Beyond Type 2: Classifying Diabetes and Screening for Hypercortisolism in the 2026 AACE Algorithm - Episode 5
How the 2026 AACE Algorithm Broadens the Lens on Diabetes
Explore signs of uncommon diabetes—from pancreatic disease and MODY to steroid, transplant, and checkpoint-inhibitor autoimmune causes.
Experts outline the broader differential diagnosis embedded in the new algorithm, spanning pancreatic, monogenic, treatment-related, and rare genetic forms of diabetes that may masquerade as type 2 in adults.
In this section, Samson invites Umpierrez to walk through the remaining categories included in the 2026 AACE diabetes classification algorithm beyond autoimmune diabetes and hypercortisolism.
Umpierrez first discusses pancreatic disorders that can lead to diabetes, including pancreatic cancer, chronic pancreatitis, cystic fibrosis–related diabetes, and hemochromatosis. He notes that in subspecialty centers with large cystic fibrosis populations, diabetes is a frequent complication and carries distinct management considerations. Weight loss and underweight status in a patient labeled as having type 2 diabetes, particularly when combined with severe insulin requirements or recurrent hypoglycemia, should prompt clinicians to consider pancreatic causes or other atypical forms of diabetes rather than assuming typical type 2 disease.
The conversation then turns to monogenic diabetes, particularly maturity-onset diabetes of the young (MODY), which Samson and Umpierrez emphasize has been recognized for decades but remains underdiagnosed. Umpierrez suggests clinicians should suspect monogenic diabetes when there is an autosomal dominant pattern of diabetes across 3 generations, diagnosis at a young age, absence of type 1 autoantibodies, and preserved or only mildly reduced C‑peptide levels. He notes that calculators and clinical prediction tools are available to estimate the likelihood of MODY and guide genetic testing. T
he algorithm also highlights glucocorticoid-induced diabetes and posttransplant diabetes, both increasingly seen in practice, as well as checkpoint inhibitor–associated autoimmune diabetes, which is emerging with broader use of immune checkpoint inhibitors in oncology. In this latter group, onset can be abrupt and reminiscent of type 1 diabetes, often with positive autoantibodies and low C‑peptide, necessitating rapid initiation of insulin therapy.
Finally, Samson emphasizes that the 2026 AACE algorithm also acknowledges rare inherited forms of diabetes, including mitochondrial diabetes syndromes that may present with diabetes and sensorineural deafness or lipodystrophic phenotypes characterized by loss of subcutaneous fat, hypertriglyceridemia, fatty liver disease, and marked insulin resistance.
Umpierrez notes that, although infrequent, these conditions are important to recognize because they carry distinct systemic risks and may respond differently to standard therapies.
Together, Samson and Umpierrez argue that the value of the new algorithm lies not only in cataloging these entities but also in providing a structured pathway that prompts clinicians to reconsider a default type 2 label when the clinical picture, family history, or treatment response is atypical.
