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The Performance of Obesity Screening Tools Among Young Thai Adults

Journal of Community Health volume 39, pages 1216–1221 (2014)Cite this article

Abstract

Obesity is a worldwide medical condition that leads to physical and psychological impairment. Specific ethnicity, gender and age group are related to different performances of anthropometric indices to predict obesity. The objectives of this study were to estimate the performance of the anthropometric indices for detecting obesity based on percentage of body fat (PBF), to study the correlation among those indices, and to determine the optimal cut-off point of the indices among young Thai adults. This is a cross-sectional study of healthy urban subjects in Khon Kaen, Thailand who were aged 20–39 years. Baseline characteristics and anthropometric measures were collected. PBF was determined using bioelectrical impedance analysis. Demographic data and anthropometric variables were analyzed using descriptive statistics. Receiver-operating characteristic (ROC) curves were used to compare the performance of anthropometric measures as predictors of obesity. One-hundred men and 100 women were recruited for this study. Body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-stature ratio (WSR) were significantly correlated to PBF. BMI demonstrated the best performance according to the area under the ROC curves in both sexes at cut-off points of 22.5 in women or 25 kg/m2 in men. WC and WSR showed better performance than WHR to detect obesity. In conclusion, anthropometric indices in young Thai adults were correlated well with PBF to predict obesity as shown in prior reports. Different cut-off points of these indices to define obesity in young Thai adults are recommended. The global cut-off points of WSR in women regardless of ethnicity are supported.

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References

  1. James, P. T. (2004). Obesity: The worldwide epidemic. Clinics in Dermatology, 22(4), 276–280.

    PubMed  Article  Google Scholar 

  2. Yang, F., Lv, J. H., Lei, S. F., Chen, X. D., Liu, M. Y., Jian, W. X., et al. (2006). Receiver-operating characteristic analyses of body mass index, waist circumference and waist-to-hip ratio for obesity: Screening in young adults in central south of China. Clinical Nutrition, 25(6), 1030–1039.

    PubMed  Article  Google Scholar 

  3. World Health Organization. (2014). Global Health Observatory (GHO). http://www.who.int/gho/ncd/risk_factors/obesity_text/en/. Accessed 14 March 2014.

  4. Tawonrungrojn, S. (2011). Indicators and their range of values to indicate obesity. Songkla Medicine Journal, 29(2), 89–96.

    Google Scholar 

  5. Cai, L., Liu, A., Zhang, Y., & Wang, P. (2013). Waist-to-height ratio and cardiovascular risk factors among Chinese adults in Beijing. PLoS One, 8(7), e69298.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  6. Cheong, K. C., Yusoff, A. F., Ghazali, S. M., Lim, K. H., Selvarajah, S., Haniff, J., et al. (2013). Optimal BMI cut-off values for predicting diabetes, hypertension and hypercholesterolaemia in a multi-ethnic population. Public Health Nutrition, 16(3), 453–459.

    PubMed  Article  Google Scholar 

  7. Okorodudu, D. O., Jumean, M. F., Montori, V. M., Romero-Corral, A., Somers, V. K., Erwin, P. J., et al. (2010). Diagnostic performance of body mass index to identify obesity as defined by body adiposity: A systematic review and meta-analysis. International Journal of Obesity (London), 34(5), 791–799.

    CAS  Article  Google Scholar 

  8. Paniagua, L., Lohsoonthorn, V., Lertmaharit, S., Jiamjarasrangsi, W., & Williams, M. A. (2008). Comparison of waist circumference, body mass index, percent body fat and other measure of adiposity in identifying cardiovascular disease risks among Thai adults. Obesity Research & Clinical Practice, 2(3), I–II.

    Article  Google Scholar 

  9. Park, S. H., Choi, S. J., Lee, K. S., & Park, H. Y. (2009). Waist circumference and waist-to-height ratio as predictors of cardiovascular disease risk in Korean adults. Circulation Journal, 73(9), 1643–1650.

    CAS  PubMed  Article  Google Scholar 

  10. Wang, F., Wu, S., Song, Y., Tang, X., Marshall, R., Liang, M., et al. (2009). Waist circumference, body mass index and waist to hip ratio for prediction of the metabolic syndrome in Chinese. Nutrition Metabolism and Cardiovascular Diseases, 19(8), 542–547.

    CAS  Article  Google Scholar 

  11. Worachartcheewan, A., Dansethakul, P., Nantasenamat, C., Pidetcha, P., & Prachayasittikul, V. (2012). Determining the optimal cutoff points for waist circumference and body mass index for identification of metabolic abnormalities and metabolic syndrome in urban Thai population. Diabetes Research and Clinical Practice, 98(2), e16–e21.

    PubMed  Article  Google Scholar 

  12. Browning, L. M., Hsieh, S. D., & Ashwell, M. (2010). A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutrition Research Reviews, 23(2), 247–269.

    PubMed  Article  Google Scholar 

  13. Cheong, K. C., Ghazali, S. M., Hock, L. K., Yusoff, A. F., Selvarajah, S., Haniff, J., et al. (2013). Optimal waist circumference cut-off values for predicting cardiovascular risk factors in a multi-ethnic Malaysian population. Obesity Research & Clinical Practice. doi: 10.1016/j.orcp.2013.03.004.

  14. Pongchaiyakul, C., Limpawattana, P., Kotruchin, P., & Rajatanavin, R. (2013). Prevalence of sarcopenia and associated factors among Thai population. Journal of Bone and Mineral Metabolism, 31(3), 346–350.

