Accurate classification of diabetes into type 1, type 2, or monogenic forms remains a major challenge. This difficulty arises from overlapping clinical features and substantial heterogeneity within these conditions, particularly in adults, due to nonconforming presentations such as obesity coexisting with autoimmunity. Consequently, misclassification is common and can contribute to suboptimal therapeutic decisions, delayed achievement of glycemic targets, and increased risk of long-term complications. Growing evidence supports a spectrum-based view of diabetes pathophysiology rather than rigid categorical definitions, highlighting the need for improved biomarkers and a deeper mechanistic understanding of disease processes. To address this knowledge gap, over the past two decades we have conducted studies of human pancreata obtained through the Network for Pancreatic Organ donors with Diabetes (nPOD) and related programs, enabling in situ investigation of disease pathogenesis. Insights derived from such tissues and their biological relevance are based on limited clinical data at the time of terminal hospitalization. Challenges with postmortem diabetes classification have led to the creation of classification pathways that include not only clinical variables but also histopathology and genetics. For example, including the presence of insulin-negative islets and insulitic lesions on histopathology and clinical features such as C-peptide loss and increased type 1 diabetes genetic risk score, over clinical history alone, supports a more holistic diagnosis of type 1 diabetes, which may include atypical forms of diabetes. Continued integration of organ donor-based research with comprehensive clinical data is essential to refine classification systems, improve diagnostic accuracy, and advance personalized approaches to diabetes care.

By 2050, diabetes care should transform into a human-centered, technology-augmented ecosystem focused on well-being. In this work, we first present a deliberately idealistic, forward-looking scenario in which advanced treatments, artificial intelligence, and integrated care delivery significantly reduce the burden on individuals with diabetes and enable person-centered health care. In this utopian vision, success in diabetes care is measured not only by glycemic metrics such as time in range but also by time in happiness, i.e., the quality time people spend unencumbered by their condition. Second, while such idealistic progress may be unlikely by 2050 due to socioeconomic and public health constraints, we build on existing gaps to outline a road map of co-designed solutions, health system reforms, and ethical innovations to move closer to it. This work serves as a "North Star" document that articulates the key values and next steps for achieving an ideal diabetes prevention and care ecosystem.