A completely new classification of diabetes that also predicts the risk of serious complications and provides treatment suggestions, are the first results of the Swedish ANDIS study. Type 2 diabetes actually consists of several subgroups, the results indicate.
“This is the first step towards personalised treatment of diabetes”, says Leif Groop, physician and professor of diabetes and endocrinology at Lund University in Sweden.
Today, about 425m people around the world have diabetes. By 2045, the number is expected to have increased to 629m*. Secondary diseases in the form of kidney failure, retinopathy (eye damage), amputations and cardiovascular diseases result in huge costs to society and major individual suffering. Thus, the need for new and better treatment options is great.
“Current diagnostics and classification of diabetes are insufficient and unable to predict future complications or choice of treatment”, explains Groop, who initiated the study. He believes that the results represent a paradigm shift in how to view the disease in the future.
“Today, diagnoses are performed by measuring blood sugar. A more accurate diagnosis can be made by also considering the factors accounted for in ANDIS (All New Diabetics In Skåne).”
Since 2008, the researchers have monitored 13,720 newly diagnosed patients between the ages 18 and 97. By combining measurements of, for example, insulin resistance, insulin secretion, blood sugar levels (BMI, HbA1c, GADA, HOMA-B and HOMA-IR) and age at onset of illness, the researchers were able to distinguish five distinct clusters that differ from today’s classification.
In addition to a more refined classification, the researchers also discovered that the different groups are more or less at risk of developing various secondary diseases.
“This will enable earlier treatment to prevent complications in patients who are most at risk of being affected”, says Emma Ahlqvist, associate professor and lead author of the publication.
Diabetes is currently divided into: type 1 diabetes (approx. 10%), type 2 diabetes (85%–90%) and a number of less common diseases such as LADA, MODY and secondary diabetes.
Group 1, SAID (severe autoimmune diabetes): essentially corresponds to type 1 diabetes and LADA (latent autoimmune diabetes in adults), and is characterised by onset at young age, poor metabolic control, impaired insulin production and the presence of GADA antibodies.
Group 2, SIDD (severe insulin-deficient diabetes): includes individuals with high HbA1C, impaired insulin secretion and moderate insulin resistance. Group 2 had the highest incidence of retinopathy.
Group 3, SIRD (severe insulin-resistant diabetes): is characterised by obesity and severe insulin resistance. Group 3 had the highest incidence of kidney damage – the secondary disease producing the highest costs to society.
Group 4, MOD (mild obesity-related diabetes): includes obese patients who fall ill at a relatively young age.
Group 5, MARD (mild age-related diabetes): is the largest group (about 40%) and consists of the most elderly patients.
“The most insulin resistant patients (Group 3) have the most to gain from the new diagnostics as they are the ones who are currently most incorrectly treated”, says Groop.
The researchers subsequently repeated the analysis in a further three studies from Sweden and Finland. “The outcome exceeded our expectations and highly corresponded with the analysis from ANDIS. The only difference was that Group 5 was larger in Finland than in Skåne. The disease progression was remarkably similar in both groups”, says Groop.
The recruitment of newly diagnosed diabetes patients continues and the researchers have several studies underway based on the data they have already acquired. “The longer the study is running, the more and better data we’ll get”, says Ahlqvist.
The researchers are also planning to launch similar studies in China and India with people of different ethnic backgrounds. “This will give us even better opportunities to tailor the treatment to each individual”, she concludes.
Dr Emily Burns, head of research communications at Diabetes UK said in a report in The Guardian that finding diabetes sub-types could help patients, but that more work was needed.
“This research takes a promising step toward breaking down type 2 diabetes in more detail, but we still need to know more about these subtypes before we can understand what this means for people living with the condition,” she said. “For example, whether we’d find the same subtypes in people of different ethnicity or nationality.”
Background: Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis.
Methods: We did data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes (n=8980) from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables (glutamate decarboxylase antibodies, age at diagnosis, BMI, HbA1c, and homoeostatic model assessment 2 estimates of β-cell function and insulin resistance), and were related to prospective data from patient records on development of complications and prescription of medication. Replication was done in three independent cohorts: the Scania Diabetes Registry (n=1466), All New Diabetics in Uppsala (n=844), and Diabetes Registry Vaasa (n=3485). Cox regression and logistic regression were used to compare time to medication, time to reaching the treatment goal, and risk of diabetic complications and genetic associations.
Findings: We identified five replicable clusters of patients with diabetes, which had significantly different patient characteristics and risk of diabetic complications. In particular, individuals in cluster 3 (most resistant to insulin) had significantly higher risk of diabetic kidney disease than individuals in clusters 4 and 5, but had been prescribed similar diabetes treatment. Cluster 2 (insulin deficient) had the highest risk of retinopathy. In support of the clustering, genetic associations in the clusters differed from those seen in traditional type 2 diabetes.
Interpretation: We stratified patients into five subgroups with differing disease progression and risk of diabetic complications. This new substratification might eventually help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.
Emma Ahlqvist, Petter Storm, Annemari Käräjämäki, Mats Martinell, Mozhgan Dorkhan, Annelie Carlsson, Petter Vikman, Rashmi B Prasad, Dina Mansour Aly, Peter Almgren, Ylva Wessman, Nael Shaat, Peter Spégel, Hindrik Mulder, Eero Lindholm, Olle Melander, Ola Hansson, Ulf Malmqvist, Åke Lernmark, Kaj Lahti, Tom Forsén, Tiinamaija Tuomi, Anders H Rosengren, Leif Groop