Anthropometric Predictors in Assessment of Metabolic Syndrome among Obese Adults

Authors

  • Vijayalaxmi Mangasuli Basaveshwara Medical College and Hospital, Chitradurga
  • Mayur S Sherkhane SDM College of Medical Sciences and Hospital, Dharwad

DOI:

https://doi.org/10.5455/njcm.20191202101708

Keywords:

Anthropomentry, Metabolic syndrom, Obesity, Risk factor, Adult, BMI

Abstract

Background: Central obesity is the important risk factor in metabolic syndrome which is in turn cardiovascular risk factor which will increase the morbidity and mortality. The study was conducted to assess the anthropometric indices in predicting metabolic syndrome among obese adults.

Methods: Cross sectional study was done among 264 adults having BMI more than 35 and visiting our hospital for a period of three months were included. A predesigned and pretested proforma was used to collect data. Anthropometric measurements, Blood pressure and diabetic profile was measured. Data was analysed using SPSS.

Results: Among 264 adults, 118 (44.69%) had metabolic syndrome; Majority belonged to 30- 39 years (40.15%); educated up to PUC (32.58%) and belonged to class II socio-economic status (38.64%). There was significant mean difference between waist circumference, diastolic blood pressure, HDL levels and FBS levels among the study participants who had and who didn’t have metabolic syndrome. Area under curve (AUC) for waist: height ratio was 0.969, whereas for waist circumference and BMI it was 0.956 and 0.689 respectively.

Conclusion: Waist: height ratio can predict metabolic syndrome better than waist circumference and BMI. Central obesity should be identified at the earliest than general obesity. So that lifestyle modifications can be advised in early life to avoid further morbidity leading to further permanent disabilities and life threatening events in future.

References

Rajput R, Rajput M, Bairwa M, Singh J, Saini O, Shankar V. Waist height ratio: a universal screening tool for prediction of metabolic syndrome in urban and rural population of Haryana. Indian J Endocr Metab 2014;18:394-9.

Harikrishnan S, Sarma S, Sanjay G, Jeemon P, Krishnan MN, Venugopal K, et al. Prevalence of metabolic syndrome and its risk factors in Kerala, South India: Analysis of a commu-nity based cross-sectional study. PLoS ONE 2018; 13(3): e0192372. https://doi.org/10.1371/ journal.pone.0192372.

Wang H, Liu A, Zhao T, Gong X, Pang T, Zhou Y et al. Comparison of anthropometric indices for predicting the risk of metabolic syndrome and its components in Chinese adults: a prospective longitudinal study. BMJ Open 2017;7:1-10.

Vatakencherry R, Saraswathy L. Prevalence of metabolic syndrome among adults in a teaching hospital in Kochi, Central Kerala: a cross sectional study. J Family Med Pri-mary Care 2019;8:2079-83.

Prasad DS, Kabir Z, Dash AK, Das BC. Prevalence and risk factors for metabolic syndrome inAsian Indians: a commu-nity study from urban Eastern India. J Cardiovasc Dis Res 2012;3:204-11.

Saklayen MG. The global epidemic of the metabolic syn-drome. Current hypertension reports 2018;20:12.

Obeidat AA, Ahmad MN, Haddad FH, Azzeh FS. Evalua-tion of several anthropometric indices of obesity as predic-tors of metabolic syndrome in Jordanian adults. Nutr Hosp 2015;32(2):667-77.

Resources and tools. [online] Idf.org. Available at: https://www.idf.org/our-activities/advocacy-awareness/ resources-and-tools/60:idfconsensus-worldwide-definition of-the-metabolic-syndrome.html [Accessed 11 Nov. 2019].

Selvaraj I, Gopalkrishnan S, Logaraj M. Prevalence of meta-bolic syndrome among rural women in a primary centre ar-ea in Tamilnadu. Indian J Public Health 2012;56:314-7.

Venugopal V, Dongre AR, Saravanan S. Prevalence and de-terminants of metabolic syndrome among the rural adult population of Puducherry. Indian J Community Med 2019;44:21-5.

Hoebel S, Ridder JD, Malan L. The association between an-thropometric parameters, the metabolic syndrome and mi-croalbuminuria in black Africans: the SABPA study. Cardi-ovas J Africa 2010;21(3):148-52.

Mastroeni SS, Mastroeni MF, Ekwaru JP, Setayeshgar S, Veugelers PJ, Goncalves MC et al. Anthropometric meas-urements as a potential non-invasive alternative for the di-agnosis of metabolic syndrome in adolescents. Arch Endo-crinol Metab 2019;63(1):30-9.

Downloads

Published

2020-06-30

How to Cite

1.
Mangasuli V, Sherkhane MS. Anthropometric Predictors in Assessment of Metabolic Syndrome among Obese Adults. Natl J Community Med [Internet]. 2020 Jun. 30 [cited 2024 Mar. 29];11(06):258-61. Available from: https://www.njcmindia.com/index.php/file/article/view/326

Issue

Section

Original Research Articles