Abstract
Background: Obesity leads to dyslipidemia and predisposes to risk of atherosclerosis and premature death. Anthropometric variables when correlated with lipid profile help to screen at risk individuals who are more susceptible for developing obesity-related morbidities.

Aim and Objective: This study aims to determine the association of dyslipidemia of obesity with body mass index (BMI) and anthropometric indices.

Materials and Methods: A cross-sectional observational study was done in the Department of Physiology, National Institute of Medical Science and Research, Jaipur. Lipid profile parameters were measured and compared between the groups and their association with BMI and other anthropometric indices was observed. SPSS software version 22.0 was used for statistical analysis. Mean and standard deviation was calculated for quantitative variables. The statistical difference in mean value was tested using unpaired t-test and one-way ANOVA. Pearson’s correlation was determined between BMI, waist circumference (WC), hip circumference (HC), waist hip ratio (WHR) and lipid profile parameters. P < 0.05 was taken as statistically significant.

Results: The anthropometric parameters such as weight, BMI, and WHR were found highest in the obese Class II; also, the intergroup difference was significant. The intergroup difference in the mean level of total cholesterol (TC), serum triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) was statistically highly significant (P < 0.001). BMI, WC, HC, and WHR had positive correlation with TC, serum TGs, and LDL-C and negative correlation with HDL in both obese groups. WHR was the best anthropometric variable to predict for dyslipidemia as it had the highest correlation coefficient compared to others.

Conclusion: This study shows that there was positive correlation between dyslipidemia and anthropometric variables in obesity. Hence, simple anthropometric measurements can be used as clinical tools to target the vulnerable within the obese population to prevent various cardiometabolic complications.

Key words: Obesity; Body Mass Index; Lipid Profile; Anthropometric indices