Toward Body Composition Reference Data for Infants, Children, and Adolescents123

Growth charts for weight and height have provided the basis for assessment of children’s nutritional status for over half a century, with charts for body mass index (BMI) introduced in the 1990s. However, BMI does not provide information on the proportions of fat and lean mass; and within the past decade, growth charts for children’s body composition have been produced by using techniques such as skinfold thicknesses, body circumferences, bioelectrical impedance analysis (BIA), and dual-energy X-ray absorptiometry (DXA). For public health research, BIA and skinfold thicknesses show negligible average bias but have wider limits of agreement than specialized techniques. For patients, DXA is the best individual method, but multicomponent models remain ideal because they address perturbations in lean mass composition. Data can be expressed in ageand sex-specific SD scores, in some cases adjusting for height. Most such reference data derive from highincome countries, but techniques such as air-displacement plethysmography allow infant body composition growth charts to be developed in lowand middle-income settings, where the data may improve understanding of the effects of low birth weight, wasting, and stunting on body composition. Recent studies suggest that between-population variability in body composition may derive in part from genetic factors, suggesting a universal human body composition reference may not be viable. Body composition growth charts may be extended into adult life to evaluate changes in fat and lean mass through the entire life course. These reference data will improve the understanding of the association between growth, body composition, health, and disease. Adv. Nutr. 5: 320S–329S, 2014. Introduction For well over a century, clinical assessment of children’s nutritional status has relied heavily on measurements of anthropometry. As early as 1835, the Belgian statistician Quetelet collected data on children’s weight and height and made use of the concept of the “normal distribution” to describe the pattern of human growth (1). In the 1870s, Bowditch collated anthropometric data on >24,000 schoolchildren from Boston, MA, and demonstrated differences in growth between the sexes and socioeconomic groups (1). Arguably the most influential contribution, however, came from the British auxologist Tanner (2,3), who pioneered more sophisticated growth charts of a format that continues to be used today. To construct growth charts, cross-sectional data are collected on a large representative sample of children, although, ideally, longitudinal data would be incorporated. The data are then subjected to statistical analysis, whereby not only the average size at each age is calculated but also the variability. Simplistically, any individual value can be expressed as a SD score (SDS), calculated as follows: SDS = (measurement 2 population mean)/population SD where both the mean and SD of the population are calculated on an ageand sex-specific basis. Providing that the data are characterized by a normal distribution, any SDS can also be expressed as a percentile, whereby, for example, an individual on the 60th centile is taller than 60% of the population (2). If the data are skewed, however, then the simple relation between SD and percentile distributions is broken (2). For this reason, more advanced statistical approaches for assessing the population distribution of growth status were developed. 1 Published in a supplement to Advances in Nutrition. Presented at the International Union of Nutritional Sciences (IUNS) 20th International Congress of Nutrition (ICN) held in Granada, Spain, September 15–20, 2013. The IUNS and the 20th ICN wish to thank the California Walnut Commission and Mead Johnson Nutrition for generously providing educational grants to support the publication and distribution of proceedings from the 20th ICN. The contents of this supplement are solely the responsibility of the authors and do not necessarily represent official views of the IUNS. The supplement coordinators were Angel Gil, Ibrahim Elmadfa, and Alfredo Martinez. The supplement coordinators had no conflicts of interest to disclose. 2 This is a free access article, distributed under terms (http://www.nutrition.org/publications/ guidelines-and-policies/license/) that permit unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 3 Author disclosures: J. C. K. Wells, no conflicts of interest. * To whom correspondence should be addressed. E-mail: [email protected]. 4 Abbreviations used: BIA, bioelectrical impedance analysis; %fat, percentage of fat; FMI, fat mass index; HIC, high-income country; LMI, lean mass index; LMIC, lowand middle-income country; SDS, SD score; TBW, total body water. 320S ã2014 American Society for Nutrition. Adv. Nutr. 5: 320S–329S, 2014; doi:10.3945/an.113.005371. To address skewness, Cole (4,5) developed a statistical approach that differentiates 3 different parameters of variability. Their “LMS” (Lambda Sigma Mu) method quantifies the median (M), the magnitude of variability (S), and the BoxCox power (L) required to transform the data to achieve a normal distribution. This approach resolved the discrepancy between SDS and centiles and has become standard practice in the construction of growth charts. The approach has also been adapted to produce software that converts raw data to ageand sex-specific SDS, enabling the longitudinal assessment of an individual’s growth status relative to the reference population. Other statistical approaches can model a wider range of covariates and distributions (6). Growth charts have remained fundamental to the assessment of basic nutritional status ever since. Most of these charts represent reference data but not growth standards. In other words, they describe the pattern of growth and its variability that is evident in a population at a given time