Estimating overweight risk in childhood from predictors during infancy.

Pediatrics. 2013 Aug;132(2):e414-21. doi: 10.1542/peds.2012-3858.Epub 2013Jul15.
Weng SF, Redsell SA, Nathan D, Swift JA, Yang M, Glazebrook C.

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.