Biometrical analysis of individual growth curves

Growth curves of Egyptian patients with Turner syndrome

This study analyzes the body anthropometric measurements in females with Turner syndrome (TS) not treated with recombinant human growth hormone. Height, weight, head circumference, and body mass index (BMI) data were collected from 93 patients. Their ages ranged from 6 months to 24 years (mean 10 ± 4.3 years). Chromosomal analysis revealed: 55 patients with 45,X and 38 patients with mosaic karyotypes. Patients were divided into yearly age groups. Standard growth curves were constructed for these Egyptian Turner syndrome (TS) patients. Mean and standard deviations were estimated across the age groups. When comparing the mean heights of patients to the Egyptian standards, short stature (≤2 SD) was found in 96.8% of patients older than 6 years. Patients' mean weight and BMI were higher than controls. The mean height of the studied Egyptian patients was slightly lower than that of females with TS in UK and European patients. Therefore, local reference values are more appropriate than International standards. The charts presented here can be used to optimize routine healthcare for Egyptian TS patients. The use of growth charts specific for Egyptian TS patients can help to discover early physical developmental delay and suggests the necessity of looking for concomitant diseases affecting growth.

Structured latent growth curves for twin data

We describe methods to fit structured latent growth curves to data from MZ and DZ twins. The well-known Gompertz, logistic and exponential curves may be written as a function of three components – asymptote, initial value, and rate of change. These components are allowed to vary and covary within individuals in a structured latent growth model. Such models are highly economical, requiring a small number of parameters to describe covariation across many occasions of measurement. We extend these methods to analyse longitudinal data from MZ and DZ twins and focus on the estimation of genetic and environmental variation and covariation in each of the asymptote, initial and rate of growth factors. For illustration, the models are fitted to longitudinal Bayley Infant Mental Development Scale data published by McArdle (1986). In these data, all three components of growth appear strongly familial with the majority of variance associated with the shared environment; differences between the models were not great. Occasion-specific residual factors not associated with the curve components account for approximately 40% of variance of which a significant proportion is additive genetic. Though the growth curve model fit less well than some others, they make restrictive, falsifiable predictions about the mean, variance and twin covariance of other (not yet measured) occasions of measurement. Twin Research (2000) 3, 165–177.

Body Size in Five-Year-Old Twins: Heritability and Comparison to Singleton Standards

The aim of this study is to examine causes of individual differences in height, weight and body mass index (BMI) in 5-year-old children registered with the Netherlands Twin Register. In addition, we examine whether the results of twin studies can be expanded to the singleton population by comparing the data from twins to Dutch reference growth data and by looking at the twins' target height, which was derived from parental height. For 2996 5-year-old twin pairs, information on height and weight and on parental height was available. Univariate and bivariate genetic analyses of height and weight and univariate analyses of BMI were conducted. In order to compare the twins to the singleton population, standard deviation scores (SDS) for height, BMI and target height were calculated based on Dutch reference growth charts for the general population from 1997. Genetic influences were an important source of variation in height, weight and BMI and the main source of covariation between height and weight. Additive genetic factors accounted for 69% and 66% of the individual differences in height in boys and girls, respectively. For weight, heritability estimates were 59% in boys and 78% in girls and for BMI 34% and 74%. The influence of common environment on height was 25% and 27%, on weight 24% and 10% and on BMI 44% and 12% in boys and girls. The bivariate model showed a large overlap between the genes influencing height and weight. Genes explain 78% (in boys) and 76% (in girls) of the covariance between weight and height. At the age of 5 years, female twins were as tall as singleton children, while male twins were shorter than singletons. For both boys and girls, however, mean height SDS was 0.6 standard deviation scores below the mean target height. All twins had lower BMI than singletons. Twins grow fairly well compared to singletons, but they grow below their target height. This may be due to the above average height of twin parents.

Changes in genetic and environmental effects on growth during infancy

BACKGROUND

Accelerated infant growth is a possible explanation for the relation between birth weight and adult diseases.

OBJECTIVE

The aim of this study was to estimate the heritability of infant growth and to examine whether the genetic contribution changes with increasing or decreasing birth weight and gestational age.

DESIGN

Growth (change in weight z score) was analyzed in 522 infants from the East Flanders Prospective Twin Survey for age windows of 0-1, 1-6, 6-12, and 12-24 mo. Structural equation modeling was performed to estimate the relative importance of additive genetic, shared environmental, and unique environmental sources of variance.

RESULTS

We showed no genetic contribution to growth in the 0-1-mo growth period. However, at later ages, the heritability of growth was high at 94% (95% CI: 90%, 96%) from 1 to 6 mo, 85% (95% CI: 80%, 89%) from 6 to 12 mo, and 86% (95% CI: 77%, 91%) in the 12-24-mo growth period. Nevertheless, in the last age window, a model without genetic factors was also statistically plausible. From 0 to 1 mo, the genetic contribution to growth was low in the average birth weight range but higher at both extremes of birth weight. The genetic contribution from 0 to 1 mo increased with increasing gestational age from 36 wk of gestation onward.

