title: Predicting impaired glucose metabolism in women with polycystic ovary syndrome by decision tree modelling.
abstract: OBJECTIVE
  Polycystic ovary syndrome (PCOS) is a risk factor of type 2 diabetes. Screening for impaired glucose metabolism (IGM) with an OGTT has been recommended, but this is relatively time-consuming and inconvenient. Thus, a strategy that could minimise the need for an OGTT would be beneficial.
  
  
  METHODS
  Consecutive PCOS patients (n=118) with fasting glucose <6.1 mmol/l were included in the study. Parameters derived from medical history, clinical examination and fasting blood samples were assessed by decision tree modelling for their ability to discriminate women with IGM (2-h OGTT value >/=7.8 mmol/l) from those with NGT.
  
  
  RESULTS
  According to the OGTT results, 93 PCOS women had NGT and 25 had IGM. The best decision tree consisted of HOMA-IR, the proinsulin:insulin ratio, proinsulin, 17-OH progesterone and the ratio of luteinising hormone:follicle-stimulating hormone. This tree identified 69 women with NGT. The remaining 49 women included all women with IGM (100% sensitivity, 74% specificity to detect IGM). Pruning this tree to three levels still identified 53 women with NGT (100% sensitivity, 57% specificity to detect IGM). Restricting the data matrix used for tree modelling to medical history and clinical parameters produced a tree using BMI, waist circumference and WHR. Pruning this tree to two levels separated 27 women with NGT (100% sensitivity, 29% specificity to detect IGM). The validity of both trees was tested by a leave-10%-out cross-validation.
  
  
  CONCLUSIONS
  Decision trees are useful tools for separating PCOS women with NGT from those with IGM. They can be used for stratifying the metabolic screening of PCOS women, whereby the number of OGTTs can be markedly reduced.
authors.affiliation: Department of Clinical Nutrition, German Institute of Human Nutrition Potsdam-Rehbruecke, 14558 Nuthetal, Germany. mmoehlig@dife.de
authors.forename: M
authors.initials: M
authors.lastname: Möhlig
authors.forename: A
authors.initials: A
authors.lastname: Flöter
authors.forename: J
authors.initials: J
authors.lastname: Spranger
authors.forename: M O
authors.initials: MO
authors.lastname: Weickert
authors.forename: T
authors.initials: T
authors.lastname: Schill
authors.forename: H W
authors.initials: HW
authors.lastname: Schlösser
authors.forename: G
authors.initials: G
authors.lastname: Brabant
authors.forename: A F H
authors.initials: AF
authors.lastname: Pfeiffer
authors.forename: J
authors.initials: J
authors.lastname: Selbig
authors.forename: C
authors.initials: C
authors.lastname: Schöfl
mesh_terms: D000328:Adult; D001786:Blood Glucose / Q000378:metabolism; D015992:Body Mass Index; D015331:Cohort Studies; D003663:Decision Trees; D005260:Female; D018149:Glucose Intolerance / Q000097:blood / Q000209:etiology*; D005951:Glucose Tolerance Test; D006728:Hormones / Q000097:blood; D006801:Humans; D015233:Models, Statistical; D011085:Polycystic Ovary Syndrome / Q000097:blood*; D011237:Predictive Value of Tests
chemical_list: D001786:Blood Glucose; D006728:Hormones