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Infant BMI trajectories are associated with young adult body composition

Published online by Cambridge University Press:  10 August 2012

M. M. Slining ,
A. H. Herring ,
B. M. Popkin ,
E. J. Mayer-Davis  and
L. S. Adair
M. M. Slining*
Affiliation:
Department of Nutrition, University of North Carolina, Chapel Hill, NC, USA Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
A. H. Herring
Affiliation:
Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
B. M. Popkin
Affiliation:
Department of Nutrition, University of North Carolina, Chapel Hill, NC, USA Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
E. J. Mayer-Davis
Affiliation:
Department of Nutrition, University of North Carolina, Chapel Hill, NC, USA
L. S. Adair
Affiliation:
Department of Nutrition, University of North Carolina, Chapel Hill, NC, USA Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
*
*Address for correspondence: Dr M. M. Slining, Carolina Population Center, University of North Carolina, University Square, 123 Franklin Street, Chapel Hill, NC 27516-3997, USA. (Email [email protected])
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Abstract

The dynamic aspect of early life growth is not fully captured by typical analyses, which focus on one specific time period. To better understand how infant and young child growth relate to the development of adult body composition, the authors characterized body mass index (BMI) trajectories using latent class growth analysis (LCGA) and evaluated their association with adult body composition. Data are from the Cebu Longitudinal Health and Nutrition Survey, which followed a birth cohort to age 22 years (n = 1749). In both males and females, LCGA identified seven subgroups of respondents with similar BMI trajectories from 0 to 24 months (assessed with bimonthly anthropometrics). Trajectory groups were compared with conventional approaches: (1) accelerated growth between two time points (0–4 months), (2) continuous BMI gain between two points (0–4 months and 0–24 months) and (3) BMI measured at one time point (24 months) as predictors of young adult body composition measures. The seven trajectory groups were distinguished by age-specific differences in tempo and timing of BMI gain in infancy. Infant BMI trajectories were better than accelerated BMI gain between 0 and 4 months at predicting young adult body composition. After controlling for BMI at age 2 years, infant BMI trajectories still explained variation in adult body composition. Using unique longitudinal data and methods, we find that distinct infant BMI trajectories have long-term implications for the development of body composition.

Keywords

anthropometrygrowth and developmentinfantinfant health
Type
Original Article
Information
Journal of Developmental Origins of Health and Disease , Volume 4 , Issue 1 , February 2013 , pp. 56 - 68
Copyright
Copyright © Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2012 