CONCLUSIONS

This study shows that genetic factors are not important in early infant growth (0-1 mo), whereas heritability is high after 1 mo. Because many (nutritional) interventions are aimed at influencing early postnatal growth, to target long-term health, these interventions may be most successful if implemented in the first month of postnatal growth.

Individual growth curve models for assessing evidence-based referral criteria in growth monitoring

The goal of this study is to assess whether a growth curve model approach will lead to a more precise detection of Turner sydnrome (TS) than conventional referral criteria for growth monitoring. The Jenss-Bayley growth curve model was used to describe the process of growth over time. A new screening rule is defined on the parameters of this growth curve model, parental height and gestational age. The rule is applied to longitudinal growth data of a group of children with TS (n=777) and a reference (n=487) group. The outcome measures are sensitivity, specificity and median referral age. Growth curve parameters for TS children were different from reference children and can therefore be used for screening. The Jenss-Bayley growth model, which uses all longitudinal measurements from birth to a maximum age of 5 years with at least one measurement after the age of 2, together with parental height and gestational age can achieve a sensitivity of 85.2 per cent with a specificity of 99.5 per cent and a median referral age of 4.2 (the last measurement between the age of 2 and 5 of each child is considered to be the moment of referral). Sensitivity increases by 2 percentage points when decreasing the specificity to 99 per cent. The Jenss-Bayley growth model from birth to a maximum age of 8 years with at least one measurement after the age of 2, together with parental height results in a sensitivity of 89.0 per cent with a specificity of 99.5 per cent and a median referral age of 6.1. For a specificity of 98 per cent, we obtain a sensitivity of 92.3 per cent. In comparison to conventional rules applied to the same data, sensitivity is about 11-30 percentage points higher at the same level of specificity for the Jenss-Bayley growth rule. We conclude that from the age of 4, growth curve models can improve the screening on TS to conventional screening rules.

Relationship between parameters of early growth in Israeli infants

A sample of 1931 Israeli infants was measured for body weight (WT), length (HT) and head circumference (HC) for approximately 2 years. The Count model with 3 parameters was chosen as the best fitting and most parsimonious function to approximate growth of all 3 studied traits. In the model parameter a relates to birth indices, b--to velocity of growth, and c--to rapid early childhood growth, or acceleration. Assuming a difference in growth patterns in the periods of different length, the whole sample was divided into 3 groups: 1) infants with last measurement around the age of 12 months; 2) infants with last measurement around the age of 18 months, and 3) infants with last measurement around the age of 24 months. The individuals measured up to 12 months were presented in all three groups. 27 curve fitting parameters, corresponding to 3 different follow-up intervals for WT, HT and HC were computed for each individual. A high correlation was detected between the a parameters regardless of time interval for 3 measured traits. A negative correlation was found between b and c parameters within the same time interval. A consistent positive correlation was indicated between a and b parameters, especially for body length and head circumference. A principal component analysis extracted five independent factors explaining 88.1% of the total variance. Three first factors retained parameters b and c, describing growth rate and pattern of each trait separately, namely, F1 was responsible for head circumference, F2 was a body length factor, F3 was a body weight factor. F4 extracted all birth indices, observed (HC0, HT0 and WT0) and expected (parameters a). The composition of principal factors allowed us to assume that there might be a strong involvement of a pleiotropic genetic source in determination of birth size traits and an independent genetic source controlling the pattern of growth for each trait separately.

Genetic variance of adolescent growth in stature

The aim of this study is to quantify the genetic and environmental variation in biological parameters of the adolescent growth in stature in both sexes, using structural equation models. Standing height was measured in 99 twin pairs from the Leuven Longitudinal Twin Study. Subjects were seen at semi-annual intervals between 10 and 16 years and at 18 years. Preece-Baines model I was fitted to the individual data and biologically meaningful parameters were derived. Path models were fitted to these parameters and a gender heterogeneity analysis was performed. A model including additive genetic and specific environmental factors (AE-model), allowing for a difference in total variance or in genetic/environmental variance components in males and females best explains most of the growth characteristics. For the timing and velocity of the adolescent growth spurt, no gender heterogeneity was observed, and the genetic (0.89 to 0.93) and specific environmental (0.07 to 0.11) contributions were equal in both sexes. For stature at take-off, stature at peak height velocity and for adult stature, gender heterogeneity was observed and different models appeared to result in the best fit for boys and girls. For height at PHV and adult stature, the significant contribution of a common environmental (0.39 to 0.56) factor in boys was noteworthy. For percentage of adult stature and distances between relevant time points of the growth curve, a non-scalar effect was observed, indicating that different genes affect these characteristics in boys and girls. It is concluded that most characteristics of the growth spurt in stature are under strong genetic control. Gender heterogeneity is present for stature and for a number of derived growth characteristics.