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. 2017 Mar;19(3):322-329.
doi: 10.1038/gim.2016.103. Epub 2016 Aug 11.

Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores

Kristi Läll  1   2 , Reedik Mägi  1 , Andrew Morris  1   3   4 , Andres Metspalu  1   5 , Krista Fischer  1
Affiliations

Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores

Kristi Läll et al. Genet Med. 2017 Mar.

Abstract

Purpose: Using effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with type 2 diabetes (T2D) status in a population-based cohort and investigated its potential for prospective T2D risk assessment.

Methods: By varying the number of single-nucleotide polymorphisms (SNPs) and their respective weights, alternative versions of GRS can be computed. They were tested in 1,181 T2D cases and 9,092 controls of the Estonian Biobank cohort. The best-fitting GRS was chosen for the subsequent analysis of incident T2D (386 cases).

Results: The best fit was provided by a novel doubly weighted GRS that captures the effect of 1,000 SNPs. The hazard for incident T2D was 3.45 times (95% CI: 2.31-5.17) higher in the highest GRS quintile compared with the lowest quintile, after adjusting for body mass index and other known predictors. Adding GRS to the prediction model for 5-year T2D risk resulted in continuous net reclassification improvement of 0.324 (95% CI: 0.211-0.444). In addition, a significant effect of the GRS on all-cause and cardiovascular mortality was observed.

Conclusion: The proposed GRS would improve the accuracy of T2D risk prediction when added to the currently used set of predictors.Genet Med 19 3, 322-329.

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Figures

Figure 1
Figure 1
Bar charts illustrating the association between the dGRS1000 quintile, BMI category and prevalent T2D status. (a) T2D prevalence in genotyped individuals aged 45–79 in the Estonian Biobank cohort by dGRS1000 quintile and BMI category. The y-axis is scaled to match the average T2D prevalence by BMI category in the entire Estonian Biobank cohort (23,538 individuals aged 45–79, including 1,936 cases of prevalent T2D). The total number of T2D cases in each BMI-dGRS1000 category is shown on the top of each bar. (b) Distribution of dGRS1000 in all 1,181 genotyped individuals with prevalent T2D. Distribution of dGRS1000 is shown among all prevalent T2D cases, with dark blue color indicating individuals with BMI >35. (c) Distribution of dGRS1000 in severely obese T2D-free individuals aged 60 and older. Distribution of dGRS1000 is shown among T2D-free individuals who have BMI >35 and are older than 60.
Figure 2
Figure 2
Cumulative incidence of type 2 diabetes in 4,881 genotyped individuals free of T2D aged 35–79 and with BMI >23 at baseline. In the figure, 6.25-year follow-up is presented because only 25% of individuals were followed for more than 6.25 years. Cumulative incidence in presented separately in three dGRS1000 categories.
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