A multifaceted approach encompassing patient-level, system-level, and policy-level interventions is crucial for enhancing population health in the context of diabetes.This approach may include the following key elements.

Diabetic eye disease (DED), including diabetic retinopathy (DR) and diabetic macular edema (DME), is a complication of diabetes and the leading cause of vision loss among working-age adults in the United States (1-3).Screening for DED can lead to its early identification and treatment, thereby preventing irreversible vision loss (4-7).However, rates of diabetic eye exams for DED screening remain suboptimal, with reported rates in the United States ranging from 11 to 70% (8,9).Autonomous artificial intelligence (AI)-based diabetic eye exams have the potential to increase access to these exams and facilitate the early identification of DED so that timely treatment can be administered to prevent blindness.Autonomous AI systems use a robotic nonmydriatic fundus camera with a built-in AI algorithm to provide feedback to the operator to acquire high-quality fundus photographs for determining the presence or absence of referable DED, with immediate results after image acquisition at the point of care (10-12).The autonomous AI system guides the operator to acquire two color fundus images determined to be of adequate quality using an image quality algorithm, with one each centered on the fovea and the optic nerve, and guides the operator to retake any images of insufficient quality.It is important to note that these systems have been rigorously validated against a prognostic standard to identify DR and DME and do not diagnose other eye conditions.The first autonomous AI diagnostic system (Luminetics-Core, formerly IDx-DR, Digital Diagnostics, Coralville, IA) was de novo-authorized by the U.S. Food and Drug Administration (FDA) in 2018 after completion of a pivotal trial demonstrating its safety, efficacy, and equity in diagnosing referable DED compared with a prognostic standard outcome (10).In the pivotal trial, the autonomous AI system demonstrated 87% sensitivity and 90% specificity in detecting referable DR and/or DME in adults (10,13).Since this first autonomous AI system was approved, two additional autonomous AI systems have been authorized under the FDA's 510(k) authorization process using it as a predicate, and there are several other autonomous AI systems in different stages of development (11,12).

Rates of type 2 diabetes in adolescents and young adults substantially increased between 1990 and 2019, according to Xie et al. (The BMJ, doi.org/jwns). Associated years lost to poor health, disability, and early death also increased, with the burden especially affecting countries with a low-middle or middle socioeconomic index. Women <30 years of age were also particularly affected. The authors found that a high BMI was the main attributable risk factor, leading them to suggest that weight control is essential. However, they also identified variable attributable risk factors between countries with differing socioeconomic statuses, meaning that specific country-level measures will likely be needed to effectively combat early-onset diabetes. "Our study showed a clear upward trend of the burden of early-onset type 2 diabetes from 1990 to 2019," the authors write. "These findings provide a basis for understanding the epidemic nature of early-onset type 2 diabetes and call for urgent actions to deal with the issue from a global perspective." The previously mentioned study examined past trends in diabetes; so, what about the future? Modeling based on data from the SEARCH for Diabetes in Youth study suggests that we can expect huge increases in the number of U.S. youth with diabetes by 2060 (Tönnies et al., Diabetes Care, doi.org/grkhkt).

Many adults with diabetes do not reach optimal glycemic targets, and, despite advances in diabetes management, diabetes technology use remains significantly lower in racial/ethnic minority groups. This study aimed to identify factors associated with achieving the recommended A1C target of <7% using data on 12,035 adults with type 1 diabetes from 15 centers participating in the T1D Exchange Quality Improvement Collaborative. Individuals attaining the target A1C were more likely to be older, White, have private health insurance, and use diabetes technology and less likely to report depressive symptoms or episodes of severe hypoglycemia or diabetic ketoacidosis than those with higher A1C levels. These findings highlight the importance of overcoming inequities in diabetes care.

