I predict this exciting article will become a highly-cited landmark publication –– at least in the field of DIABETES. A layman’s article on this topic was shared by all of GEITP on 2 March 2018 and it posted again [way below]. The current article [attached] is the scientific report that has now appeared.
Diabetes is the fastest increasing disease, world-wide, and a substantial threat to human health. Existing treatment strategies have been unable to stop the progressive course of the disease and prevent development of chronic diabetic complications. One explanation for these shortcomings is that diagnosis of diabetes is based on measurement of only one metabolite, glucose, but the disease is highly heterogeneous –– with regard to clinical presentation and progression.
Diabetes classification into type-1 and type-2 diabetes (T2D) relies primarily on the presence (type-1) or absence (T2D) of autoantibodies against pancreatic islet β-cell antigens and age at diagnosis (younger for type-1 diabetes); with this approach, 75–85% of patients are classified as having T2D. A third subgroup, latent autoimmune diabetes in adults (LADA; affecting <10% of people with diabetes), defined by the presence of glutamic acid decarboxylase antibodies (GADA), is indistinguishable from T2D at diagnosis, but becomes increasingly similar to type-1 diabetes over time. With the introduction of gene sequencing in clinical diagnostics, several rare monogenic forms of diabetes have recently been described –– including maturity-onset diabetes of the young, and neonatal diabetes. Authors [see attached] performed data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables: glutamate decarboxylase antibodies; age at diagnosis; body mass index (BMI); HbA1c levels; and homoeostatic model assessment-2 estimates of β-cell function; and insulin resistance. Replication was done in three independent cohorts: the Scania Diabetes Registry (N = 1,466), All New Diabetics in Uppsala (N = 844), and Diabetes Registry Vaasa (N = 3,485), i.e. total N was 8,980. Cox regression and logistic regression were used to compare time to medication, time to reaching the treatment goal, risk of diabetic complications, and genetic associations. Authors 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 they had all 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 T2D. This new sub-stratification 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. Lancet Diabetes Endocrinol 10.1016://S2213-8587(18)30051-2 (2018) www.thelancet.com/diabetes-endocrinology Published online March 1, 2018 http://dx.doi.org/10.1016/S2213-8587(18)30051-2 Published 2 March 2018 By Honor Whiteman Fact checked by Jasmin Collier Adults with diabetes could benefit from better treatment if the condition was categorized into five types, rather than just two. This is the conclusion of a new study published in The Lancet Diabetes & Endocrinology. The research was led by Prof. Leif Groop, of the Lund University Diabetes Centre in Sweden and the Institute for Molecular Medicine Finland in Helsinki. In the United States alone, around 30.3 million people are living with diabetes. Excluding gestational diabetes — diabetes that develops during pregnancy — there are two main types: type 1 and type 2. In type 1 diabetes, the beta cells of the pancreas — which produce insulin, the hormone that regulates blood sugar levels — are mistakingly attacked and destroyed by the immune system. Type 2 diabetes is by far the most common form, accounting for around 90–95 percent of all cases. This occurs when the body's cells stop responding to insulin, or the beta cells are unable to produce sufficient amounts of the hormone. In both forms of the condition, blood sugar levels can become too high — a condition known as hyperglycemia. Unless controlled, this can lead to a number of complications, including kidney disease, cardiovascular disease, and nerve damage. The opposite, during excessive treatment, is low blood sugar levels, known as hypoglycemia and which can cause coma and death. The heterogeneity of diabetes A diabetes diagnosis is normally made using the fasting plasma glucose (FPG) test or the A1c test. The FPG test assesses a person's blood glucose level at a single time-point, whereas the A1c test measures average blood glucose levels over the previous four months. When it comes to determining which type of diabetes a person has, healthcare professionals might look for diabetes-related auto-antibodies in the blood. These are proteins produced by the immune system that can attack the body's own cells. The presence of such auto-antibodies is an indicator of type 1 diabetes. If a person does not have these auto-antibodies, they are considered to have type 2 diabetes. But, as Prof. Groop and colleagues note, the classification guidelines for diabetes have not been updated for 20 years — despite increasing evidence that diabetes has high heterogeneity. "Diabetes is a group of chronic metabolic disorders," says Dr. Rob Sladek, of the McGill University and Génome Québec Innovation Centre in Canada, in an editorial linked to the study, "that share the common feature of hyperglycemia, meaning that, in principle, diabetes can be diagnosed via measurement of a single blood component." Prof. Groop and his team say that a "refined classification" of diabetes based on its heterogeneity could help healthcare professionals better predict which individuals are most likely to develop complications and allow a more personalized approach to treatment. In their study, the researchers propose that diabetes should no longer be categorized as two types. Instead, they say that the condition should be classified into five distinct types. The five 'clusters' of diabetes The researchers came to their proposal by analyzing the data of four study cohorts. These included a total of 14,775 adults from Sweden and Finland, all of whom had been newly diagnosed with diabetes. As part of the analysis, the scientists looked at six measures in each subject that each represent different features of diabetes. These measures were: body mass index (BMI); age at which diabetes was diagnose; hemoglobin A1c (HbA1c), a measure of long-term blood sugar control; pancreatic beta-cell functioning; insulin resistance; and presence of diabetes-related auto-antibodies. As well as conducting genetic analyses of the participants, the researchers also compared their disease progression, complications, and treatment. The study revealed five distinct forms of diabetes, three of which were severe and two that were mild. The team categorized these as follows: · Cluster 1: severe auto-immune diabetes (currently known as type 1 diabetes), characterized by insulin deficiency and the presence of auto-antibodies. This was identified in 6–15 percent of subjects. · Cluster 2: severe insulin-deficient diabetes, characterized by younger age, insulin deficiency, and poor metabolic control, but no autoantibodies. This was identified in 9–20 percent of subjects. · Cluster 3: severe insulin-resistant diabetes, characterized by severe insulin resistance and a significantly higher risk of kidney disease. This was identified in 11–17 percent of subjects. · Cluster 4: mild obesity-related diabetes, most common in obese individuals. This affected 18–23 percent of subjects. · Cluster 5: mild age-related diabetes, most common in elderly individuals. This was the most common form, affecting 39–47 percent of subjects. The researchers note that each of these five types "were also genetically distinct," meaning that there were no genetic mutations that were shared across all five clusters. A 'step toward precision medicine' When the researchers assessed the treatment received by adults in each of the five clusters, they noticed that some were being treated inappropriately. As an example, the team points out that just 42 percent of patients in cluster 1 and 29 percent of patients in cluster 2 received insulin therapy from the point of disease onset. They say that this indicates that the current classifications of diabetes fail to target the underlying features of the disease. While further research is required to refine each of these five clusters — by using biomarkers and genetic risk scores, for example — the team believes that this study represents a great stride toward tailored treatments for diabetes. "Existing treatment guidelines," concludes Prof. Groop, "are limited by the fact the patients respond to poor metabolic control when it has developed, but do not have the means to predict which patients will need intensified treatment.