Early-life exposures predicting onset and resolution of childhood overweight or obesity. Issue 10 (24th February 2017) - Kerr, Jessica A; Long, Catherine; Clifford, Susan A; Muller, Joshua; Gillespie, Alanna N; Donath, Susan; Wake, Melissa
Abstract : Objectives: To determine which of multiple early-life exposures predict onset or resolution of overweight/obesity during a 9-year period. Methods: Design : longitudinal cohort from three harmonised community-based cohorts enriched for overweight and obesity. Early-life exposures : child—gestational age; delivery; birth weight; breast feeding; solids introduction; baseline body mass index (BMI); waist circumference; diet; activity; global, physical and psychosocial health. Mother—baseline BMI; education; age; neighbourhood disadvantage; concern for child's weight. Outcome : change in BMI category. Analyses : adjusted logistic regression. Results: On average, the 363 children (57% retention) were 6 and 15 years old at baseline and follow-up. Children were classified as 'never' overweight/obese (38%), 'resolving' overweight/obese (15%), 'becoming' overweight/obese (8%) or 'always' overweight/obese (39%). Compared with 'never overweight/obese' children, odds of 'becoming overweight/obese' were greater with higher child (OR 2.33, 95% CI 1.02 to 5.29) and maternal BMI (OR 1.18, CI 1.07 to 1.31), and lower with higher maternal education (OR 0.09, CI 0.02 to 0.34). Compared with 'always overweight/obese' children, odds of 'resolving overweight/obese' were lower with higher maternal BMI (OR 0.87, CI 0.78 to 0.97), and higher with better child physical health (OR 1.06, CI 1.02 to 1.10) and higher maternal age (OR 1.11, CI 1.01 to 1.22) and education (OR 4.07, CI 1.02 to 16.19). Conclusions: Readily available baseline information (child/maternal BMI, maternal age, education and child health) were the strongest predictors of both onset and resolution of overweight/obesity between the primary school and adolescent years. Perinatal, breastfeeding and lifestyle exposures were not strongly predictive. Results could stimulate development of algorithms identifying children most in need of targeted prevention or treatment.