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 HbA1c Levels
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.
The analysis revealed the presence of five clusters of diabetes in men and women, with similar distributions between the two, as shown in the table.
Five Clusters of Diabetes
| Cluster | N (%) | Characteristics | Name |
|---|---|---|---|
| 1 | 577 (6.4) |
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 autoimmune diabetes (SAID) |
| 2 | 1575 (17.5) | Similar to cluster 1 but GADA–, high HbA1c, highest incidence of retinopathy | Severe insulin-deficient diabetes (SIDD) |
| 3 | 1373 (15.3) | Insulin resistance, high BMI, highest incidence of nephropathy | Severe-insulin resistant diabetes (SIRD) |
| 4 | 1942 (21.6) | Obesity, younger age, not insulin resistant | Mild obesity-related diabetes (MOD) |
| 5 | 3513 (39.1) | Older age, modest metabolic alterations | Mild age-related diabetes (MARD) |
Researchers then tested the clusters in 1466 patients from SDR, 844 patients from ANDIU and 3485 patients from DIREVA, and identified similar patient distributions and cluster characteristics.
Looking at disease progression and treatment, the team found that clusters 1 and 2 had substantially higher HbA1c levels than the other clusters, which persisted throughout follow-up.
Patients in clusters 1 and 2 were also more likely to have ketoacidosis at diagnosis (31% and 25%) compared with other clusters (< 5%), of which HbA1c was the strongest predictor (odds ratio [OR] per SD change, 2.73; P < .0001).
Insulin was prescribed to 42% of cluster 1 patients and 29% of cluster 2 patients, but to less than 4% of patients in other clusters. The time to sustained insulin use was also shortest in these two clusters.
A First Step Towards Precision Medicine in Diabetes
Metformin use was highest in cluster 2 and lowest in clusters 1 and 3. Kidney function and adverse reactions did not have a major effect on metformin use.
Cluster 3 was at highest risk of developing chronic disease, at a mean follow-up of 3.9 years. This cluster also had a higher risk of diabetic nephropathy and macroalbuminuria than other patients (hazard ratio [HR], 2.18; P = .0026).
Patients in cluster 3 also had a substantially higher risk of end-stage renal disease (HR vs cluster 5, 4.89; P < .0001).
Diabetic retinopathy was more common in cluster 2, at an OR of 1.6 vs cluster 5 (in ANDIS).
The team also reports that there was no one genetic variant associated with all five clusters and that each cluster had a genetic profile distinct from that of type 2 diabetes as a whole.
While acknowledging that their study has several limitations and needs confirmation in other, less homogenous populations, researchers say that the combined information provided by the variables in their analysis is "superior to measurement of only one metabolite, glucose."
"Through combining this information from diagnosis with information in the health care system, this study provides a first step towards a more precise, clinically useful stratification," they continue.
"This new substratification could change the way we think about type 2 diabetes and help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes," they add.
The team also believe the clusters they identified "can easily be applied to both existing diabetes cohorts (for example, from drug trials) and patients in diabetes clinics."
"A web-based tool to assign patients to specific clusters, provided the appropriate variables have been measured, is under development," they note.
How Do These Data Relate to Type 3c Diabetes?
The current results follow those of a study published in late 2017, which showed that patients with diabetes resulting from pancreatic dysfunction — type 3c diabetes — are often misdiagnosed as having type 2 diabetes.
As reported by Medscape Medical News, that analysis of more than 30 000 incident diabetes cases showed that type 3c diabetes, also known as diabetes of the exocrine pancreas, is almost as common as type 1 diabetes and misdiagnosed in over 87% of patients.
The misdiagnosis results in an increased risk of poor glycemic control compared with patients with type 2 diabetes and a far greater reliance on insulin.
Sladek said the current study is not directly related to type 3c diabetes, as this latter, rarer form of the disease is considered a 'secondary diabetes.' "In other words, it occurs as a result of a well-recognized disease process that does not alter insulin secretion directly and independently," he said.
Noting that the patients with type 3c diabetes had some form of exocrine pancreatic disease that affected endocrine function, Sladek said "this could occur for many reasons that share the common feature that the exocrine pancreas damage precedes the development of diabetes."
"In contrast, patients with type 1 or 2 diabetes have normal exocrine pancreatic function."
This study was funded by the Swedish Research Council, European Research Council, Vinnova, Academy of Finland, Novo Nordisk Foundation, Vasa Hospital District, Scania University Hospital, Sigrid Juselius Foundation, European Union, Swedish Foundation for Strategic Research, Jakobstadsnejden Heart Foundation, Folkhalsan Research Foundation, and Ollqvist Foundation. It was conducted by researchers from Lund University, Skåne University Hospital, Vaasa Central Hospital, Vaasa Health Care Center, Uppsala University, Lund University Hospital, Folkhalsan Research Center, Helsinki University Central Hospital, University of Helsinki, and University of Gothenburg.
Lancet Diabetes Endocrinol. Published online March 1, 2018. Abstract
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Cite this: Diabetes Consists of Five Types, Not Two, Say Researchers - Medscape - Mar 01, 2018.
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