Learning control-ready forecasters for Blood Glucose Management.

Type 1 diabetes (T1D) presents a significant health challenge, requiring patients to actively manage their blood glucose (BG) levels through regular bolus insulin administration. Automated control solutions based on machine learning (ML) models could reduce the need for manual patient intervention. However, the accuracy of current models falls short of what is needed. This is due in part to the fact that these models are often trained on data collected using a basal bolus (BB) strategy, which results in substantial entanglement between bolus insulin and carbohydrate intake. Under standard training approaches, this entanglement can lead to inaccurate forecasts in a control setting, ultimately resulting in poor BG management. To address this, we propose a novel algorithm for training BG forecasters that disentangles the effects of insulin and carbohydrates. By exploiting correction bolus values and leveraging the monotonic effect of insulin on BG, our method accurately captures the independent effects of insulin and carbohydrates on BG. Using an FDA-approved simulator, we evaluated our approach on 10 individuals across 30 days of data. Our approach achieved on average higher time in range compared to standard approaches (81.1% [95% confidence interval (CI) 80.3,81.9] vs 53.6% [95%CI 52.7,54.6], p<0.001), indicating that our approach is able to reliably maintain healthy BG levels in simulated individuals, while baseline approaches are not. Utilizing proxy metrics, our approach also demonstrates potential for improved control on three real world datasets, paving the way for advancements in ML-based BG management.

Nutrition counsellors' recommended eating patterns for individuals with type 2 diabetes in the USA.

Introduction: Multiple eating patterns can promote glycaemic control and weight loss among patients with type 2 diabetes mellitus (T2D). Clinical practice guidelines for T2D management encourage health professionals to guide patients' selection of a patient-centred eating pattern. This study aims to characterise beliefs about and recommendations for and against practice guideline-concordant eating patterns among registered dietitians (RDs) and other healthcare professionals who provide nutrition counselling to patients with T2D. Methods: This was a cross-sectional online survey. We invited 82 RDs affiliated with an academic health system in the midwestern USA to participate. We also invited health professionals who provide nutrition counselling to patients with T2D and are affiliated with 264 primary care practices within the Michigan Collaborative for Type 2 Diabetes. Participants were asked to select the eating pattern(s) that they commonly recommend or avoid for patients with T2D and why. Results: Survey respondents (n=81) most commonly recommend low-carbohydrate (77.8%); Mediterranean-style (52.8%) and energy-modified/calorie-restricted (36.1%) eating patterns. Survey respondents most commonly recommend avoiding very low-carbohydrate (51.0%) and very low-calorie (49.0%) eating patterns. Respondents who did not recommend very low-carbohydrate were most concerned about the eating pattern being too restrictive (93.0%). Conclusions: Survey respondents recommend a range of guideline-adherent eating patterns to patients with T2D but tend to recommend against very low-carbohydrate and very low-calorie eating patterns. Additional strategies are needed to increase patient-centred use of these evidence-based options in clinical practice settings.

Shortcomings in the Evaluation of Blood Glucose Forecasting.

OBJECTIVE: Recent years have seen an increase in machine learning (ML)-based blood glucose (BG) forecasting models, with a growing emphasis on potential application to hybrid or closed-loop predictive glucose controllers. However, current approaches focus on evaluating the accuracy of these models using benchmark data generated under the behavior policy, which may differ significantly from the data the model may encounter in a control setting. This study challenges the efficacy of such evaluation approaches, demonstrating that they can fail to accurately capture an ML-based model's true performance in closed-loop control settings. METHODS: Forecast error measured using current evaluation approaches was compared to the control performance of two forecasters-a machine learning-based model (LSTM) and a rule-based model (Loop)-in silico when the forecasters were utilized with a model-based controller in a hybrid closed-loop setting. RESULTS: Under current evaluation standards, LSTM achieves a significantly lower (better) forecast error than Loop with a root mean squared error (RMSE) of 11.57 ±0.05 mg/dL vs. 18.46 ±0.07 mg/dL at the 30-minute prediction horizon. Yet in a control setting, LSTM led to significantly worse control performance with only 77.14% (IQR 66.57-84.03) time-in-range compared to 86.20% (IQR 78.28-91.21) for Loop. CONCLUSION: Prevailing evaluation methods can fail to accurately capture the forecaster's performance when utilized in closed-loop settings. SIGNIFICANCE: Our findings underscore the limitations of current evaluation standards and the need for alternative evaluation metrics and training strategies when developing BG forecasters for closed-loop control systems.

