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dc.contributor.authorDennis, J. M.
dc.date.accessioned2021-02-23T13:49:37Z
dc.date.available2021-02-23T13:49:37Z
dc.date.issued2020-10
dc.identifier.citationDennis JM. Precision Medicine in Type 2 Diabetes: Using Individualized Prediction Models to Optimize Selection of Treatment. Diabetes. 2020 Oct;69(10):2075-2085. doi: 10.2337/dbi20-0002. Epub 2020 Aug 25.en_US
dc.identifier.pmid32843566
dc.identifier.doi10.2337/dbi20-0002
dc.identifier.urihttps://rde.dspace-express.com/handle/11287/621634
dc.description.abstractDespite 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.en_US
dc.language.isoenen_US
dc.publisherHighWireen_US
dc.relation.urlhttps://diabetes.diabetesjournals.org/lookup/pmidlookup?view=long&pmid=32843566en_US
dc.rights© 2020 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.en_US
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectType 2 Diabetesen_US
dc.subjectPrecision Medicineen_US
dc.subjectIndividualized Prediction Modelsen_US
dc.subjectWessex Classification Subject Headings::Endocrinology::Diabetesen_US
dc.titlePrecision Medicine in Type 2 Diabetes: Using Individualized Prediction Models to Optimize Selection of Treatmenten_US
dc.typeJournal Articleen_US
dc.identifier.journalDiabetesen_US
dc.identifier.pmcidPMC7506836
dc.description.noteThis article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.en_US
dc.description.fundingJ.D. is supported by an independent fellowship funded by Research England’s Expanding Excellence in England (E3) fund. Research discussed is this article was supported by the Medical Research Council (U.K.) (MR/N00633X/1).en_US
dc.type.versionPublisheden_US
dc.description.admin-noteaccepted version, submitted versionen_US


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© 2020 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.
Except where otherwise noted, this item's license is described as © 2020 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.