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A Global Overview of Precision Medicine in Type 2 Diabetes
Author(s) -
Hugo Fitipaldi,
Mark I. McCarthy,
José C. Florez,
Paul W. Franks
Publication year - 2018
Publication title -
diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.219
H-Index - 330
eISSN - 1939-327X
pISSN - 0012-1797
DOI - 10.2337/dbi17-0045
Subject(s) - data science , precision medicine , identification (biology) , big data , computer science , wearable technology , scale (ratio) , disease , complex disease , perspective (graphical) , wearable computer , medicine , risk analysis (engineering) , data mining , artificial intelligence , biology , pathology , botany , physics , quantum mechanics , embedded system
The detailed characterization of human biology and behaviors is now possible at scale owing to innovations in biomarkers, bioimaging, and wearable technologies; “big data” from electronic medical records, health insurance databases, and other platforms becoming increasingly accessible; and rapidly evolving computational power and bioinformatics methods. Collectively, these advances are creating unprecedented opportunities to better understand diabetes and many other complex traits. Identifying hidden structures within these complex data sets and linking these structures to outcome data may yield unique insights into the risk factors and natural history of diabetes, which in turn may help optimize the prevention and management of the disease. This emerging area is broadly termed “precision medicine.” In this Perspective, we give an overview of the evidence and barriers to the development and implementation of precision medicine in type 2 diabetes. We also discuss recently presented paradigms through which complex data might enhance our understanding of diabetes and ultimately our ability to tackle the disease more effectively than ever before.

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