Body mass index, body composition, and metabolic profile of patients with polycystic ovary syndrome


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

Objective: To compare the diagnostic value of body mass index (BMI), body composition, and their relationship with the metabolic profile of patients with polycystic ovary syndrome (RCOS). Materials and methods: A single-center, cross-sectional study included 129 women with PCOS (mean age 26.7 (5.4) years). They underwent a comprehensive clinical and laboratory examination, including BMI calculation, body composition analysis, hormonal and lipid prof iles, glucose metabolism, and pelvic ultrasound. Results: Among patients with PCOS, overweight and obesity were diagnosed only in 48/129 (37.2%) patients, and 95/129 (73.6%) had an excess of total adipose tissue located mainly in the visceral region. Forty-six of 81 (56.8%) patients with normal BMI values had an excess of total adipose tissue, indicating latent obesity. Among them, 18/46 (39.1%) had visceral obesity, which was associated with hyperinsulinemia, insulin resistance (IR), and dyslipidemia in every third case and with impaired glucose tolerance (IGT) in every 5th case. ROC analysis showed that at a threshold value of BMI≥223 kg/m2, an excess of total adipose tissue was detected in 100% of cases, NTG was more than 11 times more common, and hyperinsulinemia and dyslipidemia were three times more common. In patients with PCOS, a BMI≥223 kg/m2 can be considered a factor predisposing to the development of IR with a sensitivity of 74% and a specificity of 70%. Conclusion: It would be appropriate to include body composition analysis in the diagnostic evaluation of patients with PCOS to identify excess adipose tissue associated with metabolic disorders. BMI≥223 kg/m2 can be considered a clinical marker of excess adipose tissue and a high risk of developing metabolic disorders. With their early diagnosis, effective interventions can be selected, including lifestyle modification and pharmacotherapy to reduce the risk of type 2 diabetes and cardiovascular diseases in the long term.

Full Text

Restricted Access

About the authors

Galina E. Chernukha

V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia

Email: g_chernukha@oparina4.ru
Dr.Med.Sci., Professor, Head of the Department of Gynecologic Endocrinology

Ekaterina D. Miroshina

V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia

Email: emiroshina.md@gmail.com
PhD. Student at the Department of Gynecologic Endocrinology

Sergei Yu. Kuznetsov

V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia

Ph.D., Radiologist, Obstetrician-Gynecologist at the Department of Gynecologic Endocrinology

Ilya A. Ivanov

V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia

Email: doctor.i.ivanov@yandex.ru
Ph.D., Obstetrician-Gynecologist at the Department of Gynecologic Endocrinology

