Abstract

This commentary examines the persistent and clinically significant burden of undiagnosed diabetes in the United States and proposes the conceptual framing of this population as “Type 3 Diabetes.” Drawing on National Health and Nutrition Examination Survey data and recent estimates from the Centers for Disease Control and Prevention and the National Institute of Diabetes and Digestive and Kidney Diseases, the article highlights that millions of U.S. adults meet biochemical criteria for diabetes yet remain unaware or undiagnosed, despite measurable improvements in detection. To explain the persistence of this hidden burden, the paper integrates the Health Belief Model, PRECEDE–PROCEED, and the Social Determinants of Health frameworks, illustrating how individual risk perceptions, health system practices, and structural inequities interact to delay screening, diagnosis, and classification.

Framing undiagnosed diabetes as a distinct phase of disease underscores its clinical risks, equity implications, and the systemic factors that perpetuate under-recognition in high-burden regions and populations. The commentary further positions emerging artificial intelligence and machine learning approaches as promising multilevel tools to enhance early detection, support risk stratification, and strengthen alignment between clinical practice and public health infrastructure. Together, this integrated conceptual approach underscores the importance of naming, measuring, and addressing undiagnosed diabetes as a central challenge in diabetes prevention and control, with implications for clinicians, public health practitioners, and policymakers alike.