Skip to main content

Advertisement

SpringerLink
Log in

Waist-to-height ratio and BMI as predictive markers for insulin resistance in women with PCOS in Kolkata, India

  • Original Article
  • Published:
Endocrine Aims and scope Submit manuscript

Abstract

Purpose

Polycystic ovarian syndrome (PCOS) is most commonly presented with insulin resistance (IR). Simple anthropometric indices may serve as surrogate markers of these conditions with population-based cut-off values. The present study suggests the cut-off values of waist-to-height ratio (WHtR) and body mass index (BMI) in early prediction of PCOS and IR in PCOS women based in Kolkata, a major metropolitan city in India.

Methods

This cross-sectional study included 66 women (aged 16–30 years) from Kolkata, India, with confirmed PCOS, using Rotterdam criteria. IR was defined following the homeostasis model assessment (HOMA). Anthropometric and biochemical data were obtained using standard protocol and compared among the PCOS subjects grouped as per IR prevalence, BMI, and WHtR values. The receiver operating characteristics (ROC) curve was applied to evaluate and compare the cut-off values of WHtR and BMI in the prediction of PCOS and IR in women with PCOS.

Results

As per ROC analysis, WHtR showed significantly higher AUC in the detection of PCOS and IR in PCOS subjects respectively, than that of BMI. The cut-off values of WHtR and BMI for PCOS were 0.560 and 28.47 respectively, and for IR in PCOS patients, were 0.620 and 29.14 respectively.

Conclusions

The present study suggests a cut-off value of WHtR to be used as an inexpensive and noninvasive screening tool for early prediction of PCOS and IR among PCOS afflicted women based in Kolkata, India and for this prediction, the study also claims WHtR as a better index than BMI.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. S.M. Sirmans, K.A. Pate, Epidemiology, diagnosis, and management of polycystic ovary syndrome. Clin. Epidemiol. 6, 1 (2014)

    Google Scholar 

  2. N.M. Clark, A.J. Podolski, E.D. Brooks, D.R. Chizen, R.A. Pierson, D.C. Lehotay, M.E. Lujan, Prevalence of polycystic ovary syndrome phenotypes using updated criteria for polycystic ovarian morphology: an assessment of over 100 consecutive women self-reporting features of polycystic ovary syndrome. Reprod. Sci. 21(8), 1034–1043 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  3. M.A. Ganie, V. Vasudevan, I.A. Wani, M.S. Baba, T. Arif, A. Rashid, Epidemiology, pathogenesis, genetics & management of polycystic ovary syndrome in India. Indian J. Med. Res. 150(4), 333 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. S. Chhabra, N. Gangane, Coexistence of Endometrial Cancer, Polycystic Ovarian Syndrome and Metabolic Syndrome. EC Endocrinol. Metab. Res. 4, 91–97 (2019)

    Google Scholar 

  5. G. Hartmann, B. McEwen, Insulin resistance and Polycystic ovary syndrome (PCOS): part 2. Diet and Nutritional Medicine. J. Austr. Trad. Med. Soc. 25(1), 18 (2019)

    Google Scholar 

  6. S.M. Bhattacharya, Prevalence of metabolic syndrome in women with polycystic ovary syndrome, using two proposed definitions. Gynecol. Endocrinol. 26(7), 516–520 (2010)

    Article  PubMed  Google Scholar 

  7. S.M. Grundy, Metabolic syndrome update. Trends Cardiovas. Med. 26(4), 364–373 (2016)

    Article  Google Scholar 

  8. J.A. Boyle, J. Cunningham, R.J. Norman, T. Dunbar, K. O’Dea, Polycystic ovary syndrome and metabolic syndrome in Indigenous Australian women. Int. Med. J. 45(12), 1247–1254 (2015)

    Article  CAS  Google Scholar 

  9. M. Zaki, W. Basha, H.T. El-Bassyouni, S. El-Toukhy, T. Hussein, Evaluation of DNA damage profile in obese women and its association to risk of metabolic syndrome, polycystic ovary syndrome and recurrent preeclampsia. Gen. Dis. 5(4), 367–373 (2018)

    CAS  Google Scholar 

  10. F. Sigit, D. Tahapary, E. Sartono, S. Trompet, M. Yazdanbakhsh, F. Rosendaal, R. de Mutsert, The Prevalence Of Metabolic Syndrome And Its Association With Body Fat Distribution In A Dutch And Indonesian Population. Atherosclerosis 287, e135–e136 (2019)

