Newer v/s Classical Anthropometric Indices as a Screening Tool for Dyslipidemia in Healthy Young Adults

• Main Authors: Nikhil Mathur, Srinidhi Rai, Neveditha Sujith, Orru Gayatri
• Format: Article
• Language: English
• Published: JCDR Research and Publications Private Limited 2019-04-01
• Series: Journal of Clinical and Diagnostic Research
• Subjects: body mass index; body roundness index; body surface index; waist circumference
• Online Access: https://jcdr.net/articles/PDF/12799/40827_CE[Ra1]_F(SL)_PF1(BT_AC)_PN(SL).pdf

Introduction: The association of obesity with higher rates of dyslipidemia and cardiovascular diseases has been well documented. The most commonly used classical anthropometric indices do not provide an accurate distinction between adipose tissue and lean body mass and therefore are an unreliable indicator of obesity. Therefore, newer Anthropometric indices such A Body Shape Index (ABSI) and Body Roundness Index (BRI) were assessed and their co-relation with plasma lipid levels was determined to predict future at-risk population for dyslipidemia. Aim: To compare classical and newer anthropometric indices in their ability in predicting dyslipidemia. Materials and Methods: The cross-sectional study was conducted on 100 subjects (aged 18-35 years). The lipid profile (total cholesterol, triglycerides, high density lipoprotein, low density lipoprotein and very low density lipoprotein) was measured by enzymatic colorimetric assay. Classical anthropometric indices: Body Mass Index (BMI), Waist Circumference (WC), Waist-Hip Ratio (WHR) and Waist-Height Ratio (WHt.R) and newer anthropometric indices: ABSI and BRI were measured. Normality of the data was assessed using Kolmogorov-smirnov test. Correlation of lipid parameters with various anthropometric indices was assessed using Pearson’s correlation test. Receiver Operating Characteristics (ROC) curve analysis was done to analyse the predictive capability of various anthropometric indices for distinguishing between dyslipidemic and non-dyslipidemic individuals. Results: In the study population, 44 (44%) participants had dyslipidemia and 56 (56%) had normal lipid levels. TC showed a statistically significant (p<0.05) positive correlation with BMI (r=0.207), WC (r=0.214) and BRI (r=0.237). TG showed a statistically significant (p<0.05) positive correlation with Wt. (r=0.209), BRI (r=0.242) and a highly significant (p<0.001) (p<0.05) positive correlation with BMI (r=0.311) and WHt.R (r=0.263). HDL-c showed a statistically significant (p<0.05) negative correlation with Wt. (r=-0.232) WC (r=-0.233), WHR (r=-0.199) and highly significant (p<0.001) negative correlation with BMI (r=-0.271) and WHt.R (r=-0.257). LDL-c showed a statistically significant (p<0.05) positive correlation with WC (r=0.249), ABSI (r=0.210) and BRI (r=0.247). BRI showed the highest prediction accuracy with the area under the ROC curve (AUC=0.637). Conclusion: BRI is closely associated with dyslipidemia. BRI is a powerful index that outperforms the classical anthropometric indices in identifying dyslipidemia and thus shows a potential to be used as an alternative obesity measurement in healthy young adults.