:

    The Glycemia Risk Index (GRI) was developed in adults with diabetes and is a validated metric of quality of glycemia. Little is known about the relationship between GRI and type 1 diabetes (T1D) self-management habits, a validated assessment of youths’ engagement in habits associated with glycemic outcomes.

     Method:

    We retrospectively examined the relationship between GRI and T1D self-management habits in youth with T1D who received care from a Midwest pediatric diabetes clinic network. The GRI was calculated using seven days of continuous glucose monitor (CGM) data, and T1D self-management habits were assessed ±seven days from the GRI score. A mixed-effects Poisson regression model was used to evaluate the total number of habits youth engaged in with GRI, glycated hemoglobin A1c (HbA1c), age, race, ethnicity, and insurance type as fixed effects and participant ID as a random effect to account for multiple clinic visits per individual.

     Results:

    The cohort included 1182 youth aged 2.5 to 18.0 years (mean = 13.8, SD = 3.5) comprising 50.8% male, 84.6% non-Hispanic White, and 64.8% commercial insurance users across a total of 6029 clinic visits. Glycemia Risk Index scores decreased as total number of habits performed increased, suggesting youth who performed more self-management habits achieved a higher quality of glycemia.

     Conclusions:

    In youth using CGMs, GRI may serve as an easily obtainable metric to help identify youth with above target glycemia, and engagement/disengagement in the T1D self-management habits may inform clinicians with suitable interventions for improving glycemic outcomes.

     
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    Free, publicly-accessible full text available July 1, 2025
  2. ; ; ; ; ; ; ; ( , Journal of Diabetes Science and Technology)
    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). Furthermore, we analyzed correlations between HbA1c and the GRI high and low components, which reflect time spent with high/very high and low/very low glucose, respectively.

     Results:

    In this cohort of 719 youth (aged = 2.5-18.0 years [median = 13.4; interquartile range [IQR] = 5.2]; 50.5% male; 83.7% non-Hispanic White; 68.0% commercial insurance), baseline GRI scores positively correlated with HbA1c measures at baseline and 3, 6, 9, and 12 months later (r = 0.68, 0.65, 0.60, 0.57, and 0.52, respectively). At all time points, strong positive correlations existed between HbA1c and time spent in hyperglycemia. Substantially weaker, negative correlations existed between HbA1c and time spent in hypoglycemia.

     Conclusions:

    In youth with T1D, the GRI may be useful for evaluating quality of glycemia between scheduled clinic visits. Additional CGM-derived metrics are needed to quantify risk for hypoglycemia in this population.