    Article  Google Scholar 

  11. J. Rojas, M. Chávez, L. Olivar, M. Rojas, J. Morillo, J. Mejías, M. Calvo, V. Bermúdez, Polycystic ovary syndrome, insulin resistance, and obesity: navigating the pathophysiologic labyrinth. Int. J. Reprod. Med. 2014, 1–17 (2014). https://doi.org/10.1155/2014/719050

    Article  Google Scholar 

  12. R.S. Legro, Obesity and PCOS: implications for diagnosis and treatment. Semin Reprod. Med. 30(6), 496 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. R. Ahirwar, P.R. Mondal, Prevalence of obesity in India: a systematic review. Clin. Res. Rev. 13(1), 318–321 (2019)

    Google Scholar 

  14. NFHS. Key findings: 2015-16 India: National Family Health Survey, India; 2012 [http://rchiips.org/NFHS/factsheet_NFHS-4.shtml.] [Last accessed on 19 Jul 2020]

  15. M. Bhadra, A. Mukhopadhyay, K. Bose, Overweight and obesity among adult Bengalee Hindu women of Kolkata, India. Hum. Ecol. 13, 77–83 (2005)

    Google Scholar 

  16. P. Sengupta, P. Chaudhuri, K. Bhattacharya, Screening obesity by direct and derived anthropometric indices with evaluation of physical efficiency among female college students of Kolkata. Ann. Med. Health Sci. Res. 3(4), 517–522 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. R.S. Legro, V.D. Castracane, R.P. Kauffman, Detecting insulin resistance in polycystic ovary syndrome: purposes and pitfalls. Obstet. Gynecol. Surv. 59(2), 141–154 (2004)

    Article  PubMed  Google Scholar 

  18. L.J. Aronne, Classification of obesity and assessment of obesity‐related health risks. Obes. Res. 10(S12), 105S–115S (2002)

    Article  PubMed  Google Scholar 

  19. F. Haghighatdoost, N. Sarrafzadegan, N. Mohammadifard, S. Asgary, M. Boshtam, L. Azadbakht, Assessing body shape index as a risk predictor for cardiovascular diseases and metabolic syndrome among Iranian adults. Nutrition 30(6), 636–644 (2014)

    Article  PubMed  Google Scholar 

  20. J.S. Mokha, S.R. Srinivasan, P. DasMahapatra, C. Fernandez, W. Chen, J. Xu, G.S. Berenson, Utility of waist-to-height ratio in assessing the status of central obesity and related cardiometabolic risk profile among normal weight and overweight/obese children: the Bogalusa Heart Study. BMC Pedia. 10(1), 73 (2010)

    Article  Google Scholar 

  21. G. Belarmino, R.S. Torrinhas, P. Sala, L.M. Horie, L. Damiani, N.C. Lopes, S.B. Heymsfield, D.L. Waitzberg, A new anthropometric index for body fat estimation in patients with severe obesity. BMC Obes. 5(1), 25 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  22. B. Dong, Z. Wang, L.W. Arnold, Y. Song, H.J. Wang, J. Ma, Simplifying the screening of abdominal adiposity in Chinese children with waist‐to‐height ratio. Am. J. Hum. Biol. 28(6), 945–949 (2016)

    Article  PubMed  Google Scholar 

  23. Z.P. Huang, B.X. Huang, H. Zhang, M.F. Zhu, H.L. Zhu, Waist-to-Height Ratio Is a Better Predictor of Hyperuricemia than Body Mass Index and Waist Circumference in Chinese. Ann. Nutr. Metab. 75(3), 187–194 (2019)

    Article  CAS  PubMed  Google Scholar 

  24. H. Yang, Z. Xin, J.P. Feng, J.K. Yang, Waist-to-height ratio is better than body mass index and waist circumference as a screening criterion for metabolic syndrome in Han Chinese adults. Medicine. 96(39), e8192 (2017)

  25. E.C. Costa, J.C. Ferezini de Sá, E.M. Mafaldo Soares, T.M. Araújo Moura Lemos, T.M. de Oliveira Maranhão, G. Dantas, Azevedo, Anthropometric indices of central obesity how discriminators of metabolic syndrome in Brazilian women with polycystic ovary syndrome. Gynecol. Endocrinol. 28(1), 12–15 (2012)

    Article  PubMed  Google Scholar 

  26. The Rotterdam ESHRE/ASRM‐sponsored PCOS consensus workshop group, Revised 2003 consensus on diagnostic criteria and long‐term health risks related to polycystic ovary syndrome (PCOS). Hum. Reprod. 19(1), 41–47 (2004)

    Article  Google Scholar 

  27. S. Bhattacharya, M. Ghosh, Insulin resistance and adolescent girls with polycystic ovary syndrome. J. Pedia. Adoles. Gynecol. 23(3), 158–161 (2010)

