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Rapid advances in technology and data science have the potential to improve the precision of preventive and therapeutic interventions, and enable the right treatment to be recommended, at the right time, to the right person. There are well-described examples of successful precision medicine approaches for monogenic conditions such as specific diets for phenylketonuria, and sulfonylurea treatments for certain types of MODY. However, the majority of chronic diseases are polygenic, and it is unlikely that the research strategies used for monogenic diseases will deliver similar changes to practice for polygenic traits. Type 2 diabetes, for example, is a multifactorial, heterogeneous, polygenic palette of metabolic disorders. In this non-systematic review I highlight limitations of the evidence, and the challenges that need to be overcome prior to implementation of precision medicine in the prevention and management of type 2 diabetes. Most precision medicine approaches are spuriously precise, overly complex and too narrowly focused on predicting blood glucose levels with a limited set of characteristics of individuals rather than the whole person and their context. Overall, the evidence to date is insufficient to justify widespread implementation of precision medicine approaches into routine clinical practice for type 2 diabetes. We need to retain a degree of humility and healthy scepticism when evaluating novel strategies, and to demand that existing evidence thresholds are exceeded prior to implementation.
Simon J. Griffin (Wed,) studied this question.