    PubMed  Article  Google Scholar 

  15. Hanley, J. A., & McNeil, B. J. (1983). A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology, 148(3), 839–843.

    CAS  PubMed  Article  Google Scholar 

  16. Kyle, U. G., Gremion, G., Genton, L., Slosman, D. O., Golay, A., & Pichard, C. (2001). Physical activity and fat-free and fat mass by bioelectrical impedance in 3853 adults. Medicine and Science in Sports and Exercise, 33(4), 576–584.

    CAS  PubMed  Article  Google Scholar 

  17. Mijnarends, D. M., Meijers, J. M., Halfens, R. J., ter Borg, S., Luiking, Y. C., Verlaan, S., et al. (2013). Validity and reliability of tools to measure muscle mass, strength, and physical performance in community-dwelling older people: A systematic review. Journal of the American Medical Directors Association, 14(3), 170–178.

    PubMed  Article  Google Scholar 

  18. Chen, L. K., Liu, L. K., Woo, J., Assantachai, P., Auyeung, T. W., Bahyah, K. S., et al. (2013). Sarcopenia in Asia: Consensus report of the asian working group for sarcopenia. Journal of the American Medical Directors Association, 15(2), 95–101.

    Article  Google Scholar 

  19. Janghorbani, M., & Amini, M. (2010). Comparison of body mass index with abdominal obesity indicators and waist-to-stature ratio for prediction of type 2 diabetes: The Isfahan diabetes prevention study. Obesity Research and Clinical Practice, 4(1), e1–e82.

    PubMed  Article  Google Scholar 

  20. Cheng, C. H., Ho, C. C., Yang, C. F., Huang, Y. C., Lai, C. H., & Liaw, Y. P. (2010). Waist-to-hip ratio is a better anthropometric index than body mass index for predicting the risk of type 2 diabetes in Taiwanese population. Nutrition Research, 30(9), 585–593.

    CAS  PubMed  Article  Google Scholar 

  21. Samsen, M., Hanchaiphiboolkul, S., Puthkhao, P., Tantirittisak, T., & Towanabut, S. (2012). Appropriate body mass index and waist circumference cutoffs for middle and older age group in Thailand: Data of 19,621 participants from Thai Epidemiologic Stroke (TES) study. Journal of the Medical Association of Thailand, 95(9), 1156–1166.

    PubMed  Google Scholar 

  22. Ashwell, M., Gunn, P., & Gibson, S. (2011). Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: Systematic review and meta-analysis. Obesity Reviews, 13(3), 275–286.

    PubMed  Article  Google Scholar 

  23. Cameron, A. J., Magliano, D. J., Shaw, J. E., Zimmet, P. Z., Carstensen, B., Alberti, K. G., et al. (2012). The influence of hip circumference on the relationship between abdominal obesity and mortality. International Journal of Epidemiology, 41(2), 484–494.

    PubMed Central  PubMed  Article  Google Scholar 

  24. Oka, R., Kobayashi, J., Yagi, K., Tanii, H., Miyamoto, S., Asano, A., et al. (2008). Reassessment of the cutoff values of waist circumference and visceral fat area for identifying Japanese subjects at risk for the metabolic syndrome. Diabetes Research and Clinical Practice, 79(3), 474–481.

    PubMed  Article  Google Scholar 

  25. Ekelund, U., Neovius, M., Linne, Y., Brage, S., Wareham, N. J., & Rossner, S. (2005). Associations between physical activity and fat mass in adolescents: The Stockholm Weight Development study. American Journal of Clinical Nutrition, 81(2), 355–360.

    CAS  PubMed  Google Scholar 

  26. Wang, Z., Ma, J., & Si, D. (2010). Optimal cut-off values and population means of waist circumference in different populations. Nutrition Research Reviews, 23(2), 191–199.

    PubMed  Article  Google Scholar 

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Acknowledgments

We wish to acknowledge Professor James A. Will, University of Wisconsin-Madison, for editing the manuscript via the Faculty of Medicine Publication Clinic, Khon Kaen University, Thailand. This manuscript was funded by the Neuroscience Research and Development Group, Khon Kaen University, Thailand.

Conflict of interest

The author (s) declared no potential conflicts of interest with respect to the authorship and/or publication of this article.

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Affiliations

  1. Division of Geriatric Medicine, Department of Internal Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand

    Panita Limpawattana

  2. Department of Internal Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand

    Thepkhachi Kengkijkosol

  3. Department of Preventive and Social Medicine, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand

    Prasert Assantachai

  4. Divison of Geriatric Medicine, Department of Internal Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand

    Orapitchaya Krairit

  5. Outpatient Clinic, Srinagarind Hospital, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand

    Jiraporn Pimporm

Authors
  1. Panita Limpawattana
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  2. Thepkhachi Kengkijkosol
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  3. Prasert Assantachai
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  4. Orapitchaya Krairit
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Correspondence to Panita Limpawattana.

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Limpawattana, P., Kengkijkosol, T., Assantachai, P. et al. The Performance of Obesity Screening Tools Among Young Thai Adults. J Community Health 39, 1216–1221 (2014). https://doi.org/10.1007/s10900-014-9881-3

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  • DOI: https://doi.org/10.1007/s10900-014-9881-3

Keywords

  • Body mass index
  • Waist circumference
  • Waist-to-hip ratio
  • Waist-to-statue ratio
  • Percentage body fat