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	/:	Comparison of the Framingham 10 year risk score with a new algorithm (Jai Heart) for the estimation of coronary heart disease risk.
Sanjay K Kakkar MB BS MSc MPH,1 Jayashree Shanker PhD, 1 Mahesh Kumar Chopra1
1Thrombosis Research Institute, Bangalore, India 
Correspondence author: Dr Sanjay K Kakkar, Thrombosis Research Institute, Narayana Hrudayalaya, No 258.A, Bommasandra Industrial Area, Anekal Taluk, Bangalore 560 099, India; Tel: +91 (80) 2783 5303; E-mail: kakkar@btinternet.com. 
ABSTRACT
Background: The objective of this study was to compare Coronary Heart Disease (CHD) risk estimation using the Framingham score with a new algorithm (Jai Heart) in the Indian population. The study used a nested case control design conducted within the Indian Atherosclerosis Research Study (IARS), a case-control and ongoing prospective cohort study of 11,963 subjects investigating risk factors for CHD in the Indian population.  
Methods: 1034 cases with confirmed CHD and 1034 age and gender matched controls selected from the IARS population with the availability of conventional Framingham variables plus 9p21 allelic status (rs10757278), diabetes history, family history of premature CHD, anti-hypertensive treatment and body mass index. 10 year CHD risk calculated for each participant using both models. The main outcome measure was the odds of being a case of CHD given a Framingham or Jai Heart 10 year risk score e"20%.
Results: Mean Framingham 10 year score was 6.70 % for cases and 6.17% for controls (p=0.034). Mean Jai Heart 10 year risk score for cases was 23.08% and for controls 15.05% (p<0.001). Odds ratio for Framingham score was 1.22 (0.88-1.70, p=0.24). Odds ratio for Jai Heart score was 2.82 (2.35-3.40, p<0.001). Odds ratios for comparison of quartiles for Framingham scores were significant only for Q4 vs. Q1: 1.32 (1.03 � 1.69). Quartile comparisons for Jai Heart scores were significant: Q2 vs. Q1 1.83 (1.42-2.37), Q3 vs. Q1 2.84 (2.20-3.66) and Q4 vs. Q1 5.12 (3.93-6.66).
Conclusion: Jai Heart risk score is potentially superior to the Framingham risk score at identifying those at a high risk (e"20%) of CHD in this study population. 
KEYWORDS Coronary heart disease; primary prevention; risk assessment; genomics.


INTRODUCTION 
Coronary Heart Disease (CHD) is the world s leading cause of mortality [1] and morbidity [2]. Approximately three-quarters of the global burden of CHD is carried by developing countries [3,4]. India is estimated to have one of the highest prevalence and incidence rates of CHD in the world [5,6]. There are limited primary data documenting the epidemiology of CHD and its risk factors derived from the population in India [6,7].  At present the bulk of reliable primary data are derived from international case-control studies with the participation of Indian subjects [5,6,8]. The relationship between conventional risk factors (tobacco consumption, dyslipidaemia, high blood pressure, elevated plasma glucose and obesity) and the risk of CHD has been demonstrated in several case-control studies that include South Asian subjects from India, Pakistan, Bangladesh and Sri Lanka [5,8,9]. Screening and identifying at risk individuals and families and then managing the underlying specific risk factors is an important strategy to improve CHD outcomes in India [10].
However, there are few, if any, clinically validated total risk estimation algorithms developed specifically for the Indian population. Furthermore, conventional risk factors alone do not appear to account for the higher incidence, severity and earlier onset of CHD observed in Asian Indians [11-13]. The presence of a strong family history [14] and ethnicity [15] are additional important risk factors for CHD in this population. In recent years Genome Wide Association Studies (GWAS) have given rise to the observation that certain Single Nucleotide Polymorphisms (SNPs), in particular at chromosome locus 9p21.3, are strongly associated with the risk of CHD [16]. Importantly these findings have been replicated in studies from India and South Asia [17-19].   The Indian Atherosclerosis Research Study (IARS) is one the largest ongoing studies to be conducted on Asian Indians living in India with the objective of describing and analysing the predisposing molecular and environmental risk factors for CHD. The IARS cohort comprises 11,963 subjects which include CHD patients, their affected and unaffected family members and asymptomatic unrelated controls. The design of IARS has been described in detail elsewhere [20]. Using risk factor data from the IARS and several other published Indian and international studies and databases we have developed a new CHD risk estimation algorithm, Jai Heart, that incorporates age, gender, ethnicity, 9p21 allelic status, smoking status, diabetes, family history of premature CHD, anti-hypertensive treatment, body mass index, systolic blood pressure and ratio of total cholesterol to high-density lipoprotein level. The ability of the Jai Heart algorithm to estimate the risk of cases of CHD was tested against the Framingham algorithm using data from subjects enrolled in the IARS.  The results of this study will guide further research into the development and use of total risk estimation algorithms in India and the surrounding region.

