- Eurasian Journal of Anthropology
- Volume:7 Issue:2
- Assessment of cardiovascular risk factors among Sunni Muslims of Delhi, India
Assessment of cardiovascular risk factors among Sunni Muslims of Delhi, India
Authors : Astha BANSAL, Pc JOSHİ
Pages : 37-48
View : 17 | Download : 12
Publication Date : 2016-12-31
Article Type : Research Paper
Abstract :Cardiovascular diseases insert ignore into journalissuearticles values(CVD); are a prominent cause of mortality and morbidity in India. Environment and culture play a significant role in development of CVD in an individual. Since population base data on CVD risk factor is scarce, Sunni Muslims Mendelian population of Delhi was studied for identifying CVD risk factors using principal component analysis. The study includes 17 inter-correlated CVD risk variables i.e. anthropometric, physiological, and biochemical markers. A cross-sectional study was undertaken in urban Delhi, through conducting household survey. A total of 406 Sunni Muslims insert ignore into journalissuearticles values(125 males and 281 females); between 35-65 years of age were included in the study. The data was analysed using principal component factor analysis insert ignore into journalissuearticles values(PCFA);. The PCFA extracted seven factors which explained nearly 81.17% and 80.06% of total variance of 17 quantitative traits among females and male, respectively. A cumulative risk scale was developed from the factor scores. Waist-height ratio insert ignore into journalissuearticles values(WHtR); showed strongest correlation for high cumulative risk insert ignore into journalissuearticles values(OR = 3.402; CI 95% = 1.693-6.834); among females, while among males, Waist-hip ratio insert ignore into journalissuearticles values(OR = 3.039; CI 95% = 1.029-8.974); showed strongest correlation for high cumulative risk. The findings of the present study add depth to the limited amount of literature on PCFA of cardiovascular risk in Indian ethnic population.Keywords : cardiovascular disease, Sunni Muslims, principal component factor analysis