Discussion

In the present longitudinal study of 10 612 participants, we investigated 10 MGRS for association with CHD and established cardiovascular risk factors. Three scores were further investigated in terms of clinical use (ie, the ability to discriminate between individuals with and without the disease and to correctly classify them in clinical categories of risk). We found that both a large comprehensive genetic score and a CHD-specific score were able to significantly improve the correct classification of study participants. In addition, we showed that a polygene MGRS, developed in an external data set, including all SNPs up to a nonsignificant P value threshold, did not outperform a literature-based score. Finally, we found that the genetic risk score for HDL-cholesterol was associated both with BMI and TC, potentially indicating pleiotropic effects of HDL-associated loci. The association between the CHD-specific MGRS and the 10-year predicted risk calculated from established risk factors (FHS) was not significant. Indeed, only 14 of 44 CHD-specific loci have been observed in a previous large study² to be associated with lipid-related traits, type 2 diabetes mellitus, or blood pressure. Similarly, in an older study,¹⁵ only 1 of 14 CHD-associated SNPs was associated with a wide range of established risk factors and novel biomarkers. These previous results, together with our findings, support the hypothesis that most of the CHD loci are not involved in pathways perturbing currently known risk factors, making a genetic score of CHD-specific loci a good candidate for future improvements of current prediction algorithms, and CHD loci an interesting starting point for further studies into the pathophysiology of atherosclerosis.

Our results indicate that use of genetic profiling, including SNPs associated with intermediate traits, might improve allocation of patients to correct risk strata. Although we calculated prediction measures in a relatively small- and high-risk subsample of the study, in which we had long enough follow-up time (n=3014), the NRI (4.9%; P=0.01) was significant. This proportion of correct reclassification is clinically relevant, and is larger than that observed for HDL-cholesterol in our study (NRI, 2.7%) and for C-reactive protein and fibrinogen in a recent meta-analysis (NRI, 1.52% and 0.83%, respectively).¹⁶ However, caution should be exercised when comparing estimates from a large meta-analysis with that observed in a single study. In a hypothetical population similar to that investigated in this study, the routine use of genetic profiling in primary prevention of CHD among individuals at intermediate risk could prevent 1 event for every 300 to 400 people screened. If the risk assessment with genetic profiling is performed on the entire population, and not only on individual at intermediate risk, 3.15 [−1.30; 7.60] years free of CHD events could be saved per 1000 people undergoing the risk assessment. Clinicians and health policy makers should evaluate whether these benefits outbalance the costs of implementing such testing in clinical practice.

Genetic profiling has some advantages compared with other biomarkers that are routinely used in clinical practice. For example, genetic information remains stable throughout life and is, therefore, not sensitive to regression dilution bias. Hence, although the strength of the genotype–phenotype associations has been suggested to be modified by age,¹⁷ genetic markers are likely to be predictive throughout life. This might allow risk prediction to be performed much earlier in life, to allow for earlier primary prevention in high-risk individuals. However, to investigate this aspect, study populations with younger participants and longer follow-up are needed than what was available for the present study. Another potential advantage is that no invasive blood sampling is needed, because DNA is routinely extracted from saliva. However, several important obstacles remain to be considered before genetic profiling can be introduced in routine healthcare. These include demonstration of usefulness and cost-effectiveness for risk prediction in several independent samples and the fact that genetic markers, in contrast to modifiable risk markers, are not useful for assessing efficacy of treatment or other risk-reduction strategies.

The alternative scoring approach (polygene approach), which includes all SNPs associated with the outcome in an external population up to a certain probability value, has shown promising results for other traits and has been proposed to increase the variance explained for common complex diseases.¹⁸ For the first time, we applied this approach to CHD. In our study, this score was not associated with CHD, probably because of the relatively small data set used for generating the score, resulting in lower accuracy of the weights. Theoretical frameworks have predicted polygenic scores to substantially increase the discriminatory ability of the model, with sample size larger than those currently available from meta-analysis consortia.^19,20 Therefore, future studies using larger development data sets are needed to understand whether a large number of SNPs with small effect size not exceeding the genome-wide threshold could be able to improve the clinical use beyond established genetic variants. The polygene score and the overall MGRS embrace a similar approach; they include a large number of SNPs, both true signals and noise, trying to capture the polygene architecture of CHD. The SNP selection strategy for these 2 scores was, however, different. The polygene score uses a probability value threshold for SNP selection and, therefore, the predictive power is much dependent on the sample size of the study where the selection was made. In contrast, the overall MGRS uses a priori information from the literature and, therefore, the prediction ability is determined by the quality of the literature selection. The overall MGRS was significantly associated with CHD, even after the exclusion of CHD-specific SNPs, indicating that a selection strategy based on literature-based information can be useful to improve the prediction ability of genetic risk scores. Moreover, we performed 2 sensitivity analyses to investigate whether the inclusion of a large number of SNPs in the overall MGRS might have caused a spurious association with CHD. First, we plotted the association results across the 6 investigated studies to address consistency (Figure V in the online-only Supplement). Second, we created 100 random overall MGRS and studied their association with CHD in TwinGene (Figure VI in the online-only Supplement). The association was consistent across studies and almost identical in the 2 larger studies. The average association of the 100 random scores was significantly lower (P=0.01) than the associations observed in the original score. These analyses suggest that the association between the overall MGRS and CHD is unlikely to be explained by chance.

Previous studies did not find a significant association between an overall MGRS and a CHD.^21,22 A smaller number of traits included in the score, different outcomes definitions, and heterogeneity between study populations might explain such differences. In addition, strengths of our study include the large number of incident CHD events investigated, as well as the up-to-date literature-based MGRS, including genetic variants from the most recent studies. Moreover, we used information from an external study population to assign weights in our scoring. This is in contrast to previous studies that have used association coefficients reported in the literature⁸; an approach that biases the score attributable to the winner’s curse. We also acknowledge several limitations to our study. We do not have any information on family history. However, this information is not included in the FHS risk score, and a previous report indicates that inclusion of family history does not influence the magnitude of the association between a CHD-specific MGRS and CHD.^8,21 Moreover, although family history remains an important contributing factor to risk prediction, it has recently been shown that genetic scores can substantially increase the discriminatory abilities of the model above family history alone.²⁰ However, larger sample sizes for deriving these scores are needed.

Furthermore, our study was undertaken in Swedish middle-aged to elderly individuals; hence, the generalizability to other ethnicities or age groups is unknown. We expect early onset CHD events to be more influenced by genetic factors compared with late-onset CHD. Therefore, the predictive ability of a genetic score in younger individuals is likely to be higher.²³ Finally, we only included myocardial infarction and unstable angina in our outcome, but not stable angina, because the validity of this diagnosis in Swedish National Patient Register is unknown. Differences in the definition of the cardiovascular outcomes across studies might limit the comparability and generalizability of the results.

In conclusion, using data from 6 Swedish prospective cohort studies with 10 612 healthy participants from the community, we have investigated the clinical use of genetic scores in primary prevention of cardiovascular diseases. Current efforts to discover additional genetic loci associated with CHD and related traits, as well as the use of sequencing approaches along with integration of other omics technologies, are likely to further improve the performance of existing predictive equations. Our results indicate that genetic information could be of some clinical value for prediction of CHD, although further studies are needed to address aspects such as feasibility, ethics, and cost efficiency of genetic profiling in the primary prevention setting.