ArticleIndian heart journal2018
Utility of anthropometric traits and indices in predicting the risk of coronary artery disease in the adult men of southern Andhra Pradesh.
Article in Indian heart journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
aimOptimal cutoff values are influenced by ethnicity, geography, lifestyles, and physical activity, and hence, there is a need for establishing population- and disease-specific cutoff values to screen individuals/populations. Therefore, the present study was carried out to determine the optimal cutoff values of anthropometric variables for coronary artery disease (CAD) for the population of southern Andhra Pradesh.
methodsOne hundred sixty five patients with CAD and 87 controls were recruited, and 52 anthropometric variables were measured for them.
resultsHigher means in 22 anthropometric variables covering circumferences, skinfold thickness (sft), and indices were observed in patients than those in controls. Receiver operator curve analysis revealed that 18 variables including circumference, sft, and fat measures with an area under curve ranging from 0.61 to 0.72 were found to have the ability of predicting the risk of CAD. A stepwise discriminant analysis showed 9 variables to correctly classify 87.4% of subjects into CAD and controls. In logistic regression analysis, among these 9 variables, only circumferences of abdomen and foot; sft of supratellar, thigh and calf; and sum of subscapular/suprailiac, waist-hip ratio and lean body mass were associated with CAD and explained 73.4% of its variation.
conclusionsEighteen anthropometric variables were found to have the ability of predicting the risk of CAD. Longitudinal studies are needed to confirm the use of anthropometric variables in predicting the risk of CAD.
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