Korean Journal of Clinical Laboratory Science (대한임상검사과학회지)

Korean Society for Clinical Laboratory Science (대한임상검사과학회)
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Assessing a Body Shape Index and Waist to Height Ratio as a Risk Predictor for Insulin Resistance and Metabolic Syndrome among Korean Adults

한국 성인의 인슐린저항성 및 대사증후군 위험 예측인자로서 체형지수와 허리둘레/신장 비율의 효용성

  • Shin, Kyung-A (Department of Clinical Laboratory Science, Shinsung University)
  • Received : 2017.12.26
  • Accepted : 2018.01.25
  • Published : 2018.03.31
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Abstract

The WHtR (waist to height ratio) and ABSI (a body shape index) are indicators that reflect abdominal obesity. This study examined the insulin resistance and metabolic syndrome prediction ability of ABSI and WHtR. In this study, 4,395 people aged 20 years or older, who underwent physical examinations at a General Hospital in Gyeonggi-do from January 2017 to September 2017 were assessed on a cross section survey. Metabolic syndrome was defined according to the criteria of the AHA/NHLBI. Insulin resistance was judged to be insulin resistance when the HOMA-IR value was 3.0 or more. Both men and women showed a stronger correlation between WHtR and the metabolic risk factors than ABSI. The AUC value of WHtR and ABSI was 0.849 and 0.676, respectively (p<0.001). The AUC value of WHtR and ABSI for predicting insulin resistance was 0.818 and 0.641, respectively (p<0.001). In conclusion, the ABSI has low predictive power of insulin resistance and metabolic syndrome whereas the WHtR has good predictive power for metabolic syndrome and insulin resistance.

WHtR과 ABSI는 기존 비만지표의 단점을 보완하기 위해 개발된 복부비만을 반영한 지표이다. 이 연구는 성인남녀를 대상으로 ABSI와 WHtR의 인슐린저항성과 대사증후군에 대한 예측능력을 허리둘레, WHR과 비교하여 알아보고자 하였다. 이 연구는 횡단면 조사에 기초하여 2017년 1월부터 2017년 9월까지 경기지역 일개 종합병원에서 건강진단을 받은 20세 이상 4,395명을 대상으로 하였다. 대사증후군은 AHA/NHLBI (American Heart Association/National Heart, Lung, and Blood Institute)의 진단기준에 따라 정의하였다. 인슐린저항성은 HOMA-IR값이 3.0 이상인 경우 인슐린저항성으로 판정하였다. WHtR 및 ABSI와 대사적 위험요인간에 상관계수를 비교한 결과 남성과 여성 모두에서 ABSI보다 WHtR과 대사적 위험요인간의 상관성이 더 높았다. 대사증후군을 예측하기 위한 WHtR의 AUC 값은 0.849, ABSI의 AUC 값은 0.676 이었다(각각 p<0.001). 인슐린저항성을 예측하기 위한 WHtR의 AUC 값은 0.818, ABSI의 AUC 값은 0.641 이었다(각각 p<0.001). 결론적으로, 한국인을 대상으로 ABSI가 허리둘레, WHR, WHtR 지표보다 인슐린저항성과 대사증후군에 대한 예측력이 낮은 지표였으며, WHtR은 인슐린저항성 및 대사증후군 위험 예측력이 가장 높은 지표임을 확인하였다.

