title: Identification of insulin resistance in Asian Indian adolescents: classification and regression tree (CART) and logistic regression based classification rules.
abstract: OBJECTIVE
  Biochemical measures for assessment of insulin resistance are not cost-effective in resource-constrained developing countries. Using classification and regression tree (CART) and multivariate logistic regression, we aimed to develop simple predictive decision models based on routine clinical and biochemical parameters to predict insulin resistance in apparently healthy Asian Indian adolescents.
  
  
  METHODS
  Community based cross-sectional study.
  
  
  METHODS
  Data of apparently healthy 793 adolescents (aged 14-19 years) were used for analysis. WHO's multistage cluster sampling design was used for data collection.
  
  
  METHODS
  Homeostasis Model of Assessment value > 75th centile was used as cut-off for defining the main outcome variable insulin resistance. CART was used to develop the decision tree models and multivariate logistic regression used to develop the clinical prediction score.
  
  
  RESULTS
  Three classification trees and an equation for prediction score were developed and internally validated. The three decision trees were termed as CART I, CART II and CART III, respectively. CART I based on anthropometric parameters alone has sensitivity 88.2%, specificity 50.1% and area under receiver operating characteristic curve (aROC) 77.8%. CART II based on anthropometric and routine biochemical parameters has sensitivity 94.5%, specificity 38.3% and aROC 73.6%. CART III based on all anthropometric, biochemical and clinical parameters together has sensitivity 70.7%, specificity 79.2% and aROC 77.4%. Prediction score for insulin resistance = 1 x (waist circumference) + 1.1 x (percentage body fat) + 1.6 x (triceps skin-fold thickness) - 1.9 x (gender). A score cut-off of > 0 (using values marked for each) was a marker of insulin resistance in the study population (sensitivity 82.4%, specificity 56.7%, and aROC 73.4%).
  
  
  CONCLUSIONS
  These simple and cost-effective classification rules may be used to predict insulin resistance and implement population based preventive interventions in Asian Indian adolescents.
authors.affiliation: Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA.
authors.forename: Ruchika
authors.initials: R
authors.lastname: Goel
authors.forename: Anoop
authors.initials: A
authors.lastname: Misra
authors.forename: Dimple
authors.initials: D
authors.lastname: Kondal
authors.forename: Ravindra M
authors.initials: RM
authors.lastname: Pandey
authors.forename: Naval K
authors.initials: NK
authors.lastname: Vikram
authors.forename: Jasjeet S
authors.initials: JS
authors.lastname: Wasir
authors.forename: Vibha
authors.initials: V
authors.lastname: Dhingra
authors.forename: Kalpana
authors.initials: K
authors.lastname: Luthra
mesh_terms: D050154:Adiposity; D000293:Adolescent; D000328:Adult; D044466:Asian People; D002965:Classification / Q000379:methods; D003430:Cross-Sectional Studies; D003663:Decision Trees; D003924:Diabetes Mellitus, Type 2 / Q000209:etiology; D005260:Female; D006801:Humans; D007194:India; D007333:Insulin Resistance / Q000208:ethnology; D016015:Logistic Models; D008297:Male; D012372:ROC Curve; D012044:Regression Analysis; D012307:Risk Factors; D012885:Skinfold Thickness; D055815:Young Adult