Bidirectional Associations Between Mental Health Disorders and Chronic Diabetic Complications in Individuals With Type 1 or Type 2 Diabetes.

OBJECTIVE: To determine bidirectional associations between the timing of chronic diabetes complications (CDCs) and mental health disorders (MHDs) in individuals with type 1 or type 2 diabetes. RESEARCH DESIGN AND METHODS: We used a nationally representative health care claims database to identify matched individuals with type 1 or 2 diabetes or without diabetes using a propensity score quasirandomization technique stratified by age (0-19, 20-39, 40-59, and ≥60 years). CDCs and MHDs were identified using ICD-9/10 codes. We fit Cox proportional hazards models with time-varying diagnoses of CDCs or MHDs to investigate their association with the hazard of developing MHDs or CDCs, respectively. RESULTS: From 2001 to 2018, a total of 553,552 individuals were included (44,735 with type 1 diabetes, 152,187 with type 2 diabetes, and 356,630 without diabetes). We found that having a CDC increased the hazard of developing an MHD (hazard ratio [HR] 1.9-2.9; P < 0.05, with higher HRs in older age strata), and having an MHD increased the hazard of developing a CDC (HR 1.4-2.5; P < 0.05, with the highest HR in age stratum 0-19 years). In those aged <60 years, individuals with type 1 diabetes were more likely to have CDCs, whereas individuals with type 2 diabetes were more likely to have MHDs. However, the relationship between CDCs and MHDs in either direction was not affected by diabetes type (P > 0.05 for interaction effects). CONCLUSIONS: We found a consistent bidirectional association between CDCs and MHDs across the life span, highlighting the important relationship between CDCs and MHDs. Prevention and treatment of either comorbidity may help reduce the risk of developing the other.

A Primary Care-Based Weight Navigation Program.

Importance: Evidence-based weight management treatments (WMTs) are underused; strategies are needed to increase WMT use and patients' weight loss. Objective: To evaluate the association of a primary care-based weight navigation program (WNP) with WMT use and weight loss. Design, Setting, and Participants: This cohort study comprised a retrospective evaluation of a quality improvement program conducted from October 1, 2020, to September 30, 2021. Data analysis was performed from August 2, 2022, to March 7, 2024. Adults with obesity and 1 or more weight-related condition from intervention and control sites in a large academic health system in the Midwestern US were propensity matched on sociodemographic and clinical factors. Exposure: WNP, in which American Board of Obesity Medicine-certified primary care physicians offered weight-focused visits and guided patients' selection of preference-sensitive WMTs. Main Outcomes and Measures: Primary outcomes were feasibility measures, including rates of referral to and engagement in the WNP. Secondary outcomes were mean weight loss, percentage of patients achieving 5% or more and 10% or more weight loss, referral to WMTs, and number of antiobesity medication prescriptions at 12 months. Results: Of 264 patients, 181 (68.6%) were female and mean (SD) age was 49.5 (13.0) years; there were no significant differences in demographic characteristics between WNP patients (n = 132) and matched controls (n = 132). Of 1159 WNP-eligible patients, 219 (18.9%) were referred to the WNP and 132 (11.4%) completed a visit. In a difference-in-differences analysis, WNP patients lost 4.9 kg more than matched controls (95% CI, 2.11-7.76; P < .001), had 4.4% greater weight loss (95% CI, 2.2%-6.4%; P < .001), and were more likely to achieve 5% or more weight loss (odds ratio [OR], 2.90; 95% CI, 1.54-5.58); average marginal effects, 21.2%; 95% CI, 8.8%-33.6%) and 10% or more weight loss (OR, 7.19; 95% CI, 2.55-25.9; average marginal effects, 17.4%; 95% CI, 8.7%-26.2%). Patients in the WNP group were referred at higher rates to WMTs, including bariatric surgery (18.9% vs 9.1%; P = .02), a low-calorie meal replacement program (16.7% vs 3.8%; P < .001), and a Mediterranean-style diet and activity program (10.6% vs 1.5%; P = .002). There were no between-group differences in antiobesity medication prescribing. Conclusions and Relevance: The findings of this cohort study suggest that WNP is feasible and associated with greater WMT use and weight loss than matched controls. The WNP warrants evaluation in a large-scale trial.

