Izzuddin M Aris1, Sheryl L Rifas-Shiman2, Ling-Jun Li3, Ken Kleinman4, Brent A Coull5, Diane R Gold6, Marie-France Hivert7, Michael S Kramer8, Emily Oken9. 1. Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA; Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Agency for Science, Technology and Research, Singapore Institute for Clinical Sciences, Singapore, Singapore. Electronic address: izzuddin_aris@harvardpilgrim.org. 2. Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA. 3. Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA; Division of Obstetrics and Gynecology, KK Women's and Children's Hospital, Singapore, Singapore; Obstetrics and Gynecology, Academic Clinician Programme, Duke-National University of Singapore Graduate Medical School, Singapore, Singapore. 4. Department of Biostatistics, School of Public Health and Human Sciences, University of Massachusetts Amherst, Amherst, MA. 5. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA. 6. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Department of Environmental Medicine, Harvard T.H. Chan School of Public Health, Boston, MA. 7. Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA; Diabetes Unit, Massachusetts General Hospital, Boston, MA. 8. Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Pediatrics and of Epidemiology, Biostatistics and Occupational Health, McGill University Faculty of Medicine, Montreal, Québec, Canada. 9. Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
Abstract
OBJECTIVE: To assess associations of pre-, perinatal, and parental factors with age and magnitude at body mass index (BMI) peak and rebound. STUDY DESIGN: Among 1681 children with BMI data from birth to mid-childhood in Project Viva, we fitted individual BMI trajectories using mixed-effect models with natural cubic spline functions and estimated age and magnitude at peak in infancy and rebound in early childhood. We used stepwise multivariable regression to identify predictors of peak and rebound in the 1354 (63.6%) children with estimable trajectory milestones. RESULTS: The mean (SD) of age at BMI peak was 8.4 (2.7) months and at rebound was 59.8 (19.6) months, and the mean (SD) of magnitude at peak was 18.0 (1.4) kg/m2 and at rebound was 15.9 (1.2) kg/m2. Girls had a later age at peak, earlier age at rebound, and lower magnitudes at peak and rebound than boys. Maternal isolated hyperglycemia (vs normoglycemia: β 0.7 months [95% CI 0.2-1.2]) and pre-eclampsia (vs normal blood pressure: 1.6 months [0.8-2.4]) were associated with a later peak, and impaired glucose tolerance (vs normoglycemia: -0.5 kg/m2 [-0.9, -0.1]) was associated with a lower magnitude at peak. Greater maternal first-trimester weight gain, smoking during pregnancy, no breastfeeding, parental obesity, and no university education were associated with greater BMI at rebound. CONCLUSIONS: We have identified modifiable prenatal and parental predictors of BMI peak in infancy and rebound in childhood. Early-life interventions that address these factors may be effective in changing BMI peak and rebound and potentially preventing later obesity.
OBJECTIVE: To assess associations of pre-, perinatal, and parental factors with age and magnitude at body mass index (BMI) peak and rebound. STUDY DESIGN: Among 1681 children with BMI data from birth to mid-childhood in Project Viva, we fitted individual BMI trajectories using mixed-effect models with natural cubic spline functions and estimated age and magnitude at peak in infancy and rebound in early childhood. We used stepwise multivariable regression to identify predictors of peak and rebound in the 1354 (63.6%) children with estimable trajectory milestones. RESULTS: The mean (SD) of age at BMI peak was 8.4 (2.7) months and at rebound was 59.8 (19.6) months, and the mean (SD) of magnitude at peak was 18.0 (1.4) kg/m2 and at rebound was 15.9 (1.2) kg/m2. Girls had a later age at peak, earlier age at rebound, and lower magnitudes at peak and rebound than boys. Maternal isolated hyperglycemia (vs normoglycemia: β 0.7 months [95% CI 0.2-1.2]) and pre-eclampsia (vs normal blood pressure: 1.6 months [0.8-2.4]) were associated with a later peak, and impaired glucose tolerance (vs normoglycemia: -0.5 kg/m2 [-0.9, -0.1]) was associated with a lower magnitude at peak. Greater maternal first-trimester weight gain, smoking during pregnancy, no breastfeeding, parental obesity, and no university education were associated with greater BMI at rebound. CONCLUSIONS: We have identified modifiable prenatal and parental predictors of BMI peak in infancy and rebound in childhood. Early-life interventions that address these factors may be effective in changing BMI peak and rebound and potentially preventing later obesity.
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