Case Studies| July 01 2022 Impact of Insulin-Derived Amyloidosis on Glycemic Control and Insulin Dosing Aaron Estep; Aaron Estep 1Department of Family Medicine, Carilion Clinic and Virginia Tech Carilion Family Medicine Residency, Roanoke, VA Search for other works by this author on: This Site PubMed Google Scholar Jarrod Uhrig 0000-0001-7137-4905 ; Jarrod Uhrig 2Department of Endocrinology, Carilion Clinic and Virginia Tech Carilion School of Medicine, Roanoke, VA Search for other works by this author on: This Site PubMed Google Scholar Curtis Bower; Curtis Bower 3Department of Surgery, Carilion Clinic and Virginia Tech Carilion School of Medicine, Roanoke, VA Search for other works by this author on: This Site PubMed Google Scholar Robert Jarrett Robert Jarrett 4Dominion Pathology Associates, Roanoke, VA5Department of Basic Science Education, Virginia Tech Carilion School of Medicine, Roanoke, VA Search for other works by this author on: This Site PubMed Google Scholar Corresponding author: Jarrod Uhrig, jluhrig@carilionclinic.org Clin Diabetes 2022;40(3):380–384 https://doi.org/10.2337/cd21-0118 PubMed: 35983426 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Cite Icon Cite Get Permissions Citation Aaron Estep, Jarrod Uhrig, Curtis Bower, Robert Jarrett; Impact of Insulin-Derived Amyloidosis on Glycemic Control and Insulin Dosing. Clin Diabetes 1 July 2022; 40 (3): 380–384. https://doi.org/10.2337/cd21-0118 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsClinical Diabetes Search Advanced Search A 57-year-old woman with longstanding type 2 diabetes on basal-bolus insulin therapy for 15–20 years and with a distant history of gastric bypass surgery presented to her primary care physician’s (PCP’s) office with concern about a subcutaneous right-sided abdominal mass. She had first noticed the painless mass about 2 months prior and did not notice any growth afterward. She had no known injury in the area and had not had any surgical procedure in the area. Physical examination demonstrated a firm mass the size of a golf ball with no overlying skin changes such as erythema, ulceration, or drainage. The PCP ordered a limited abdominal ultrasound to help further characterize the mass. The ultrasound imaging showed a 3.3 × 1.5 × 2.0–cm heterogeneous, hypoechoic structure with similar echogenicity to the adjacent subcutaneous fat (Figure 1). The radiologist noted that it was suggestive of a hernia, although the differential... You do not currently have access to this content.

Foot ulceration is a serious problem among people with diabetes. This study assessed the knowledge, attitude, and practice of 483 people with diabetes with regard to the prevention of diabetic foot ulcers (DFUs). Data were collected using a valid and reliable questionnaire, and logistic regression was carried out to predict the effect of statistically significant independent variables on the scores of dependent variables. A significant association was found between knowledge and attitude about DFU prevention. Individuals who were unmarried were more likely to have higher knowledge scores than those who were married, and those who had university and secondary education were more likely to have higher attitude and practice scores than other groups. Overall, however, only a small percentage of study participants achieved high scores for knowledge, practice, and attitude in this study.

The work of Elijah P. Mays, Jr., described in an article in this issue (1), connects the past, present, and future of quality improvement (QI). The author begins with a question: what is the rate of screening for vitamin B12 for people with type 2 diabetes who are prescribed metformin in my clinic? An initial chart review reveals a rate of 68%—low enough for the clinic to decide to do something about it. The gap between the current and ideal states was sufficiently large that energies were devoted to creating change. The structural and process changes introduced in Dr. Mays’ clinic take us back to the Chronic Care Model, as introduced by Dr. Ed Wagner years ago (2). Dr. Wagner’s model (Figure 1) puts the clinic in the context of the community, with all its resources that clinic staff ideally should be aware of and connect patients to. In the clinic itself are four levers that, when pulled, allow for productive interactions between patients and their care team. First is “self-management,” through which patients are provided the tools, education, and skills they need to proactively and effectively care for …