The method Dynamic Phenotype of Body Mass or of the body height was used for the interpretation of growth in children and adolescents from birth to the age of 18 years. Modelling of the body mass growth curve in boys by means of Dynamic Phenotype of Body Mass is expressed in the form of three individual curves which are compatible with the three I, C, P, components of Karlberg's body height growth curve. However the Dynamic Phenotype of Body Mass is based on the direct use of the measured biological values as input parameters of the simulated growth curve e.g. body mass in the origin of the growth curve (GO, kg), the genetic limit of body mass (GLi, kg) inherited from parents and the inherited physiological potency to produce the appropriate body mass increase (dG max, kg/d) in conditions of adequate nutrition and in convenient environment. The components I, C, P, of children and adolescents growth curve do exhibit principal difference in comparison with the growth curves of the other mammals. This difference is characterized by the long lasting (C) component with extremely slow body mass increase indicating the very low growth velocity of body mass growth. This long lasting (C) component of childhood postpones the puberty component (P) from the infancy component (I). This phenomenon makes the principal difference between the body mass growth in man and that of other mammals where immediately after the short episode of postnatal growth follows puberty, sexual and corporeal maturity. Some primates carry out the body mass growth similar to man. The method of Dynamic Phenotypes may be helpful for investigation of the brain's function ontogeny in relation to neural and humoral regulatory mechanisms of body mass growth during childhood and transition into puberty.

Research Institute of Preventive and Social Paediatrics of the Medical Faculty Masaryk University Brno Czech Republic

Estimating Growth in Height from Limited Longitudinal Growth Data Using Full-Curves Training Dataset: A Comparison of Two Procedures of Curve Optimization-Functional Principal Component Analysis and SITAR