Details

Autor(en) / Beteiligte

• Leslie, R. David
• Palmer, Jerry
• Schloot, Nanette C.
• Lernmark, Ake

Titel

Diabetes at the crossroads: relevance of disease classification to pathophysiology and treatment

Ist Teil von

• Diabetologia, 2016-01, Vol.59 (1), p.13-20

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2016

Link zum Volltext

Quelle

SpringerLink

Beschreibungen/Notizen

• Diabetes is not a single homogeneous disease but composed of many diseases with hyperglycaemia as a common feature. Four factors have, historically, been used to identify this diversity: the age at onset; the severity of the disease, i.e. degree of loss of beta cell function; the degree of insulin resistance and the presence of diabetes-associated autoantibodies. Our broad understanding of the distinction between the two major types, type 1 diabetes mellitus and type 2 diabetes mellitus, are based on these factors, but it has become apparent that they do not precisely capture the different disease forms. Indeed, both major types of diabetes have common features, encapsulated by adult-onset autoimmune diabetes and maturity-onset diabetes of the young. As a result, there has been a repositioning of our understanding of diabetes. In this review, drawing on recent literature, we discuss the evidence that autoimmune type 1 diabetes has a broad clinical phenotype with diverse therapeutic options, while the term non-autoimmune type 2 diabetes obscures the optimal management strategy because it encompasses substantial heterogeneity. Underlying these developments is a general progression towards precision medicine with the need for precise patient characterisation, currently based on clinical phenotypes but in future augmented by laboratory-based tests. Key points • The need to clarify diabetes classification, which is currently imprecise in distinguishing major disease types, using laboratory tests • The importance of predictors of disease progression, including genetic, immune and metabolic features • The potential for predicting therapeutic responses to provide a more personalised approach to therapy