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The wide variety of infant formula available on the market can be confusing for parents and physicians. We aimed to determine associations between predominant type of formula used from birth to 4 months and parental and child characteristics and type of physician consulted, and then to describe relations between type of formula used and growth. Our analyses included 1349 infants from the EDEN mother-child cohort. Infant's feeding mode and type of formula used were assessed at 4 months by maternal self-report. Infant's weight and height from birth to 4 months, measured in routine follow-up, were documented by health professionals in the infant's personal health record. Anthropometric z-scores were calculated by using World Health Organization growth standards. Multinomial logistic regression was used to identify factors associated with the type of formula predominantly used; relations with growth were analysed by linear regressions. Partially hydrolysed formulas were more likely to be used by primiparous women (P < 0.001), those breastfeeding longer (P < 0.001) and for infants with family history of allergies (P = 0.002). Thickened formulas were more often used by mothers returning to employment in the first 4 months (P = 0.05) and breastfeeding shortly (P < 0.001). No significant relation was found between infant's growth and type of formula (P > 0.20). Infants breastfed shorter showed higher weight-for-age (P < 0.001) and length-for-age (P = 0.001) z-score changes between birth and 4 months. The use of a specific type of infant formula seems to be mainly related to parental characteristics. Infant's growth in the first 4 months is related to other factors than to the type of formula used., (© 2012 John Wiley & Sons Ltd.)

In the era of the obesity epidemic in children, characterizing childhood growth trajectories (weight, length/height, or body mass index/weight for length) is becoming essential for surveillance. Clinicians routinely use growth curves to identify abnormal growth trajectories. Clinical epidemiologists are interested in both the determinants of growth and the consequences of certain patterns of growth on later health and diseases. Characterizing growth trajectories is also helpful to predict future growth based on past growth and might be useful in the future to compare the impact of various interventions., (© 2014 S. Karger AG, Basel.)

Weight, height and body mass index (BMI) growth trajectories have been associated with several chronic diseases in later life. Our aim was to describe a method to model individual weight and height growth curves during infancy and to show how it can be used to study their determinants and relationships with later health outcomes as well as to predict BMI trajectories. In the EDEN mother-child cohort, we collected 17 measurements of weight and 16 of length/height per child between birth and 3 years of age in 1,900 infants from their health care booklet and during the study clinical examinations at 1 and 3 years; 1,436 (76%) had at least 1 measurement between 2 and 3 years. We fitted individual weight and height growth trajectories using the Jenss nonlinear model including random effects using the 'SAEMIX' package (R software). We studied whether individual growth model parameters were associated with gender in one- and two-step approaches. We indirectly calculated BMI increase against time from both weight and height growth models combined and compared the fit with a direct multilevel spline model. By modeling observed growth data, we homogenized the data in terms of number and age of measurements and were able to calculate other specific parameters as growth velocities., (© 2014 S. Karger AG, Basel.)

Background/aims: The objective of our study was to compare the fit of four growth models for weight and height in contemporary US children between birth and 9 years., Methods: In Project Viva, we collected weight and height growth data between birth and 9 years. We compared the Jenss model, the adapted Jenss model that adds a quadratic term, and the Reed 1st and 2nd order models. We used the log likelihood ratio test to compare nested models and the Akaike (AIC)/Bayesian information criterion (BIC) to compare nonnested models., Results: For weight and height, the adapted Jenss model had a better fit than the Jenss model (for weight: p < 0.0001), and the Reed 2nd order model had a better fit than the Reed 1st order model (for weight: p < 0.0001). Compared with the Reed 2nd order model, the adapted Jenss model had a better fit for both weight (adapted Jenss vs. Reed 2nd order, AIC: 66,974 vs. 82,791, BIC: 67,066 vs. 82,883) and height (adapted Jenss vs. Reed 2nd order, AIC: 87,108 vs. 87,612, BIC: 87,196 vs. 87,700)., Conclusions: In this pre-birth study of children aged 0-9 years, for both weight and height the adapted Jenss model presented the best fit of all four tested models., (© 2014 S. Karger AG, Basel.)