Reference body composition and anthropometry

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Children grow and horses race: is the adiposity rebound a critical period for later obesity?

BACKGROUND: The adiposity rebound is the second rise in body mass index that occurs between 3 and 7 years. An early age at adiposity rebound is known to be a risk factor for later obesity. The aim here is to clarify the connection between the age at rebound and the corresponding pattern of body mass index change, in centile terms, so as to better understand its ability to predict later fatness. DISCUSSION: Longitudinal changes in body mass index during adiposity rebound, measured both in original (kg/m(2)) and standard deviation (SD) score units, are studied in five hypothetical subjects. Two aspects of the body mass index curve, the body mass index centile and the rate of body mass index centile crossing, determine a child's age at rebound. A high centile and upward centile crossing are both associated separately with an early rebound, while a low centile and/or downward centile crossing correspond to a late rebound. Early adiposity rebound is a risk factor for later fatness because it identifies children whose body mass index centile is high and/or crossing upwards. Such children are likely to have a raised body mass index later in childhood and adulthood. This is an example of Peto's "horse racing effect". The association of centile crossing with later obesity is statistical not physiological, and it applies at all ages not just at rebound, so adiposity rebound cannot be considered a critical period for future obesity. Body mass index centile crossing is a more direct indicator of the underlying drive to fatness. SUMMARY: An early age at adiposity rebound predicts later fatness because it identifies children whose body mass index centile is high and/or crossing upwards. Such children are likely to have a raised body mass index later. Body mass index centile crossing is more direct than the timing of adiposity rebound for predicting later fatness

Childhood body mass index trajectories: modeling, characterizing, pairwise correlations and socio-demographic predictors of trajectory characteristics

Background: Modeling childhood body mass index (BMI) trajectories, versus estimating change in BMI between specific ages, may improve prediction of later body-size-related outcomes. Prior studies of BMI trajectories are limited by restricted age periods and insufficient use of trajectory information. Methods: Among 3,289 children seen at 81,550 pediatric well-child visits from infancy to 18 years between 1980 and 2008, we fit individual BMI trajectories using mixed effect models with fractional polynomial functions. From each child's fitted trajectory, we estimated age and BMI at infancy peak and adiposity rebound, and velocity and area under curve between 1 week, infancy peak, adiposity rebound, and 18 years. Results: Among boys, mean (SD) ages at infancy BMI peak and adiposity rebound were 7.2 (0.9) and 49.2 (11.9) months, respectively. Among girls, mean (SD) ages at infancy BMI peak and adiposity rebound were 7.4 (1.1) and 46.8 (11.0) months, respectively. Ages at infancy peak and adiposity rebound were weakly inversely correlated (r = -0.09). BMI at infancy peak and adiposity rebound were positively correlated (r = 0.76). Blacks had earlier adiposity rebound and greater velocity from adiposity rebound to 18 years of age than whites. Higher birth weight z-score predicted earlier adiposity rebound and higher BMI at infancy peak and adiposity rebound. BMI trajectories did not differ by birth year or type of health insurance, after adjusting for other socio-demographics and birth weight z-score. Conclusions: Childhood BMI trajectory characteristics are informative in describing childhood body mass changes and can be estimated conveniently. Future research should evaluate associations of these novel BMI trajectory characteristics with adult outcomes

Body mass index, adiposity rebound and early feeding in a longitudinal cohort (Raine study)

Objective: This study examined the influence of type and duration of infant feeding on adiposity rebound and the tracking of body mass index (BMI) from birth to 14 years. Methods: A sample of 1330 individuals over eight follows-ups was drawn from the Western Australian Pregnancy Cohort (Raine) Study. Trajectories of BMI from birth to adolescence using linear mixed model (LMM) analysis investigated the influence of age breastfeeding stopped and age other milk introduced (binomial 4-month cut-point). A sub-sample of LMM predicted BMI was used to determine BMI and age at nadir for early infant feeding groups. Results: Chi square analysis between early feeding and weight status (normal weight, overweight and obese) groups found a significant difference between age breastfeeding stopped (p Conclusions: Early infant feeding was important in the timing and BMI at adiposity rebound. The relationship between infant feeding and BMI remained up to age 14 years. Although confounding factors cannot be excluded, these findings support the importance of exclusive breastfeeding for longer than four months as a protective behaviour against the development of adolescent obesity

Comparison of BMI Derived from Parent-Reported Height and Weight with Measured Values: Results from the German KiGGS Study

The use of parent-reported height and weight is a cost-efficient instrument to assess the prevalence of children’s weight status in large-scale surveys. This study aimed to examine the accuracy of BMI derived from parent-reported height and weight and to identify potential predictors of the validity of BMI derived from parent-reported data. A subsample of children aged 2–17 years (n = 9,187) was taken from the 2003–2006 cross-sectional German KiGGS study. Parent-reported and measured height and weight were collected and BMI was calculated. Besides descriptive analysis, linear regression models with BMI difference and logistic regression models with weight status misclassification as dependent variables were calculated. Height differences varied by gender and were generally small. Weight and BMI were under-reported in all age groups, the under-reporting getting stronger with increasing age. Overall, the proportion for overweight and obesity based on parental and measured reports differed slightly. In the youngest age group, the proportion of overweight children was overestimated, while it was underestimated for older children and adolescents. Main predictors of the difference between parent reported and measured values were age, gender, weight status and parents’ perception of the child’s weight. In summary, the exclusive use of uncorrected parental reports for assessment of prevalence rates of weight status is not recommended

Weight status is associated with cross-sectional trajectories of motor co-ordination across childhood

Background Research indicates the development of motor co-ordination (MC) may be an important contributing factor to positive or negative weight trajectories across childhood. Objectives To analyse cross-sectional associations between MC and weight status in children (boys n = 3344 – girls n = 3281), aged 6–11 years and assess overweight/obese risk across different ages. Methods Body mass index (BMI) was calculated [body mass (kg)/height (m2)]. MC was evaluated using the Körperkoordination Test für Kinder (KTK) and a motor quotient (MQ) was calculated.MQ distribution data were split into tertiles. The effect of age, sex and MQ tertiles on BMI and MC was tested with a factorial ANOVA. A logistic regression also was performed to calculate odd ratios (OR) for being overweight/obese at each age. Results Children with higher MQ demonstrated lower BMI levels (F(2,6224) = 222.09; P < 0.001). Differences in BMI among MQ tertiles became larger across age (F(10,6224) = 4.53; P < 0.001). The OR of being overweight/obese in both sexes within the lowest MQ tertile increased in each age group from 6 to 11 years. Specifically, OR increased from 2.26 to 27.77 and from 1.87 to 6.81 in boys and girls respectively. Conclusions Children with low levels of MC have a higher risk of being overweight/obese and this risk increases with age