International Journal of Endocrinology and Metabolism

Published by: Kowsar

Optimal Cutoff Points for Anthropometric Variables to Predict Insulin Resistance in Polycystic Ovary Syndrome

Hossein Hatami 1 , Seyed Ali Montazeri 2 , * , Nazanin Hashemi 2 and Fahimeh Ramezani Tehrani 2
Authors Information
1 Department of Public Health, School of Public Health and Environmental and Occupational Hazards Control Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2 Reproductive Endocrinology Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Article information
  • International Journal of Endocrinology and Metabolism: October 2017, 15 (4); e12353
  • Published Online: July 30, 2017
  • Article Type: Research Article
  • Received: February 2, 2017
  • Revised: July 18, 2017
  • Accepted: July 22, 2017
  • DOI: 10.5812/ijem.12353

To Cite: Hatami H, Montazeri S A, Hashemi N, Ramezani Tehrani F. Optimal Cutoff Points for Anthropometric Variables to Predict Insulin Resistance in Polycystic Ovary Syndrome, Int J Endocrinol Metab. 2017 ; 15(4):e12353. doi: 10.5812/ijem.12353.

Copyright © 2017, International Journal of Endocrinology and Metabolism. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
1. Background
2. Methods
3. Results
4. Discussion
  • 1. Teede HJ, Hutchison S, Zoungas S, Meyer C. Insulin resistance, the metabolic syndrome, diabetes, and cardiovascular disease risk in women with PCOS. Endocrine. 2006; 30(1): 45-53[DOI][PubMed]
  • 2. Azziz R, Carmina E, Dewailly D, Diamanti-Kandarakis E, Escobar-Morreale HF, Futterweit W, et al. The Androgen Excess and PCOS Society criteria for the polycystic ovary syndrome: the complete task force report. Fertil Steril. 2009; 91(2): 456-88[DOI][PubMed]
  • 3. Hosseinpanah F, Barzin M, Erfani H, Serahati S, Ramezani Tehrani F, Azizi F. Lipid accumulation product and insulin resistance in Iranian PCOS prevalence study. Clin Endocrinol (Oxf). 2014; 81(1): 52-7[DOI][PubMed]
  • 4. Hosseinpanah F, Barzin M, Keihani S, Ramezani Tehrani F, Azizi F. Metabolic aspects of different phenotypes of polycystic ovary syndrome: Iranian PCOS Prevalence Study. Clin Endocrinol (Oxf). 2014; 81(1): 93-9[DOI][PubMed]
  • 5. Huang J, Ni R, Chen X, Huang L, Mo Y, Yang D. Metabolic abnormalities in adolescents with polycystic ovary syndrome in south China. Reprod Biol Endocrinol. 2010; 8: 142[DOI][PubMed]
  • 6. Barber TM, Wass JA, McCarthy MI, Franks S. Metabolic characteristics of women with polycystic ovaries and oligo-amenorrhoea but normal androgen levels: implications for the management of polycystic ovary syndrome. Clin Endocrinol (Oxf). 2007; 66(4): 513-7[DOI][PubMed]
  • 7. Bonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care. 2000; 23(1): 57-63[DOI][PubMed]
  • 8. Singh B, Saxena A. Surrogate markers of insulin resistance: A review. World J Diabetes. 2010; 1(2): 36-47[DOI][PubMed]
  • 9. Vasques AC, Rosado L, Rosado G, Ribeiro Rde C, Franceschini S, Geloneze B. Anthropometric indicators of insulin resistance. Arq Bras Cardiol. 2010; 95(1)-23[DOI][PubMed]
  • 10. Mueller NT, Pereira MA, Buitrago-Lopez A, Rodriguez DC, Duran AE, Ruiz AJ, et al. Adiposity indices in the prediction of insulin resistance in prepubertal Colombian children. Public Health Nutr. 2013; 16(2): 248-55[DOI][PubMed]
  • 11. Manios Y, Kourlaba G, Kafatos A, Cook TL, Spyridaki A, Fragiadakis GA. Associations of several anthropometric indices with insulin resistance in children: The Children Study. Acta Paediatr. 2008; 97(4): 494-9[DOI][PubMed]
