Diabetes subphenotypes and precision medicine

Precision medicine is a new direction in diabetes therapy which considers a stepwise approach, incorporating the evidence from clinical studies, in order to achieve quantifiable, implementable outcomes based on diabetes etiology and risk of developing certain complications.
The traditional classification into type 1 and type 2 diabetes has proven useful in differentiating distinct pathophysiological mechanisms with clear therapeutic implications, yet it remains insufficient in explaining the wide variety of clinical manifestations of this disease. Furthermore, algorithms of prediction and prevention of diabetes complications, the rate of beta-cell failure, the proper methods of weight management, or medication suitability remain scarce. Precision medicine is the concept that specific treatments can be targeted to groups of individuals with specific genetic or metabolic features.
New technological and clinical developments coupled with increased statistical sophistication introduced diagnostic and therapeutic methods with a comprehensive understanding of the physiopathology behind type 2 diabetes. Thus it could be revealed that patterns of metabolic phenotype variation in humans can also distinguish subgroups of patients with similar metabolic traits. This was valid for both overt type 2 diabetes and prediabetes. By using clustering algorithms, research groups from Düsseldorf and Tübingen showed, using the well phenotyped German cohorts of patients with diabetes (German Diabetes Study) and prediabetes (PLIS Study), that specific metabolic features can be used to subdivide cohorts of patients into groups with similar patterns. Specific clusters were then showed to have rapid progression of microvascular and/or macrovascular complications or require aggressive escalation of therapy.
Making the correct and precise diagnosis can be challenging, but it is crucial to prevent long-term morbidity and mortality. Given the plethora of treatment options and clinical examinations, the challenge lies in finding simple clinical measures to identify patients at risk and subsequently matching the right drug with the right patient at the right time to obtain the best clinical outcome.

Patricia Zaharia

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