III. Chapter 2. Cardiomics
3.3 Results and Discussion
3.3.3 PRS ascertainment to distinguish patients and predict subsequent cardiovascular events
13
cardiovascular events
14
To utilize the genome data produced herein, the polygenic risk score for coronary artery disease was 15
obteained herein among individuals owing to the cumulative effect of all variants among individuals.
16
Accordingly, genetic effects were ascertained from a meta-analysis of the coronary artery disease 17
studies.81, 82 The distribution of PRS was significantly higher in the patients than in the controls (average 18
PRS: 0.40 and -0.17 for the patients and controls, respectively; P < 0.001; Figure 5). The odds ratio of 1
the PRS of the patients compared with that of the controls was 1.83 (95 % CI: 1.69–1.99; P < 0.001).
2
The proportion of individuals showing a high PRS was significantly greater in patients (58 of 265;
3
21.9 %) than in the controls (32 of 636; 5.0 %; P < 0.001). There was a significant negative correlation 4
between the PRS and age (age-at-event) in the patients (Spearman’s rho coefficient = -0.14; P = 0.025).
5
However, there was no significant correlation between the PRS and age in the controls.
6 7
8
Figure 5 The Distribution of PRS Between Early-onset AMIs and Controls 9
10
The AUC of the PRS was 0.65 (95 % CI: 0.61–0.69) when the classification accuracy of 11
conventional risk factors and the additive contribution of the PRS in discrimination of patients and 12
controls, was assessed (Figure 6). Among the conventional risk factors, current smoking showed the 13
highest AUC of 0.80 (95 % CI: 0.77–0.83) compared to other factors such as hypercholesterolemia 14
(AUC of 0.70; 95 % CI: 0.68–0.73), body mass index (AUC of 0.64; 95 % CI: 0.60–0.68), hypertension 15
(AUC of 0.58; 95 % CI: 0.55–0.61), family history of CAD (AUC of 0.57; 95 % CI: 0.54–0.59), and 16
diabetes mellitus (AUC of 0.55; 95 % CI: 0.52–0.57). The AUC of the model including current smoking 17
and PRS was 0.85 (95 % CI: 0.82–0.88) and the contribution of PRS to current smoking was significant 18
(P = 0.016). The AUC of the model including all six conventional risk factors was 0.91 (95 % CI: 0.89–
19
0.93) and that of the model including the six conventional risk factors and PRS was 0.92 (95 % CI:
20
0.90–0.94). Further, the contribution of the PRS to the six conventional risk factors was significant (P 21
= 0.015) as well. The AUC of the model including five conventional risk factors (excluding smoking) 1
was 0.80 (95 % CI: 0.76–0.83) and that of the model including these five conventional risk factors and 2
PRS was 0.90 (95 % CI: 0.88–0.92). The contribution of PRS to the five conventional risk factors was 3
significant (P < 0.001).
4 5
6
Figure 6 Receiver Operator Characteristic Curve and Area Under the Curve of Conventional 7
Risk Factors and Combined Models for the Classification of Early-onset AMI 8
PRS indicates the polygenic risk score; AUC, Area under the curve; conventional risk factors, combined 1
model including current smoking, hypercholesterolemia, body mass index, hypertension, family history 2
of CAD and diabetes mellitus; family history of CAD, 1st degree family history of coronary artery 3
disease.
4 5
Next, the classification accuracy of PRS between the younger participants (25 < age ≤ 45 years;
6
130 patients and 248 controls) and the older participants (45 < age ≤ 50 years; 134 patients and 72 7
controls) was compared (Figure 7). The AUC of the PRS was significantly higher in the younger group 8
(AUC of 0.69; 95 % CI: 0.63–0.75) than in the older group (AUC of 0.58; 95 % CI: 0.50–0.66; P = 9
0.029). Combining the PRS with the conventional risk factors increased the classification accuracy in 10
both the younger (AUC of conventional factors = 0.92; 95 % CI: 0.90– 0.95; AUC of conventional 11
factors and PRS = 0.94, 95 % CI: 0.91–0.96; P = 0.038) and older group (AUC of conventional factors 12
= 0.90, 95 % CI: 0.86–0.95; AUC of conventional factors and PRS = 0.91, 95 % CI: 0.86– 0.95; P = 13
0.423). The additional increase in discrimination was significant only in the younger group (Figure 8).
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1
Figure 7 Receiver Operator Characteristic Curve and Area Under the Curve of Polygenic Risk 2
Score for the Classification of Early-onset AMI Stratified by Age 3
4
1
Figure 8 The Age Dependent Contribution of Polygenic Risk Score for Patient Classification 2
3
The AUC of the current smoking status was higher than that of the other predictors. Our patient 4
cohort contained a higher proportion of current smokers (patients: 72.7 %, controls: 12.9 %) compared 5
with a previous study on early-onset AMI (patients: 51 %, controls: 12 %).26 This difference in the 6
proportions of smokers could be due to the difference in the proportion of males among the patients 7
groups between the previous (34 %) and present studies (95.1 %). Moreover, the proportion of the 8
current smokers among both the male patients (75.1 %) and controls (22.6 %) in the present study was 9
not similar to the reported proportion of smokers among the Korean males (40–50%).90 This low 10
proportion of current smokers in the control group might have resulted in our analyses. Another 11
sampling bias could be in the way the controls were recruited; that is, healthy volunteers who were 12
probably more interested in maintaining good health would have been more suitable for such a study.
