title: Using cluster analysis to identify a homogeneous subpopulation of women with polycystic ovarian morphology in a population of non-hyperandrogenic women with regular menstrual cycles.
abstract: OBJECTIVE
  Can cluster analysis can be used to identify a homogeneous subpopulation of women with polycystic ovarian morphology (PCOM) within a very large population of control women in a non-subjective way?
  
  
  CONCLUSIONS
  Identification and exclusion of the cluster corresponding to women with PCOM from controls improved the diagnostic power of serum anti-Müllerian hormone (AMH) level and follicle number per ovary (FNPO) in discriminating between women with or without polycystic ovary syndrome (PCOS).
  
  
  BACKGROUND
  There is disagreement as to whether women with PCOM should be excluded from the control population when establishing FNPO and AMH diagnostic thresholds for the definition of PCOS and how to identify such women. It has been demonstrated that cluster analysis can detect women with PCOM within the control population through a set of classifying variables among which the most relevant was AMH. The adequacy of this approach has not been confirmed in other clinical settings.
  
  
  METHODS
  This was a retrospective study using clinical and laboratory data derived from the computerized database. The data were collected from March 2011 to May 2013.
  
  
  METHODS
  The study included 893 patients referred for routine infertility evaluation and treatment. The patients were divided into three groups: (i) the control group (n = 621) included women with regular menstrual cycles and no signs of hyperandrogenism (HA), (ii) the full-blown PCOS group (n = 95) consisted of women who were diagnosed as having PCOS based on the presence of both HA and oligo/amenorrhoea (OA), (iii) the mild PCOS group included women with only two items of the Rotterdam classification, i.e. PCOM at ultrasonography according to the FNPO threshold of 12 or more and either OA (n = 110) or HA (n = 67).
  
  
  RESULTS
  After exclusion of women with PCOM from the controls, the AMH threshold of 28 pmol/l with specificity 97.5% and sensitivity 84.2% [area under the curve (AUC) 0.948 (95% confidence interval (CI) 0.915-0.982)] and FNPO threshold of 12 with specificity 92.5% and sensitivity 83.2% [AUC 0.940 (95% CI 0.909-0.971)] for identifying PCOS were derived from the receiver operating characteristic curve analysis. The AMH threshold of 28 pmol/l had the same specificity for discriminating the mild and the full-blown PCOS phenotypes from controls.
  
  
  CONCLUSIONS
  There was no selection bias other than being evaluated for infertility treatment, however, this could also be considered as a limitation of the study as these women may not necessarily be a representative sample of the general population. The study demonstrated that serum AMH has intrinsically a very high potency to detect women with PCOM within a control group. However, the AMH threshold is specific for this method and clinical setting.
  
  
  CONCLUSIONS
  AMH threshold value for the definition of PCOM is method specific and cannot be universally applied. This study confirmed the results of previous studies that cluster analysis can identify women with PCOM within the very large control population without using a predefined diagnostic threshold for FNPO, and that AMH can detect women with PCOM with a high specificity. The exclusion of PCOM women from the controls by using a cluster analysis should be considered when establishing reference intervals and decision threshold values for various parameters used to characterize PCOS.
  
  
  BACKGROUND
  The authors have no funding/competing interest(s) to declare.
authors.affiliation: Service de Gynécologie Endocrinienne et Médecine de la Reproduction, Hôpital Jeanne de Flandre, C.H.R.U., Lille 59037, France didier.dewailly@chru-lille.fr.
authors.forename: D
authors.initials: D
authors.lastname: Dewailly
authors.affiliation: Department of Human Reproduction, Merkur University Hospital, Zagreb 10 000, Croatia.
authors.forename: M Š
authors.initials: MŠ
authors.lastname: Alebić
authors.affiliation: Unité de Biostatistiques, EA2694, Université Lille Nord de France, Lille, France.
authors.forename: A
authors.initials: A
authors.lastname: Duhamel
authors.affiliation: Department of Medical Biochemistry and Laboratory Medicine, Merkur University Hospital, Zagreb 10 000, Croatia.
authors.forename: N
authors.initials: N
authors.lastname: Stojanović
mesh_terms: D000328:Adult; D054304:Anti-Mullerian Hormone / Q000097:blood*; D001786:Blood Glucose; D016000:Cluster Analysis; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008597:Menstrual Cycle / Q000097:blood*; D010053:Ovary / Q000473:pathology*; D011085:Polycystic Ovary Syndrome / Q000097:blood / Q000175:diagnosis* / Q000473:pathology; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
chemical_list: D001786:Blood Glucose; D054304:Anti-Mullerian Hormone
keywords: anti-Müllerian hormone; ovarian follicle; polycystic ovarian morphology; polycystic ovary syndrome; ultrasonography