David M. Maahs

Purpose: Non-timely Diagnosis of type 1 diabetes is associated with complications, hinders the therapeutic relationship, and compromises diabetes care delivery. This study aims to identify sociodemographic and clinical risk factors for Non-timely Diagnosis of pediatric type 1 diabetes. Methods: We identified youth (6m–21yrs) diagnosed with type 1 diabetes (ADA definition and 1+autoantibody) at Stanford Children’s Hospital from 01/2021-11/2023. Through ICD-10 code review in the STAnford Research Repository followed by chart review of all clinical data, two-person coding team determined if type 1 diabetes was diagnosed Timely or Non-timely. Non-timely was categorized as Unrecognized (symptoms attributed to another diagnosis) or Misclassified (type 2 diabetes diagnosis). Analysis via multivariate logistic regression identified sociodemographic and clinical risk factors for Non-timely Diagnosis. Results: Of 197 youth with type 1 diabetes, 49 (25%) were Non-timely Diagnosed (7% Unrecognized, 18% Misclassified). A majority of those who were Unrecognized (78%) and Misclassified (77%) were seen initially in the primary care setting. Acanthosis (aOR=11.25 [3.18,39.80]) was a risk factor for Non-timely Diagnosis, while hospitalization (aOR=0.25 [0.10,0.61]), and female sex (aOR=0.24 [0.09,0.66]) were protective associations. Conclusions: A quarter of youth in our study did not receive a timely diagnosis of type 1 diabetes. This study highlights the need for timely and accurate type 1 diabetes diagnosis to reduce delays, establish strong therapeutic relationships, and ensure access to disease modifying immunotherapy. Interventions to combat Non-timely Diagnosis should focus on primary care and diabetes clinic settings. With over 1 in 6 youth Misclassified, autoantibodies are critical to improving diagnostic clarity.

Rates of type 1 diabetes (T1D) and type 2 diabetes (T2D) in the pediatric population have risen over the past two decades. Insulin therapy remains critical for diabetes management, and proper injection technique has been shown to improve injection experience, glycemic management, and health outcomes. Youth with diabetes (YWD), ranging from infancy to young adulthood, require unique considerations for injection technique and education best practices due to their distinct physiological, developmental, emotional, psychological, and psychosocial needs relative to adults. Considering these needs, comprehensive recommendations addressing both physiological and behavioral health aspects of YWD are warranted. Nine pediatric diabetes specialists from five countries collaborated virtually to develop pediatric and young adult recommendations for proper injection technique updated from the adult-focused Forum for Injection Technique and Therapy Expert Recommendations (FITTER) Forward guidelines published in 2025. The resulting ‘FITTER Forward Youth’ recommendations address insulin initiation, injection technique, storage, injection method, and lipodystrophy. Psychosocial concerns for YWD and their caregivers, including diabetes distress, depressive symptoms, and anxiety, are also discussed. Additionally, technical and psychosocial education around insulin administration is key for YWD, their primary caregivers, and community-based secondary caregivers through daycare or school. Due to variable resources across geographical regions and socio-economic groups, FITTER Forward Youth also encompasses proper injection technique education for manual injection devices, which are typically more accessible than advanced diabetes technologies, such as insulin pumps and automated insulin delivery systems.

Diabetes is one of the most prevalent chronic diseases worldwide, with rates that continue to increase. More than 30 years ago, the Diabetes Control and Complications Trial demonstrated that intensive glucose management decreased long-term vascular complications. Unfortunately, many people with diabetes still struggle to meet glycemic goals. In this mini-review, we highlight advances in diabetes technology that are associated with improvements in glycemic management. Continuous glucose monitoring (CGM) and automated insulin delivery systems are associated with significant improvements in glycated hemoglobin (HbA1c), time in range (70-180 mg/dL), and decreases in severe hypoglycemia and diabetic ketoacidosis in people with both type 1 and type 2 diabetes. Recent data show that these technologies improve outcomes early in the course of type 1 diabetes. CGM is also being explored as a tool to monitor progression through early-stage type 1 diabetes (2 antibodies positive) to identify individuals who may benefit from disease-modifying therapies as well as to prevent the onset of diabetic ketoacidosis at onset of stage 3 (insulin-requiring) type 1 diabetes. Adjunctive pharmacologic therapies and artificial intelligence may further expand and improve therapies, offering potential synergistic benefits. However, there continue to be significant disparities in access to diabetes technologies and access to insulin worldwide. This mini-review summarizes recently published data, highlights emerging applications, and underscores the need to pair technological innovation with strategies that promote equitable access and support for diabetes care to improve outcomes for all people with diabetes.

