Diabetes Type 1,2,3,4,5 and the path towards a web based tool to determine the prognosis
Diabetes is presently classified into two main forms, type 1 (T1D) and type 2 diabetes (T2D), but especially T2D is highly heterogeneous. T1D relied by now primarily on presence resp. absence of autoantibodies against pancreatic islet beta cell autoantigens and age at diagnosis, which made diagnosis of T2D more or less a diagnosis by exclusion.
Now Swedish researcher have shed light into the heterogenous nature of diabetes, stepping closer to personalized medicine. They performed a data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes (n=8980) from the Swedish All New Diabetics in Scania cohort and replicated these findings in three Swedish independent cohorts.
This new sub-stratification as described in the table below may help to tailor and target early treatment to patients who would benefit most. But it may also help underwriters in life and health insurance soon to assess diabetes more reliably concerning the longterm prognosis by identifying individuals at high risk of diabetic complications due to different underlying disease mechanisms. What even matters most is the idea, that the differentiation and prediction can be made already at stage of first diagnosis. It is small wonder that a web-tool to assign patients to specific clusters, provided by the below specified variables is already under development.
The table below indicates that the combined information from a few variables appears to be central to the development of diabetes and is surprisingly even superior to the measurement of only one metabolitic parameter, namely glucose.
Conclusions and caveats
It still needs to be shown in prospective studies whether patients (especially from the periphery of clusters) can move between clusters. It might also be possible to refine the stratification further by including additional cluster variables e.g. biomarkers, genotypes or genetic risk scores, but this seems to be a fisrt major step towards a more efficient treatment and maybe even a better understanding of diabetes.
References: https://www.medscape.com/viewarticle/893305