     
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    Free, publicly-accessible full text available July 1, 2025
  3. ; ; ; ; ( , 41st International Conference on Machine Learning (ICML))
    Hybrid models composing mechanistic ODE- based dynamics with flexible and expressive neural network components have grown rapidly in popularity, especially in scientific domains where such ODE-based modeling offers important interpretability and validated causal grounding (e.g., for counterfactual reasoning). The incorporation of mechanistic models also provides inductive bias in standard blackbox modeling approaches, critical when learning from small datasets or partially observed, complex systems. Unfortunately, as the hybrid models become more flexible, the causal grounding provided by the mechanistic model can quickly be lost. We address this problem by leveraging another common source of domain knowledge: ranking of treatment effects for a set of interventions, even if the precise treatment effect is unknown. We encode this information in a causal loss that we combine with the standard predictive loss to arrive at a hybrid loss that biases our learning towards causally valid hybrid models. We demonstrate our ability to achieve a win-win, state-of-the-art predictive performance and causal validity, in the challenging task of modeling glucose dynamics post-exercise in individuals with type 1 diabetes. 
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    Free, publicly-accessible full text available July 21, 2025
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Nature Medicine)
    Free, publicly-accessible full text available July 1, 2025
  5. ; ; ; ; ; ; ; ; ; ( , Diabetes Technology & Therapeutics)
    Free, publicly-accessible full text available March 1, 2025
  6. ; ; ; ; ; ; ; ( , NEJM Evidence)
    Free, publicly-accessible full text available January 23, 2025
  7. ; ; ; ; ; ; ; ; ; ( , Journal of Diabetes and its Complications)
    Full Text Available 
  8. ; ; ; ; ; ; ; ; ( , Endocrinology, Diabetes & Metabolism)
    Abstract Introduction Algorithm‐enabled remote patient monitoring (RPM) programs pose novel operational challenges. For clinics developing and deploying such programs, no standardized model is available to ensure capacity sufficient for timely access to care. We developed a flexible model and interactive dashboard of capacity planning for whole‐population RPM‐based care for T1D. Methods Data were gathered from a weekly RPM program for 277 paediatric patients with T1D at a paediatric academic medical centre. Through the analysis of 2 years of observational operational data and iterative interviews with the care team, we identified the primary operational, population, and workforce metrics that drive demand for care providers. Based on these metrics, an interactive model was designed to facilitate capacity planning and deployed as a dashboard. Results The primary population‐level drivers of demand are the number of patients in the program, the rate at which patients enrol and graduate from the program, and the average frequency at which patients require a review of their data. The primary modifiable clinic‐level drivers of capacity are the number of care providers, the time required to review patient data and contact a patient, and the number of hours each provider allocates to the program each week. At the institution studied, the model identified a variety of practical operational approaches to better match the demand for patient care. Conclusion We designed a generalizable, systematic model for capacity planning for a paediatric endocrinology clinic providing RPM for T1D. We deployed this model as an interactive dashboard and used it to facilitate expansion of a novel care program (4 T Study) for newly diagnosed patients with T1D. This model may facilitate the systematic design of RPM‐based care programs. 
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  9. ; ; ; ; ; ; ; ; ; ; et al ( , Diabetes)
    Psychosocial impacts of early CGM initiation in youth soon after T1D diagnosis are underexplored. We report parent/guardian (PG) and youth trends in Patient Reported Outcomes (PROs) for families in the 4T Study 1. Of the 133 participants in the 4T Study 1, 125 PG and 60 youth (≥11 years) were eligible for PROs. PROs included Diabetes Distress Scale - Parent (mean DDS-P) for PG and for youth, Diabetes Distress Scale (DDS sum), PROMIS Pediatric Global Health (PGH sum), Diabetes Technology Attitudes (DTA sum), and CGM Benefits/Burden (BenCGM and BurCGM sum). Kruskal Wallis rank sum test evaluated temporal trends and sociodemographics were evaluated (Numerical: Wilcoxon rank; Categorical: Fisher's if n<5, Chi-squared if n≥5). PROs completion rates were higher for PG than youth at baseline (74% v 59%), 3 months (70% v 53%), and 6 months (66% v 50%). PG DDS-P remained low throughout the study (Table). Youth had favorable psychosocial trends (low DDS and high PGH), and perceived technology positively (high DTA and BenCGM with low BurCGM). Age, DKA at diagnosis, gender, ethnicity, insurance status, and language spoken were not associated with PROs scores in PG or youth. CGM initiation shortly after T1D diagnosis is not associated with poor or worsening PROs for PG and youth. These data suggest that early CGM initiation does not adversely impact psychosocial states for families and youth with T1D. Disclosure A.Addala: None. F.K.Bishop: None. D.P.Zaharieva: Advisory Panel; Dexcom, Inc., Research Support; Hemsley Charitable Trust, International Society for Pediatric and Adolescent Diabetes, Insulet Corporation, Speaker's Bureau; American Diabetes Association, Ascensia Diabetes Care, Medtronic. P.Prahalad: None. M.Desai: None. D.M.Maahs: Advisory Panel; Medtronic, LifeScan Diabetes Institute, MannKind Corporation, Consultant; Abbott, Research Support; Dexcom, Inc. K.K.Hood: Consultant; Cecelia Health. V.Ritter: None. B.Shaw: None. E.Pang: None. A.L.Cortes-navarro: None. I.Balistreri: None. A.Loyola: None. S.A.Alamarie: None. A.Schneider-utaka: None. Funding National Institutes of Health (K23DK13134201, R18DK122422) 
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  10. ; ; ; ; ; ; ; ; ( , Diabetes)
    Continuous glucose monitoring (CGM) use soon after T1D diagnosis in the 4T Study was associated with improved glycemic outcomes. We evaluated participant factors associated with elevated versus in target A1c for youth in the 4T Study. All youth from the 4T Study 1 (n=133) were evaluated. In this analysis, the 110 youth who had a final A1c between 6-13 months were included in a complete case analysis. These 110 youth were comparable to the 133 4T Study 1 youth by race/ethnicity, insurance, preferred language, and age. Group differences by non-ordered A1c categories were evaluated for categorical (race/ethnicity, insurance, gender, and language) and continuous (age and time from CGM start) variables via chi-square and ANOVA, respectively. A majority of youth in the 4T Study 1 met glycemic targets (65% with A1c ≤7% between 6-13 months post-diagnosis). Age, race/ethnicity, and insurance status were significantly associated with A1c categories (p=0.02 for all; Table). Higher A1c categories were more likely to include Hispanic youth and youth with public insurance. In the 4T Study 1, Hispanic youth and youth with public insurance had higher A1c categories despite similar CGM access and training. These findings suggest the need to address additional drivers of disparities in addition to CGM access. Approaches focused on health equity are required to improve glycemic outcomes in all youth newly diagnosed with T1D. Disclosure J. Kim: None. D. P. Zaharieva: Advisory Panel; Dexcom, Inc., Research Support; Hemsley Charitable Trust, International Society for Pediatric and Adolescent Diabetes, Insulet Corporation, Speaker's Bureau; American Diabetes Association, Ascensia Diabetes Care, Medtronic. F. K. Bishop: None. D. Scheinker: None. R. Johari: None. M. Desai: None. K. K. Hood: Consultant; Cecelia Health. D. M. Maahs: Advisory Panel; Medtronic, LifeScan Diabetes Institute, MannKind Corporation, Consultant; Abbott, Research Support; Dexcom, Inc. A. Addala: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (K23DK13134201, R18DK122422) 
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