Diabetes Receives New 5-Cluster Classification

For a long time, the medical industry has associated diabetes into two categories.  Type 1 diabetes, of course, is genetic; you are born with it. Type 2 diabetes is also known as Adult-Onset diabetes and is a lifestyle disease that evolves over time, associated with the body’s eventual resistance to insulin.

More recent studies, however, now suggest that there are actually 5 different types of diabetes. Or, rather, Scandinavian researchers argue that the two types could further divide into five total clusters with different physiological and genetic distinctions.

From data on roughly 15,000 patients, taken from five cohorts in Finland and Sweden, the researchers used six standard measurements to identify these five clusters.  Essentially there are two mild forms and three severe forms.

The first form is basically Type 1 diabetes, attributed to a genetic predisposition. The remaining four dissect Type 2 diabetes into different degrees of severity.

  • Cluster 1:  Severe Autoimmune Diabetes (SAID)
  • Cluster 2:  Severe Insulin-Deficient Diabetes (SIDD)
  • Cluster 3:  Severe Insulin-Resistant Diabetes (SIRD)
  • Cluster 4:  Mild Obesity-Related Diabetes (MOD)
  • Cluster 5:  Mild Age-Related Diabetes (MARD)

According to consultant and Imperial College London clinical scientist Dr. Victoria Salem, most specialists have long pursued that are 2-tier diabetes definitions is “not a terribly accurate classification system.” She goes on to say, “There is still a massively unknown quantity – it may well be that worldwide there are 500 subgroups depending on genetic and local environment effects,” adding that this is definitely going to improve the way we think about diabetes as a disease.

Lead study author Leif Groop, MD, PhD, of the Lund University Diabetes Center in Malmö, Sweden, and Folkhalsan Research Centre, Helsinki, Finland, comments,

“Existing treatment guidelines are limited by the fact they respond to poor metabolic control when it has developed, but do not have the means to predict which patients will need intensified treatment.”

Finally, McGill University’s Rob Sladek, MD, explains in an accompanying editorial, “This study moves us towards a more clinically useful diagnosis, and represents an important step towards precision medicine in diabetes. Nevertheless, the finding that simple parameters assessed at the time of diagnosis could reliably stratify patients with diabetes according to prognosis is compelling and poses the challenge of development of methods to predict outcomes of patients with type 2 diabetes that are more generalizable and comprehensive.”

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