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References

1.Black, RE, Allen, LH, Bhutta, ZA, et al. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet. 2008; 371, 243–260.Google Scholar
2.Mendez, MA, Adair, LS. Severity and timing of stunting in the first two years of life affect performance on cognitive tests in late childhood. J Nutr. 1999; 129, 1555–1562.CrossRefGoogle ScholarPubMed
3.Victora, CG, Adair, L, Fall, C, et al. Maternal and child undernutrition: consequences for adult health and human capital. Lancet. 2008; 371, 340–357.CrossRefGoogle ScholarPubMed
4.Victora, CG, Barros, FC. Commentary: the catch-up dilemma-relevance of Leitch's ‘low–high’ pig to child growth in developing countries. Int J Epidemiol. 2001; 30, 217–220.Google Scholar
5.Ong, KK, Loos, RJ. Rapid infancy weight gain and subsequent obesity: systematic reviews and hopeful suggestions. Acta Paediatr. 2006; 95, 904–908.Google Scholar
6.Fisher, D, Baird, J, Payne, L, et al. Are infant size and growth related to burden of disease in adulthood? A systematic review of literature. Int J Epidemiol. 2006; 35, 1196–1210.Google Scholar
7.Popkin, BM. Nutrition in transition: the changing global nutrition challenge. Asia Pac J Clin Nutr. 2001; 10(Suppl.), S13–S18.Google Scholar
8.Bhargava, SK, Sachdev, HS, Fall, CH, et al. Relation of serial changes in childhood body-mass index to impaired glucose tolerance in young adulthood. N Engl J Med. 2004; 350, 865–875.Google Scholar
9.Lawlor, DA, Leon, DA, Rasmussen, F. Growth trajectory matters: interpreting the associations among birth weight, concurrent body size, and systolic blood pressure in a cohort study of 378,707 Swedish men. Am J Epidemiol. 2007; 165, 1405–1412.Google Scholar
10.Barker, DJ, Osmond, C, Forsen, TJ, Kajantie, E, Eriksson, JG. Trajectories of growth among children who have coronary events as adults. N Engl J Med. 2005; 353, 1802–1809.CrossRefGoogle ScholarPubMed
11.World Health Organization (WHO), Multicentre Growth Reference Study Group (MGRSG). WHO Child Growth Standards based on length/height, weight and age. Acta Paediatr Suppl. 2006; 450, 76–85.Google Scholar
12.Monteiro, PO, Victora, CG. Rapid growth in infancy and childhood and obesity in later life – a systematic review. Obes Rev. 2005; 6, 143–154.Google Scholar
13.Gillman, MW. The first months of life: a critical period for development of obesity. Am J Clin Nutr. 2008; 87, 1587–1589.Google Scholar
14.Victora, CG, de Onis, M, Hallal, PC, Blossner, M, Shrimpton, R, et al. Worldwide timing of growth faltering: revisiting implications for interventions. Pediatrics. 2010; 125, e473–e480.Google Scholar
15.Uauy, R, Kain, J, Mericq, V, Rojas, J, Corvalan, C. Nutrition, child growth, and chronic disease prevention. Ann Med. 2008; 40, 11–20.Google Scholar
16.Martorell, R. Results and implications of the INCAP follow-up study. J Nutr. 1995; 125(Suppl 4), 1127S–1138S.Google Scholar
17.Durnin, JV, Womersley, J. Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr. 1974; 32, 77–97.Google Scholar
18.Deurenberg, P, Deurenberg-Yap, M. Validation of skinfold thickness and hand-held impedance measurements for estimation of body fat percentage among Singaporean Chinese, Malay and Indian subjects. Asia Pac J Clin Nutr. 2002; 11, 1–7.Google Scholar
19.Heymsfield, SB, Gallagher, D, Mayer, L, Beetsch, J, Pietrobelli, A. Scaling of human body composition to stature: new insights into body mass index. Am J Clin Nutr. 2007; 86, 82–91.Google Scholar
20.Ballard, JL, Novak, KK, Driver, MA. A simplified score of assessment of fetal maturation of newly born infants. J Pediatr. 1979; 95, 769–774.Google Scholar
21.Gardosi, J. New definition of small for gestational age based on fetal growth potential. Horm Res. 2006; 65(Suppl. 3), 15–18.Google ScholarPubMed
22.Dahly, DL, Adair, LS. Quantifying the urban environment: a scale measure of urbanicity outperforms the urban–rural dichotomy. Soc Sci Med. 2007; 64, 1407–1419.Google Scholar
23.Nagin, D. Analyzing developmental trajectories: a semiparametric, group-based approach. Psychol Methods. 1999; 4, 139–157.Google Scholar
24.Jones, B, Nagin, D, Roeder, K. A SAS procedure based on mixture models for estimating developmental trajectories. Sociol Methods Res. 2001; 29, 374–393.Google Scholar
25.Bollen, KA, Curran, PJ. Latent Curve Models: A Structural Equation Perspective, 2006. Hoboken, NJ: Wiley-Interscience.Google Scholar
26.Schwarz, G. Estimating the dimension of a model. Ann Stat. 1978; 6, 461–464.Google Scholar
27.Wedel, M, Kamakura, W. Market Segmentation: Conceptual and Methodological Foundations International Series in Quantitative Marketing, Vol. 2, 2000. Norwell, MA: Kluwer Academic Publishers.Google Scholar
28.WHO. The Asia Pacific perspective: redefining obesity and its treatment. In International Association for the study of Obesity and International Obesity Task Force, 2000. International Diabetes Institute: Melbourne.Google Scholar
29.Lampl, M, Thompson, AL, Frongillo, EA. Sex differences in the relationships among weight gain, subcutaneous skinfold tissue and saltatory length growth spurts in infancy. Pediatr Res. 2005; 58, 1238–1242.CrossRefGoogle ScholarPubMed
30.Popkin, BM, Adair, LS, Ng, SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev. 2012; 70, 3–21.Google Scholar
31.Adair, LS. Dramatic rise in overweight and obesity in adult Filipino women and risk of hypertension. Obes Res. 2004; 12, 1335–1341.Google Scholar
32.Adair, LS, Gultiano, S, Suchindran, C. 20-year trends in Filipino women's weight reflect substantial secular and age effects. J Nutr. 2011; 141, 667–673.Google Scholar
33.Wells, JC. A Hattori chart analysis of body mass index in infants and children. Int J Obes Relat Metab Disord. 2000; 24, 325–329.Google Scholar
34. WHO. Physical Status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser. 1995; 854, 1776–1780.Google Scholar
35.Andruff, H, Carraro, N, Thompson, A, Gaudreau, P, Louvet, B. Latent class growth models: a tutorial. Tutor Quant Methods Psychol. 2009; 5, 11–24.Google Scholar
36.Curran, PJ, Muthen, BO. The application of latent curve analysis to testing developmental theories in intervention research. Am J Community Psychol. 1999; 27, 567–595.CrossRefGoogle ScholarPubMed
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