MATERIALS AND METHODS 
Study Population
This was a case-control study of a cohort of 1034 cases and 1034 age and gender matched controls from the IARS database. Cases with CHD in the IARS were recruited from all regions of India with the majority coming from multispecialty hospitals in Bangalore, South India and from Mumbai, Western India. Cases were diagnosed on the presence of angiographically confirmed CHD with >70% stenosis in any one of the major epicardial arteries or >50% stenosis in two or more epicardial arteries, and/or having undergone percutaneous transluminal coronary angioplasty (PTCA) or bypass graft surgery (CBG). Control subjects were recruited from the community and clinically asymptomatic for CHD demonstrated by normal electrocardiogram readings. Detailed demographics, anthropometrics, vital parameters, personal and medical history were recorded for each participant. Presence of type 2 diabetes and hypertension was ascertained based on self-report of physician�s diagnosis and/or use of prescription medications along with a review of relevant medical records. In addition, each of the 2068 subjects included in this analysis had allelic status data available for the 9p21.3 SNP rs10757278. The IARS was approved by the ethics committee of the Thrombosis Research Institute. All participants provided informed signed consent to participate in the study. The IARS has been designed according to the guidelines of the Indian Council of Medical Research for undertaking research on human subjects and the study has been carried out according to the principles expressed in the Declaration of Helsinki.

Exposure and outcome measures
Using age, gender, total cholesterol, HDL-cholesterol, systolic blood pressure and smoking status, we generated the Framingham risk score [21] for each subject reported as a 10 year risk of CHD expressed as a percentage. 9p21 allelic status (homozygous or heterozygous for the risk allele or homozygous for the protective allele) age, gender, smoking status, diabetes, anti-hypertensive treatment, height (cm), weight (kg), systolic blood pressure, total serum cholesterol and high-density lipoprotein level for each subject were entered into the Jai Heart model. Ethnicity was entered as Indian for all subjects and, because verifiable family history data was not available, family history of premature CHD was entered as zero for all subjects. Using the Jai Heart algorithm we also generated a 10 year risk of CHD expressed as a percentage. 

Statistical analysis
Means values (+/-standard error of the mean (SEM)) for each of the inputs used in the Framingham and Jai Heart algorithms were calculated for cases and controls (baseline values). Mean Framingham and Jai Heart risk scores for cases and controls were also calculated. Student t-testing with a two sided p-values was used to compare means. 
The standard accepted 10 year CHD risk e"20% threshold was used to categorise exposure and the number of cases and controls above and below this threshold was counted. 2x2 contingency tables were generated for calculation of Odds Ratios with 95% confidence intervals and significance testing.   Cases and controls were divided into quartiles according to Framingham and Jai Heart 10-year risk scores in order to estimate the odds of CHD across the quartiles, using quartile 1 (Q1) as the reference group. Data were analyzed using SPSS Version 17 (SPSS Inc., Chicago, IL, USA).