Dispensing of Glucagon-Like Peptide-1 Receptor Agonists to Adolescents and Young Adults, 2020-2023.

This study uses data from US retail pharmacies to assess national GLP-1RA dispensing to adolescents and young adults from 2020-2023.

The Glycemia Risk Index Predicts Performance of Diabetes Self-Management Habits in Youth With Type 1 Diabetes Mellitus.

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.

Utilizing the Glucose and Insulin Response Shape of an Oral Glucose Tolerance Test to Predict Dysglycemia in Children with Overweight and Obesity, Ages 8-18 Years.

Common dysglycemia measurements including fasting plasma glucose (FPG), oral glucose tolerance test (OGTT)-derived 2 h plasma glucose, and hemoglobin A1c (HbA1c) have limitations for children. Dynamic OGTT glucose and insulin responses may better reflect underlying physiology. This analysis assessed glucose and insulin curve shapes utilizing classifications-biphasic, monophasic, or monotonically increasing-and functional principal components (FPCs) to predict future dysglycemia. The prospective cohort included 671 participants with no previous diabetes diagnosis (BMI percentile ≥ 85th, 8-18 years old); 193 returned for follow-up (median 14.5 months). Blood was collected every 30 min during the 2 h OGTT. Functional data analysis was performed on curves summarizing glucose and insulin responses. FPCs described variation in curve height (FPC1), time of peak (FPC2), and oscillation (FPC3). At baseline, both glucose and insulin FPC1 were significantly correlated with BMI percentile (Spearman correlation r = 0.22 and 0.48), triglycerides (r = 0.30 and 0.39), and HbA1c (r = 0.25 and 0.17). In longitudinal logistic regression analyses, glucose and insulin FPCs predicted future dysglycemia (AUC = 0.80) better than shape classifications (AUC = 0.69), HbA1c (AUC = 0.72), or FPG (AUC = 0.50). Further research should evaluate the utility of FPCs to predict metabolic diseases.

Association Between Psychosocial Acuity and Glycemic Control in a Pediatric Type 1 Diabetes Clinic.

PURPOSE: The purpose of this study was to describe the frequency of psychosocial risk and its associations with glycemic levels in youth with type 1 diabetes (T1D) seen by social work staff during regular clinical care. METHODS: A retrospective longitudinal analysis of observational clinical data was conducted. Individuals (1-26 years) with known T1D who were seen at a pediatric diabetes clinic in a US academic medical center between 2014 and 2021 were included. Variables included psychosocial acuity, A1C, and demographic characteristics. Chi-square tests, Wilcoxon rank sum tests, and mixed linear regressions were used to examine associations between demographic variables, psychosocial acuity, and A1C. RESULTS: Of 966 patients, 513 (53.1%) were male, 76 (7.9%) were non-Hispanic Black, and 804 (83.2%) were non-Hispanic White. There was a mean of 6.9 annual social work encounters per patient, with 3 psychosocial domains measured at each visit. Results showed that as psychosocial acuity level increased, glycemic control decreased. There were significant differences in A1C according to race/ethnicity, insurance, age, and psychosocial acuity. CONCLUSIONS: In a real-world clinical population, psychosocial acuity was associated with glycemic control. Presenting for psychosocial issues in their diabetes clinic was associated with reduced glycemic control among youth with T1D. There is an opportunity to connect pediatric patients with appropriate mental health services and psychosocial supports.

Associations between food insecurity and diabetes risk factors in US adolescents in the National Health and Nutrition Examination Survey (NHANES) 2007-2016.

