Adult-onset diabetes consists of five types of disease that have different physiological and genetic profiles, rather than the traditional type 1 and 2 classification, say Scandinavian researchers, findings that could bring the promise of personalized medicine a step closer.
Gathering data on almost 15 000 patients from across five cohorts in Sweden and Finland, they found that using six standard measurements identified five clusters of patients with diabetes.
These divided into three severe and two mild forms of disease: one corresponding to type 1 diabetes and the remaining four representing subtypes of type 2 diabetes.
The clusters included one of very insulin-resistant individuals at significantly higher risk of diabetic nephropathy, another of relatively young insulin deficient individuals with poor metabolic control (high HbA1c), and a large group of elderly patients with the most benign disease course.
Crucially, treatment often did not correspond to the type of diabetes.
The research, published online March 1 in the Lancet Diabetes & Endocrinology, could have important implications not only for the diagnosis and management of diabetes but for future therapeutic guidance.
“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,” lead author Leif Groop, MD, PhD, Lund University Diabetes Center, Malmö, Sweden, and Folkhalsan Research Centre, Helsinki, Finland, said in a press release by the journal.
“This study moves us towards a more clinically useful diagnosis, and represents an important step towards precision medicine in diabetes.”
In an accompanying editorial, Rob Sladek, MD, McGill University and Genome Quebec Innovation Centre, Montreal, Canada, points out that future studies will have to take into account the effect of age on patient outcomes, and that other factors not included in the current analysis may also have an impact.
“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,” he writes.
“Additionally, the physiological basis of the features characterizing each cluster provides a strong rationale to investigate the genetic architecture and molecular mechanisms that lead to heterogeneity in the presentation and progression of diabetes in adults.”
Sladek told Medscape Medical News that he “was not completely surprised” that there were as many as five clusters of diabetes.
“We already know that there is a group of adult-onset patients that are severely insulin deficient. In addition, we think of diabetes as being a balance between insulin needs or insulin resistance, say from obesity, and insulin production,” he said.
“So I might have expected that a couple [of the] groups would identify patients with insulin resistance.”
Clusters 1 and 2 Had Highest HbA1cLevels
Diabetes is currently classified as type 1 diabetes, type 2 diabetes, and a number of less common diseases such as latent autoimmune diabetes in adults (LADA), maturity-onset diabetes in the young (MODY), and secondary diabetes.
The classification of diabetes into type 1 and type 2 relies predominantly on the presence or absence, respectively, of autoantibodies against pancreatic beta-cell antigens and younger age. On this basis, 75% to 85% of patients are identified as having type 2 diabetes.
Recent research on glutamate acid decarboxylase antibodies (GADA) and gene sequencing has demonstrated that type 2 diabetes in particular is highly heterogeneous.
Furthermore, Groop noted, “evidence suggests that early treatment for diabetes is crucial to prevent life-shortening complications.”
“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.”
The researchers therefore set out to establish a more refined diabetes classification that could allow individualized treatment and identify patients at diagnosis who are most at risk of complications.
They gathered data from five cohorts: Swedish All New Diabetics in Scania (ANDIS), Scania Diabetes Registry (SDR), All New Diabetics in Uppsala (ANDIU), Diabetes Registry Vaasa (DIREVA), and Malmö Diet and Cancer Cardiovascular Arm (MDC-CVA).
The team used six variables to conduct a data-driven cluster analysis of 8980 patients from ANDIS, all of whom were newly diagnosed with diabetes between 2008 and 2016.
Variables included the presence of GADA; age at diagnosis; body mass index (BMI); HbA1c; and homeostatic model assessment 2 (HOMA2) estimates of beta-cell function (HOMA2-B) and insulin resistance (HOMA2-IR), based on C-peptide concentrations (which performs better than insulin in patients with diabetes) calculated using the HOMA calculator.
Five clusters of Diabetes
Cluster 1- Severe autoimmune diabetes (SAID)
Characteristics: Early disease onset (at a young age), essentially corresponds with type 1 diabetes and LADA, relatively low BMI, poor metabolic control, insulin deficiency (impaired insulin production), GADA+
Severe insulin-deficient diabetes (SIDD)
Characteristics Similar to cluster 1 but GADA–, high HbA1c, highest incidence of retinopathy