Clinics continue to adopt care models shaped by the algorithmic analysis of continuous glucose monitoring (CGM) data, such as remote patient monitoring for type 1 diabetes. No clinic-facing quantitative framework currently exists to track the impact of such algorithm-directed care on patient outcomes and clinical workload. We used CGM data from the Teamwork, Targets, Technology, and Tight Control (4T) Study (Pilot n=135 and Study 1 n=133), in which algorithms enable precision, whole-population care by directing clinician attention to patients with deteriorating glucose management. Youth meeting criteria for clinical review are then contacted by Certified Diabetes Care and Education Specialists. Through iterative data analysis and meetings with a variety of stakeholders, we identified metrics for reviewing and revising clinical workloads, glucose management, and timeliness of care. For each metric, we developed an interactive dashboard to provide clinical and administrative leaders with an overview of the program. The metrics to track clinical workload were the total number of youths (1) in the program, (2) in each study, and (3) cared for by each clinician. The metrics to track glucose management were the number of youths meeting each criterion for review, including (4) total, (5) for each clinician, and (6) for each study. The metric to track timeliness of care was (7) the number of days since meeting criteria for clinical review. When presented at regular program leadership meetings, the metrics facilitated data-driven decision-making about clinical and operational components of the program. In this paper, we describe the process of developing and operationalizing this reproducible, clinician-facing key performance indicator tool to monitor an algorithm-enabled remote patient monitoring program. As the role of algorithms grows in directing clinical effort and prioritizing patients for care, this framework may help clinics track clinical workload, patient outcomes, and the timeliness of care.

A panel of experts in the use of continuous glucose monitoring (CGM) data in the treatment of diabetes met in Burlingame, California on October 27, 2025 to discuss the utility of the glycemia risk index (GRI) for clinical care research and population health management. The GRI composite metric is a single number (on a 0-100 percentile scale—lower is better) based on an expert-determined weighting of the seven individual components in the existing ambulatory glucose profile (AGP). The GRI describes the quality of glycemia based on glucose values collected in a 14-day CGM tracing, thus providing additional insights into CGM profiles beyond the AGP. During the meeting, the mathematical derivation of the GRI metric was presented along with its use for adult and pediatric individuals with diabetes and cancer who require medications that can adversely affect the glucose concentration. Examples where the GRI provided useful insights into the quality of CGM tracings were also discussed by the expert panel. In addition, a new smartphone application, the GRI Calculator, was presented. This app calculates the GRI of a CGM tracing and provides visualization of sequential CGM tracings for a specific individual. The GRI provides a reference measurement for the accuracy of artificial intelligence (AI) models assigning levels of glycemic quality to CGM tracings intended to match the assessments of clinicians. The GRI is now part of the data visualization panel for the Integration of Connected Diabetes Device Data into the Electronic Health Record (iCoDE-2) project, which standardizes both CGM and insulin dosing data. Further exploration of the potential value of the GRI for non-insulin users needs to be undertaken. The panel unanimously recommended that CGM manufacturers and developers of data visualization software for CGMs add the GRI to their data platforms for insulin users.