Keywords

References

  1. Ministry of Health and Welfare, Korea Centers for Disease Control and Prevention. Korea Health Statistics 2014: Korea National Health and Nutrition Examination Survey (KNHANES VI-2). Report. Cheongju: Korea Centers for Disease Control and Prevention; 2015.
  2. Reaven GM. Role of insulin resistance in human disease (syndrome X): an expanded definition. Annu Rev Med. 1993;44:121-131. https://doi.org/10.1146/annurev.me.44.020193.001005
  3. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes. 1988;37(12):1595-1607. https://doi.org/10.2337/diab.37.12.1595
  4. Behboudi-Gandevani S, Ramezani Tehrani F, Cheraghi L, Azizi F. Could "a body shape index" and "waist to height ratio" predict insulin resistance and metabolic syndrome in polycystic ovary syndrome? Eur J Obstet Gynecol Reprod Biol. 2016;205:110-114.
  5. National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). 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):3143-3421.
  6. Kim JM, Back MK, Ju SH, Shin MY, Kim MJ, Park YH, et al. Validation of waist-to-height ratio for predicting metabolic syndrome in patients with prediabetes. Korean J Obes. 2015;24(1):36-43. https://doi.org/10.7570/kjo.2015.24.1.36
  7. World Health Organization. Obesity: preventing and managing the global epidemic report of a WHO Consultation (WHO Technical Report Series 894). Report. Geneva: World Health Organization; 2010.
  8. Nevill AM, Stewart AD, Olds T, Holder R. Relationship between adiposity and body size reveals limitations of BMI. Am J Phys Anthropol. 2006;129(1):151-156. https://doi.org/10.1002/ajpa.20262
  9. Heymsfield SB, Scherzer R, Pietrobelli A, Lewis CE, Grunfeld C. Body mass index as a phenotypic expression of adiposity: quantitative contribution of muscularity in a population-based sample. Int J Obes (Lond). 2009;33(12):1363-1373. https://doi.org/10.1038/ijo.2009.184
  10. Kang SM, Yoon JW, Ahn HY, Kim SY, Lee KH, Shin H, et al. Android fat depot is more closely associated with metabolic syndrome than abdominal visceral fat in elderly people. PLoS One. 2011;6(11):e27694. https://doi.org/10.1371/journal.pone.0027694
  11. de Koning L, Merchant AT, Pogue J, Anand SS. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur Heart J. 2007;28(7):850-856. https://doi.org/10.1093/eurheartj/ehm026
  12. Hsieh SD, Yoshinaga H. Do people with similar waist circumference share similar health risks irrespective of height? Tohoku J Exp Med. 1999;188(1):55-60. https://doi.org/10.1620/tjem.188.55
  13. Hsieh SD, Yoshinaga H, Muto T. Waist-to-height ratio, a simple and practical index for assessing central fat distribution and metabolic risk in Japanese men and women. Int J Obes Relat Metab Disord. 2003;27(5):610-616. https://doi.org/10.1038/sj.ijo.0802259
  14. Browning LM, Hsieh SD, Ashwell M. 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. Nutr Res Rev. 2010;23(2):247-269. https://doi.org/10.1017/S0954422410000144
  15. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275-286. https://doi.org/10.1111/j.1467-789X.2011.00952.x
  16. Langenberg C, Shipley MJ, Batty GD, Marmot MG. Adult socioeconomic position and the association between height and coronary heart disease mortality: findings from 33 years of follow-up in the Whitehall Study. Am J Public Health. 2005;95(4):628-632. https://doi.org/10.2105/2004.046219
  17. Lin WY, Lee LT, Chen CY, Lo H, Hsia HH, Liu IL, et al. Optimal cut-off values for obesity: using simple anthropometric indices to predict cardiovascular risk factors in Taiwan. Int J Obes Relat Metab Disord. 2002;26(9):1232-1238. https://doi.org/10.1038/sj.ijo.0802040
  18. Krakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS One 2012;7(7):e39504. https://doi.org/10.1371/journal.pone.0039504
  19. Bozorgmanesh M, Sardarinia M, Hajsheikholeslami F, Azizi F, Hadaegh F. CVD-predictive performances of "a body shape index" versus simple anthropometric measures: Tehran lipid and glucose study. Eur J Nutr. 2016;55(1):147-157. https://doi.org/10.1007/s00394-015-0833-1