    Article  CAS  Google Scholar 

  28. R.S. Legro, A.R. Kunselman, W.C. Dodson, A. Dunaif, Prevalence and predictors of risk for type 2 diabetes mellitus and impaired glucose tolerance in polycystic ovary syndrome: a prospective, controlled study in 254 affected women. J. Clin. Endocrinol. Metab. 84(1), 165–169 (1999)

    CAS  PubMed  Google Scholar 

  29. World Health Organization, BMI classification. Global Database on body mass index. Switzerland (World Health Organization, 2006)

  30. M. Gutch, S. Kumar, S.M. Razi, K.K. Gupta, A. Gupta, Assessment of insulin sensitivity/resistance. Indian J. Endocrinol. Metab. 19(1), 160 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. M.A. Olamoyegun, R. Oluyombo, S.O. Asaolu, Evaluation of dyslipidemia, lipid ratios, and atherogenic index as cardiovascular risk factors among semi-urban dwellers in Nigeria. Ann. Afr. Med. 15(4), 194 (2016)

    Article  PubMed  PubMed Central  Google Scholar 

  32. K. Alberti, R.H. Eckel, S.M. Grundy, P.Z. Zimmet, J.I. Cleeman, K.A. Donato, J.-C. Fruchart, W.P.T. James, C.M. Loria, S.C. Smith Jr, Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Circulation 120(16), 1640–1645 (2009)

    Article  CAS  PubMed  Google Scholar 

  33. D. Matthews, J. Hosker, A. Rudenski, B. Naylor, D. Treacher, R. Turner, Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28(7), 412–419 (1985)

    Article  CAS  PubMed  Google Scholar 

  34. A. Ramachandran, C. Snehalatha, R. Vinitha, M. Thayyil, C.S. Kumar, L. Sheeba, S. Joseph, V. Vijay, Prevalence of overweight in urban Indian adolescent school children. Diab. Res. Clin. Prac. 57(3), 185–190 (2002)

    Article  CAS  Google Scholar 

  35. D. Sacks, C.P. Society, A.H. Committee, Age limits and adolescents. Paediat. Child Heal. 8(9), 577–577 (2003)

    Article  Google Scholar 

  36. A. Misra, P. Chowbey, B. Makkar, N. Vikram, J. Wasir, D. Chadha, S.R. Joshi, Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management. J. Assoc. Phys. India 57(2), 163–170 (2009)

    CAS  Google Scholar 

  37. US Department of Health and Human Services, Polycystic ovary syndrome. 2016 [cited 2020 31.05.2020]; https://www.womenshealth.gov/a-z-topics/polycystic-ovary-syndrome [last accessed on 19 July, 2020]

  38. H.J. Teede, S. Hutchison, S. Zoungas, C. Meyer, Insulin resistance, the metabolic syndrome, diabetes, and cardiovascular disease risk in women with PCOS. Endocrine 30(1), 45–53 (2006)

    Article  CAS  PubMed  Google Scholar 

  39. E. Bonora, G. Targher, M. Alberiche, R.C. Bonadonna, F. Saggiani, M.B. Zenere, T. Monauni, M. Muggeo, 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. Diab. Care 23(1), 57–63 (2000)

    Article  CAS  Google Scholar 

  40. B. Singh, A. Saxena, Surrogate markers of insulin resistance: a review. World J. Diab. 1(2), 36 (2010)

    Article  Google Scholar 

  41. A. Vasques, L. Rosado, G. Rosado, R. Ribeiro, S. Franceschini, B. Geloneze, Anthropometric Indicators Of Insulin Resistance [indicadores Antropométricos De Resistência à Insulina]. Arq. Brasil. Cardiol. 95(1), e14-e23 (2010)

  42. K. Kondaki, E. Grammatikaki, D.J. Pavón, Y. Manios, M. González-Gross, M. Sjöstrom, F. Gottrand, D. Molnar, L.A. Moreno, A. Kafatos, Comparison of several anthropometric indices with insulin resistance proxy measures among European adolescents: The Helena Study. Eur. J. Pedia. 170(6), 731–739 (2011)

    Article  Google Scholar 

  43. M.I.B. Silva, C.C. da Silva Lemos, M.R.S.G. Torres, R. Bregman, 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 30(3), 279–285 (2014)

    Article  PubMed  Google Scholar 

  44. A. Nadeem, A.K. Naveed, M.M. Hussain, S.I. Raza, Cut-off values of anthropometric indices to determine insulin resistance in Pakistani adults. Age 51(12.1), 51.16–10.62 (2013)