Aims: To examine prescription of guideline-recommended therapies and achievement of treatment targets across the span of older adulthood in type 1 diabetes (T1D) in the United States and Germany/Austria. Materials and Methods: Cross-sectional data of adults aged ≥60 years with T1D for ≥1 year seen in 2022 in the T1D Exchange Quality Improvement Collaborative (T1DX-QI) and the Diabetes Prospective Follow-up (DPV) registry. Descriptive statistics and within-registry comparisons across age groups using analysis of variance and chi-squared tests were used to analyze the data. Results: Thirty-six hundred adults aged ≥60 years, median age 67.5 [interquartile range (IQR) 63.4, 72.8] in T1DX-QI (n = 1549) and 68.9 (IQR 63.6, 75.7) in DPV (n = 2051) were included. The prevalence of atherosclerotic cardiovascular disease (ASCVD) (34.6% vs 16.8%) and chronic kidney disease (28.5% vs 11.8%) was higher in the DPV than the T1DX-QI. Lipid-lowering therapy for secondary prevention (52.9% vs 38%) and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker use (55.3% vs 44.8%) were higher in the DPV. Continuous glucose monitoring use was similar (50.3% vs 47.9%), insulin pump use was >2 × higher (40.7% vs 17%), and automated insulin delivery use was >3 × higher (20.4% vs 6.4%) in the T1DX-QI as compared to the DPV. Conclusion: Despite a high prevalence of ASCVD and risks of hypoglycemia, guideline-recommended treatments including lipid-lowering therapy for secondary prevention and diabetes technologies were used in approximately half or fewer of older adults with T1D. Additional attention to prescribing and practices to support clinicians and older adults in the use of diabetes technologies is urgently needed.

Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH’s Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article’s cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH’s GSS principles.

