Type 2 diabetes is diagnosed on the basis of one metabolite, glucose, and on the basis of exclusion criteria. If the individual does not have type 1 diabetes or monogenic diabetes, they are classified as having type 2 diabetes. However, this does not consider the underlying pathogenic causes or disease outcomes. Elevated glycaemia can be the consequence of multiple pathogenic processes occurring in myriad combinations, including rising insulin resistance and defective insulin secretion; consequently, type 2 diabetes is a heterogeneous disease. Disease severity, progression, treatment strategies, and response can vary widely between individuals. The palette model of type 2 diabetes has been proposed, in which each person’s individual risk for dysregulation in one or more component pathways can contribute to the overall development of type 2 diabetes. Three primary approaches have emerged to address this heterogeneity. The first involves utilizing a set of clinical variables to divide individuals into subgroups based on manifestation of disease patterns. To this end, we performed data- driven machine learning on commonly measured clinical variables (age at diagnosis, sex, body mass index, glycated haemoglobin [HbA1c], and homeostatic model 2 of assessment for insulin secretion [HOMA- B] and resistance [HOMA- IR]) in individuals with newly diagnosed diabetes. Five reproducible clusters were identified: severe autoimmune diabetes (SAID), which included both type 1 diabetes and LADA; severe insulin- deficient diabetes (SIDD); severe insulin- resistant diabetes (SIRD); mild obesity- related diabetes (MOD); and mild age- related diabetes (MARD). Each of these groups differed with respect to characteristics, risk of complications, and progression. These subgroups were replicated in several populations and are partially genetically different.

Given that phenotypes may vary across time and with different exposures, a second approach uses genetic information rather than phenotypes to subgroup individuals. This strategy minimizes the contribution of environmental factors and produces subgroups reflecting genetically driven pathways that predispose individuals differentially to type 2 diabetes–related metabolic disease out comes. A third approach combined both phenotype information including variables derived from oral glucose tolerance tests, magnetic resonance imaging (MRI)- measured body fat distribution, and liver fat content as well as genetic information, and identified six subtypes (implemented in individuals with prediabetes thus far). The subgrouping also opens up new avenues for research allowing better definitions of underlying pathogenic defects and refining of stratification. Moreover, more advanced methods using artificial intelligence and detailed phenotypes together with genetic information could allow for better subclassification and identification of individuals at high risk for the disease and development of complications. Genetic studies into these subgroups would further help unravel their underlying pathophysiology.

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مواضيع ذات صلة

Stepwise development of tissue- specific insulin resistance

Insulin resistance in adipose tissue Adipose tissue

Insulin resistance in the liver

Insulin resistance in skeletal muscle

Insulin resistance as a risk factor for type 2 diabetes

Definition and measurement of insulin resistance in humans

Hypoglycemia without Diabetes

Targeting the brain to restore normoglycaemia

Blood Glucose Regulation

Male Hypothalamic-Pituitary-Gonadal Axis

Hormone Secretion, Transport, and Clearance From the Blood

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