Early stature prediction method using stature growth parameters

The creation of an accurate growth prediction method for human stature at a stage of growth has been an interesting challenge in medical science and human biology.

The aim of this study was to develop a non-radiographic final stature prediction method that is applicable in the early pubertal growth period.

Randomly selected 12-year serial stature growth data for 400 Koreans were fitted with two nonlinear growth curves: Preece and Baines model 1 (PB1) and Jolicoeur-Pontier-Pernin-Sempe (JPPS) functions. Five biological parameters, including take-off (TO) related parameters, were derived by differentiation of the two curves, respectively. Those five variables were composed into a multiple linear regression equation for final stature prediction. In the cross-validation subjects, TO-related variables were estimated by linear interpolation from the partial growth data prior to estimation age, then incorporated into the prediction equation.

The final stature prediction model had excellent validity and accuracy when applied to the cross-validation samples. Prediction accuracy increased according to increasing years after take-off.

This study suggests that a final stature prediction method using multiple regression analysis that includes biological parameters can predict stature growth with sufficient validity and accuracy. Incorporation of TO-related parameters allowed us to develop earlier growth evaluation and prediction methods compared with other previous methods.