OBJECTIVE: To evaluate the associations between household food insecurity and diabetes risk factors among lower-income US adolescents. DESIGN: Cross-sectional analysis. Household food security status was measured using the 18-item Food Security Survey Module. Simple and multivariable linear and logistic regressions were used to assess the association between food security status and fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), HbA1C and homoeostatic model assessment - insulin resistance (HOMA-IR). The analyses were adjusted for household and adolescent demographic and health characteristics. SETTING: USA. PARTICIPANTS: 3412 US adolescents aged 12-19 years with household incomes ≤300 % of the federal poverty line from the National Health and Nutrition Examination Survey cycles 2007-2016. RESULTS: The weighted prevalence of marginal food security was 15·4 % and of food insecurity was 32·9 %. After multivariate adjustment, adolescents with food insecurity had a 0·04 % higher HbA1C (95 % CI 0·00, 0·09, P-value = 0·04) than adolescents with food security. There was also a significant overall trend between severity of food insecurity and higher HbA1C (Ptrend = 0·045). There were no significant mean differences in adolescents' FPG, OGTT or HOMA-IR by household food security. CONCLUSIONS: Food insecurity was associated with slightly higher HbA1c in a 10-year sample of lower-income US adolescents aged 12-19 years; however, other associations with diabetes risk factors were not significant. Overall, this suggests slight evidence for an association between food insecurity and diabetes risk in US adolescents. Further investigation is warranted to examine this association over time.

Weight Loss Treatment and Longitudinal Weight Change Among Primary Care Patients With Obesity.

Importance: Among individuals with obesity, 5% or greater weight loss can improve health. Weight management treatments (WMT) include nutrition counseling, very low-calorie meal replacement (MR), antiobesity medications (AOM), and bariatric surgery; however, little is known about how these WMT are associated with weight change among individual patients and populations. Objective: To characterize weight status and WMT use among primary care patients and assess associations between WMT and weight trajectories. Design, Setting, and Participants: Retrospective, population-based cohort study of primary care patients from 1 academic health system in Michigan between October 2015 and March 2020 using cross-sectional analysis to compare obesity prevalence and WMT utilization. For patients with obesity and WMT exposure or matched controls, a multistate Markov model assessing associations between WMT and longitudinal weight status trajectories was used. Data were analyzed from October 2021 to October 2023. Exposures: Cross-sectional exposure was year: 2017 or 2019. Trajectory analysis exposures were WMT: nutrition counseling, MR, AOM, and bariatric surgery. Main Outcomes and Measures: Cross-sectional analysis compared mean body mass index (BMI), obesity prevalence, and, among patients with obesity, prospective WMT use. The trajectory analysis examined longitudinal weight status using thresholds of ±5% and 10% of baseline weight with primary outcomes being the 1-year probabilities of 5% or greater weight loss for each WMT. Results: Adult patients (146 959 participants) consisted of 83 636 female participants (56.9%); 8940 (6.1%) were Asian, 14 560 (9.9%) were Black, and 116 664 (79.4%) were White. Patients had a mean (SD) age of 49.6 (17.7) years and mean (SD) BMI of 29.2 (7.2). Among 138 682 patients, prevalence of obesity increased from 39.2% in 2017 to 40.7% in 2019; WMT use among patients with obesity increased from 5.3% to 7.1% (difference: 1.7%; 95% CI, 1.3%-2.2%). In a multistate model (10 180 patients; 33 549 patient-years), the 1-year probability of 5% or greater weight loss without WMT exposure was 15.6% (95% CI, 14.3%-16.5%) at reference covariates. In contrast, the probability of 5% or greater weight loss was more likely with year-long exposures to any WMT (nutrition counseling: 23.1%; 95% CI, 21.3%-25.1%; MR: 54.6%; 95% CI, 46.5%-61.2%; AOM: 27.8%; 95% CI, 25.0%-30.5%; bariatric surgery: 93.0%; 95% CI, 89.7%-95.0%). Conclusions and Relevance: In this cohort study of primary-care patients with obesity, all WMT increased the patient-level probability of achieving 5% or greater weight loss, but current rates of utilization are low and insufficient to reduce weight at the population level.

Real-World Depression Screening Practices Among Primary Care Providers Across Patient-Level and Provider-Level Characteristics.

This study examined primary care provider (PCP) alignment with guideline-based care for adolescent depression screening and identified factors associated with post-screening responses. A retrospective chart review was conducted across 17 primary care clinics. Logistical regressions were estimated across provider specialties, sociodemographic factors, and patient clinical histories. Significant differences in follow-up and identification of depression were found among patients with more severe depression presentation. Follow-up screening was also more likely to be completed among patients with private insurance and less likely to occur among Black patients. Patients with significant mental health history of a mood concern, history of being prescribed psychotropic medication, were currently on medications at the time of the screening, or had a history of an internal mental health referral had a higher predicted probability of being identified as depressed on the patient problem list.

