The Ahlqvist Subphenotypes of Type 2 Diabetes: A Narrative Review of Scientific Evidence and Its Potential Contribution to Precision Diabetes

ABSTRACT Background Among several approaches, k‐means clustering has established as a widely used method to classify people with type 2 diabetes (T2D) into distinct subphenotypes, and the Ahlqvist T2D subphenotypes of Severe Insulin Deficient Diabetes (SIDD), Mild Age‐Related Diabetes (MARD), Mild Obesity Diabetes (MOD) and Severe Insulin Resistant Diabetes (SIRD) became the most replicated ones in diabetes cohorts since its introduction in 2018. This review summarises the current evidence gained from the Ahlqvist subphenotypes focusing on distribution patterns, key characteristics, comorbidities and their clinical implementation. Methods We conducted a narrative literature search of PubMed for English‐language articles published from inception to June 2026 using combinations of the terms ‘k‐means clustering’, ‘classification of diabetes’, ‘ Ahlqvist methodology’, ‘type 2 diabetes’, ‘diabetes subphenotypes’ and ‘diabetes subtypes’. Literature was eligible which reported the same Ahlqvist classification method as originally applied. Results Twenty‐eight cohorts were identified incorporating more than 97 000 people with diabetes. Ahlqvist subphenotypes have been studied mainly in White/European and East/South Asian populations but seldom in Black/Africans and Hispanic/Latin Americans. MARD and MOD had the highest prevalences among populations; SIDD had severe insulin deficiency and hyperglycaemia, and low fasting C‐peptide (FCP) levels; SIRD was insulin resistant with high FCP levels and incidence risk of nephropathy. Retrospective analyses of glycaemic responses to glucose‐lowering treatments did not clearly favour any drug regimen for subphenotypes. Conclusions Ahlqvist T2D subphenotypes reflect the heterogeneity of T2D. However, clinical implementation is associated with many barriers, and further studies must prove the value of subphenotype‐guided management of T2D towards precision diabetes.