Comparison of Two Statistical Methods to Determine Normal Range of Androgen Hormones: K-Means Cluster Analysis and Receiver Operating Characteristic Curve
AbstractObjective: To assess and compare the normal ranges of androgen hormones level, total testosterone (TT), free testosterone (FT), dehydrotestosterone (DHT), androstenedione (A4), dehydroepiandrosterone (DHEA), and dehydroepiandrosterone sulfate (DHEAS), in Iranian women based on different statistical methods.Materials and methods: This study was conducted on previous data collected in Iranian PCOS Prevalence Study, which details have been published before. A total of 1772 women of 18-45 years were recruited from urban areas of five randomly selected provinces in different geographic regions of Iran. The natural range of androgen hormones was determined and compared by two statistical methods including k-means cluster analysis, and receiver operating characteristic curve.Results: In women younger than 35 years old with any BMI, cut-off points obtained for FAI hormone were in lower percentiles; however, in older women, the results of the two methods were almost the same. Cut-off points of DHEAS in under 35 years old women of normal and obese weight and women older than 35 years old with normal weight calculated by ROC curve method was in higher percentiles than that in the cluster analysis method. In >35 years obese women, obtained cut-off points for DHEAS ROC curve was in lower percentiles in comparison to cluster analysisConclusion: Although our study depicts the differences among the cutoff values among two statistical methods; however, lacking a gold standard test to define hyperandrogenism, we need further studies to obtain more comprehensive results.
Lizneva D, Gavrilova-Jordan L, Walker W, et al. Androgen excess: Investigations and management. Best Pract Res Clin Obstet Gynaecol 2016;37:98-118.
Knochenhauer ES, Key TJ, Kahsar-Miller M, et al. Prevalence of the polycystic ovary syndrome in unselected black and white women of the southeastern United States: a prospective study. J Clin Endocrinol Metab 1998;83(9):3078-82.
Azziz R, Woods KS, Reyna R, et al. The prevalence and features of the polycystic ovary syndrome in an unselected population. Journal of Clinical Endocrinology & Metabolism 2004;89(6):2745-9.
Ovalle F, Azziz R. Insulin resistance, polycystic ovary syndrome, and type 2 diabetes mellitus. Fertil Steril 2002;77(6):1095-105.
Wild RA. Long-term health consequences of PCOS. Hum Reprod Update 2002;8(3):231-41.
Hardiman P, Pillay OC, Atiomo W. Polycystic ovary syndrome and endometrial carcinoma. Lancet 2003;361(9371):1810-2.
Legro RS. Polycystic ovary syndrome and cardiovascular disease: a premature association? Endocr Rev 2003;24(3):302-12.
Stanczyk FZ. Diagnosis of hyperandrogenism: biochemical criteria. Best Pract Res Clin Endocrinol Metab 2006;20(2):177-91.
Azziz R, Sanchez L, Knochenhauer E, et al. Androgen excess in women: experience with over 1000 consecutive patients. The Journal of Clinical Endocrinology & Metabolism 2004;89(2):453-62.
Stener-Victorin E, Holm G, Labrie F, et al. Are there any sensitive and specific sex steroid markers for polycystic ovary syndrome? J Clin Endocrinol Metab 2010;95(2):810-9.
Azziz R, Carmina E, Dewailly D, et al. Criteria for defining polycystic ovary syndrome as a predominantly hyperandrogenic syndrome: an androgen excess society guideline. The Journal of Clinical Endocrinology & Metabolism 2006;91(11):4237-45.
Yildiz BO, Bolour S, Woods K, et al. Visually scoring hirsutism. Human reproduction update 2009;16:51-64.
Hashemi S, Ramezani Tehrani F, Noroozzadeh M, et al. Normal cut-off values for hyperandrogenaemia in Iranian women of reproductive age. European Journal of Obstetrics & Gynecology and Reproductive Biology 2014;172:51-5.
DeUgarte CM, Woods K, Bartolucci AA, et al. Degree of facial and body terminal hair growth in unselected black and white women: toward a populational definition of hirsutism. The Journal of Clinical Endocrinology & Metabolism 2006;91(4):1345-50.
Tehrani FR, Simbar M, Tohidi M, et al. The prevalence of polycystic ovary syndrome in a community sample of Iranian population: Iranian PCOS prevalence study. Reprod Biol Endocrinol 2011;9(39):39.
Kumarapeli V, Seneviratne RdA, Wijeyaratne C, et al. A simple screening approach for assessing community prevalence and phenotype of polycystic ovary syndrome in a semiurban population in Sri Lanka. American journal of epidemiology 2008;168(3):321-8.
Chen X, Yang D, Mo Y, et al. Prevalence of polycystic ovary syndrome in unselected women from southern China. European Journal of Obstetrics & Gynecology and Reproductive Biology 2008;139(1):59-64.
Ehrmann DA, Liljenquist DR, Kasza K, et al. Prevalence and predictors of the metabolic syndrome in women with polycystic ovary syndrome. The Journal of Clinical Endocrinology & Metabolism 2006;91(1):48-53.
March WA, Moore VM, Willson KJ, et al. The prevalence of polycystic ovary syndrome in a community sample assessed under contrasting diagnostic criteria. Human Reproduction 2010;25(2):544-51.
Kaufman L, Rousseeuw PJ. Finding groups in data: an introduction to cluster analysis. John Wiley & Sons; 2009.
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143(1):29-36.
Subhash S. Applied multivariate techniques. John Wily & Sons Inc, Canada 1996.
Halligan S, Altman DG, Mallett S. Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: a discussion and proposal for an alternative approach. Eur Radiol 2015;25(4):932-9.
Krebs D, Berger M, Ferligoj A. Approaching achievement motivation-comparing factor analysis and cluster analysis. New approaches in applied statistics, Metodoloski zvezki 2000;16.
Alpaydin E. Introduction to machine learning. MIT press; 2014.
Munro BH. Statistical methods for health care research. Lippincott Williams & Wilkins; 2005.
Thompson B. Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association; 2004.
Tucker LR, MacCallum RC. Exploratory factor analysis. Unpublished manuscript, Ohio State University, Columbus 1997.
Sharifi F, Mousavinasab N, Mellati AA. Defining a cutoff point for vitamin D deficiency based on insulin resistance in children. Diabetes Metab Syndr 2013;7(4):210-3.
Burns RD, Brusseau TA, Fu Y, et al. Establishing school day pedometer step count cut-points using ROC curves in low-income children. Prev Med 2016;86:117-22.
Zhao X, He Z, Mo Y, et al. Determining the normal cut-off levels for hyperandrogenemia in Chinese women of reproductive age. European Journal of Obstetrics & Gynecology and Reproductive Biology 2011;154(2):187-91.