|Year : 2021 | Volume
| Issue : 4 | Page : 133-138
Genotype-based prediction for cardiovascular disease risk using polymorphism in rs10757278 at 9p21 locus
Geetha Bhaktha Research Scientist-C 1, Shivananda Nayak Professor 2
1 Multidisciplinary Research Unit, Shimoga Institute of Medical Sciences, Shimoga, Karnataka, India
2 Department of Biochemistry, Subbaiah Institute of Medical Sciences, Shimoga, Karnataka, India
|Date of Submission||16-Jan-2021|
|Date of Decision||30-Mar-2021|
|Date of Acceptance||05-Jun-2021|
|Date of Web Publication||24-Dec-2021|
Dr. Geetha Bhaktha
Research Scientist-C, Multidisciplinary Research Unit, Shimoga Institute of Medical Sciences, Shimoga, Karnataka
Source of Support: None, Conflict of Interest: None
Background and Aims: Along with the conventional risk factors and based on the Framingham risk score, a preventive measure can be targeted in those subjects who are in risk category. The use of genotype-based assessment in these subjects can be much benefited in clinical decision-making. Hence, we aimed to match the risk frequency with genotype score for rs10757278 in asymptomatic coronary heart disease (CHD) individuals. Methods: This is a cross-sectional study with 105 participants. These subjects were without any clinical presentation of CHD. Single-nucleotide polymorphism 10757278 was genotyped using tetra-primer amplification refractory mutation system–polymerase chain reaction. Results: The minor allele frequency was 0.84 higher though the subjects were asymptomatic. When the group was categorized using Framingham risk score (low, moderate, and high), it was observed that the risk allele was 0.74 versus 0.77 versus 0.93. The risk allele frequency (male) in low, moderate, and high groups was 0.76 versus 0.79 versus 0.94. This incremental rise was lost in females with risk allele frequency to be 0.81 versus 0.76 versus 0.87. It is observed that the association between gender and risk status was significant (P < 0.001) both while considering risk wise and even after considering the risk allele. Conclusion: A good individual predicted risk can be assessed using global risk stratification along with the knowledge of the interaction of genetics. Further, to determine the accuracy and clinical utility of such reclassification, more prospective studies are needed.
Keywords: ANRIL, coronary heart disease, Framingham risk score, genetic risk score
|How to cite this article:|
Bhaktha G, Nayak S. Genotype-based prediction for cardiovascular disease risk using polymorphism in rs10757278 at 9p21 locus. J Clin Prev Cardiol 2021;10:133-8
|How to cite this URL:|
Bhaktha G, Nayak S. Genotype-based prediction for cardiovascular disease risk using polymorphism in rs10757278 at 9p21 locus. J Clin Prev Cardiol [serial online] 2021 [cited 2022 Jan 19];10:133-8. Available from: https://www.jcpconline.org/text.asp?2021/10/4/133/333703
| Introduction|| |
Cardiovascular disease (CVD) in the current scenario has become the greatest basis of morbidity and mortality in this world. The Indian population is the most seriously affected among the other ethnic races. An early prevention may decrease the rate of mortality among Indians. Recent studies have shown that CVD death and its effect in low-income countries has risen sharply. An estimate by Gupta et al. had reported that in India, nearly 25% of the death is because of CVD alone, and when the whole world is considered, it may rise to nearly 50% within few years. It is observed that a huge burden to the family is seen among the CVD suffers and these subjects are mostly in their early age of life. Hence, this vast loss in their productive age imposes disturbing consequence in the coming years. It was reported that the greatest health challenge to the developing countries was to control the status of noncommunicable disasters from the CVD. Its risk-associated conditions were seen to cause double death rates than the communicable diseases.
The onset of coronary heart disease (CHD) is not sudden. It has a long asymptomatic time period during which preventive interventions could prevent the developing of CHD. It now happens to be important to develop a useful program and an approach to lower the population burden due to CVD. An important step is the primary prevention of CVD in the early stage and easily quantifies the risk and provides an easy way to communicate it to the subject and his dependents. Various guidelines and education programs by an expert panel have been detected., Variety of risk scoring algorithm has been framed. Framingham risk score (FRS) is one such scoring system which is well used in different populations. The main objective of FRS is to identify the risk of developing CHD much early than it could actually happen. These scoring system are now been globally used to estimate the risk of developing the disease wherein the measurement of risk factors will be considered.