13
This recruitment bias could have caused the underestimation of the PRS accuracy. Hence, the high AUC 14
for the current smoking status in the present study might be biased and the classification accuracy based 15
on the current smoking status may be lower in actual practice.
16
Therefore, after considering the cost and time for sequencing, a PRS measurement can be 17
performed when the conventional risk factors cannot be determined. Identifying the genetic risk at birth 18
or in early life could be the most cost-effective option. The negative correlation between age-at-event 1
and the PRS of the patient group and the higher discrimination accuracy of the PRS in the younger 2
group than in the older group indicates an age-dependence on the weight of the genetic effects, at least 3
for early-onset AMI among Koreans. This implies that the contribution of the PRS to the conventional 4
model can be improved by age-adjustment of the effect weight; thus, as one becomes older, the early- 5
assessed PRS can be combined with the periodically measured conventional risk factors to identify 6
individuals with different AMI risk trajectories and predict the early events of AMI. Hence, this 7
prediction of risk trajectory with an age-adjusted PRS weighing model may be beneficial, especially for 8
the young individuals, because the effects of the genetic risk could be relieved by adhering to a healthy 9
lifestyle as early as possible.91, 92 Coronary atherosclerosis exists in the young and asymptomatic people;
10
9 as the number of cardiovascular risk factors increases, so does the severity of asymptomatic coronary 11
atherosclerosis in young people.10 Thus, PRS can serve as a guide for achieving primary prevention by 12
identifying the risk of CAD in young people before any cardiovascular risk factors are noticed. This 13
would facilitate early risk modifications, prevention, and reduction in the prevalence of asymptomatic 14
coronary atherosclerosis.
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Figure 9 Cumulative Event of Repeat Revascularization between Upper 50% of PRS Group and 18
Lower 50% of PRS Group 19
1
Next, the classification power of PRS for predicting a subsequent cardiovascular event after 2
PCI was assessed; a significant cumulative event was identified for repeat revascularization. The 3
cumulative event of death and AMI were not significant, possibly because of a small sample size and 4
small number of events, respectively (death: n = 5, P = 0.944; AMI: n = 4, P = 0.957). The upper 50 % 5
of the PRS group (patients with normalized PRS among patients ≥0) showed a significantly higher 6
frequency of repeat revascularization (hazard ratio = 2.19; 95 % CI: 1.47–3.36; P = 0.049; Figure 8).
7
The PRS was significantly and independently associated with repeat revascularization after PCI in both 8
univariable and multivariable analyses conducted with all the conventional risk factors (Table 5).
9
Meanwhile, the association of conventional risk factors with repeat revascularization after PCI, was 10
insignificant.
11 12
Table 4 Predictive power of conventional risk factors and PRS for repeat revascularization after 13
14 PCI
Predictors
Univariable analysis Multivariable analysis
Hazard ratio
95% CI P-value
Hazard ratio
95% CI P-value
Body mass index 0.96 0.88 - 1.04 0.314 0.92 0.83 - 1.03 0.135
Hypertension 0.74 0.32 - 1.75 0.495 0.88 0.36 - 2.18 0.787
Current smoking 0.67 0.31 - 1.45 0.313 0.58 0.26 - 1.31 0.191
Diabetes mellitus 1.44 0.59 - 3.55 0.424 1.41 0.54 - 3.69 0.478
Hypercholesterolemia 3.33 0.45 - 24.5 0.238 3.56 0.43 - 29.40 0.238
Family history of CAD
0.77 0.27 - 2.2 0.621 0.63 0.21 - 1.91 0.411
Polygenic risk score 1.64 1.12 - 2.38 0.010 1.65 1.11 - 2.46 0.014
1
Thus, PRS may be a practical measure for guiding secondary prevention strategies and aid in 2
suggesting a closer follow-up with optimal medical treatments in the high PRS group. For example, 3
after PCI, a clinician may recommend patients with a high PRS to visit the hospital more frequently 4
than those with a low PRS.
5
The classification accuracy and the additive contribution of PRS to the conventional risk 6
factors modestly improved the prediction; this is in accordance with a previous PRS study.72 However, 7
this PRS performance could have been affected by various factors such as the population, ethnicity, 8
disease type, and biological pathway specificity.93-97 Therefore, improved applicability may be expected 9
if the PRS is fine-tuned for these factors. Further, major study limitations include the small cohort size, 10
biased sex proportion (number of women = 13; 4.9 %), and the small number of clinical events such as 11
death or recurrent acute myocardial infarction during patient follow-up. Hence, further studies with 12
more diverse and larger sample size and a longer follow-up period would potentially yield single omics 13
markers, which the present study could not provide, and optimize the current PRS system for early- 14
onset AMI for the Korean population.
15 16 17