RESULTS 
Baseline characteristics and risk scores
We did not observe any difference between the mean age and gender mix of cases and controls. We found that the frequency of rs10757278 risk alleles, smoking, body mass index (BMI) and presence of diabetes and hypertension were all significantly higher amongst cases as compared to controls (p<0.001 for all except smoking (p=0.025) and BMI (p=0.009)). Conversely systolic blood pressure, total serum cholesterol (TC), high density lipoprotein levels (HDL) and TC/HDL ratio were all lower in cases as compared to controls (p<0.001). We observed mean Framingham 10 year CHD risk scores were 6.70% in cases and 6.17% in controls (p=0.034).  The mean Jai Heart 10 year CHD risk score for cases was 23.08% and 15.05% in controls (p<0.001). The baseline characteristics and risk scores are shown in table 1. 
Table 1 Baseline characteristics of the study cohort 
VariablesCasesControlsp-valueAge (years)50.03�0.2550.17�0.26NSGender (males (%) /females (%))801 (77.5) / 233 (22.5)785 (75.9) /249 (24.1) NSDiabetes n (no (%) / yes (%))582 (56.3) / 452 (43.7)868 (83.9) / 166 (16.1)<0.001Hypertension n (no (%) / yes (%)) (%))477 (46.1)/ 557 (53.9)876 (84.7) / 158 (15.3)<0.001Smoking n (no (%) / yes (%))628 (60.7) / 406 (39.3)   802 (77.6) / 232 (22.4) / / <0.001BMI (kg/m2)25.97�0.1325.47�0.140.008Systolic blood pressure (mmHg)123.30�0.49126.16�0.59<0.001Total cholesterol (mg/dl)152.42�1.34176.71�1.21<0.001HDL-cholesterol (mg/dl)37.52�0.2740.30�0.32<0.001TC/HDL-c ratio (mg/dl)4.22�0.044.62�0.05<0.00110757278 n(%)
AA
AG
GG
161(15.6)
542 (52.4)
331 (32.0)

234 (22.6)
522 (50.5)
278 (26.9)
<0.001
<0.001
<0.001Framingham 10 year (%)6.706.170.034Jai Heart 10 year (%)	23.0815.05<0.001Mean values � SEM or numbers and (%)

Distribution of Framingham risk score and Jai Heart score amongst cases and controls 
Our analysis showed that 83 of the 1034 cases (8.0%) had a Framingham risk score e"20% whilst 69 of the 1034 controls (6.6%) had a Framingham risk score e"20%. Jai Heart scores were e"20% in 506 cases (48.9%) and in 262 controls (25.3%). The distribution of risk scores amongst cases and controls is shown in figure 1. 

Figure 1 Frequency of Jai Heart and Framingham scores e"20% and <20% in cases and controls 

Number of cases and controls shown in each risk category

Estimation of Odds of CHD using the Framingham and Jai Heart algorithms
Based on Chi-square analysis, we found that the odds of being a case given a Framingham score e"20% was 1.22 (0.88-1.70, p=0.24). However, we observed that the odds of being a case if the Jai Heart score was e"20% was substantially higher at 2.82 (2.35-3.40, p<0.001).   
As shown in Table 2, when distributing subjects into quartiles for each risk score, compared to Q1, only the top quartile (Q4) showed a significant Odds Ratio (OR) of 1.32 for CHD (OR 1.32, 95% CI 1.03 � 1.69, p=0.026) for the Framingham risk score. On the other hand, estimation of risk between the 1st quartile and the 2nd, 3rd and 4th quartiles yielded significant odds ratios for the Jai Heart 10-year score, with the highest OR being obtained when Q1 was compared to Q4 (OR 5.12, 95% CI 3.93 � 6.66, p<0.001).

Table 2: Association of risk of CHD according to Framingham and Jai Heart risk score by quartile 
Quartile comparisonOR (95% CI)p valueFramingham risk scoreQ2 versus Q11.17 (0.92-1.50)0.198Q3 versus Q11.25 (0.97-1.61)0.086Q4 versus Q11.32 (1.03 � 1.69)0.026Jai Heart risk scoreQ2 versus Q11.83 (1.42-2.37)<0.001Q3 versus Q12.84 (2.20-3.66)<0.001Q4 versus Q15.12 (3.93-6.66)<0.001Odds Ratio with 95% confidence intervals 
Q1 used as the reference quartile for each risk score