It is also seen that Framingham risk equation sometimes overestimates the incidence of CHD, hence the addition of genetic information to create a more added personalized effect and accurate risk evaluation is to be validated. To the extent that the published associations identify useful genetic risk factors, our approach may more accurately reflect the potential of current genetic markers to improve risk prediction on a population basis. Furthermore, Asian Indians are known to be more prone to CVD than Europeans along with higher levels of pro-inflammatory markers. A study by Tillin et al. has reported that the patterns of CHD differ among South Asian men from European counterparts. Genomic wide association study has shown that the genetic polymorphism in the genes is more prevalent among the Asian populations,, which may be the reason for the inherent susceptibility for the varying patterns of CHD.
Studies have implicated that variant in 9p21 has provided a comparative information about the implication of gene polymorphism with heart diseases., The locus encodes antisense noncoding RNA in the INK4 locus designated as ANRIL which, like other lncRNAs, regulates genome methylation via interacting with specific DNA sequences and proteins, thus affecting expression of multiple genes. It is observed that genetic variation can interact and modify an individual's susceptibility toward the heart disease and hence play an important role in evaluation of the cause. A new insight into the gene polymorphism was given by Bellary et al. This along with the associated risk factors would help in establishing an underlying link between the outcome and the mechanism. With the intention of identifying and employing an effective strategy for averting the Indians from the CVD, a good perspective of the associated risk factors and its implementation in calibrating the most fitting risk pattern which efficiently develops a new risk scoring algorithm to provide an accurate estimate of CVD risk was very much needed. Hence, identifying the traditional risk factors with the genotype in the asymptomatic CHD subject was the rationale of the study.
| Methods|| |
This was a cross-sectional study and comprised 105 participants with no clinical symptoms of CHD. The subjects were asked for any of these symptoms such as a feel of pressure or squeezing in the chest, with burning or tightness in the chest area, cold sweats, lightheadedness, nausea or a feeling of indigestion, dizziness, neck pain, shortness of breath with activity, having sleep disturbances, or fatigue. If these symptoms were not observed in the subjects, then they were eligible for enrollment in the study. The subjects who had any of these symptoms recurring were excluded from the study.
The objective and contents of this study were explained to the participants who were willing to volunteer the study. All the individuals volunteered their willingness to participate in the study by signing the written informed consent on the day of recruitment. Information about the subjects regarding various factors such as age, sex, smoking, medication for hypertension, medication for diabetes, and usage of any hypolipidemic drugs was collected on the same day. Hypertensive subjects were those who had systolic blood pressure ≥140 mmHg and diastolic blood pressure ≥90 mmHg at the time of measurement as per the standard procedure. Subjects were considered diabetic when they had over a year fasting blood glucose ≥126 mg/dl and postprandial blood glucose ≥200 mg/dl. The FRS has evolved as a validated means of expecting CVD risk in asymptomatic subjects. Categorization of the risk for a period of 10 years is if the risk score in percentage is <10, it is considered as a low-risk category. If the risk score is between 10 and 20, then they are grouped as a moderate-risk category. If the percentage of risk score is >20, then they are considered as a high-risk category. CVD 10-year risk was identified by CVD risk calculator – https://www.omnicalculator.com/health/cvd-risk. It takes into consideration of six factors such as age, gender, total cholesterol (TC), high-density lipoprotein (HDL) cholesterol, smoking habits, and systolic blood pressure. We calculated the FRS with the 9p21 information, and a genetic risk score value of 1, 0, and −1 was specified. The total absence of risk allele is depicted as “−1” as in the case of AA, “0” for the genotype AG, and for the genotype GG as “1.” It is observed that the allele G is considered as a risk allele. Hence, the value is assigned. Moreover, the number of subjects was reclassified after adding the score and risk was determined.
The study protocol was approved by the Institutional Ethics Committee, SIMS/IEC/312/2017-18.
Blood sample collection
Five milliliters of venous blood was collected in a plain vacutainer tube and 2 ml in an ethylenediaminetetraacetic acid-containing vacutainer tube from the study group with informed consent and was used for lipid profile and DNA isolation. The samples were collected in a nonfasting state. HDL cholesterol and TC concentration was determined by the procedure described in the reagent kits instruction manual (AGAPPE Diagnostics Ltd., India) using Mispa Nano biochemistry analyzer.
