Incorporating polygenic risk into the Leicester Risk Assessment score for 10-year risk prediction of type 2 diabetes
Aims
We evaluated whether incorporating information on ethnic background and polygenic risk enhanced the Leicester Risk Assessment (LRA) score for predicting 10-year risk of type 2 diabetes.
Methods
The sample included 202,529 UK Biobank participants aged 40–69 years. We computed the LRA score, and developed two new risk scores using training data (80% sample): LRArev, which incorporated additional information on ethnic background, and LRAprs, which incorporated polygenic risk for type 2 diabetes. We assessed discriminative and reclassification performance in a test set (20% sample). Type 2 diabetes was ascertained using primary care, hospital inpatient and death registry records.
Results
Over 10 years, 7476 participants developed type 2 diabetes. The Harrell's C indexes were 0.796 (95% Confidence Interval [CI] 0.785, 0.806), 0.802 (95% CI 0.792, 0.813), and 0.829 (95% CI 0.820, 0.839) for the LRA, LRArev and LRAprs scores, respectively. The LRAprs score significantly improved the overall reclassification compared to the LRA (net reclassification index [NRI] = 0.033, 95% CI 0.015, 0.049) and LRArev (NRI = 0.040, 95% CI 0.024, 0.055) scores.
Conclusions
Polygenic risk moderately improved the performance of the existing LRA score for 10-year risk prediction of type 2 diabetes.
Funding
Cancer Research UK (grant no C16077/A29186)
National Institute for Health and Care Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and Leicester NIHR Biomedical Research Centre (BRC)
Nuffield Department of Population Health, Oxford University
History
Author affiliation
College of Life Sciences/Population Health SciencesVersion
- AM (Accepted Manuscript)
Published in
Diabetes & Metabolic Syndrome: Clinical Research & ReviewsPagination
102996 - 102996Publisher
Elsevier BVissn
1871-4021Copyright date
2024Available date
2024-04-09Publisher DOI
Language
enPublisher version
Deposited by
Professor Laura GrayDeposit date
2024-04-05Data Access Statement
This research has been conducted using the UK Biobank Resource under Application Number 33952. The data reported in this paper are available via application directly to the UK Biobank, https://www.ukbiobank.ac.uk. All code used to set up and run the statistical analyses are available at https://github.com/xiaonanl1996/LRArevprs.Rights Retention Statement
- No