DISCUSSION
Several CHD risk assessment algorithms have been developed using epidemiological data from western populations and have been extensively validated in those groups. The commonly used tools include the Framingham risk score (Framingham) [21], the Joint British Societies Guidelines on Prevention of Cardiovascular Disease risk charts (JBS2)[22], QRISK [23], the  European Heart Score (SCORE) [24,25] and the World Health Organisation (WHO)/ International Society of Hypertension (ISH) charts [26]. The purpose of these tools is the easy, accurate and comprehensive identification of high risk individuals who can then be managed actively and motivated to prevent CHD [22,25-27].  A major strength of Framingham, JBS2 and SCORE is their use of prospective studies with standardised measurements. These risk estimation tools discriminate effectively and perform well in predicting events over a 10 year period having been, except for the WHO/ISH charts, validated both internally and externally in numerous studies [27]. One issue with the models mentioned above is that, with the exception of the WHO/ISH charts, the algorithms are based on data from epidemiological studies in western populations for which they are primarily designed. Whilst these scores have sometimes been adapted for ethnic populations living in the west they have not been developed using data from non-White populations living outside of Western countries. The WHO/ISH risk prediction charts have been developed by creating a hypothetical data set for each WHO sub-region based on risk factor prevalence and then relative risk for each sub-region has been calculated. 
The risk factors used in Framingham, namely age, gender, blood pressure, cholesterol levels and smoking are well described and extensively validated in the Indian population [5,8,9]. The results from our study are consistent with previous findings [28] and suggest that the Framingham risk score is not an effective risk estimation tool in a setting, such as India, where the vast majority of CHD events tend to occur at a younger age and where conventional (Framingham) risk factors alone do not fully explain disease. The limitations of Framingham in younger and non-Western populations have been described previously [27].
As far as we are aware, Jai Heart is the first CHD risk assessment algorithm that has been developed and tested using data from an Indian population living in India.  The results of our study suggest that the Jai Heart algorithm might be superior to the Framingham risk score at identifying those at a high risk (e"20%) of CHD in the Indian population. Further confirmation through prospective studies would provide a screening tool that enables a larger number of at risk individuals to benefit from preventive strategies and cardio-protective medication.
Adding a single 9p21 SNP to a risk score based on traditional risk factors has been shown to improve discrimination and risk prediction in intermediate risk categories which is the objective of modifying risk scores [29]. Jai Heart is the first risk prediction tool that attempts to incorporate a genetic marker validated in South Asians [17-19] with conventional risk factors.
 
Strengths and limitations
A strength of this study is the confirmation of cases and controls. Cases were clinically diagnosed as having CHD and were well matched by age and gender with controls that were verified as being free of disease. Another strength is the use of standardised measurements in this study. The size of study sample is an additional strength enabling odds calculations to be performed with a high degree of confidence. 
This is a case-control study and therefore does not assess the power of either algorithm to predict risk over a time course in those free of disease at baseline. We are planning a prospective study in order to test this hypothesis and determine the area under the receiver operator characteristic curve (AUROC) for the Jai Heart versus the Framingham algorithms. However, the current study does provide initial internal validation of Jai Heart algorithm for the setting used.    
Cases are treated and therefore blood pressure and cholesterol levels entered into the models are lower than controls and below the levels that would be observed in the clinical situation. However, this in itself is not a major limitation as it would result in an underestimation of strength of association of both Framingham and Jai Heart algorithms both of which incorporate blood pressure and cholesterol. Even so the effect on the Framingham model is likely to be greater as it uses fewer inputs than the Jai Heart model and has a greater reliance on blood pressure and cholesterol. This alone would not account for the magnitude of difference in performance observed between the two algorithms.

CONCLUSION
The Jai Heart algorithm uses a wide range of known risk factors, is ethnically adjusted and incorporates an extensively validated genetic marker. Therefore, it might be more relevant for Asian populations than existing risk assessment tools.  We have demonstrated in the IARS population that Jai Heart is potentially a better risk estimation model than the Framingham risk score. The current data suggests this tool can be used clinically and as a basis for the further development of risk prediction algorithms designed specifically for populations in South Asia and the surrounding region.

Acknowledgements We wish to thank Professor Vijay V Kakkar for his support and encouragement. In addition, we wish to thank Ankit Pal for his critical input to the project.

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23. Hippisley Cox J, Coupland C, Vinogradova Y, Robson J, May M, et al. (2007) Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ 335: 136.
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