  • 12. Hirschler V, Ruiz A, Romero T, Dalamon R, Molinari C. Comparison of different anthropometric indices for identifying insulin resistance in schoolchildren. Diabetes Technol Ther. 2009; 11(9): 615-21[DOI][PubMed]
  • 13. Kondaki K, Grammatikaki E, Pavon DJ, Manios Y, Gonzalez-Gross M, Sjostrom M, et al. Comparison of several anthropometric indices with insulin resistance proxy measures among European adolescents: The Helena Study. Eur J Pediatr. 2011; 170(6): 731-9[DOI][PubMed]
  • 14. Matos LN, Giorelli Gde V, Dias CB. Correlation of anthropometric indicators for identifying insulin sensitivity and resistance. Sao Paulo Med J. 2011; 129(1): 30-5[PubMed]
  • 15. Silva MI, Lemos CC, Torres MR, Bregman R. Waist-to-height ratio: an accurate anthropometric index of abdominal adiposity and a predictor of high HOMA-IR values in nondialyzed chronic kidney disease patients. Nutrition. 2014; 30(3): 279-85[DOI][PubMed]
  • 16. Nadeem A, Naveed AK, Hussain MM, Raza SI. Cut-off values of anthropometric indices to determine insulin resistance in Pakistani adults. J Pak Med Assoc. 2013; 63(10): 1220-5[PubMed]
  • 17. Lee CM, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol. 2008; 61(7): 646-53[DOI][PubMed]
  • 18. 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): 275-86[DOI][PubMed]
  • 19. Yang CY, Peng CY, Liu YC, Chen WZ, Chiou WK. Surface anthropometric indices in obesity-related metabolic diseases and cancers. Chang Gung Med J. 2011; 34(1): 1-22[PubMed]
  • 20. Rotterdam EAPCWG. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). Hum Reprod. 2004; 19(1): 41-7[DOI][PubMed]
  • 21. Krakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS One. 2012; 7(7)[DOI][PubMed]
  • 22. Thomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring). 2013; 21(11): 2264-71[DOI][PubMed]
  • 23. Freedman DS, Thornton JC, Pi-Sunyer FX, Heymsfield SB, Wang J, Pierson RJ, et al. The body adiposity index (hip circumference / height(1.5)) is not a more accurate measure of adiposity than is BMI, waist circumference, or hip circumference. Obesity (Silver Spring). 2012; 20(12): 2438-44[DOI][PubMed]
  • 24. Zadeh-Vakili A, Tehrani FR, Hosseinpanah F. Waist circumference and insulin resistance: a community based cross sectional study on reproductive aged Iranian women. Diabetol Metab Syndr. 2011; 3: 18[DOI][PubMed]
  • 25. Brudecki J, Chrzanowska M. Anthropometric indicators as predictors of the risk of metabolic syndrome in adult working men. Anthropol Rev. 2015; 78(1): 67-77[DOI]
  • 26. Akobeng AK. Understanding diagnostic tests 3: Receiver operating characteristic curves. Acta Paediatr. 2007; 96(5): 644-7[DOI][PubMed]
  • 27. Ramezani Tehrani F, Montazeri SA, Hosseinpanah F, Cheraghi L, Erfani H, Tohidi M, et al. Trend of Cardio-Metabolic Risk Factors in Polycystic Ovary Syndrome: A Population-Based Prospective Cohort Study. PLoS One. 2015; 10(9)[DOI][PubMed]
  • 28. Stepto NK, Cassar S, Joham AE, Hutchison SK, Harrison CL, Goldstein RF, et al. Women with polycystic ovary syndrome have intrinsic insulin resistance on euglycaemic-hyperinsulaemic clamp. Hum Reprod. 2013; 28(3): 777-84[DOI][PubMed]
  • 29. DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol. 1979; 237(3)-23[PubMed]
  • 30. Carmienke S, Freitag MH, Pischon T, Schlattmann P, Fankhaenel T, Goebel H, et al. General and abdominal obesity parameters and their combination in relation to mortality: a systematic review and meta-regression analysis. Eur J Clin Nutr. 2013; 67(6): 573-85[DOI][PubMed]
  • 31. 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): 247-69[DOI][PubMed]
