Risk tools that combine clear-cut, questionnaire-based risk factors with contemporary polygenic risk scores compared to clinical risk scores for type 2 diabetes and coronary heart disease prevention show equivalent or better risk discernment, improved risk reclassification, and the possibility of being additionally improved with standard blood pressure and lipid measurements. These findings were published in Communications Biology.
With genome data expanding exponentially, genetic prediction algorithms focused on prevention of type 2 diabetes and coronary heart disease are being improved to calculate polygenic risk scores (PRSs). These scores combine the effects of multiple genetic markers to a single measure of genetic risk, but with limited application if not incorporated with other known risk factors. Genetic information is not being used by the current set of risk tools. “Identifying high-risk individuals using both genetic and non-genetic risk factors at an early stage for targeted preventative efforts could therefore have substantial benefits over the current prevention strategies for coronary heart disease (CHD) and type 2 diabetes (T2D),” the study authors wrote.
Leveraging the wealth of genomic and health data in disease prevention requires the right risk tools. For CHD and T2D, researchers sought to describe and evaluate genomic-enhanced risk tools. To accomplish this, they conducted a cross-biobank analysis utilizing longitudinal health and genomics data from the FinnGen study to develop the risk tools and validate them with data from the UK Biobank project. Questionnaire risk factors that included lifestyle, demographic, medication, and comorbidity data were combined with genome-wide PRSs, allowing researchers to make risk calculations.
Researchers constructed genome-wide PRSs for CHD and T2D and tested the association in FinnGen (N=309,154 individuals in Finland) with 33,628 cases of CHD and 44,266 cases of T2D, and in the UK Biobank (N = 343,672) with 18,698 cases of CHD and 24,192 cases of T2D, with better scores than previous studies. CHD model derivation inclusion criteria was met by 61,878 FinnGen participants, with 69,159 for T2D. Median follow-up time for CHD was 10.0 years (IQR, 7.8–10.0) and 10.0 years (IQR, 7.5–10.0) for T2D. Sex-specific risk tools were established for each disease: Baseline; Genomics-enhanced RIsk Tool (GRIT-CHD and GRIT-T2D combining easily surveyed risk factors with PRS); and, GRIT requiring systolic blood pressure and lipid measurements. Predictive performance of the PRSs and GRIT scores were estimated for 10-year incident disease in the UK Biobank.
Some study limitations were that the algorithms were optimized to predict 10-year risk of cardiovascular disease rather than CHD and rare genetic variants were not considered. Participants were also limited to middle-aged individuals of European ancestry, and investigators used the effect sizes from previous analyses of FinnGen subcohorts. There was also overlapping input weight samples.
Risk prediction for CHD and T2D is improved when PRSs are combined with risk factors easily obtained via questionnaire. GRIT-CHD and GRIT-T2D, which integrate risk factors obtained by questionnaires with PRSs, “have at least comparable performance to established risk scores recommended in respective clinical practice guidelines.” Researchers noted that, “…further enriching the GRIT scores with routine clinical measurements results in further performance improvements, improving prediction beyond established clinical risk scores.” Among individuals who need clinical risk assessment and targeted prevention, PRS combined with the questionnaire risk factors could help early risk detection.
Tamlander M, Mars N, Pirinen M; FinnGen, Widén E, Ripatti S. Integration of questionnaire-based risk factors improves polygenic risk scores for human coronary heart disease and type 2 diabetes. Commun Biol. Published online February 23, 2022. doi:10.1038/s42003-021-02996-0