Preventing obesity is a major public health priority because of the high health, social, and economic costs associated with it. Obesity is increasingly seen at earlier ages, and prevention of obesity in children is important to lower the risks of the metabolic consequences that lead to disease during childhood and adulthood. The increase in obesity globally is attributed to multiple forces acting on entire populations to change both social and physical environments and dietary and physical-activity behaviors.1 Successful prevention will involve elimination of, compensation for, and/or buffering of these forces population-wide. Prevention also may benefit from selective intervention for those at highest risk. Taveras et al2 suggest that identifying children in the early postnatal period (1-24 months of age) who are at highest risk of obesity is potentially important to obesity prevention. They conclude that counting weight-for-length percentile crossings in the early postnatal period may provide a practical tool to identify children who may be at high risk of obesity in clinical settings and that “efforts to curb excess weight gain in infancy may be useful in preventing later obesity.” Given the high frequency of percentile crossings found in the early postnatal period,3 identifying infants and toddlers at risk of developing obesity and targeting them for appropriate intervention is challenging. A better understanding of the following 4 issues will be crucial before attempting to do so: (1) the validity of the proposed identification tool, (2) the practicality for clinicians, (3) the role played by growth dynamics and body composition in the early postnatal period, and (4) the potential for benefit and harm with selective early intervention. First, demonstrating the validity of the proposed screening tool will be an important step to ascertain that early percentile crossing usefully identifies children who later develop obesity, and this is done with a high level of accuracy (ie, sensitivity and specificity) and a high yield (ie, positive and negative predictive values).4 Estimates of population-attributable risk, a function of both the relative risk and the population size for each exposure category during the early postnatal period, can convey what proportion of later obesity is accounted by the early identification tool. The goal of this validation work would be to establish that the proposed tool can sensitively identify (ie, not miss too many) children at high risk for later obesity while being specific (ie, not identifying children at high risk who are actually not) and to ensure that the yield and value added of doing this identification (compared with just placing children in exposure categories) are high. Second, any identification tool must be practical enough for busy clinicians to incorporate it into their daily work. As a follow-up to identification, they must be able to act on the information provided. It is often assumed that the use of growth reference charts representing percentile distributions of size is the easiest and most practical approach to growth status assessment, superior to a graphical representation based on z scores.2 The apparent closeness of values near the asymptotic upper bound of 100% is problematic, however, because this is the region associated with children at high risk of obesity. For percentile crossing to be most useful, charts need to include more extreme percentile curves (eg, 97th and 99th percentile curves). Alternatively, the international consensus is that z scores are superior to percentile representations in most situations,5 and the Centers for Disease Control and Prevention could provide such charts (as has been done for the World Health Organization growth standards6), notwithstanding limitations in the growth-reference data from the Centers for Disease Control and Prevention.7 The response to risk identification for obesity is equally challenging, and a better understanding of how clinicians can follow up both practically and effectively will be needed.8 The barriers that clinicians cite include a lack of preparation to counsel children, a belief that counseling is ineffective and that reimbursement and time available to counsel are insufficient, an unfamiliarity with billing codes for weight management services, and a lack of success with unmotivated families.8,9 Third, during the early postnatal period, physical growth is highly dynamic, so high rates of crossing percentiles on a growth chart are expected.3,10 For both weight and length, rapid deceleration of growth occurs during the early postnatal period. If deceleration of growth in the length of the child is greater than deceleration of growth in the weight of the child, then the child will cross weight-for-length percentiles upward. During periods of rapid acceleration or deceleration (such as puberty), these dynamics cannot be assessed well using cross-sectional growth charts because they do not depict how children actually grow.10 Although the weight and length of a child are often measured, it is not clear whether these measurements are useful for predicting later obesity. Despite recommendations to conduct routine assessments of obesity in children using body mass index,11,12 only about half of clinicians assess body mass index in children older than 2 years of age.9 Moreover, recent evidence cautions that obesity screening and possible intervention in the early postnatal period should not focus on early weight and length gain alone, prior to our better understanding of the dynamics of body composition development in infants and toddlers. Excess body fat is the real concern with obesity. Although