Institutional Barriers to the Successful Implementation of Telemedicine for Type 1 Diabetes Care.

The aim of this study was to describe rates of telemedicine use 18 months after the start of the coronavirus disease 2019 pandemic and to assess the institutional barriers to its implementation for type 1 diabetes care across centers of the T1D Exchange Quality Improvement Collaborative. Observational electronic health record data capturing telemedicine rates from 15 U.S. centers between September 2020 and September 2021 and a survey of 33 centers capturing telemedicine rates and key components of telemedicine were analyzed. A capacity score was developed and summed to a total capacity score and compared with overall telemedicine rates across centers. Telemedicine visits decreased by 17.4% from September 2020 to September 2021. Generally, it was observed that the lower the average telemedicine capacity score, the lower the rate of telemedicine visits. Despite a decline in the utilization of telemedicine 18 months after the start of the pandemic, visit rates were still 20% higher than in the pre-pandemic period. However, there is a need to improve structural components to ensure telemedicine capacity and robust telemedicine utilization.

The Design of the Electronic Health Record in Type 1 Diabetes Centers: Implications for Metrics and Data Availability for a Quality Collaborative.

BACKGROUND: Systematic and comprehensive data acquisition from the electronic health record (EHR) is critical to the quality of data used to improve patient care. We described EHR tools, workflows, and data elements that contribute to core quality metrics in the Type 1 Diabetes Exchange Quality Improvement Collaborative (T1DX-QI). METHOD: We conducted interviews with quality improvement (QI) representatives at 13 T1DX-QI centers about their EHR tools, clinic workflows, and data elements. RESULTS: All centers had access to structured data tools, nine had access to patient questionnaires and two had integration with a device platform. There was significant variability in EHR tools, workflows, and data elements, thus the number of available metrics per center ranged from four to 17 at each site. Thirteen centers had information about glycemic outcomes and diabetes technology use. Seven centers had measurements of additional self-management behaviors. Centers captured patient-reported outcomes including social determinants of health (n = 9), depression (n = 11), transition to adult care (n = 7), and diabetes distress (n = 3). Various stakeholders captured data including health care professionals, educators, medical assistants, and QI coordinators. Centers that had a paired staffing model in clinic encounters distributed the burden of data capture across the health care team and was associated with a higher number of available data elements. CONCLUSIONS: The lack of standardization in EHR tools, workflows, and data elements captured resulted in variability in available metrics across centers. Further work is needed to support measurement and subsequent improvement in quality of care for individuals with type 1 diabetes.

Clinician Experiences With Hybrid Closed Loop Insulin Delivery Systems in Veterans With Type 1 Diabetes: Qualitative Study.

BACKGROUND: Hybrid closed loop (HCL) insulin pumps adjust insulin delivery based on input from a continuous glucose monitor. Several systems are FDA approved and associated with improved time in range, reduction in hemoglobin A1c, and decreased incidence of hypoglycemia. Major diabetes guidelines differ in their strength of recommendations regarding the use of HCL systems. Overall, limited information about the factors that influence HCL pump clinical decision-making is available, especially among endocrinology clinicians. OBJECTIVE: The study objective is to describe the knowledge and attitudes, network support, and self-efficacy regarding HCL insulin delivery systems among endocrinology clinicians in one Veterans Affairs (VA) Healthcare System in the Midwest. METHODS: Following a descriptive approach, this qualitative study used semistructured interviews and inductive thematic analysis. All endocrinologists, endocrinology fellows, and nurses in the endocrinology and metabolism department at one VA Healthcare System in the Midwest were invited to participate in one-on-one phone interviews. Thematic analysis explored clinician perspectives on HCL insulin pump systems. RESULTS: Participants (n=11) had experience within VA and university health care system endocrinology clinics. From their experiences, 4 themes were identified involving the evaluation and assessment of insulin pump candidates, prescribing challenges, clinical benefits of HCL pumps, and overall clinician confidence. CONCLUSIONS: Findings suggest that clinicians believe HCL systems have significant glycemic benefits but are not appropriate for all patients, especially those with cognitive impairment. HCL pump initiation is a multi-step process requiring an interdisciplinary team of health care clinicians to ensure patient and pump success. Furthermore, HCL systems improve clinician confidence in overall diabetes management.

