The role of traditional obesity parameters in predicting the number of stenosed coronary arteries (≥ 60%) among patients undergoing cardiac catheterization.
Sci Rep 2022;
12:13830. [PMID:
35970873 PMCID:
PMC9378653 DOI:
10.1038/s41598-022-17517-0]
[Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 07/26/2022] [Indexed: 12/12/2022] Open
Abstract
The correlation between obesity and coronary artery disease (CAD) has been well-documented in the literature. Body mass index, waist–height ratio, waist–hip ratio, body adiposity index, body shape index, waist circumference, and hip circumference are traditional obesity parameters used to measure obesity. This study aimed to investigate the role of these traditional obesity parameters in the prediction of the number of stenosed coronary arteries (≥ 60%) among patients undergoing cardiac catheterization. A descriptive cross-sectional study was conducted among 220 hospitalized patients undergoing cardiac catheterization in two hospitals in Jordan. Bivariate Pearson’s correlation and forward linear regression analysis were used in the current study. Hip circumference was identified as being the best predictor of CAD (r = 0.5), with the best cut-off value of 103 cm (sensitivity = 0.92, specificity = 0.58). Hip circumference had significant regression levels with the number of stented coronary arteries (P = 0.002) and the number of severe stenosed coronary arteries (P = 0.04). The second-best obesity parameter in predicting CAD was waist circumference (r = 0.4), with a cut-off value of 0.95 m (sensitivity = 0.76, specificity = 0.68). High-sensitivity C-reactive protein (HS-CRP), triglycerides, and smoking had significant positive correlations with the number of stented coronary arteries (P < 0.05). Hip circumference of ≥ 103 cm, increased serum level of triglycerides, HS-CRP, and being a smoker are all factors which can predict CAD or the risk of developing it.
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