Optimal cut-off values of anthropometric markers to predict hypertension in North Indian population
Shilpi Gupta1, Satwanti Kapoor
1Department of Anthropology, Obesity Research Unit, Physiological Anthropology Laboratory, University of Delhi, New Delhi, India.
Abstract:
The aim of the study was to determine the cut-offs of anthropometric markers for detecting hypertension in an endogamous North Indian population. A cross-sectional study was carried out to collect data from 578 adult Aggarwal Baniya subjects (271 men and 307 women, mean age: 43.4 ± 5.3 and 38.7 ± 4.9 respectively) using multistage, stratified sampling method. Individual body weight, height, waist circumference (WC), hip circumference, blood pressure were assessed. Receiver operating characteristic (ROC) analysis was used to find out the optimal cut-off values of various anthropometric markers to predict hypertension. The likelihood ratios for having hypertension in subjects with various cut-off values were calculated. Logistic regression analysis was used to examine the independent relationship between the anthropometric markers and odds of having hypertension. The BMI cut-off to predict hypertension was 22.8 kg/m(2) in men and 28.8 kg/m(2) in women. The optimal WC cut-offs varied from 91-92 cm in both men and women. The WHR cut-off was about 0.90 in men and 0.78 in women respectively, and the optimal WHtR cut-off was 0.56 in men and 0.43 in women. The cut-off levels for BMI, WC and WHtR corresponded to the inflexion points in the likelihood ratio graphs. The area under curve (AUC) and odds ratios showed that the risk of having hypertension was highest with respect to increased BMI and that BMI is the best predictor of having hypertension. The cut-off points for detecting cardiovascular risk factors among our population are lower than the criteria by the World Health Organization. Although these results may not be readily applied to the rest of the Indian populations due to the multiethnic composition, they point to the necessity of similar studies with large randomized samples to find the cut-off levels for chronic conditions in different populations.
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