Bone area (BA) and bone mineral content (BMC) were measured from childhood to young adulthood at the total body (TB), lumbar spine (LS), total hip (TH), and femoral neck (FN). BA and BMC values were expressed as a percentage of young-adult values to determine if and when values reached a plateau. Data were aligned on biological ages [years from peak height velocity (PHV)] to control for maturity. TB BA increased significantly from −4 to +4 years from PHV, with TB BMC reaching a plateau, on average, 2 years later at +6 years from PHV (equates to 18 and 20 years of age in girls and boys, respectively). LS BA increased significantly from −4 years from PHV to +3 years from PHV, whereas LS BMC increased until +4 from PHV. FN BA increased between −4 and +1 years from PHV, with FN BMC reaching a plateau, on average, 1 year later at +2 years from PHV. In the circumpubertal years (−2 to +2 years from PHV): 39% of the young-adult BMC was accrued at the TB in both males and females; 43% and 46% was accrued in males and females at the LS and TH, respectively; 33% (males and females) was accrued at the FN. In summary, we provide strong evidence that BA plateaus 1 to 2 years earlier than BMC. Depending on the skeletal site, peak bone mass occurs by the end of the second or early in the third decade of life. The data substantiate the importance of the circumpubertal years for accruing bone mineral. © 2011 American Society for Bone and Mineral Research

Dietary patterns established during adolescence might play a role in adulthood disease. We examined the stability of dietary patterns (DPs) from childhood through adolescence and into young adulthood (from age 8 to 34 years). Data from 130 participants (53 females) of Saskatchewan Pediatric Bone Mineral Accrual Study (aged 8–15 years, at baseline) were included. Multiple 24-h recalls were collected annually from 1991 to 1997, 2002 to 2005, and 2010 and 2011. Using principal component analysis, “Vegetarian-style”, “Western-like”, “High-fat, high-protein”, “Mixed”, and “Snack” DPs were derived at baseline. Applied DP scores for all annual measurements were calculated using factor loading of baseline DPs and energy-adjusted food group intakes. We analyzed data using generalized estimating equations. The tracking coefficient represents correlation between baseline dietary pattern scores and all other follow-up dietary pattern scores. We found a moderate tracking for the “Vegetarian-style” (β = 0.44, p < 0.001) and “High-fat, high-protein” (β = 0.39, p < 0.001) DPs in females and “Vegetarian-style” DP (β = 0.30, p < 0.001) in males. The remaining DPs showed poor-to-fair tracking in both sexes. No tracking for “Western-like” DP in females was observed. Assessing overall change in DP scores from childhood to young adulthood showed an increasing trend in adherence to “Vegetarian-style” DP and decreasing trend in adherence to “High-fat, high-protein” DP by age in both sexes (p < 0.001), while “Western-like” and “Mixed” DP scores increased only in males (p < 0.001). These findings suggest that healthy dietary habits established during childhood and adolescence moderately continue into adulthood.

Background : Although early in life there is little discernible difference in bone mass between boys and girls, at puberty sex differences are observed. It is uncertain if these differences represent differences in bone mass or just differences in anthropometric dimensions. Aim : The study aimed to identify whether sex independently affects bone mineral content (BMC) accrual in growing boys and girls. Three sites are investigated: total body (TB), femoral neck (FN) and lumbar spine (LS). Subjects and methods : 85 boys and 67 girls were assessed annually for seven consecutive years. BMC was assessed by dual energy X-ray absorptiometry (DXA). Biological age was defined as years from age at peak height velocity (PHV). Data were analysed using a hierarchical (random effects) modelling approach. Results : When biological age, body size and body composition were controlled, boys had statistically significantly higher TB and FN BMC at all maturity levels ( p < 0.05). No independent sex differences were found at the LS ( p > 0.05). Conclusion : Although a statistical significant sex effect is observed, it is less than the error of the measurement, and thus sex difference are debatable. In general, sex difference are explained by anthropometric difference.

During childhood, physical activity is likely the most important modifiable factor for the development of lean mass. However, the effects of normal growth and maturation must be controlled. To distinguish effects of physical activity from normal growth, longitudinal data are required. One hundred nine boys and one hundred thirteen girls, participating in the Saskatchewan Pediatric Bone Mineral Accrual Study, were repeatedly assessed for 6 yr. Age at entry was 8–15 yr. Stature, body mass, and physical activity were assessed biannually. Body composition was assessed annually by dual-energy X-ray absorptiometry. Physical activity was determined using the physical activity questionnaires for children and adolescence. Biological age was defined as years from age of peak height velocity. Data were analyzed using multilevel random-effects models. In boys, it was found that physical activity had a significant time-dependent effect on lean mass accrual of the total body (484.7 ± 157.1 g), arms (69.6 ± 27.2 g), legs (197.7 ± 60.5 g), and trunk (249.1 ± 91.4 g) ( P < 0.05). Although the physical activity effects were similar in the girls (total body: 306.9 ± 96.6 g, arms: 31.4 ± 15.5 g, legs: 162.9 ± 40.0 g, and trunk: 119.6 ± 58.2 g; P < 0.05), boys for the same level of activity accrued, depending on the site, between 21 and 120% more absolute lean mass (g). In conclusion, habitual physical activity had a significant independent influence on the growth of lean body mass during adolescence, once biological maturity and stature were controlled.

Background: Longitudinal data analysis can improve our understanding of the influences on health trajectories across the life-course. There are a variety of statistical models which can be used, and their fitting and interpretation can be complex, particularly where there is a nonlinear trajectory. Our aim was to provide an accessible guide along with applied examples to using four sophisticated modelling procedures for describing nonlinear growth trajectories.Methods: This expository paper provides an illustrative guide to summarising nonlinear growth trajectories for repeatedly measured continuous outcomes using (i) linear spline and (ii) natural cubic spline linear mixed-effects (LME) models, (iii) Super Imposition by Translation and Rotation (SITAR) nonlinear mixed effects models, and (iv) latent trajectory models. The underlying model for each approach, their similarities and differences, and their advantages and disadvantages are described. Their application and correct interpretation of their results is illustrated by analysing repeated bone mass measures to characterise bone growth patterns and their sex differences in three cohort studies from the UK, USA, and Canada comprising 8500 individuals and 37,000 measurements from ages 5–40 years. Recommendations for choosing a modelling approach are provided along with a discussion and signposting on further modelling extensions for analysing trajectory exposures and outcomes, and multiple cohorts.Results: Linear and natural cubic spline LME models and SITAR provided similar summary of the mean bone growth trajectory and growth velocity, and the sex differences in growth patterns. Growth velocity (in grams/year) peaked during adolescence, and peaked earlier in females than males e.g., mean age at peak bone mineral content accrual from multicohort SITAR models was 12.2 years in females and 13.9 years in males. Latent trajectory models (with trajectory shapes estimated using a natural cubic spline) identified up to four subgroups of individuals with distinct trajectories throughout adolescence.Conclusions: LME models with linear and natural cubic splines, SITAR, and latent trajectory models are useful for describing nonlinear growth trajectories, and these methods can be adapted for other complex traits. Choice of method depends on the research aims, complexity of the trajectory, and available data. Scripts and synthetic datasets are provided for readers to replicate trajectory modelling and visualisation using the R statistical computing software.