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• W3080886764 abstract "Despite the known heterogeneity of type 2 diabetes and variable response to glucose lowering medications, current evidence on optimal treatment is predominantly based on average effects in clinical trials rather than individual-level characteristics. A precision medicine approach based on treatment response would aim to improve on this by identifying predictors of differential drug response for people based on their characteristics and then using this information to select optimal treatment. Recent research has demonstrated robust and clinically relevant differential drug response with all noninsulin treatments after metformin (sulfonylureas, thiazolidinediones, dipeptidyl peptidase 4 [DPP-4] inhibitors, glucagon-like peptide 1 [GLP-1] receptor agonists, and sodium-glucose cotransporter 2 [SGLT2] inhibitors) using routinely available clinical features. This Perspective reviews this current evidence and discusses how differences in drug response could inform selection of optimal type 2 diabetes treatment in the near future. It presents a novel framework for developing and testing precision medicine-based strategies to optimize treatment, harnessing existing routine clinical and trial data sources. This framework was recently applied to demonstrate that subtype approaches, in which people are classified into subgroups based on features reflecting underlying pathophysiology, are likely to have less clinical utility compared with approaches that combine the same features as continuous measures in probabilistic individualized prediction models." @default.
• W3080886764 created "2020-09-01" @default.
• W3080886764 creator A5020931016 @default.
• W3080886764 date "2020-08-25" @default.
• W3080886764 modified "2023-10-12" @default.
• W3080886764 title "Precision Medicine in Type 2 Diabetes: Using Individualized Prediction Models to Optimize Selection of Treatment" @default.
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• W3080886764 doi "https://doi.org/10.2337/dbi20-0002" @default.
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