Objective: The objective was to investigate whether early life exposures influence the timing of puberty, as defined by both early and late markers, in healthy German girls and boys.

Design: Term participants (n = 215; 49.8% female) of the DONALD (DOrtmund Nutritional and Anthropometric Longitudinally Designed) Study, with sufficient repeated anthropometric measurements between 6 and 13 y to allow estimation of age at take-off of the pubertal growth spurt (ATO) and information on a variety of early life exposures, including birth weight, breastfeeding status, velocity of weight gain, and parental characteristics, were studied. Age at peak height velocity (APHV) and menarche were also considered.

Results: Children who weighed between 2500 and <3000 g at birth were 7 mo younger at ATO than were the other children (β ± SE: –0.56 ± 0.20 y; P = 0.006). Children who had gained weight rapidly between birth and 24 mo (increase in weight SD score >0.67) experienced ATO 4 mo earlier than those who had gained weight normally (–0.34 ± 0.15 y; P = 0.02). Rapid weight gain was also associated with an earlier APHV (P = 0.0006) and, in girls, with an earlier menarche (P = 0.002). Adjustment for body mass index SD score or body fat percentage 1, 2, or 3 y before ATO did not account for these effects.

Conclusion: In both boys and girls, intrauterine and early postnatal growth factors appear to influence both early and later markers of puberty onset independently of prepubertal body composition.

Objective: The objective was to examine the perinatal risk factors related to childhood obesity.

Design: In a prospective study, 89 women with normal glucose tolerance (NGT) or gestational diabetes mellitus (GDM) and their offspring were evaluated at birth and at 8.8 ± 1.8 y. At birth, obstetrical data, parental anthropometric measures, and neonatal body composition were assessed; at follow-up, diet and activity were assessed and laboratory studies were conducted. Weight was classified by using weight for age and sex, and body composition was measured by using dual-energy X-ray absorptiometry. In childhood, data were analyzed as tertiles and prediction models were developed by using logistic and stepwise regression.

Results: No significant differences in Centers for Disease Control and Prevention weight percentiles, body composition, and most metabolic measures were observed between children of mothers with NGT and GDM at follow-up. Children in the upper tertile for weight had greater energy intake (P = 0.02), skinfold thickness (P = 0.0001), and leptin concentrations (P < 0.0001) than did those in tertiles 1 and 2. Children in the upper tertile for percentage body fat had greater waist circumference (P = 0.0001), insulin resistance (P = 0.002), and triglyceride (P = 0.009) and leptin (P = 0.0001) concentrations than did children in tertiles 1 and 2. The correlation between body fat at birth and follow-up was r = 0.29 (P = 0.02). The strongest perinatal predictor for a child in the upper tertile for weight was maternal pregravid body mass index (BMI; kg/m²) >30 (odds ratio: 3.75; 95% CI: 1.39, 10.10; P = 0.009) and for percentage body fat was maternal pregravid BMI >30 (odds ratio: 5.45; 95% CI: 1.62, 18.41; P = 0.006).

Conclusion: Maternal pregravid BMI, independent of maternal glucose status or birth weight, was the strongest predictor of childhood obesity.

Objective: The objective was to evaluate different approaches to describing more extreme values of body mass index (BMI)-for-age by using simple functions of the CDC growth charts.

Design: Empirical data for the 99th and the 1st percentiles of BMI-for-age were calculated from the data set used to construct the growth charts and were compared with estimates extrapolated from the CDC-supplied LMS parameters and to various functions of other smoothed percentiles. A set of reestimated LMS parameters that incorporated a smoothed 99th percentile were also evaluated.

Results: Extreme percentiles extrapolated from the CDC-supplied LMS parameters did not match well to the empirical data for the 99th percentile. A better fit to the empirical data was obtained by using 120% of the smoothed 95th percentile. The empirical first percentile was reasonably well approximated by extrapolations from the LMS values. The reestimated LMS parameters had several drawbacks and no clear advantages.

Conclusions: Several approximations can be used to describe extreme high values of BMI-for-age with the use of the CDC growth charts. Extrapolation from the CDC-supplied LMS parameters does not provide a good fit to the empirical 99th percentile values. Simple approximations to high values as percentages of the existing smoothed percentiles have some practical advantages over imputation of very high percentiles. The expression of high BMI values as a percentage of the 95th percentile can provide a flexible approach to describing and tracking heavier children.

Objective: The objective was to examine whether body composition and certain dietary intakes are associated with AA production in children after accounting for urinary indicators of major adrenarche-related steroidogenic enzymes.

Design: Androgen and glucocorticoid metabolites were profiled by gas chromatography–mass spectrometry in 24-h urine samples of 137 healthy prepubertal children aged 3–12 y, for whom birth characteristics, growth velocity data, and 3-d weighed-diet record information were available. Associations of the sum of C19 metabolites (reflecting daily AA secretion) with nutritional factors [fat mass (FM), fat-free mass (FFM), nutrient intakes, glycemic index, and glycemic load] and AA-relevant estimates of steroidogenic enzyme were examined in stepwise multiple regression models adjusted for age, sex, urine volume, and total energy intake. Enzyme activity estimates were calculated by using specific urinary steroid metabolite ratios.

Results: Of the nutrition-relevant predictors, FM (P < 0.0001) explained most of the variation of AA secretion (R² = 5%). Animal protein intake was also positively associated with AA secretion (P < 0.05), which explained 1% of its variation. FFM (P = 0.1) and total protein intake (P = 0.05) showed positive trends. The difference in daily AA secretion between the lowest and highest quartile of FM was comparable to that between the lowest and highest estimated activity of one of the major steroidogenic enzymes.

Conclusions: Body fat mass may relevantly influence prepubertal adrenarchal androgen status. In addition, animal protein intake may also make a small contribution to AA secretion in children.

Objective: The objective was to assess relations between growth from age 0 to 4 y and CVD status at 4 y in 323 Chilean children with normal birth weight.

Design: From health records we obtained weight and height every 6 mo from age 0 to 3 y and calculated body mass index (BMI; weight/height²). At age 4 y, we measured height, waist circumference, insulin, glucose, and plasma lipids; infant feeding information was provided by the mothers. Outcomes were metabolic score (waist-to-height ratio + glucose + insulin + triglycerides – HDL-cholesterol z scores/5), total cholesterol (TC):HDL cholesterol, and homeostasis model of assessment of insulin resistance.

Results: At 4 y, the prevalence of obesity was 13%. Changes in BMI, particularly from 6 to 24 mo, predicted a higher metabolic score (standardized regression coefficient = 0.29; 95% CI: 0.16, 0.42) but were unrelated to homeostasis model of assessment of insulin resistance and TC:HDL cholesterol. Height changes were not associated with CVD risks at the age of 4 y. Mode of infant feeding was unrelated to CVD status at 4 y; however, in children who were exclusively breastfed at 4 mo, an increase in BMI from 0 to 6 mo was positively associated with TC:HDL cholesterol at 4 y (standardized regression coefficient = 0.24; 95% CI: –0.02, 0.50), whereas in children who were partially or nonbreastfed at 4 mo, it was negatively associated with TC:HDL cholesterol at 4 y (standardized regression coefficient = –0.30; 95% CI: –0.52, –0.08).

Conclusion: In children with normal birth weight and a high prevalence of obesity at 4 y, changes in BMI after 6 mo predicted a higher overall CVD risk at 4 y.