  • 32. Hadaegh F, Zabetian A, Sarbakhsh P, Khalili D, James WP, Azizi F. Appropriate cutoff values of anthropometric variables to predict cardiovascular outcomes: 7.6 years follow-up in an Iranian population. Int J Obes (Lond). 2009; 33(12): 1437-45[DOI][PubMed]
  • 33. Petursson H, Sigurdsson JA, Bengtsson C, Nilsen TI, Getz L. Body configuration as a predictor of mortality: comparison of five anthropometric measures in a 12 year follow-up of the Norwegian HUNT 2 study. PLoS One. 2011; 6(10)[DOI][PubMed]
  • 34. Hirschler V, Molinari C, Beccaria M, Maccallini G, Aranda C. Comparison of various maternal anthropometric indices of obesity for identifying metabolic syndrome in offspring. Diabetes Technol Ther. 2010; 12(4): 297-305[DOI][PubMed]
  • 35. Onat A, Ugur M, Can G, Yuksel H, Hergenc G. Visceral adipose tissue and body fat mass: predictive values for and role of gender in cardiometabolic risk among Turks. Nutrition. 2010; 26(4): 382-9[DOI][PubMed]
  • 36. Dhana K, Ikram MA, Hofman A, Franco OH, Kavousi M. Anthropometric measures in cardiovascular disease prediction: comparison of laboratory-based versus non-laboratory-based model. Heart. 2015; 101(5): 377-83[DOI][PubMed]
  • 37. Kim JY, Oh S, Chang MR, Cho YG, Park KH, Paek YJ, et al. Comparability and utility of body composition measurement vs. anthropometric measurement for assessing obesity related health risks in Korean men. Int J Clin Pract. 2013; 67(1): 73-80[DOI][PubMed]
  • 38. Yumi M, Toru N, Shuichiro Y, Yoshihiko T, Tetsuji Y, Tetsuya M, et al. Visceral fat area cutoff for the detection of multiple risk factors of metabolic syndrome in Japanese: the Hitachi Health Study. Obesity (Silver Spring). 2012; 20(8): 1744-9[DOI][PubMed]
  • 39. Nakamura K, Nanri H, Hara M, Higaki Y, Imaizumi T, Taguchi N, et al. Optimal cutoff values of waist circumference and the discriminatory performance of other anthropometric indices to detect the clustering of cardiovascular risk factors for metabolic syndrome in Japanese men and women. Environ Health Prev Med. 2011; 16(1): 52-60[DOI][PubMed]
  • 40. Liu P, Ma F, Lou H, Liu Y. The utility of fat mass index vs. body mass index and percentage of body fat in the screening of metabolic syndrome. BMC Public Health. 2013; 13: 629[DOI][PubMed]
  • 41. Dhana K, Kavousi M, Ikram MA, Tiemeier HW, Hofman A, Franco OH. Body shape index in comparison with other anthropometric measures in prediction of total and cause-specific mortality. J Epidemiol Community Health. 2016; 70(1): 90-6[DOI][PubMed]
  • 42. Mooney SJ, Baecker A, Rundle AG. Comparison of anthropometric and body composition measures as predictors of components of the metabolic syndrome in a clinical setting. Obes Res Clin Pract. 2013; 7(1)-66[DOI][PubMed]
  • 43. Bosy-Westphal A, Geisler C, Onur S, Korth O, Selberg O, Schrezenmeir J, et al. Value of body fat mass vs anthropometric obesity indices in the assessment of metabolic risk factors. Int J Obes (Lond). 2006; 30(3): 475-83[DOI][PubMed]
  • 44. 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)[DOI][PubMed]
  • 45. Moreira SR, Ferreira AP, Lima RM, Arsa G, Campbell CS, Simoes HG, et al. Predicting insulin resistance in children: anthropometric and metabolic indicators. J Pediatr (Rio J). 2008; 84(1): 47-52[DOI][PubMed]
Creative Commons License Except where otherwise noted, this work is licensed under Creative Commons Attribution Non Commercial 4.0 International License .

Search Relations:



Create Citiation Alert
via Google Reader

Readers' Comments