Historical Insights and Current Perspectives on the Diagnosis and Management of Presymptomatic Type 1 Diabetes.

Objective: The article provides practical guidance for (1) interpreting and confirming islet autoantibody screening results for type 1 diabetes (T1D) and (2) follow-up of individuals with early stages of T1D with the goal of ensuring medical safety and providing patients and their families with an assessment of risk for progression to a clinical diagnosis of T1D. Research Design and Methods: We used an explicit a priori methodology to identify areas of agreement and disagreement in how to manage patients with early T1D. We used a modified Delphi method, which is a systematic, iterative approach to identifying consensus. We developed a list of topic questions, ranked them by importance, and developed consensus statements based on available evidence and expert opinion around each of the 30 topic questions consistently ranked as being most important. Results: Consensus statements for screening and monitoring are supported with figures proposing an algorithm for confirmation of T1D diagnosis and management of early T1D until clinical diagnosis. Conclusions: Disseminating and increasing knowledge related to how to interpret T1D screening tests, confirm early T1D diagnosis and monitor for medical safety and clinical disease risk prediction is critically important as there are currently no clinical recommendations. Published guidance will promote better management of T1D screening-detected individuals.

Racial Disparities in Diabetes Technology Adoption and Their Association with HbA1c and Diabetic Ketoacidosis.

Aim: Poorer glycemic control and higher diabetic ketoacidosis (DKA) rates are seen in racial/ethnic minorities with type 1 diabetes (T1D). Use of diabetes technologies such as continuous glucose monitors (CGM), continuous subcutaneous insulin infusion (CSII) and automated insulin delivery (AID) systems has been shown to improve glycemic control and reduce DKA risk. We examined race/ethnicity differences in diabetes technology use and their relationship with HbA1c and DKA. Methods: Data from patients aged ≥12 years with T1D for ≥1 year, receiving care from a single diabetes center, were examined. Patients were classified as Non-Hispanic White (n=3945), Non-Hispanic Black (Black, n=161), Hispanic (n=719), and Multiracial/Other (n=714). General linear models and logistic regression were used. Results: Black (OR=0.22, 0.15-0.32) and Hispanic (OR=0.37, 0.30-0.45) patients were less likely to use diabetes technology. This disparity was greater in the pediatric population (p-interaction=0.06). Technology use associated with lower HbA1c in each race/ethnic group. Among technology users, AID use associated with lower HbA1c compared to CGM and/or CSII (HbA1c of 8.4% vs 9.2%, respectively), with the greatest difference observed for Black adult AID users. CSII use associated with a lower odds of DKA in the past year (OR=0.73, 0.54-0.99), a relationship that did not vary by race (p-interaction =0.69); this inverse association with DKA was not observed for CGM or AID. Conclusion: Disparities in diabetes technology use, DKA, and glycemic control were apparent among Black and Hispanic patients with T1D. Differences in technology use ameliorated but did not fully account for disparities in HbA1c or DKA.

Racial-Ethnic Inequity in Young Adults With Type 1 Diabetes.

CONTEXT: Minority young adults (YA) currently represent the largest growing population with type 1 diabetes (T1D) and experience very poor outcomes. Modifiable drivers of disparities need to be identified, but are not well-studied. OBJECTIVE: To describe racial-ethnic disparities among YA with T1D and identify drivers of glycemic disparity other than socioeconomic status (SES). DESIGN: Cross-sectional multicenter collection of patient and chart-reported variables, including SES, social determinants of health, and diabetes-specific factors, with comparison between non-Hispanic White, non-Hispanic Black, and Hispanic YA and multilevel modeling to identify variables that account for glycemic disparity apart from SES. SETTING: Six diabetes centers across the United States. PARTICIPANTS: A total of 300 YA with T1D (18-28 years: 33% non-Hispanic White, 32% non-Hispanic Black, and 34% Hispanic). MAIN OUTCOME: Racial-ethnic disparity in HbA1c levels. RESULTS: Non-Hispanic Black and Hispanic YA had lower SES, higher HbA1c levels, and much lower diabetes technology use than non-Hispanic White YA (P < 0.001). Non-Hispanic Black YA differed from Hispanic, reporting higher diabetes distress and lower self-management (P < 0.001). After accounting for SES, differences in HbA1c levels disappeared between non-Hispanic White and Hispanic YA, whereas they remained for non-Hispanic Black YA (+ 2.26% [24 mmol/mol], P < 0.001). Diabetes technology use, diabetes distress, and disease self-management accounted for a significant portion of the remaining non-Hispanic Black-White glycemic disparity. CONCLUSION: This study demonstrated large racial-ethnic inequity in YA with T1D, especially among non-Hispanic Black participants. Our findings reveal key opportunities for clinicians to potentially mitigate glycemic disparity in minority YA by promoting diabetes technology use, connecting with social programs, and tailoring support for disease self-management and diabetes distress to account for social contextual factors.

Comparison of autoantibody-positive and autoantibody-negative pediatric participants enrolled in the T1D Exchange clinic registry.

OBJECTIVE: To compare characteristics of autoantibody (aAb)-positive and -negative cases of type 1 diabetes (T1D) <18 years old in the T1D Exchange clinic registry. METHODS: An aAb-positive status (n = 6239) required at least one of the aAbs to be positive; an aAb-negative status (n = 485) required negative results on testing of at least two different aAbs. RESULTS: The percentage of males was higher (58% vs. 51%; P = 0.002) and total daily insulin dose lower (P = 0.003) in aAb-negative compared with aAb-positive groups, but both groups had similar distributions of race-ethnicity, diagnosis age, family history of T1D, ketoacidosis at diagnosis, body mass index at diagnosis and at most recent office visit, and current HbA1c. CONCLUSIONS: Male gender and lower total daily insulin dose were more likely in aAb-negative than aAb-positive children with T1D, but no other distinguishing characteristics were identified. Further examination of characteristics of aAb-negative cases may help characterize the heterogeneous nature of T1D.