Genomic DNA isolation
HiPurA Blood Genomic DNA Mini Purification Kit from HiMedia PVT was used. This kit provides silica membrane-based DNA purification from fresh, old (more than 24 h), and frozen blood. Protocol involves blood cell lysis, which is achieved by incubation of whole blood in a solution containing chaotropic ions in the presence of proteinase K at 55°C. The lysate is prepared for initial binding of DNA to the spin column, and impurities such as proteins, polysaccharides, low-molecular-weight metabolites, and salts are removed by short washing steps. HiMedia's spin column format allows rapid processing of a multiple number of samples in various volumes. The columns have a high binding capacity, and high-quality DNA is obtained which is suitable for downstream processes. The concentration and purity of DNA was revealed using Eppendorf Microcuvette G1.0 BioSpectrometer basic. The DNA was aliquoted and stored at −20°C.
Single Nucleotide Polymorphism for rs10757278 was identified using tetra-primer amplification refractory mutation system PCR (Tetra-ARMS-PCR). Primers were ordered from Bioserve Pvt. Ltd. The primers used for TARMS-PCR are shown in [Table 1].
PCR reaction was performed using Taq 2× master mix (HiMedia Pvt. Ltd.) in a thermal cycler (Bio-Rad) with a total reaction volume of 50 μL. The cycling PCR protocol was composed of an initial denaturation at 95°C for 5 min, followed by 35 cycles of 95°C for 30 s, annealing temperature 60°C for 35 s and 72°C for 35 s, with a final extension of 72°C for 10 min and hold for 4°C for infinite.
The PCR products were run on 1.8% agarose gel to visualize the bands. The subjects with GG allele had two bands at 445, 238 and GA allele had three bands 445, 263, 238 and AA allele will have two bands at 445, 263 bp.
For analysis, the data were entered in an Excel sheet, and frequency in percentage was calculated based on the genotype.
| Results|| |
[Table 2] highlights the characteristics of the study participants. The mean age of the study participants was similar. 52% of the males and 7.5% of the females in the study population were currently smoking. In the study, nearly 49% among males and 60% among females were on medication for hypertension during their enrollment. Although the lipid profile of the study population was almost similar, 54% of the male participants and 13% of the female participants were on statin drugs that lower cholesterol. Furthermore, 62% among male participants and 83% among female participants were diabetic. The table also shows the classification of study subjects into groups of low, moderate, and high based on FRS category. A Chi-square test was performed to examine the relation between gender and risk status. It shows that the association between gender and risk status was significant (P < 0.001).
[Table 3] shows the genotype and allele frequency of the study subjects. The study population is in Hardy–Weinberg equilibrium. No statistically significant association was observed among the genotype (P = 0.16, Chi-square [df = 2] =3.64). It is obvious that risk allele G is higher in both genders. [Figure 1] shows the banding pattern of different genotypes.
|Figure 1: Agarose gel electrophoresis of tetra-primer amplification refractory mutation system–polymerase chain reaction product of rs10757278 gene polymorphism with ladder in the last lane|
Click here to view
[Table 4] shows the classification of study subjects into groups of low, moderate, and high. A Chi-square test was performed to examine the relation between gender and risk status. It shows that the association between gender and risk status after considering the risk allele and was significant, P < 0.001. High risk was prominent among males and moderate risk was evident among females.
|Table 4: Framingham risk score category after considering risk allele in males and females|
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The allele frequency of both genders is shown in [Table 5]. It is clear that G allele frequency showed a steady rise from low to high, whereas this steady rise in frequency was not seen among the females.
| Discussion|| |
Genetic risk variants are the most predictable risk stratifiers in the clinical application, particularly the variants that predispose to CHD. These genetic risk scores when used with the clinical risk score improve our ability to categorize individuals who are at risk. Further, by modifying the modifiable risk factor and by decreasing the exposure to such risk factor, incidents of various cardiac causalities can be decreased. This has been proven by several observational studies, and expectation in predicting the success in these intervention studies is good.,
An observation by Harismendy et al. in the rs10757278 was that G allele weakens the binding region for transcription factor STAT1, hence linking to differential expression of splice variants of ANRIL., This may lead to the development and succession of atherosclerosis.
