Figure 1. Overview of genetic epidemiologic study conducted by Yamada and colleagues. 【Click to enlarge】

A large-scale genetic epidemiological study to identify gene polymorphisms that may lead to cardiovascular disease was conducted by Yamada and colleagues of Mie University. The details of this study in 7,000 Japanese persons are shown in Figure 1. The goal of their research is the development of personalized prevention systems. This group has completed genome-wide association studies (GWAS) for myocardial infarction and stroke with 500,000 SNP chips.

Personalized prevention of myocardial infarction

Figure 2. The burden of myocardial infarction in Japan. 【Click to enlarge】

Figure 2 illustrates the current burden of myocardial infarction (MI) in Japan. Yamada stated that identification of markers of disease risk is a key step to develop potential interventions for disease prevention.

The GWAS for MI included 3,433 persons. The 1,328 persons with MI were 63 years old, 22% women and had a BMI of 24. Twenty-five percent were smokers, 72% had hypertension, 48% diabetes, and 57% hypercholesterolemia.

Of 300 candidate gene polymorphisms in gene association studies, 3 were significantly associated with MI (MTHFR, LPL, IPF1);  a total of 15 were associated with MI. Adjustment for age, sex, BMI, and the prevalence of smoking, hypertension, diabetes mellitus, and hypercholesterolemia revealed 10 SNPs were associated with MI, of which 4 were significantly associated (FABP2, IPF1, GP1BA, MTHFR).

The results of this multivariable analysis were used to determine the prediction probability, which is a reflection of the individual susceptibility to MI. Based on the risk level, 5 risk groups were defined. Of the control group, 75% had an average (8-36% probability) or less risk of MI, while about 85% of the MI patients had a relatively high (36-69% probability) or greater risk.

Figure 3. Simulation of personalized prevention of myocardial infarction. 【Click to enlarge】

Simulation of personalized prevention of MI is shown in Figure 3, illustrating a person at high risk for MI as a result of their traditional risk factors and presence of 14 polymorphisms for MI; this 70% probability could be reduced to 15% with appropriate lifestyle changes and medical treatment. This personalized prevention system was significantly validated through cross-validation, confirming its contribution to MI prevention.

Personalized prevention of stroke

Figure 4. The burden of stroke in Japan. 【Click to enlarge】

The burden of ischemic stroke in Japan is described in Figure 4. Yamada and colleagues studied 300 candidate gene polymorphisms in 2,892 Japanese subjects. The 822 ischemic stroke subjects were 68 years old, 41% women, with a BMI of 23, and their medical history included smoking (current and former) in 24%, hypertension 69%, diabetes 37%, hypercholesterolemia in 33%.

Seven SNPs were associated with atherothrombotic cerebral infarction (ACI), of which 3 were significantly related (ABCA1, IL6, IRAK1). Adjustment for age, sex, BMI, and the prevalence of smoking, hypertension, diabetes mellitus, and hypercholesterolemia showed 6 SNPs were associated with ACI, 1 of which (IL6) was significantly related to ACI.

Figure 5. Simulation of personalized prevention of stroke. 【Click to enlarge】

Five risk groups were then defined based on the prediction probability, showing that persons with ACI have a higher risk for stroke compared to controls. A simulation for personalized prevention of ACI showed that the risk of 43% as a result of the traditional risk factors and genetic factors could be reduced to 8% with lifestyle changes and medical treatment over time (Figure 5). This was significantly validated in the cross-validation testing.

Personalized prevention system

Figure 6. Overview of personalized prevention system for myocardial infarction and stroke. 【Click to enlarge】

Figure 6 illustrates a personalized prevention system for MI and stroke, incorporating data from both traditional laboratory examinations and genetic analysis to determine a comprehensive risk prediction. This system will also predict reductions in risk with treatment of individual risk factors. In the future, it will be possible to address specific genetic polymorphisms, for example, taking a small dose of aspirin if the GP1BA polymorphism is present.