Classifying diabetes patients into five types, rather than two, may help to tailor and target early treatment, according to a study published in The Lancet Diabetes and Endocrinology (online, 1 March 2018)
Researchers carried out a cluster analysis of data from five cohorts of adult patients with newly diagnosed diabetes. The clusters were based on six variables, including age at diagnosis, body mass index (BMI) and insulin resistance, and were related to prospective data from the patient’s records on prescriptions and diabetic complications.
The researchers compared time to medication, time to reaching the treatment goal, and the risk of diabetic complications and genetic associations.
They found that there were five clear subgroups of diabetes, including three severe and two mild forms of the disease, each with significantly different patient characteristics and risks of diabetic complications. The five types were also genetically distinct, with no mutations associated with all types of the disease.
The 77 patients in group one, which was labelled as severe autoimmune diabetes (SAID), were characterised as having early-onset disease, relatively low BMI, poor metabolic control, insulin deficiency, and presence of glutamate decarboxylase autoantibodies (GADA) — a predictive marker for type 1 diabetes.
Group two, which was made up of 1,575 of the patients, was labelled as severe insulin-deficient diabetes (SIDD), and was similar to group one, apart from the fact that patients in that group were GADA negative.
Group three was labelled as severe insulin-resistant diabetes (SIRD) and contained 1,373 of the patients. This group was characterised by insulin resistance and a high BMI. While group four, with 1,942 patients, was characterised by obesity and not insulin resistance and was labelled as mild obesity-related diabetes (MOD).
Finally, the fifth group, labelled as mild age-related diabetes (MARD) contained 3,513 patients who were older than patients in the other groups, but, similar to group four, showed only modest metabolic derangements.
The same groupings were also replicated in three more cohorts of patients.
“More accurately diagnosing diabetes could give us valuable insights into how it will develop over time, allowing us to predict and treat complications before they develop,” said lead author of the study Leif Groop from Lund University Diabetes Centre, Sweden, and the Institute for Molecular Medicine Finland.
“Existing treatment guidelines are limited by the fact that they respond to poor metabolic control when it has developed, but do not have the means to predict which patients will need intensified treatment.
“This study moves us towards a more clinically useful diagnosis, and represents an important step towards precision medicine in diabetes.”
The researchers concluded that the results suggested that this new, more personalised, classification of patients with adult-onset diabetes is superior to the classic diabetes classification because it identifies patients at high risk of diabetic complications, thereby helping to guide clinicians to a more suitable choice of therapy.
However, Philip Newland-Jones, a consultant pharmacist specialising in diabetes and endocrinology at University Hospital Southampton NHS Foundation Trust, said that he had concerns about complicating a disease area where misdiagnosis and clinical incidents related to the misunderstanding and management of type 1 diabetes are already common.
“Type 2 diabetes is often described as a spectrum disorder where at one end you may have a perfectly working pancreas and severe insulin resistance and at the other no insulin resistance but progressively declining beta cell function,” he explained.
“In practice this means we should be treating type 2 diabetes diagnosed for the first time in old age very differently from type 2 diabetes diagnosed below the age of 40 years.
“I think most clinicians would welcome the recognition that diabetes is more complex than type 1 or type 2 diabetes, but I do not think this research will change practice until we have further evidence that treating these subgroups of type 2 diabetes differently affects clinical outcomes,” he added.
A web-based tool to help assign patients to the correct group is now under development. The authors said that the addition of further variables, such as genotype, and looking at other populations may help to refine the stratification further.
 Ahlqvist E, Storm P, KÃ¤rÃ¤jÃ¤mÃ¤ki A et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol 2018. doi: 10.1016/S2213-8587(18)30051-2