Point of Decision Design to Address Adolescent Overweight and Obesity.

OBJECTIVES: This study aims to utilize the point of decision design framework to understand how, where, and why adolescents and families make decisions about diet and physical activity and to explore how modifications to the environment can help to promote healthier choices and reduce obesity. BACKGROUND: Child and adolescent obesity is a critical public health problem. As environmental factors are a primary contributor, understanding the role of design in our surrounding environment highlights an important area of interdisciplinary study. Design strategies have been used successfully to increase stair use and reduce sedentary behavior and can be used to further promote healthier diet and activity choices among adolescents and families. METHODS: We leveraged the human-centered design-thinking process through (1) qualitative interviews and survey instruments, (2) persona and prompt development, and (3) a design workshop with multidisciplinary stakeholders. RESULTS: Five personas were developed from the qualitative data and used in a design-thinking workshop. During the workshop, participants generated 12 influential factors and nine points of decision which were used to generate 33 solutions spanning the design continuum (from information and policy design to the design of urban, architectural, and interior environments) aimed at improving nutrition and physical activity among adolescents. Additionally, a tool kit was prototyped, which includes interview guides, a persona framework, and a workshop facilitation guide. CONCLUSIONS: Our novel process led to the generation of design solutions that can be implemented to expand and improve upon existing interventions for childhood obesity and create environments that encourage positive health outcomes.

Prospective Test Performance of Nonfasting Biomarkers to Identify Dysglycemia in Children and Adolescents.

INTRODUCTION: Test performance screening measures for dysglycemia have not been evaluated prospectively in youth. This study evaluated the prospective test performance of random glucose (RG), 1-h nonfasting glucose challenge test (1-h GCT), hemoglobin A1c (HbA1c), fructosamine (FA), and 1,5-anhydroglucitol (1,5-AG) for identifying dysglycemia. METHODS: Youth ages 8-17 years with overweight or obesity (body mass index, BMI, ≥85th percentile) without known diabetes completed nonfasting tests at baseline (n = 176) and returned an average of 1.1 years later for two formal fasting 2-h oral glucose tolerance tests. Outcomes included glucose-defined dysglycemia (fasting plasma glucose ≥100 mg/dL or 2-h plasma glucose ≥140 mg/dL) or elevated HbA1c (≥5.7%). Longitudinal test performance was evaluated using receiver-operating characteristic (ROC) curves and calculation of area under the curve (AUC). RESULTS: Glucose-defined dysglycemia, elevated HbA1c, and either dysglycemia or elevated HbA1c were present in 15 (8.5%), 11 (6.3%), and 23 (13.1%) participants at baseline, and 16 (9.1%), 18 (10.3%), and 28 (15.9%) participants at follow-up. For prediction of glucose-defined dysglycemia at follow-up, RG, 1-h GCT, and HbA1c had similar performance (0.68 (95% CI: 0.55-0.80), 0.76 (95% CI: 0.64-0.89), and 0.70 (95% CI: 0.56-0.84)), while FA and 1,5-AG performed poorly. For prediction of HbA1c at follow-up, baseline HbA1c had strong performance (AUC 0.93 [95% CI: 0.88-0.98]), RG had moderate performance (AUC 0.67 [95% CI: 0.54-0.79]), while 1-h GCT, FA, and 1,5-AG performed poorly. CONCLUSION: HbA1c and nonfasting glucose tests had reasonable longitudinal discrimination identifying adolescents at risk for dysglycemia, but performance depended on outcome definition.

Impact of diabetes status and related factors on COVID-19-associated hospitalization: A nationwide retrospective cohort study of 116,370 adults with SARS-CoV-2 infection.