information on relative weight (ie, weight-for-height or body mass index) is routinely used to assess obesity in children and adults, relative weight is an imperfect measure of body fat. Weight is determined by the amount of fat, muscle, bone, organ, and other tissue, and differences or changes in weight or relative weight do not necessarily reflect differences or changes in the amount of fat. For example, the increase in body mass index commonly seen in children at around 5 to 6 years of age (the so-called adiposity rebound) is actually not a function of increased fat tissue, as has been assumed, but a function of increased lean mass coupled with cessation of the decrease in fat mass.13 Finally, early identification of the risk of later obesity brings further risks that come with interventions, even if unintended or unknown.14,15 Identifying the subset of individuals who may potentially benefit from selective intervention is essential to ensuring that specific interventions are applied to only those who are likely to benefit.16-18 Focusing on percentile crossing in the critical developmental period of up to 24 months (as a call to action) opens the way for several possible unintended consequences: poor nutrition, linear growth restriction, altered emotional and social development, and social alienation (eg, stigma). Thus, it is most important to identify those children for whom the potential benefit of specific interventions outweighs risks from intervention. Back to top Article Information Correspondence: Dr Frongillo, Department of Health Promotion, Education, and Behavior, University of South Carolina, 800 Sumter St, Rm 216, Columbia, SC 29208 (email@example.com). Author Contributions:Study concept and design: Frongillo and Lampl. Drafting of the manuscript: Frongillo and Lampl. Critical revision of the manuscript for important intellectual content: Frongillo and Lampl. Financial Disclosure: None reported. References 1. Institute of Medicine of the National Academies; Committee on Progress in Preventing Childhood Obesity. Progress in Preventing Childhood Obesity: How Do We Measure Up? Washington, DC: National Academies Press; 2007 2. Taveras EM, Rifas-Shiman SL, Sherry B, et al. Crossing growth percentiles in infancy and risk of obesity in childhood. Arch Pediatr Adolesc Med. 2011;165(11):993-998Google ScholarCrossref 3. Mei Z, Grummer-Strawn LM, Thompson D, Dietz WH. Shifts in percentiles of growth during early childhood: analysis of longitudinal data from the California Child Health and Development Study. Pediatrics. 2004;113(6):e617-e62715173545PubMedGoogle ScholarCrossref 4. Frongillo EA Jr. Validation of measures of food insecurity and hunger. J Nutr. 1999;129(2S):(suppl) 506S-509S10064319PubMedGoogle Scholar 5. World Health Organization (WHO). Physical Status: The Use and Interpretation of Anthropometry: Report of a WHO Expert Committee. Geneva, Switzerland: WHO Press; 1995. WHO Technical Report Series No. 854 6. World Health Organization (WHO). WHO Child Growth Standards: Length/Height-for-Age, Weight-for-Age, Weight-for-Length, Weight-for-Height and Body Mass Index-for-Age: Methods and Development. Geneva, Switzerland: WHO Press; 2006 7. Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC Growth Charts for the United States: methods and development. Vital Health Stat 11. 2002;(246):1-19012043359PubMedGoogle Scholar 8. Barlow SE, Richert M, Baker EA. Putting context in the statistics: paediatricians' experiences discussing obesity during office visits. Child Care Health Dev. 2007;33(4):416-42317584397PubMedGoogle ScholarCrossref 9. Voelker R. Improved use of BMI needed to screen children for overweight. JAMA. 2007;297(24):2684-268517595264PubMedGoogle ScholarCrossref 10. Hall DMB. Monitoring children's growth. BMJ. 1995;311(7005):583-5847663245PubMedGoogle ScholarCrossref 11. Barlow SE.Expert Committee. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007;120:(suppl 4) S164-S19218055651PubMedGoogle ScholarCrossref 12. Reilly JJ. Assessment of obesity in children and adolescents: synthesis of recent systematic reviews and clinical guidelines. J Hum Nutr Diet. 2010;23(3):205-21120337839PubMedGoogle ScholarCrossref 13. Campbell MW, Williams J, Carlin JB, Wake M. Is the adiposity rebound a rebound in adiposity? Int J Pediatr Obes. 2011;6(2-2):e207-e21521091126PubMedGoogle ScholarCrossref 14. Le Roith D. Although both rhGh and rhIGF-I have the potential to provide clinical benefit, each also has the potential to produce unwanted side-effects. Growth Horm IGF Res. 1999;9(2):8310373340PubMedGoogle ScholarCrossref 15. Mitka M. Experts weigh pros and cons of screening and treatment for childhood obesity. JAMA. 2008;300(12):1401-140218812525PubMedGoogle ScholarCrossref 16. Weber C, Neeser K. Using individualized predictive disease modeling to identify patients with the potential to benefit from a disease management program for diabetes mellitus. Dis Manag. 2006;9(4):242-25616893337PubMedGoogle ScholarCrossref 17. Weir CJ, Kaste M, Lees KR.Glycine Antagonist in Neuroprotection (GAIN) International Steering Committee and Investigators. Targeting neuroprotection clinical trials to ischemic stroke patients with potential to benefit from therapy. Stroke. 2004;35(9):2111-211615243146PubMedGoogle ScholarCrossref 18. Shi H, Shu X, Wang F, et al. Longitudinal two-dimensional strain rate imaging: a potential approach to predict the response to cardiac resynchronization therapy. Int J Cardiovasc Imaging. 2009;25(7):677-68719639392PubMedGoogle ScholarCrossref
Archives of Pediatrics & Adolescent Medicine – American Medical Association
Published: Nov 1, 2011
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