
==== Front
medRxiv
MEDRXIV
medRxiv
Cold Spring Harbor Laboratory

10.1101/2024.08.22.24312440
preprint
2
Article
Integrated clinical risk prediction of type 2 diabetes with a multifactorial polygenic risk score
Ritchie Scott C. http://orcid.org/0000-0002-8454-9548

Taylor Henry J. http://orcid.org/0000-0003-2088-5240

Liang Yujian
Manikpurage Hasanga D. http://orcid.org/0000-0002-2365-6956

Pennells Lisa
Foguet Carles http://orcid.org/0000-0001-8494-9595

Abraham Gad http://orcid.org/0000-0003-4853-0118

Gibson Joel T.
Jiang Xilin http://orcid.org/0000-0001-6773-9182

Liu Yang
Xu Yu
Kim Lois G. http://orcid.org/0000-0002-4552-3820

Mahajan Anubha http://orcid.org/0000-0001-5585-3420

McCarthy Mark I. http://orcid.org/0000-0002-4393-0510

Kaptoge Stephen
Lambert Samuel A http://orcid.org/0000-0001-8222-008X

Wood Angela http://orcid.org/0000-0002-7937-304X

Sim Xueling
Collins Francis S.
Denny Joshua C.
Danesh John
Butterworth Adam S. http://orcid.org/0000-0002-6915-9015

Di Angelantonio Emanuele http://orcid.org/0000-0001-8776-6719

Inouye Michael http://orcid.org/0000-0001-9413-6520

26 8 2024
2024.08.22.24312440https://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
http://medrxiv.org/lookup/doi/10.1101/2024.08.22.24312440
nihpp-2024.08.22.24312440.pdf
Abstract

Combining information from multiple GWASs for a disease and its risk factors has proven a powerful approach for development of polygenic risk scores (PRSs). This may be particularly useful for type 2 diabetes (T2D), a highly polygenic and heterogeneous disease where the additional predictive value of a PRS is unclear. Here, we use a meta-scoring approach to develop a metaPRS for T2D that incorporated genome-wide associations from both European and non-European genetic ancestries and T2D risk factors. We evaluated the performance of this metaPRS and benchmarked it against existing genome-wide PRS in 620,059 participants and 50,572 T2D cases amongst six diverse genetic ancestries from UK Biobank, INTERVAL, the All of Us Research Program, and the Singapore Multi-Ethnic Cohort. We show that our metaPRS was the most powerful PRS for predicting T2D in European population-based cohorts and had comparable performance to the top ancestry-specific PRS, highlighting its transferability. In UK Biobank, we show the metaPRS had stronger predictive power for 10-year risk than all individual risk factors apart from BMI and biomarkers of dysglycemia. The metaPRS modestly improved T2D risk stratification of QDiabetes risk scores for 10-year risk prediction, particularly when prioritising individuals for blood tests of dysglycemia. Overall, we present a highly predictive and transferrable PRS for T2D and demonstrate that the potential for PRS to incrementally improve T2D risk prediction when incorporated into UK guideline-recommended screening and risk prediction with a clinical risk score.
==== Body
pmc