References

  1. Moran L.J., Misso M.L., Wild R.A., Norman R.J. Impaired glucose tolerance, type 2 diabetes and metabolic syndrome in polycystic ovary syndrome: a systematic review and meta-analysis. Hum Reprod Update. 2010; 16(4): 347-63. https://dx.doi.org/10.1093/humupd/dmq001.
  2. Cooney L.G., Lee I., Sammel M.D., Dokras A. High prevalence of moderate and severe depressive and anxiety symptoms in polycystic ovary syndrome: a systematic review and meta-analysis. Hum Reprod. 2017; 32(5): 1075-91. https://dx.doi.org/10.1093/humrep/dex044.
  3. Attaoua R., El Mkadem S.A., Radian S., Fica S., Hanzu F., Albu A. et al. FTO gene associates to metabolic syndrome in women with polycystic ovary syndrome. Biochem Biophys Res Commun. 2008; 373(2): 230-4. https://dx.doi.org/10.1016/j.bbrc.2008.06.039.
  4. Morgan C.L., Jenkins-Jones S., Currie C.J., Rees D.A. Evaluation of adverse outcome in young women with polycystic ovary syndrome versus matched, reference controls: a retrospective, observational study. J. Clin Endocrinol Metab. 2012; 97(9): 3251-60. https://dx.doi.org/10.1210/jc.2012-1690.
  5. Zhu S., Zhang B., Jiang X., Li Z., Zhao S., Cui L. et al. Metabolic disturbances in non-obese women with polycystic ovary syndrome: a systematic review and meta-analysis. Fertil Steril. 2019; 111(1): 168-77. https://dx.doi.org/10.1016/j.fertnstert.2018.09.013.
  6. Vrbikova J., Fanta M., Cibula D., Vondra K., Bendlova B. Impaired glucose metabolism in women with polycystic ovary syndrome. Gynecol Obstet Invest. 2009; 68(3): 186-90. https://dx.doi.org/10.1159/000232574.
  7. Ollila M.-M., West S., Keinaanen-Kiukaanniemi S., Jokelainen J., Auvinen J., Puukka K. et al. Overweight and obese but not normal weight women with PCOS are at increased risk of Type 2 diabetes mellitus - a prospective, population-based cohort study. Hum Reprod. 2017; 32(2): 423-31. https://dx.doi.org/10.1093/humrep/dew329.
  8. Garvey W.T., Garber A.J., Mechanick J.I., Bray G.A., Dagogo-Jack S., Einhorn D. et al. The Aace Obesity Scientific Committee. American association of clinical endocrinologists and american college of endocrinology position statement on the 2014 advanced framework for a new diagnosis of obesity as a chronic disease. Endocr Pract. 2014; 20(9): 977-89. https://dx.doi.org/10.4158/EP14280.PS.
  9. Dickey R.A., Bartuska D.G., Bray G.W., Callaway C.W., Davidson E.T., Feld S. et al. AACE/ACE Position statement on the prevention, diagnosis, and treatment of obesity (1998 revision). Endocr Pract. 1998; 4: 297-350.
  10. Чернуха Г.Е., Табеева Г.И., Гусев Д.В., Кузнецов С.Ю. Оценка показателей жировой ткани при функциональной гипоталамической аменорее. Акушерство и гинекология. 2018; 2: 74-80. [Chernukha G.E., Tabeeva G.I., Gusev D.V., Kuznetsov S.Yu. Estimation of adipose tissue indicators in functional hypothalamic amenorrhea. Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2018; (2): 74-80. (in Russian)]. https://dx.doi.org/10.18565/aig.2018.2.74-80.
  11. Kim J.Y., Han S.H., Yang B.M. Implication of high-body-fat percentage on cardiometabolic risk in middle-aged, healthy, normal-weight adults. Obesity (Silver Spring). 2013; 21(8): 1571-7. https://dx.doi.org/10.1002/oby.20020.
  12. Ru derm an N.B., Schneider S.H., Berchtold P. The ‘metabolically-obese,’ normal-weight individual. Am J. Clin Nutr. 1981; 34(8): 1617-21. https://dx.doi.org/10.1093/ajcn/34.8.1617.
  13. Fan B., Shepherd J.A., Levine M.A., Steinberg D., Wacker W., Barden H.S. et al. National Health and Nutrition Examination Survey whole-body dual-energy X-ray absorptiometry reference data for GE Lunar systems. J. Clin Densitom. 2014; 17(3): 344-77. https://dx.doi.org/10.1016/j.jocd.2013.08.019.
  14. World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO Consultation on Obesity, Geneva, 3-5 June 1997. Geneva: WHO; 1998.
  15. Sim S.J., Park H.S. The cut-off values of body fat to identify cardiovascular risk among Korean adults. Korean J. Obes. 2004; 13(1): 14-21.
  16. Cho Y.G., Song H.J., Kim J.M., Park K.H., Paek Y.J., Cho J.J. et al. The estimation of cardiovascular risk factors by body mass index and body fat percentage in Korean male adults. Metabolism. 2009; 58(6): 765-71. https://dx.doi.org/10.1016/j.metabol.2009.01.004.
  17. Dudeja V., Misra A., Pandey R.M., Devina G., Kumar G., Vikram N.K. BMI does not accurately predict overweight in Asian Indians in northern India. Br J. Nutr. 2001; 86(1): 105-12. https://dx.doi.org/10.1079/bjn2001382.