AIMS: We examined diabetes status (no diabetes; type 1 diabetes [T1D]; type 2 diabetes [T2D]) and other demographic and clinical factors as correlates of coronavirus disease 2019 (COVID-19)-related hospitalization. Further, we evaluated predictors of COVID-19-related hospitalization in T1D and T2D. METHODS: We analyzed electronic health record data from the de-identified COVID-19 database (December 2019 through mid-September 2020; 87 US health systems). Logistic mixed models were used to examine predictors of hospitalization at index encounters associated with confirmed SARS-CoV-2 infection. RESULTS: In 116,370 adults (>=18 years old) with COVID-19 (93,098 no diabetes; 802 T1D; 22,470 T2D), factors that independently increased risk for hospitalization included diabetes, male sex, public health insurance, decreased body mass index (BMI; <25.0-29.9 kg/m2), increased BMI (>25.0-29.9 kg/m2), vitamin D deficiency/insufficiency, and Elixhauser comorbidity score. After further adjustment for concurrent hyperglycemia and acidosis in those with diabetes, hospitalization risk was substantially higher in T1D than T2D and in those with low vitamin D and elevated hemoglobin A1c (HbA1c). CONCLUSIONS: The higher hospitalization risk in T1D versus T2D warrants further investigation. Modifiable risk factors such as vitamin D deficiency/insufficiency, BMI, and elevated HbA1c may serve as prognostic indicators for COVID-19-related hospitalization in adults with diabetes.

Self-Regulation as a Protective Factor for Diabetes Distress and Adherence in Youth with Type 1 Diabetes During the COVID-19 Pandemic.

OBJECTIVE: The COVID-19 pandemic increased economic, social, and health stressors for families, yet its impacts on families of youth with chronic conditions, such as type 1 diabetes (T1D), are not well understood. Self-regulation (SR)-or the capacities to control emotions, cognition, and behavior in response to challenge-is known to support T1D management and coping in the face of stress. Strong SR may have protected youth with T1D from the impacts of pandemic-related stressors. This study compared youth and parent emotional functioning and T1D management before and after the pandemic's onset in relation to family pandemic-related stress and youth SR. METHODS: Parents of youth with T1D (N = 88) and a subset of these youth (N = 43; Mean age 15.3 years [SD 2.2]) completed surveys regarding SR, stress, emotional functioning, and T1D-related functioning prior to and after March 2020. Outcomes were compared using mixed effects models adjusting for covariates. Family pandemic-related stress experiences and youth SR were tested as moderators of change. RESULTS: Parents' responsibility for T1D management increased across pandemic onset and their diabetes-related distress decreased. Family pandemic-related stress was associated with decreased emotional functioning over time. Youth SR, particularly emotional and behavioral aspects, predicted better emotional and T1D-related functioning. DISCUSSION: While youth with T1D whose families experienced higher pandemic-related stress had poorer adjustment, strong emotional and behavioral SR appeared to protect against worsening youth mood and adherence across pandemic onset. Both social-contextual and individual factors are important to consider when working with families managing T1D.

Increase in newly diagnosed type 1 diabetes in youth during the COVID-19 pandemic in the United States: A multi-center analysis.

BACKGROUND: An increase in newly diagnosed type 1 diabetes (T1D) has been posited during the COVID-19 pandemic, but data are conflicting. We aimed to determine trends in newly diagnosed T1D and severity of presentation at diagnosis for pediatric and adolescent patients during COVID-19 (2020) as compared to the previous year (2019) in a multi-center analysis across the United States. METHODS: This retrospective study from seven centers in the T1D Exchange Quality Improvement Collaborative (T1DX-QI) included data on new onset T1D diagnosis and proportion in DKA at diagnosis from January 1 to December 31, 2020, compared to the prior year. Chi-square tests were used to compare differences in patient characteristics during the pandemic period compared to the prior year. RESULTS: Across seven sites, there were 1399 newly diagnosed T1D patients in 2020, compared to 1277 in 2019 (p = 0.007). A greater proportion of newly diagnosed patients presented in DKA in 2020 compared to 2019 (599/1399(42.8%) vs. 493/1277(38.6%), p = 0.02), with a higher proportion presenting with severe DKA (p = 0.01) as characterized by a pH <7.1 and/or bicarbonate of <5 mmol/L. Monthly data trends demonstrated a higher number of new T1D diagnoses over the spring and summer months (March to September) of 2020 compared to 2019 (p < 0.001). CONCLUSIONS: We found an increase in newly diagnosed T1D and a greater proportion presenting in DKA at diagnosis during the COVID-19 pandemic compared to the prior year. Future longitudinal studies are needed to confirm these findings with population level data and determine the long-term impact of COVID-19 on diabetes trends.