Izzuddin M Aris1,2,3, Sheryl L Rifas-Shiman1, Ling-Jun Li1,4,5, Ken P Kleinman6, Brent A Coull7, Diane R Gold8,9, Marie-France Hivert1,10, Michael S Kramer2,11,12, Emily Oken1,13. 1. Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA. 2. Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. 3. Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore. 4. Division of Obstetrics and Gynecology, KK Women's and Children's Hospital, Singapore, Singapore. 5. Obstetrics and Gynecology Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore. 6. Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA. 7. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 8. Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. 9. Department of Environmental Medicine, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 10. Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA. 11. Departments of Pediatrics. 12. Department of Epidemiology, Biostatistics and Occupational Health, McGill University Faculty of Medicine, Montreal, QC, Canada. 13. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Abstract
BACKGROUND: Few studies have examined the independent and combined relationships of body mass index (BMI) peak and rebound with adiposity, insulin resistance and metabolic risk later in life. We used data from Project Viva, a well-characterized birth cohort from Boston with repeated measures of BMI, to help fill this gap. METHODS: Among 1681 children with BMI data from birth to mid childhood, we fitted individual BMI trajectories using mixed-effects models with natural cubic splines and estimated age, and magnitude of BMI, at peak (in infancy) and rebound (in early childhood). We obtained cardiometabolic measures of the children in early adolescence (median 12.9 years) and analysed their associations with the BMI parameters. RESULTS: After adjusting for potential confounders, age and magnitude at infancy BMI peak were associated with greater adolescent adiposity, and earlier adiposity rebound was strongly associated with greater adiposity, insulin resistance and metabolic risk score independently of BMI peak. Children with a normal timing of BMI peak plus early rebound had an adverse cardiometabolic profile, characterized by higher fat mass index {β 2.2 kg/m2 [95% confidence interval (CI) 1.6, 2.9]}, trunk fat mass index [1.1 kg/m2 (0.8, 1.5)], insulin resistance [0.2 units (0.04, 0.4)] and metabolic risk score [0.4 units (0.2, 0.5)] compared with children with a normal BMI peak and a normal rebound pattern. Children without a BMI peak (no decline in BMI after the rise in infancy) also had adverse adolescent metabolic profiles. CONCLUSIONS: Early age at BMI rebound is a strong risk factor for cardiometabolic risk, independent of BMI peak. Children with a normal peak-early rebound pattern, or without any BMI decline following infancy, are at greatest risk of adverse cardiometabolic profile in adolescence. Routine monitoring of BMI may help to identify children who are at greatest risk of developing an adverse cardiometabolic profile in later life and who may be targeted for preventive interventions.
BACKGROUND: Few studies have examined the independent and combined relationships of body mass index (BMI) peak and rebound with adiposity, insulin resistance and metabolic risk later in life. We used data from Project Viva, a well-characterized birth cohort from Boston with repeated measures of BMI, to help fill this gap. METHODS: Among 1681 children with BMI data from birth to mid childhood, we fitted individual BMI trajectories using mixed-effects models with natural cubic splines and estimated age, and magnitude of BMI, at peak (in infancy) and rebound (in early childhood). We obtained cardiometabolic measures of the children in early adolescence (median 12.9 years) and analysed their associations with the BMI parameters. RESULTS: After adjusting for potential confounders, age and magnitude at infancy BMI peak were associated with greater adolescent adiposity, and earlier adiposity rebound was strongly associated with greater adiposity, insulin resistance and metabolic risk score independently of BMI peak. Children with a normal timing of BMI peak plus early rebound had an adverse cardiometabolic profile, characterized by higher fat mass index {β 2.2 kg/m2 [95% confidence interval (CI) 1.6, 2.9]}, trunk fat mass index [1.1 kg/m2 (0.8, 1.5)], insulin resistance [0.2 units (0.04, 0.4)] and metabolic risk score [0.4 units (0.2, 0.5)] compared with children with a normal BMI peak and a normal rebound pattern. Children without a BMI peak (no decline in BMI after the rise in infancy) also had adverse adolescent metabolic profiles. CONCLUSIONS: Early age at BMI rebound is a strong risk factor for cardiometabolic risk, independent of BMI peak. Children with a normal peak-early rebound pattern, or without any BMI decline following infancy, are at greatest risk of adverse cardiometabolic profile in adolescence. Routine monitoring of BMI may help to identify children who are at greatest risk of developing an adverse cardiometabolic profile in later life and who may be targeted for preventive interventions.
Authors: Rachael W Taylor; Sheila M Williams; Philippa J Carter; Ailsa Goulding; David F Gerrard; Barry J Taylor Journal: Int J Pediatr Obes Date: 2011-02-03
Authors: Rachael W Taylor; Andrea M Grant; Ailsa Goulding; Sheila M Williams Journal: Curr Opin Clin Nutr Metab Care Date: 2005-11 Impact factor: 4.294
Authors: Robert J Kuczmarski; Cynthia L Ogden; Shumei S Guo; Laurence M Grummer-Strawn; Katherine M Flegal; Zuguo Mei; Rong Wei; Lester R Curtin; Alex F Roche; Clifford L Johnson Journal: Vital Health Stat 11 Date: 2002-05
Authors: Elizabeth A O'Connor; Corinne V Evans; Brittany U Burda; Emily S Walsh; Michelle Eder; Paula Lozano Journal: JAMA Date: 2017-06-20 Impact factor: 56.272
Authors: Amrik Singh Khalsa; Rui Li; Joseph Rausch; Mark A Klebanoff; Taniqua T Ingol; Kelly M Boone; Sarah A Keim Journal: Pediatr Obes Date: 2022-03-20 Impact factor: 3.910
Authors: Caitriona McGovern; Sheryl L Rifas-Shiman; Karen M Switkowski; Jennifer A Woo Baidal; Jenifer R Lightdale; Marie-France Hivert; Emily Oken; Izzuddin M Aris Journal: Am J Clin Nutr Date: 2022-08-04 Impact factor: 8.472
Authors: Allison Kupsco; Haotian Wu; Antonia M Calafat; Marianthi-Anna Kioumourtzoglou; Alejandra Cantoral; Marcela Tamayo-Ortiz; Ivan Pantic; Maria Luisa Pizano-Zárate; Emily Oken; Joseph M Braun; Andrea L Deierlein; Robert O Wright; Martha M Téllez-Rojo; Andrea A Baccarelli; Allan C Just Journal: Environ Res Date: 2021-09-23 Impact factor: 8.431
Authors: Izzuddin M Aris; Joanne E Sordillo; Sheryl L Rifas-Shiman; Jessica G Young; Diane R Gold; Carlos A Camargo; Marie-France Hivert; Emily Oken Journal: Paediatr Perinat Epidemiol Date: 2021-03-22 Impact factor: 3.103