OBJECTIVE: The purpose of this study was to investigate the association between large-for-gestational-age (LGA) infants and the development of childhood obesity in an inner-city primarily African American population. STUDY DESIGN: Maternal, neonatal, socioeconomic, and nutritional histories were collected for mothers with children who were 2-5 years old. Associations between Alexander and customized birthweight percentiles and body mass index for the age of the child were examined. RESULTS: One hundred ninety-five mother-child pairs were enrolled; the childhood obesity rate was 18%. Increasing Alexander and customized birthweight percentiles were related to increasing obesity. LGA newborn infants were 2.5 times more likely to be obese in childhood than average size newborn infants. Maternal smoking was also associated with childhood obesity. CONCLUSION: LGA infants have the highest likelihood of childhood obesity in this inner-city predominantly African American population. Customized growth percentiles perform best in the identification of the highest risk population.
OBJECTIVE: The purpose of this study was to investigate the association between large-for-gestational-age (LGA) infants and the development of childhood obesity in an inner-city primarily African American population. STUDY DESIGN: Maternal, neonatal, socioeconomic, and nutritional histories were collected for mothers with children who were 2-5 years old. Associations between Alexander and customized birthweight percentiles and body mass index for the age of the child were examined. RESULTS: One hundred ninety-five mother-child pairs were enrolled; the childhood obesity rate was 18%. Increasing Alexander and customized birthweight percentiles were related to increasing obesity. LGA newborn infants were 2.5 times more likely to be obese in childhood than average size newborn infants. Maternal smoking was also associated with childhood obesity. CONCLUSION: LGA infants have the highest likelihood of childhood obesity in this inner-city predominantly African American population. Customized growth percentiles perform best in the identification of the highest risk population.
Authors: Vanessa M Oddo; Noel T Mueller; Keshia M Pollack; Pamela J Surkan; Sara N Bleich; Jessica C Jones-Smith Journal: Public Health Nutr Date: 2017-08-04 Impact factor: 4.022
Authors: Katharina Reiss; Jürgen Breckenkamp; Theda Borde; Silke Brenne; Wolfgang Henrich; Matthias David; Oliver Razum Journal: Int J Public Health Date: 2016-05-10 Impact factor: 3.380
Authors: Anny H Xiang; Mary Helen Black; Bonnie H Li; Mayra P Martinez; David A Sacks; Jean M Lawrence; Thomas A Buchanan; Steven J Jacobsen Journal: Diabetologia Date: 2014-10-24 Impact factor: 10.122
Authors: Corina Lesseur; David A Armstrong; Alison G Paquette; Devin C Koestler; James F Padbury; Carmen J Marsit Journal: Mol Cell Endocrinol Date: 2013-07-30 Impact factor: 4.102