  20. Maessen MF, Eijsvogels TM, Verheggen RJ, Hopman MT, Verbeek AL, de Vegt F. Entering a new era of body indices: the feasibility of a body shape index and body roundness index to identify cardiovascular health status. PLoS One. 2014;9(9):e107212. https://doi.org/10.1371/journal.pone.0107212
  21. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement: Executive Summary. Crit Pathw Cardiol. 2005;4(4):198-203. https://doi.org/10.1097/00132577-200512000-00018
  22. World Health Organization. The Asia-Pacific Perspective: Redefining obesity and its treatment. Sydney, Australia: Health Communications Australia; 2000. p19-20.
  23. Moon K, Sung SH, Chang YK, Park IK, Paek YM, Kim SG, et al. The association between Apolipoprotein E genotype and lipid profiles in healthy woman workers. J Prev Med Public Health. 2010;43(3):213-221. https://doi.org/10.3961/jpmph.2010.43.3.213
  24. Lee S, Choi S, Kim HJ, Chung YS, Lee KW, Lee HC, et al. Cutoff values of surrogate measures of insulin resistance for metabolic syndrome in Korean non-diabetic adults. J Korean Med Sci. 2006;21(4):695-700. https://doi.org/10.3346/jkms.2006.21.4.695
  25. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240(4857):1285-1293. https://doi.org/10.1126/science.3287615
  26. Gomez-Ambrosi J, Silva C, Galofre JC, Escalada J, Santos S, Millan D, et al. Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. Int J Obes (Lond). 2012;36(2):286-294. https://doi.org/10.1038/ijo.2011.100
  27. Bray GA, Smith SR, de Jonge L, Xie H, Rood J, Martin CK, et al. Effect of dietary protein content on weight gain, energy expenditure, and body composition during overeating: a randomized controlled trial. JAMA. 2012;307(1):47-55. https://doi.org/10.1001/jama.2011.1918
  28. Hadaegh F, Shafiee G, Azizi F. Anthropometric predictors of incident type 2 diabetes mellitus in Iranian women. Ann Saudi Med. 2009;29(3):194-200. https://doi.org/10.5144/0256-4947.51788
  29. Hsieh SD, Yoshinaga H. Waist/height ratio as a simple and useful predictor of coronary heart disease risk factors in women. Intern Med. 1995;34(12):1147-1152. https://doi.org/10.2169/internalmedicine.34.1147
  30. Hsieh SD, Yoshinaga H. Abdominal fat distribution and coronary heart disease risk factors in men-waist/height ratio as a simple and useful predictor. Int J Obes Relat Metab Disord. 1995;19(8):585-589.
  31. Lee JS, Aoki K, Kawakubo K, Gunji A. A study on indices of body fat distribution for screening for obesity. Sangyo Eiseigaku Zasshi. 1995;37(1):9-18. https://doi.org/10.1539/sangyoeisei.37.9
  32. Shin KA, Hong SB, Shin KS. Body adiposity index and metabolic syndrome risk factors in Korean adults: a comparison with body mass index and other parameters. Biomed Sci Letters. 2017;23(2):57-63. https://doi.org/10.15616/BSL.2017.23.2.57
  33. Ashwell M, Gibson S. Waist-to-height ratio as an indicator of 'early health risk': simpler and more predictive than using a 'matrix' based on BMI and waist circumference. BMJ Open. 2016;6(3):e010159. https://doi.org/10.1136/bmjopen-2015-010159
  34. Liu L, Ping Z, Li L, Yang Y, Li C, Zhang M. Power and the cutoff value of waist-to-height ratio predicting metabolism syndrome. Wei Sheng Yan Jiu. 2012;41(6):992-996.
  35. Haghighatdoost F, Sarrafzadegan N, Mohammadifard N, Asgary S, Boshtam M, Azadbakht L. Assessing body shape index as a risk predictor for cardiovascular diseases and metabolic syndrome among Iranian adults. Nutrition. 2014;30(6):636-644. https://doi.org/10.1016/j.nut.2013.10.021
  36. Bertoli S, Leone A, Krakauer NY, Bedogni G, Vanzulli A, Redaelli VI, et al. Association of Body Shape Index (ABSI) with cardio-metabolic risk factors: A cross-sectional study of 6081 Caucasian adults. PLoS One. 2017;12(9):e0185013. https://doi.org/10.1371/journal.pone.0185013
  37. Duncan MJ, Mota J, Vale S, Santos MP, Ribeiro JC. Associations between body mass index, waist circumference and body shape index with resting blood pressure in Portuguese adolescents. Ann Hum Biol. 2013;40(2):163-167. https://doi.org/10.3109/03014460.2012.752861
  38. He S, Chen X. Could the new body shape index predict the new onset of diabetes mellitus in the Chinese population? PLoS One. 2013;8(1):e50573. https://doi.org/10.1371/journal.pone.0050573

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