In:
Diabetes, American Diabetes Association, Vol. 68, No. Supplement_1 ( 2019-06-01)
Abstract:
With the rise in the prevalence of type 2 diabetes (T2D), as well as undiagnosed cases of T2D and prediabetes (25% and 90%, respectively), early detection is imperative to minimize individual and societal burden. T2D is highly heritable, and personal genetic information is increasingly available to the general public. Studies have suggested that T2D risk reduction strategies may be more effective for individuals with high T2D genetic risk, supporting the use of genetics as a screening tool to inform cost-effective interventions. We trained a polygenic risk score (PRS) for T2D based on & gt;1,200 genotyped variants in & gt;600,000 European consented research participants from a consumer genetic database who self-reported if they had been diagnosed with T2D. We tested the PRS' performance in separate sets of participants covering five different ancestries (African-American, East-Asian, European, Latino, and South-Asian; ~ 600,000 participants). The area under the receiver-operator curve of this PRS varied from 0.65 to 0.57, performing best in European and worst in African ancestries. The PRS was calibrated separately in each ancestry to account for differences in T2D prevalence. European participants with a PRS in the top 5% of the distribution have a T2D odds ratio of more than 3, and lifetime risk for this group exceeds 65%. Our PRS is strongly correlated with an independently derived T2D PRS from (Scott et al. 2017 GWAS, Spearman rho=0.44, p & lt; 1x10^-200) but is more predictive in our dataset (AUC 0.65 vs. 0.59). Lastly, we defined an "increased likelihood" result based on the PRS threshold at which risk of T2D from genetics alone exceeds the risk of T2D due to being overweight. In our database, 19% of individuals met this criterion. We find that personalized PRS' have the potential to identify large numbers of individuals with increased T2D susceptibility equal to or greater than known risk factors and could prove useful in evaluating T2D risk at individual as well as population levels. Disclosure M.L. Multhaup: Employee; Self; 23andMe. R. Kita: Employee; Self; 23andMe. N. Eriksson: None. S. Aslibekyan: Employee; Self; 23andMe. J. Shelton: None. R.I. Tennen: Employee; Self; 23andMe. E. Kim: Employee; Self; 23andMe, inc. B. Koelsch: Employee; Self; 23andMe.
Type of Medium:
Online Resource
ISSN:
0012-1797
,
1939-327X
Language:
English
Publisher:
American Diabetes Association
Publication Date:
2019
detail.hit.zdb_id:
1501252-9
