Diabetes is one of those non-communicable diseases which is steadily increasing everywhere: both in developed and developing countries. According to World Health Organisation (WHO), diabetes not only can lead to complications but also can increase the risk of premature death.
For a long period of time, cases of diabetes have been categorised as type 1 or type 2. However, a recent research article, published in The Lancet, classified this fast-moving disease into five categories.
Currently diabetes is diagnosed by measuring only one metabolite — glucose; and that could be one explanation for the ineffectivity of the ongoing treatment strategies. Therefore, for further in-depth exploration of this emerging disease, the researchers from Sweden attempted to update the classification system of diabetes diagnosis.
At present diabetes has been classified according to age of onset and presence or absence of antibodies which attack pancreatic beta cells. But in this research article, the authors proposed five “clusters” of the disease by focusing on six different variables: age, body mass index, presence of beta-cell antibodies, level of metabolic control and measures of beta-cell function and insulin resistance.
The newly classified clusters are:
- Severe Insulin-Resistant Diabetes (SIRD) — not only bringing in insulin resistance but also the risk of diabetic kidney disease;
- Severe Insulin-Deficient Diabetes (SIDD), comprising of poor metabolic control mainly among young adults;
- Severe Autoimmune Diabetes, coinciding with type 1 diabetes;
- Mild Age-Related Diabetes (MARD); and
- Mild Obesity-Related Diabetes (MOD).
The last two types are benign forms of diabetes. The researchers mentioned that SIRD and SIDD were masked with type 2 diabetes.
According to study results, these subtypes of diabetes will come to the aid in identifying people who could be at high risk of developing diabetes-related chronic complications and guiding healthcare professionals about treatment of choice.
The researchers also hoped that this newly proposed classification would be beneficial not only for newly diagnosed patient but also for those who are suffering from diabetes type 2 for a while.
Although the researchers did not claim this as the best classification system, their data suggested that this classification system is superior to the prevalent one. Therefore, adopting this new classification system could be an important step towards precision medicine in diabetes.
Last but not the least, further research on diabetes diagnosis among more diverse population is necessary before implementing this classification into everyday clinical practice.