Utility of Anthropometric Indices as a Predictor of Dyslipidemia
Background and Aim: It is a well-known fact that many health indices have a relationship with anthropometric indices. With this in mind, the correlation of dyslipidemia with anthropometric indices was done to bring to light the best anthropometric index in predicting the same. Materials and Methods...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
ADICHUNCHANAGIRI INSTITUTE OF MEDICAL SCIENCES
2015-10-01
|
| Series: | Journal of Medical Sciences and Health |
| Subjects: | |
| Online Access: | http://jmsh.ac.in/index.php?option=com_k2&view=item&id=28:utility-of-anthropometric-indices-as-a-predictor-of-dyslipidemia&Itemid=73 |
| Summary: | Background and Aim: It is a well-known fact that many health indices have a relationship with anthropometric indices. With this in mind, the correlation of dyslipidemia with anthropometric indices was done to bring to light the best anthropometric index in predicting the same.
Materials and Methods: A retrospective analysis of the data obtained from The Community Outreach Program (COP) at Holalu village conducted by the Department of Biochemistry, Mandya Institute of Medical Sciences Mandya was done. 460 participants above the age of 30 years, both males and females were a part of this COP. During the COP, physical measurements such as height, weight, waist circumference (WC), hip circumference (HC) were measured and various biochemical parameters such as total cholesterol, triglycerides, high-density lipoprotein and low-density lipoproteins were investigated. Anthropometric indices such as body mass index (BMI), waist hip ratio, WC, and waist height ratio were calculated from the data. The data analysis was done using statistical software Epi data analysis V.2.2.2.178.
Results: Regression analysis and receiver operating characteristic analysis was done to determine the best anthropometric index. Of all the anthropometric indices, the area under the curve of BMI was the highest for most of the lipid profile parameters (0.6 and above). Regression analysis showed BMI as the best anthropometric index for dyslipidemia (0.078). In this study, we found that BMI is the single best anthropometric index for predicting dyslipidemia.
Conclusion: Anthropometric indices can be used to predict dyslipidemia which in turn can be used as a predictor of cardiovascular diseases. BMI can be considered as a better tool to determine dyslipidemia compared to other anthropometric indices. |
|---|---|
| ISSN: | 2394-9481 2394-949X |