  18. Goh V.H., Tain C.F., Tong T.Y., Mok H.P., Wong M.T. Are BMI and other anthropometric measures appropriate as indices for obesity? A study in an Asian population. J. Lipid Res. 2004; 45(10): 1892-8. https://dx.doi.org/10.1194/jlr.M400159-ЛR200.
  19. Miazgowski T., Krzyzanowska-Swiniarska B., Dziwura-Ogonowska J., Widecka K. The associations between cardiometabolic risk factors and visceral fat measured by a new dual-energy X-ray absorptiometry-derived method in lean healthy Caucasian women. Endocrine. 2014; 47(2): 500-5. https://dx.doi.org/10.1007/s12020-014-0180-7.
  20. Sumner A.E., Cowie C.C. Ethnic differences in the ability of triglyceride levels to identify insulin resistance. Atherosclerosis. 2008; 196(2): 696-703. https://dx.doi.org/10.1016/j.atherosclerosis.2006.12.018.
  21. Randeva H.S., Tan B.K., Weickert M.O., Lois K., Nestler J.E., Sattar N. et al. Cardiometabolic aspects of the polycystic ovary syndrome. Endocr Rev. 2012; 33(5): 812-41. https://dx.doi.org/10.1210/er.2012-1003.
  22. Чернуха Г.Е., Блинова И.В., Купрашвили М.И. Эндокринно-метаболические характеристики больных с различными фенотипами синдрома поликистозных яичников. Акушерство и гинекология. 2011; 2: 70-6.
  23. Чернуха Г.Е., Блинова И.В. СПКЯ. Кардиоваскулярные риски и влияние на них терапии сиофором. Трудный пациент. 2008; 6(1): 18-22.
  24. Kirchengast S., Huber J. Body composition characteristics and fat distribution patterns in young infertile women. Fertil Steril. 2004; 81(3): 539-44. https://dx.doi.org/10.1016/j.fertnstert.2003.08.018.
  25. Yildrim B., Sabir N., Kaleli B. Relation of intra-abdominal fat distribution to metabolic disorders in nonobese patients with polycystic ovary syndrome. Fertil Steril. 2003; 79(6): 1358-64. https://dx.doi.org/10.1016/s0015-0282(03)00265-6.
  26. Yucel A., Noyan V., Sagsoz N. The association of serum androgens and insulin resistance with fat distribution in polycystic ovary syndrome. Eur J. Obstet Gynecol Reprod Biol. 2006; 126(1): 81-6. https://dx.doi.org/10.1016/j.ejogrb.2005.11.012.
  27. Carmina E., Bucchieri S., Esposito A., Del Puente A., Mansueto P., Orio F. et al. Abdominal fat quantity and distribution in women with polycystic ovary syndrome and extent of its relation to insulin resistance. J. Clin Endocrinol Metab. 2007; 92(7): 2500-5. https://dx.doi.org/10.1210/jc.2006-2725.
  28. Puder J.J., Varga S., Kraenzlin M., De Geyter C., Keller U., Muller B. Central fat excess in polycystic ovary syndrome: relation to low grade inflammation and insulin resistance. J. Clin Endocrinol Metab. 2005; 90(11): 6014-21. https://dx.doi.org/10.1210/jc.2005-1002.
  29. Faloia E., Canibus P., Gatti C., Frezza F., Santangelo M., Garrappa G.G. et al. Body composition, fat distribution and metabolic characteristics in lean and obese women with polycystic ovary syndrome. J. Endocrinol Invest. 2004; 27(5): 424-9. https://dx.doi.org/10.1007/BF03345285.
  30. Samuel V.T., Shulman G.I. The pathogenesis of insulin resistance: integrating signaling pathways and substrate flux. J. Clin Invest. 2016; 126(1): 12-22. https://dx.doi.org/10.1172/JCI77812.
  31. Ding C., Chan Z., Magkos F. Lean, but not healthy: the 'metabolically obese, normal-weight' phenotype. Curr Opin Clin Nutr Metab Care. 2016; 19(6): 408 17. https://dx.doi.org/10.1097/MCO.0000000000000317
  32. Hestiantoro A., Kapnosa Hasani R.D., Shadrina A., Situmorang H., Ilma N., Muharam R. et al. Body fat percentage is a better marker than body mass index for determining inflammation status in polycystic ovary syndrome. Int J. Reprod Biomed. 2018; 16(10): 623-8.
  33. Rojas J., Chavez M., Olivar L., Rojas M., Morillo J., Mejias J. et al. Polycystic ovary syndrome, insulin resistance, and obesity: navigating the pathophysiologic labyrinth. Int J. Reprod Med. 2014; 2014: 719050. https://dx.doi.org/10.1155/2014/719050.
  34. Lim S.S., Davies M.J., Norman R.J., Moran L.J. Overweight, obesity and central obesity in women with polycystic ovary syndrome: a systematic review and metaanalysis. Hum Reprod Update. 2012; 18(6): 618-37. https://dx.doi.org/10.1093/humupd/dms030.
  35. International Evidence-Based Guideline for the Assessment and Management of Polycystic Ovary Syndrome. 2018. Available at: https://www.monash.edu/medicine/sphpm/mchri/pcos/guideline Accessed 14 June 2019.

Supplementary files

Supplementary Files
Action
1. JATS XML

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies