Assessment of the Validity of Multiple Obesity Indices Compared with Obesity-related Co-morbidities

Overview

Journal Public Health Nutr

Publisher Cambridge University Press

Date 2019 Feb 19

PMID 30773165

Citations 4

Authors

Jaeeun Myung

Kyung Yoon Jung

Tae Hyun Kim

Euna Han

Affiliations

Soon will be listed here.

Abstract

Objective: The aim of the present study was to compare selected obesity indicators with comprehensive health status.

Design: The study employed a pooled cross-sectional design.

Setting: BMI, waist circumference, waist-to-height ratio (WHtR) and body fat percentage were considered as indirect obesity indicators. The Edmonton Obesity Staging System (EOSS) was used as a composite indicator to comprehensively reflect obesity-related co-morbidities. Cohen's κ coefficient was used to evaluate inter-measurement agreement for obesity. Conformity of indirect obesity indicators to the EOSS was assessed based on percentage agreement (proportion classified as obese and severely unhealthy as a result of obesity among the total sample), sensitivity (proportion classified as obese among individuals severely unhealthy as a result of obesity) and specificity (proportion classified as non-obese among fairly healthy individuals). Logistic regression analysis was used to identify the sociodemographic factors most strongly associated with conformity.ParticipantsThe study included 17338 adults from the Korea National Health and Nutrition Examination survey conducted between July 2008 and May 2011.

Results: Level of conformity to the EOSS was highest for WHtR (60·77 %) and lowest for BMI (35·96 %). WHtR and BMI had the highest sensitivity (53·7 %) and specificity (98·4 %), respectively. Predictability of conformity was lower among men for all indirect obesity indicators.

Conclusions: WHtR has the greatest potential to identify individuals at risk of health problems due to obesity. Individual demographic factors must be considered in selecting the most appropriate obesity measurement.

Citing Articles

Correlation and agreement between the body mass index and abdominal perimeter with the waist-to-height ratio in peruvian adults aged 18 to 59 years.

Aparco J, Cardenas-Quintana H Rev Peru Med Exp Salud Publica. 2023; 39(4):392-399.

PMID: 36888800 PMC: 11397726. DOI: 10.17843/rpmesp.2022.394.11932.

Meal occasion, overweight, obesity and central obesity in children and adults: a cross-sectional study based on a nationally representative survey. Colombia, 2015.

Herran O, Herran-Fonseca C BMJ Open. 2022; 12(9):e064832.

PMID: 36123072 PMC: 9486272. DOI: 10.1136/bmjopen-2022-064832.

Level of injury is an independent determining factor of gut dysbiosis in people with chronic spinal cord injury: A cross-sectional study.

Pattanakuhar S, Kaewchur T, Saiyasit N, Chattipakorn N, Chattipakorn S Spinal Cord. 2022; 60(12):1115-1122.

PMID: 35835855 DOI: 10.1038/s41393-022-00832-8.

Correlations between percent body fat measured by dual-energy X-ray absorptiometry and anthropometric measurements in Thai persons with chronic traumatic spinal cord injury.

Kuvijitsuwan B, Fongkaew K, Tengpanitchakul K, Dolkittanasophon J, Chunsanit S, Pattanakuhar S Spinal Cord. 2022; 60(12):1094-1099.

PMID: 35773356 DOI: 10.1038/s41393-022-00828-4.

References

. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002; 106(25):3143-421. View

Kuk J, Ardern C, Church T, Sharma A, Padwal R, Sui X . Edmonton Obesity Staging System: association with weight history and mortality risk. Appl Physiol Nutr Metab. 2011; 36(4):570-6. DOI: 10.1139/h11-058. View

An R . Educational disparity in obesity among U.S. adults, 1984-2013. Ann Epidemiol. 2015; 25(9):637-642.e5. DOI: 10.1016/j.annepidem.2015.06.004. View

Schneider H, Friedrich N, Klotsche J, Pieper L, Nauck M, John U . The predictive value of different measures of obesity for incident cardiovascular events and mortality. J Clin Endocrinol Metab. 2010; 95(4):1777-85. DOI: 10.1210/jc.2009-1584. View

Hauer A, Ruigrok Y, Algra A, van Dijk E, Koudstaal P, Luijckx G . Age-Specific Vascular Risk Factor Profiles According to Stroke Subtype. J Am Heart Assoc. 2017; 6(5). PMC: 5524074. DOI: 10.1161/JAHA.116.005090. View

Wang Z . Age-dependent decline of association between obesity and mortality: a systematic review and meta-analysis. Obes Res Clin Pract. 2015; 9(1):1-11. DOI: 10.1016/j.orcp.2014.01.006. View

Vijan S . In the clinic. Type 2 diabetes. Ann Intern Med. 2010; 152(5):ITC31-15. DOI: 10.7326/0003-4819-152-5-201003020-01003. View

Rahman M, Berenson A . Accuracy of current body mass index obesity classification for white, black, and Hispanic reproductive-age women. Obstet Gynecol. 2010; 115(5):982-988. PMC: 2886596. DOI: 10.1097/AOG.0b013e3181da9423. View

Page J, Rexrode K, Hu F, Albert C, Chae C, Manson J . Waist-height ratio as a predictor of coronary heart disease among women. Epidemiology. 2009; 20(3):361-6. PMC: 4012298. DOI: 10.1097/EDE.0b013e31819f38f1. View

10.

Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J . Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk--a review of the literature. Eur J Clin Nutr. 2009; 64(1):16-22. DOI: 10.1038/ejcn.2009.68. View

11.

Kim Y . The Korea National Health and Nutrition Examination Survey (KNHANES): current status and challenges. Epidemiol Health. 2014; 36:e2014002. PMC: 4017741. DOI: 10.4178/epih/e2014002. View

12.

Thijssen E, van Caam A, van der Kraan P . Obesity and osteoarthritis, more than just wear and tear: pivotal roles for inflamed adipose tissue and dyslipidaemia in obesity-induced osteoarthritis. Rheumatology (Oxford). 2014; 54(4):588-600. DOI: 10.1093/rheumatology/keu464. View

13.

Rothman K . BMI-related errors in the measurement of obesity. Int J Obes (Lond). 2008; 32 Suppl 3:S56-9. DOI: 10.1038/ijo.2008.87. View

14.

Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Bohm M . 2013 ESH/ESC Guidelines for the management of arterial hypertension: the Task Force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). J Hypertens. 2013; 31(7):1281-357. DOI: 10.1097/01.hjh.0000431740.32696.cc. View

15.

Gadar L, Abonyi J . Graph configuration model based evaluation of the education-occupation match. PLoS One. 2018; 13(3):e0192427. PMC: 5839539. DOI: 10.1371/journal.pone.0192427. View

16.

Davis T, Millns H, Stratton I, Holman R, Turner R . Risk factors for stroke in type 2 diabetes mellitus: United Kingdom Prospective Diabetes Study (UKPDS) 29. Arch Intern Med. 1999; 159(10):1097-103. DOI: 10.1001/archinte.159.10.1097. View

17.

Janssen I, Katzmarzyk P, Ross R . Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr. 2004; 79(3):379-84. DOI: 10.1093/ajcn/79.3.379. View

18.

Klein S, Allison D, Heymsfield S, Kelley D, Leibel R, Nonas C . Waist circumference and cardiometabolic risk: a consensus statement from Shaping America's Health: Association for Weight Management and Obesity Prevention; NAASO, The Obesity Society; the American Society for Nutrition; and the American Diabetes.... Am J Clin Nutr. 2007; 85(5):1197-202. DOI: 10.1093/ajcn/85.5.1197. View

19.

Aune D, Sen A, Schlesinger S, Norat T, Janszky I, Romundstad P . Body mass index, abdominal fatness, fat mass and the risk of atrial fibrillation: a systematic review and dose-response meta-analysis of prospective studies. Eur J Epidemiol. 2017; 32(3):181-192. PMC: 5380695. DOI: 10.1007/s10654-017-0232-4. View

20.

Dyson J, Anstee Q, McPherson S . Non-alcoholic fatty liver disease: a practical approach to treatment. Frontline Gastroenterol. 2014; 5(4):277-286. PMC: 4173737. DOI: 10.1136/flgastro-2013-100404. View