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Comparisons of polyexposure, polygenic, and clinical risk scores in risk prediction of type 2 diabetes

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Title: Comparisons of polyexposure, polygenic, and clinical risk scores in risk prediction of type 2 diabetes
Authors: He, Y
Lakhani, CM
Rasooly, D
Manrai, AK
Tzoulaki, I
Patel, CJ
Item Type: Journal Article
Abstract: OBJECTIVE: To establish a polyexposure score (PXS) for type 2 diabetes (T2D) incorporating 12 nongenetic exposures and examine whether a PXS and/or a polygenic risk score (PGS) improves diabetes prediction beyond traditional clinical risk factors. RESEARCH DESIGN AND METHODS: We identified 356,621 unrelated individuals from the UK Biobank of White British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 nongenetically ascertained exposure and lifestyle variables for the PXS in prospective T2D. We computed the clinical risk score (CRS) and PGS by taking a weighted sum of eight established clinical risk factors and >6 million single nucleotide polymorphisms, respectively. RESULTS: In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Individuals in the top 10% of PGS, PXS, and CRS had 2.00-, 5.90-, and 9.97-fold greater risk, respectively, compared to the remaining population. Addition of PGS and PXS to CRS improved T2D classification accuracy, with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. CONCLUSIONS: For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. However, the concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.
Issue Date: 9-Feb-2021
Date of Acceptance: 13-Jan-2021
URI: http://hdl.handle.net/10044/1/86882
DOI: 10.2337/dc20-2049
ISSN: 0149-5992
Publisher: American Diabetes Association
Start Page: 935
End Page: 943
Journal / Book Title: Diabetes Care
Volume: 44
Issue: 4
Copyright Statement: © 2021 by the American Diabetes Association https://www.diabetesjournals.org/content/licenseReaders 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.
Sponsor/Funder: Health Data Research Uk
Health Data Research Uk
Funder's Grant Number: Health Data Research UK
Keywords: Endocrinology & Metabolism
11 Medical and Health Sciences
Publication Status: Published
Conference Place: United States
Online Publication Date: 2021-02-09
Appears in Collections:Faculty of Medicine
School of Public Health