Secular trends in physical growth, biological maturation, and intelligence in children and adolescents born between 1978 and 1993.

Introduction: Human physical growth, biological maturation, and intelligence have been documented as increasing for over 100 years. Comparing the timing of secular trends in these characteristics could provide insight into what underlies them. However, they have not been examined in parallel in the same cohort during different developmental phases. Thus, the aim of this study was to examine secular trends in body height, weight, and head circumference, biological maturation, and intelligence by assessing these traits concurrently at four points during development: the ages of 4, 9, 14, and 18 years. Methods: Data derived from growth measures, bone age as an indicator of biological maturation, and full-scale intelligence tests were drawn from 236 participants of the Zurich Longitudinal Studies born between 1978 and 1993. In addition, birth weight was analyzed as an indicator of prenatal conditions. Results: Secular trends for height and weight at 4 years were positive (0.35 SD increase per decade for height and an insignificant 0.27 SD increase per decade for weight) and remained similar at 9 and 14 years (height: 0.46 SD and 0.38 SD increase per decade; weight: 0.51 SD and 0.51 SD increase per decade, respectively) as well as for weight at age 18 years (0.36 SD increase per decade). In contrast, the secular trend in height was no longer evident at age 18 years (0.09 SD increase per decade). Secular trends for biological maturation at 14 years were similar to those of height and weight (0.54 SD increase per decade). At 18 years, the trend was non-significant (0.38 SD increase per decade). For intelligence, a positive secular trend was found at 4 years (0.54 SD increase per decade). In contrast, negative secular trends were observed at 9 years (0.54 SD decrease per decade) and 14 years (0.60 SD decrease per decade). No secular trend was observed at any of the four ages for head circumference (0.01, 0.24, 0.17, and - 0.04 SD increase per decade, respectively) and birth weight (0.01 SD decrease per decade). Discussion: The different patterns of changes in physical growth, biological maturation, and intelligence between 1978 and 1993 indicate that distinct mechanisms underlie these secular trends.

Child eating behavior predicts body mass index after 1 year: results from the Swiss Preschooler's Health Study (SPLASHY).

Child obesity is a growing global issue. Preventing early development of overweight and obesity requires identifying reliable risk factors for high body mass index (BMI) in children. Child eating behavior might be an important and malleable risk factor that can be reliably assessed with the parent-report Child Eating Behavior Questionnaire (CEBQ). Using a hierarchical dataset (children nested within child care centers) from a representative cohort of Swiss preschool children, we tested whether eating behavior, assessed with a 7-factor solution of the CEBQ, and BMI at baseline predicted the outcome BMI after 1 year, controlling for socioeconomic status (n = 555; 47% female; mean age = 3.9 years, range: 2.2-6.6; mean BMI = 16 kg/m2, range: 11.2-23; mean age- and sex-corrected z-transformed BMI, zBMI = 0.4, range -4 to +4.7). The statistical model explained 65.2% of zBMI at follow-up. Baseline zBMI was a strong positive predictor, uniquely explaining 48.8% of outcome variance. A linear combination of all CEBQ scales, taken together, explained 10.7% of outcome variance. Due to their intercorrelations, uniquely explained variance by any individual scale was of negligible clinical relevance. Only food responsiveness was a significant predictor, when accounting for all other predictors and covariates in the model, and uniquely explained only 0.4% of outcome variance. Altogether, our results confirm, extend, and refine previous research on eating behavior and zBMI in preschool children, by adjusting for covariates, accounting for intercorrelations between predictors, partitioning explained outcome variance, and providing standardized beta estimates. Our findings show the importance of carefully examining the contribution of predictors in multiple regression models for clinically relevant outcomes.