Diabetes Mellitus is caused by several different disease subtypes, which are often misclassified especially in young patients. Correctly classifying patients is vital to select an optimal treatment. Researchers at the BIST community centre CRG, led by Dr. Jorge Ferrer, are embarking on a new project to improve the diagnostic accuracy of the disease – a project which has been selected for funding by the European Research Council with a €150,000 Proof-of-Concept grant.
Diabetes presents a major global health challenge, affecting nearly 600 million people worldwide. Early diagnosis is critical to help manage the condition with appropriate treatments and lifestyle changes. Without early or correct diagnosis, the condition can lead to severe complications such as blindness, limb amputation, or premature death.
Diagnosis is challenging because there are many subtypes of diabetes, such as type-1 diabetes (T1D), an autoimmune condition where the body attacks and destroys insulin-producing cells in the pancreas, and type-2 diabetes (T2D), the most common form of the disease characterised by the body’s ineffective use of insulin. There are also several less common forms of diabetes resulting from mutations in a single gene, with the most common being Maturity-Onset Diabetes of the Young (MODY) caused by a mutation in the HNF1A gene.
In clinical practice, doctors might measure autoantibodies if they suspect T1D, or order a genetic panel to detect the presence of a rare genetic form. However, these aren’t always determinant. For example, around 50% of patients with clinically suspected monogenic diabetes whose DNA is sequenced have negative results in conventional genetic tests. Clinical judgement remains a major guide to diagnose one of the many subtypes of the disease. The dearth of objective tests means many patients have their subtype either misclassified or not classified at all. These patients fail to benefit from personalised medicine.
An example of misclassification is when patients have MODY but are incorrectly diagnosed with T1D, receiving daily insulin injections for many years despite the preferred treatment being the oral use of sulfonylurea tablets. Non-classification is also a widespread problem, with around 1 in 20 patients diagnosed before the age of 45 being classified as having “unspecified diabetes” because they do not have clinical features associated with any known subtype.
The newly funded project plans to tackle the challenge of non- and misclassified diabetes by developing GenomeDia, a new tool which will collect and use information from the entire genome of patients. The solution is unique because it will include not just the parts of the DNA that are well understood (like genes that code for proteins) but also the more complex regions that don’t make proteins but still play critical roles in health and disease.
“There is overwhelming evidence that many different regions of the genome contribute to the development of both type-1 and type-2 diabetes. Rather than look for the most common culprit genes found in less than 2% of the protein coding sequences of DNA, we want to look at the entire human genome. This can help detect the presence of noncoding variants that are most likely to be clinically deleterious, assist subtype diagnosis and facilitate personalised medicine for many more patients than currently possible,” explains Dr. Jorge Ferrer, GenomeDia lead, Coordinator of the Computational Biology and Health Genomics programme at the Centre for Genomic Regulation and Group Leader at CIBERDEM.
GenomeDia will build upon findings from a previous EU-funded project DecodeDiabetes, which has already compiled extensive genetic data from patients with diabetes. The researchers will begin by collecting whole genome sequences from patients with a focus on young patients with diabetes in which the disease subtype is unclear. GenomeDia will then be tested in several large studies encompassing diverse types of patients with diabetes.
“Given the prevalence of diabetes in absolute numbers, tens of millions of patients worldwide face delayed or inappropriate treatment, greatly affecting the prognosis and management of the disease. Our aim is to develop tools that help create new standards in diabetes diagnostics, moving away from the current one-size-fits-all approach to a more nuanced, genetics-based understanding of the disease. If we succeed, it will mark a significant step forward in the field of personalized medicine,” concludes Dr. Ferrer.