Volume 32, Issue 2, February 2026, Pages 293-294

Author links open overlay panelGuillermo E. Umpierrez MD, Maria Cecilia Lansang MD, MPH

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Access through your organizationSection snippetsCGM: Redefining Glycemic Assessment

Several studies in the special issue expand our understanding of CGM beyond traditional use in type 1 diabetes. Agarwal et al1 argue for a conceptual shift from glycated hemoglobin to glucose pattern assessment, emphasizing the physiologic insights gained from 24-hour glucose profiling. Similarly, Zahalka et al2 review CGM’s emerging role in prediabetes, where dynamic monitoring may identify subtle dysglycemia and guide early behavioral interventions to prevent disease progression. The study by

AID and Closed-Loop Systems

The integration of CGM and insulin pumps has ushered in a new era of semiautomated insulin therapy. Hughes and Levy4 and Lei et al5 highlight the impressive progress of hybrid and open-source AID systems in optimizing time in range and minimizing hypoglycemia, even among children and adolescents. These data underscore the potential of user-driven platforms to complement commercial systems through flexibility and customization.

From a health economics perspective, Khan-Mirón et al6 demonstrate

Technology in the Aging Population

In an often-overlooked population, Urbina et al7 focus on diabetes technology in older adults. Aging brings unique challenges—cognitive decline, frailty, and heightened hypoglycemia risk—that require nuanced clinical decision-making. The evidence now suggests that technology can safely enhance glycemic control in well-selected older patients, if education, caregiver involvement, and individualized targets guide implementation. This geriatric-focused framework ensures that innovation remains

AI: From Innovation to Integration

As discussed by Parab et al,8 AI applications in diabetes management now span the spectrum from early disease detection to precision treatment. Deep learning algorithms have achieved near-clinician accuracy in screening for diabetic retinopathy and macular edema, whereas predictive models can forecast disease progression or hospitalization risk. AI-driven clinical decision support tools, integrated into electronic health records, offer real-time therapeutic recommendations. However, translating

Looking Ahead: Toward Precision and Personalization

Taken together, these contributions illustrate how digital innovation is reshaping every facet of diabetes care—from prevention to automation. The integration of AI, CGM, and AID systems represents not merely technologic progress but also a fundamental redefinition of how we conceptualize glycemia, risk, and treatment success. However, as technology advances, the clinician’s role becomes ever more critical. Empathy, contextual understanding, and clinical judgment remain indispensable in

Disclosure

G.E.U. is partly supported by research grants from the NIH and the National Center for Research Resources (NIH/NIDDK 2P30DK111024-06). G.E.U. has received research support for Emory University from Bayer, Corcept, Abbott, Glucotrack, and Dexcom, and has participated in advisory boards for Dexcom, Corcept, Glucotrack, and Glycare. M.C.L. has received research support from Dexcom, Abbott, Lilly and Insulet, and has participated in advisory board for Willow Laboratories.

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