Type 1 diabetes (T1D) is caused by the autoimmune destruction of pancreatic β-cells, creating a lifelong need for exogenous insulin and glucose monitoring (1). Despite therapeutic advances in T1D, cardiovascular disease (CVD) remains high in this population with up to 45% developing ≥2 CVD risk factors within ten years after diagnosis (1,2). Additionally, CVD-related mortality remains approximately twice as high in T1D compared to the general population, even when glycemic targets are achieved (3,4).Individualized medical nutrition therapy (MNT) is a pillar for improving glycemic outcomes, promoting adequate growth, and reducing or delaying the development of chronic complications (such as CVD) and comorbidities (such as dyslipidemia or hypertension) in persons with T1D (5). MNT delivered by a registered dietitian is associated with improved cardiometabolic markers, including a 1.0-1.9% reduction in HbA1c in people living with T1D (5). In practice, MNT for T1D management has focused on carbohydrates as the main contributor to postprandial glycemic fluctuations, but compelling evidence and practice guidelines suggest that MNT should additionally incorporate broader eating patterns, food preferences, cultural practices, relationships with food and body, culinary skills, and food security to optimize health for people living with diabetes (5)(6)(7). In this commentary, we discuss the evolution of MNT for T1D management and opportunities to revise nutritional guidelines to reflect recent therapeutic advances and improve outcomes beyond glucose management, including those related to cardiometabolic risk and quality of life.Although MNT has transformed over the past century, it has always been a cornerstone of T1D management (Figure 1). Before the discovery of insulin, life expectancy after T1D diagnosis was estimated to be less than three years (8). MNT primarily took the form of carbohydrate restriction (as low as 10g of carbohydrates per day or ≤10% of daily caloric intake) to delay mortality associated with T1D (9). In some cases, high fat diets (i.e., up to 80% caloric intake from fats) and intermittent fasting regimens (i.e., once a week complete fast with just water or bouillon) were also recommended to manage glucosuria and prolong life expectancy (10,11). Other more drastic measures like prolonged fasting (up to ten days) and hypocaloric diets were effective in reducing ketoacidosis and glucosuria but were not sustainable as they resulted in significant nutrient deficiencies, starvation, and eventual death (10,12).With the discovery of insulin in the early 1920s, recommended carbohydrate intake was increased to 40 to 70% of daily intake (9). Specifically, recommendations for people with T1D were to follow fixed diet plans to match carbohydrate intake to insulin doses administered (13). By the mid-20th century, and as insulin formulations and glucose monitoring developed to better reflect physiological responses to nutrient intake, a carbohydrate exchange system (1 exchange = 10 to 16 grams of carbohydrates) was proposed (14). People with T1D were prescribed a fixed number of insulin units which dictated the number and distribution of recommended carbohydrate exchanges to consume to approach euglycemia.The exchange system allowed people with T1D to choose from a variety of carbohydrate sources while matching carbohydrate amounts to their prescribed insulin dosing (15).The development of intensive insulin therapy (i.e., three or more daily injections of insulin) and capillary blood glucose meters transformed T1D management (15). People with T1D could monitor their glycemia at home and match their insulin dose to their carbohydrate intake instead of having to rigidly adhere to predetermined carbohydrate amounts based on a fixed insulin dose. Before this, glucose monitoring relied on urine tests as blood glucose monitoring strips only became available in 1965 for use in the clinic and 1980s for at home use (16). This advancement provided added flexibility in what, when, and how to eat (15). Specifically, in the 1990s carbohydrate counting (CC, i.e., counting the number of carbohydrates found in food and using this value to estimate prandial insulin needs) became a topic of interest in the US, solidified with Diabetes Control and Complications Trial results (15).Although CC has remained a mainstay of the dietary management of T1D, its efficacy is equivocal, and unintended effects on diabetes distress and disordered eating are of concern (17). While some studies showed improvement in HbA1c values (standard mean difference [95%CI]: -0.51 [-0.83, -019] %) when insulin dosing was combined with precise CC and was compared to standard diabetes education (16), evidence for CC superiority is less clear when compared to other dietary approaches of matching insulin to food intake (-0.31 [-0.99, 1.61]%) such as glycemic index or fixed carbohydrate amounts (18).Discrepancies between insulin doses estimated using CC and postprandial glycemic responses can be accounted for by both dietary and non-dietary factors including fiber and other macronutrients, previous physical activity, and time of day (7,(19)(20)(21). CC is complex, takes time, requires nutritional literacy and numerical skills, and is generally inaccurate, especially when estimating carbohydrate intake from fresh and non-prepackaged foods which are the basis of a healthy diet (22,23,19,24). Additionally, parallel to the general population (25), low diet quality remains a persistent challenge for people with T1D and has implications for both glycemic outcomes and downstream cardiometabolic risk, thus supporting the need to shift from a carbohydrate-centric approach to target overall diet quality (26,27). Current national and international guidelines state that there is no one ideal diet approach for MNT in T1D; however, dietary patterns that are rich in sources of fiber and unsaturated fatty acids such as whole grains,...

Background: Type 2 diabetes (T2D) disproportionately affects youth with public insurance of minority and lower socioeconomic status backgrounds. We aimed to determine feasibility of CGM use in this understudied population. Methods: We enrolled youth 75% wear-time as goal) from downloaded report prior to clinic visit. Exploratory outcomes included: 14-day CGM wear time in range (TIR: % time spent between 70 and 180 mg/dl), HbA1c, and patient-reported outcomes (PROs) collected from youth and parents. Results: We enrolled 30 youth (age 15.1 years [SD 2.48]; HbA1c 10.2%, range: 6.5%-15.5%), 46.7% female, 90% Hispanic. At baseline, 37% previously used CGM and 53% lacked glucometer data. CGM use was 50% at three months and 23% at 12 months. CGM wear time decreased by 6.4 days per two weeks by 12 months. Mean HbA1c was 9.8% at 12 months and median TIR decreased from 71% to 42%. Parents and youth had moderate-to-positive attitudes about diabetes technology. Youth endorsed fair levels of global health; and youth and parents endorsed fair general and diabetes-related health-related quality of life. Conclusions: Strategies for sustained CGM use in youth with T2D may differ from adults with T2D or youth with type 1 diabetes. Additional studies are needed to evaluate facilitators and barriers of sustained CGM use to optimize CGM use in youth with T2D. Clinicaltrials.gov registration: NCT05074667.