Our study reported a minor allele (G) frequency of 0.84 and a major allele (A) frequency of 0.16. It was observed in studies from Bhanushali et al. that cases had a higher frequency of G allele than the A allele. As observed from a study by Abdulla et al., it was also seen that the high-risk allele frequency of 0.59 versus 0.45 was observed in cases and was higher than the controls, and several studies have supported this.,,,,, It is speculated that high-risk allele subjects present with heart disease in early stages of life.
As per the study by Helgadottir et al., the minor allele (G) has shown an association with myocardial infarction (MI) in subjects with early-onset MI in both cases and controls. The odds ratio relative to single-nucleotide polymorphism (SNP) rs10757278, in no-risk to high-risk individuals was 1.64 (confidence interval [CI]: 1.47–1.82), and for carriers of one-risk allele (A; G) individuals was 1.26 (CI: 1.16–1.36). Furthermore, homozygous for the risk allele was 1.64 times greater than the non risk group.
Since our study participants had no symptoms of CHD, the heterozygous for G allele was less in both genders than the homozygous for G allele. This mode of inheritance for this SNP was different in a multiethnic study done by Assimes et al. A study by Patel et al. has shown that the risk allele G is responsible for the severity and progression of the CHD, which may be true. In our study population, though they had no clinical symptoms of heart problem, probably they maybe a silent carrier.
It is a well known fact that probability of having a CVD is not same in both the genders and this is a matter of concern. In male gender, as the age advances, the risk profile of having CVD increases linearly.Although the disparity is mainly caused in the inherited gene and a good share of contribution is from the influence in the environment. Although the risk profile of CVD among men is seen to increase linearly over time. Attentiveness toward the CVDs will help to have a better prevention of cardiovascular events. It is established that the women during their reproductive age have a good amount of estrogen which has an advantageous effect on CHD.
We had used FRS score to classify the subjects into low-, moderate-, and high-risk categories. It is observed that the relation between gender and risk status was highly significant even after considering risk allele. When this risk scoring was applied, it was observed that a major share of our study population were in the high-risk group among the males and a major share among the females, 47.5%, were in the moderate-risk category. A shift of 14.31% increase from the low to moderate group was observed in male gender only with a reclassification of subjects on using genetic risk score; a similar shift was also seen in the Atherosclerosis Risk in Communities study.
It was observed looking into the genotype of the low, moderate, and high groups that there was a sequential increase in the frequency of risk allele (G = 0.74, 0.77, and 0.93), whereas this was not found in genotype of female gender. This makes it obvious that in this study population among the females, the allele frequency did not match the traditional risk factor along with FRS. Also there was no shift in the category as seen among the males.
It is noticed that both the genders in our population had a similar age. Maybe men at younger age tend to develop CHD than females, which may be reflected in shifting of 14.31% from the low to moderate group. Hence, we recommend that for risk prediction, gender-related factor needs to be defined, and using genetic risk score, more studies should be conducted.
| Conclusion|| |
We believe that the study was small in looking into the assessment of traditional risk factors with the genotype. rs10757278 has played an important role in risk classification of the subjects. Looking into the results, it seems that new way of individualized classification of risk is necessary wherein higher predicted risks for cases and lower predicted risks for noncases should be introduced. It should be focused in such a way that subjects at low risk need no further testing for risk assessment because any intensive interventions are not necessary in this stage, however candidates with high risk should be subjected for intensive treatment.
In subjects with intermediate-risk status, the appropriate test modalities defined by the clinician should be such that they prove helpful in selected patients in reverting back into the low-risk status. The genotype of our study subjects showed risk allele G to be higher. In the male population, the number of subjects in the high-risk group was higher than the females. Furthermore, risk allele frequency in the high-risk group was higher among the other categorized groups, but this was not observed in the female group. Hence, we conclude that the summative use of conventional risk factors along with the genetic score will be particularly useful to those individuals who are in a low predictive ability but actually at an increased risk of CHD.
Authors acknowledge the Multidisciplinary Research Unit, shimoga of Department of Health Research, New Delhi, India for providing the training for TARM technique.
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]