The basic, routine measures are very easy to achieve in any healthcare clinic, such as age, at the time of diagnosis and body mass index, which can be used efficiently to choose the best treatment for a person suffering from type-II diabetes.
This conclusion was derived by the researchers at the University of Exeter, UK, after they related their simple approach with a “subgroup model” earlier proposed by researchers in Finland and Sweden.
The study findings based on recent research are published in the journal Lancet Diabetes & Endocrinology.
The authors suggested that five subgroups or replicable clusters of adults with diabetes were created in earlier studies, which could be a significant way of carrying out the effective treatment of diabetes. But the recent study has revealed that using very basic clinical features, such as evaluation of age, gender, body mass index (BMI), and kidney function, it could be a more sensible and effective way of deciding treatments.
Owing to the diversifying nature of type-II diabetes, researchers are concerned in further segmenting the class of adults into sub-categories, as it could “boost care and results” for several affected people.
Earlier study findings revealed that the researchers divided the volunteers into four sub-groups, who were suffering from type-II diabetes.
The researchers stated that the earlier study had efficiently generated the clusters but was not able to express the benefits of the clusters in selecting the favorable way of therapy.
The researchers thoroughly analyzed the medical data of 8,500 candidates suffering from type-II diabetes. The complete data set was obtained from the two independent clinical trials, which revealed that the patients were prescribed different medications for the same disease. Along with this, the researchers found that the occurrence of kidney disease also varied among the clusters. However, chronic kidney disease occurrence was better predicted by examining the actual glomerular filtration rate of the participants.