    Google Scholar 

  45. C.M.Y. Lee, R.R. Huxley, R.P. Wildman, M. Woodward, Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J. Clin. Epidemiol. 61(7), 646–653 (2008)

    Article  PubMed  Google Scholar 

  46. Q. A. Acton, Issues in global environment-Climate and Climate Change. Climate Research, ed. Q. A. Acton (Atlanta, Georgia, ScholarlyEditions, 2013)

  47. E.G. Yoo, Waist-to-height ratio as a screening tool for obesity and cardiometabolic risk. Kor. J. Pedia. 59(11), 425 (2016)

    Article  Google Scholar 

  48. J. Cresswell, R.B. Fraser, C. Bruce, P. Egger, D. Phillips, D.J. Barker, Relationship between polycystic ovaries, body mass index and insulin resistance. Acta Obstet. Gynecol. Scand. 82(1), 61–64 (2003)

    Article  PubMed  Google Scholar 

  49. S. Behboudi-Gandevani, F.R. Tehrani, L. Cheraghi, F. Azizi, 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. 205, 110–114 (2016)

    Article  CAS  PubMed  Google Scholar 

  50. M. Ashwell, S. Gibson, 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 6(3), e010159 (2016)

    Article  PubMed  PubMed Central  Google Scholar 

  51. L.M. Browning, S.D. Hsieh, M. Ashwell, 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. 23(2), 247–269 (2010)

    Article  PubMed  Google Scholar 

  52. M. Van Hemelrijck, H. Ulmer, G. Nagel, R.S. Peter, J. Fritz, R. Myte, B. Van Guelpen, B. Föger, H. Concin, C. Häggström, Longitudinal study of body mass index, dyslipidemia, hyperglycemia, and hypertension in 60,000 men and women in Sweden and Austria. PloS ONE. 13(6), e0197830 (2018)

  53. F.R. Tehrani, S. Minooee, F. Azizi, Comparison of various adiposity indexes in women with polycystic ovary syndrome and normo-ovulatory non-hirsute women: a population-based study. Eur. J. Endocrinol. 171, 199–207 (2014)

    Article  CAS  Google Scholar 

  54. T. Liu, Q. Wang, W. Huang, J. Tan, D. Liu, T. Pei, X. Li, G. Zhou, Anthropometric indices to predict insulin resistance in women with polycystic ovary syndrome in China. Reprod. Biomed. Online 38(1), 101–107 (2019)

    Article  CAS  PubMed  Google Scholar 

  55. F. Bacopoulou, V. Efthymiou, G. Landis, A. Rentoumis, G.P. Chrousos, Waist circumference, waist-to-hip ratio and waist-to-height ratio reference percentiles for abdominal obesity among Greek adolescents. BMC Pedia. 15(1), 50 (2015)

    Article  Google Scholar 

  56. X. Guan, G. Sun, L. Zheng, W. Hu, W. Li, Y. Sun, Associations between metabolic risk factors and body mass index, waist circumference, waist‐to‐height ratio and waist‐to‐hip ratio in a Chinese rural population. J. Diab. Investig. 7(4), 601–606 (2016)

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Post Graduate Department of Physiology, Hooghly Mohsin College, University of Burdwan, Bardhaman, West Bengal, India

    Koushik Bhattacharya & Alak Kumar Syamal

  2. Department of Physiology, Rungta College of Dental Sciences and Research, Bhilai, Chhattisgarh, India

    Koushik Bhattacharya

  3. Faculty of Medicine, Bioscience and Nursing, MAHSA University, Jenjarom, Selangor, Malaysia

    Pallav Sengupta

  4. Faculty of Dentistry, MAHSA University, Jenjarom, Selangor, Malaysia

    Sulagna Dutta

  5. Department of Physiology, Government General Degree College, Paschim Medinipur, West Bengal, India

    Prasenjit Chaudhuri

  6. Department of Obstetrics and Gynecology, KPC Medical College, Jadavpur, Kolkata, West Bengal, India

    Lipika Das Mukhopadhyay

Authors
  1. Koushik Bhattacharya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pallav Sengupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sulagna Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Prasenjit Chaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lipika Das Mukhopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alak Kumar Syamal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Lipika Das Mukhopadhyay or Alak Kumar Syamal.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The Institutional Human Ethical Committee of Burdwan University (IECH/OCH/02/CC) has approved the present study.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhattacharya, K., Sengupta, P., Dutta, S. et al. Waist-to-height ratio and BMI as predictive markers for insulin resistance in women with PCOS in Kolkata, India. Endocrine 72, 86–95 (2021). https://doi.org/10.1007/s12020-020-02555-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12020-020-02555-3

Keywords

Search

Navigation