Micro and macro perspectives in auxology: findings and considerations upon the variability of short term and individual growth and the stability of population derived parameters

A time-varying biased random walk approach to human growth

Growth and development are dominated by gene-environment interactions. Many approaches have been proposed to model growth, but most are either descriptive or describe population level phenomena. We present a random walk-based growth model capable of predicting individual height, in which the growth increments are taken from time varying distributions mimicking the bursting behaviour of observed saltatory growth. We derive analytic equations and also develop a computational model of such growth that takes into account gene-environment interactions. Using an independent prospective birth cohort study of 190 infants, we predict height at 6 years of age. In a subset of 27 subjects, we adaptively train the model to account for growth between birth and 1 year of age using a Bayesian approach. The 5-year predicted heights compare well with actual data (measured height = 0.838*predicted height + 18.3; R² = 0.51) with an average error of 3.3%. In one patient, we also exemplify how our growth prediction model can be used for the early detection of growth deficiency and the evaluation of the effectiveness of growth hormone therapy.

Genetic Determinants of Prepubertal and Pubertal Growth and Development

This article surveys the current general understanding of genetic influences on within- and between-population variation in growth and development in the context of establishing an International Growth Standard for Preadolescent and Adolescent Children. Traditional genetic epidemiologic analysis methods are reviewed, and evidence from family studies for genetic effects on different measures of growth and development is then presented. Findings from linkage and association studies seeking to identify specific genomic locations and allelic variants of genes influencing variation in growth and maturation are then summarized. Special mention is made of the need to study the interactions between genes and environments. At present, specific genes and polymorphisms contributing to variation in growth and maturation are only beginning to be identified. Larger genetic epidemiologic studies are needed in different parts of the world to better explore population differences in gene frequencies and gene—environment interactions. As advances continue to be made in molecular and statistical genetic methods, the genetic architecture of complex processes, including those of growth and development, will become better elucidated. For now, it can only be concluded that although the fundamental genetic underpinnings of the growth and development of children worldwide are likely to be essentially the same, there are also likely to be differences between populations in the frequencies of allelic gene variants that influence growth and maturation and in the nature of gene–environment interactions. This does not necessarily preclude an international growth reference, but it does have important implications for the form that such a reference might ultimately take.

Growth, telomere dynamics and successful and unsuccessful human aging

This paper links mass trajectories with telomere dynamics to construct theoretical models of successful and unsuccessful aging in human beings. It couples parameters of telomere length in somatic cells, as expressed by the terminal restriction fragment (TRF), at birth and the rate of telomere attrition thereafter with nonlinear models of somatic growth to predict the probability of surviving disease free, based on the assumption that telomere length in replicating somatic cells is a surrogate indicator of aging determinants in humans. The models capture aspects of individual variation in successful and unsuccessful aging and the long-term consequences of rapid growth early in life.

Synthetic standards for body weight

Growth charts represent body stature, body weight, and body mass index (BMI) from birth to maturity. Due to secular changes in these parameters, growth charts tend to become outdated, and must be revised from time to time. Recently, we developed alternative strategies that facilitate developing and renewing growth charts, and suggested synthetic standards for body stature. The increasing prevalence of obesity has made it necessary to develop similar techniques also for monitoring body weight and BMI. Two-hundred-and-forty historic and modern growth studies (108 studies of male growth, 132 studies of female growth) were selected from 22 European, 6 American, 3 African, and 6 Asian nations, published between 1831 and 2001. The studies contained annual information on weight and stature, either between birth and 6 years, or between 6 years and maturity, or information on the whole age range between birth and maturity. Since historic studies up to the mid-20th century usually ignore the fact that body weight (in contrast to body stature) is not normally distributed, a group of 92 more recent studies (45 male, 47 female), published between 1943 and 2001, presenting centiles for weight, was chosen for additional analysis. Furthermore, the skewness of body weight distributions, was investigated in original raw data of body weight obtained from five well reputed longitudinal growth studies, performed at Jena, Germany, Lublin, Poland, Paris, France, Prague, Czech Republic, and Zürich, Switzerland. Average body stature and average weight differ markedly between different populations. But within the same population, both parameters are closely interrelated. In males, birth length and weight correlated with r = 0.503, stature and weight correlated with r = 0.873 at the age of 2 years; with r = 0.882 at the age of 6 years; with r = 0.935 at the age of 14 years, and with r = 0.891 at the age of 18 years. Similar results were obtained in females. At birth, length and weight correlated with r = 0.619. Stature and weight correlated with r = 0.863 at the age of 2; with r = 0.912 at the age of 6; with r = 0.935 at the age of 12; and with r = 0.918 at the age of 15 years. Tables of linear regression coefficients for relative stature and weight at all ages enable the reversal of the process of the meta-analysis and allow the generation of synthetic growth references for stature and weight. Synthetic reference charts help in the revision of current growth charts without much additional effort, and may be used for populations for which autochthonous growth standards are not available.