title: Estimating overweight risk in childhood from predictors during infancy.
abstract: OBJECTIVE
  The aim of this study was to develop and validate a risk score algorithm for childhood overweight based on a prediction model in infants.
  
  
  METHODS
  Analysis was conducted by using the UK Millennium Cohort Study. The cohort was divided randomly by using 80% of the sample for derivation of the risk algorithm and 20% of the sample for validation. Stepwise logistic regression determined a prediction model for childhood overweight at 3 years defined by the International Obesity Task Force criteria. Predictive metrics R(2), area under the receiver operating curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.
  
  
  RESULTS
  Seven predictors were found to be significantly associated with overweight at 3 years in a mutually adjusted predictor model: gender, birth weight, weight gain, maternal prepregnancy BMI, paternal BMI, maternal smoking in pregnancy, and breastfeeding status. Risk scores ranged from 0 to 59 corresponding to a predicted risk from 4.1% to 73.8%. The model revealed moderately good predictive ability in both the derivation cohort (R(2) = 0.92, AUROC = 0.721, sensitivity = 0.699, specificity = 0.679, PPV = 38%, NPV = 87%) and validation cohort (R(2) = 0.84, AUROC = 0.755, sensitivity = 0.769, specificity = 0.665, PPV = 37%, NPV = 89%).
  
  
  CONCLUSIONS
  Using a prediction algorithm to identify at-risk infants could reduce levels of child overweight and obesity by enabling health professionals to target prevention more effectively. Further research needs to evaluate the clinical validity, feasibility, and acceptability of communicating this risk.
authors.affiliation: Division of Psychiatry, Institute of Mental Health, University of Nottingham Innovation Park, Nottingham NG7 2TU, United Kingdom.
authors.forename: Stephen F
authors.initials: SF
authors.lastname: Weng
authors.forename: Sarah A
authors.initials: SA
authors.lastname: Redsell
authors.forename: Dilip
authors.initials: D
authors.lastname: Nathan
authors.forename: Judy A
authors.initials: JA
authors.lastname: Swift
authors.forename: Min
authors.initials: M
authors.lastname: Yang
authors.forename: Cris
authors.initials: C
authors.lastname: Glazebrook
mesh_terms: D000465:Algorithms; D001724:Birth Weight; D015992:Body Mass Index; D002675:Child, Preschool; D015331:Cohort Studies; D005260:Female; D005544:Forecasting; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D009765:Obesity / Q000453:epidemiology* / Q000209:etiology / Q000235:genetics / Q000517:prevention & control; D050177:Overweight / Q000453:epidemiology* / Q000209:etiology / Q000235:genetics / Q000517:prevention & control; D011247:Pregnancy; D011446:Prospective Studies; D012306:Risk; D012737:Sex Factors; D006113:United Kingdom; D015430:Weight Gain
keywords: children; early-life; infants; obesity; overweight; risk algorithm