point, but they do not assume that any particular level of growth is optimal. Longitudinal measurements showed that from early childhood onward, the majority of children do not cross up or down through the centiles but tend to track along a given centile, indicating that growth is self-regulating and target-seeking (7). Thus, regardless of whether a child is large or small at any given time point, centile crossing gives an indication of a clinical growth abnormality. On this basis, growth charts are used both in clinical monitoring to detect individual abnormalities in growth trajectory, but also in public health research and monitoring to understand variability and secular trends in children’s growth. The logic of these growth charts is simple but very effective: at any age, a child can be ranked relative to others of the same age and sex to assess immediate growth status. Longitudinal data allow change in growth status to be assessed. Although early data prioritized weight and height, many other components of growth can be addressed in the same way. Assessment of nutritional status. Because weight and height are strongly associated in children, various efforts were made to produce charts for weight that took height into account. By the 1970s, opinion was converging on the use of BMI (calculated as weight in kilograms divided by the square of height in meters) as the optimal approach in adults. First developed by Quetelet in the 19th century (8), the utility of BMI is that it is highly correlated with weight and body fatness but has a relatively low correlation in adults with height (9). BMI thus became adopted as the primary index of adult overweight. In both sexes, the thresholds for overweight and obesity were defined as 25 and 30 kg/m, respectively (10). Subsequently, an additional cutoff for chronic energy deficiency was added, at 18.5 kg/m (11). In children, however, BMI has a characteristic curvilinear shape with age, a scenario for which growth charts provide the ideal solution. BMI growth charts were first produced for French children in 1982 (12) and for UK children in 1995 using the LMS method (13). The curvilinear association of BMI with age means that no simple invariant cutoff can be used to define overweight or obesity. Rather, British pediatricians adopted the 85th centile as the threshold for overweight and the 95th centile for obesity. These charts allowed the nutritional status of children to be assessed over time and enabled a standardized approach to be used in early clinical monitoring of childhood obesity. Nevertheless, as the approach was replicated in other countries and individual national charts were produced, there was no international consensus on the BMI that is equivalent to obesity at any given age. To address this issue, Cole et al. (14) analyzed data from 6 countries, and in this large data set identified age-specific BMI cutoffs that were statistically equivalent to the adult BMI cutoffs of 25 and 30 kg/m. Thus, international cutoffs for pediatric overweight and obesity were now available, and shortly thereafter they were followed by equivalent cutoffs for different degrees of pediatric underweight (15). Limitations of BMI for body composition assessment. These international pediatric BMI cutoffs have made a major contribution to the monitoring of nutritional status in children and adolescents worldwide and provide a template against which nutritional status can be assessed in the clinic. However, BMI is a global proxy of nutritional status. It is highly correlated with many different components of weight, such as lean mass (used here synonymously with fat-free mass), skeletal muscle mass, fat mass, and bone mass; yet, it cannot differentiate between them. The primary evidence favoring BMI as an index of adiposity is that across a wide range of BMIs, there is a strong correlation between BMI and the proportion of fat in body weight, or percentage of fat (%fat) (16). However, this strong correlation emerges because of the tendency for low-BMI children to have low %fat and high-BMI children to have high %fat. In the middle of the range, children of a given BMI value can have very different %fat. This is shown clearly by disentangling BMI into its fat and lean components; however, before examining this issue it is first helpful to discuss the limitations of using %fat itself as an index of adiposity. The logic of %fat is that it adjusts fat mass for an index of body size—in this case, weight. Clearly, 3 kg of fat mass is a substantial amount for a 3 y old weighing 15 kg but very little for an adolescent weighing 60 kg. However, dividing fat mass by weight is statistically problematic, because the fat is present in both numerator and denominator (17,18). As absolute fat mass increases, %fat rises increasingly slowly, eventually trending toward an asymptote at ~60% fat. In obese individuals, even large gains or losses in adipose tissue mass may induce only small changes in %fat. A second conceptual problem with %fat is that it is not an index of adiposity that is fully independent of body size. High %fat values might reflect high adiposity or low lean mass as, for example, in some patient groups (17,19). The use of %fat as the primary body composition outcome therefore directs attention to fat at the expense of lean mass. Historically, this approach has resulted in extensive interest in height as the Pediatric body composition reference data 321S primary index of growth and similar interest in %fat as the primary index of body composition, with minimal interest being directed to lean mass, despite the fact that it comprises multiple functional tissues. To resolve this problem in adults, VanItallie et al. (20) proposed splitting BMI into 2 components: the lean mass index (LMI; lean mass/height) and the fat mass index (FMI; fat mass/height), each expressed in the same kg/m units as BMI. This approach has 2 benefits: it adjusts tissue masses for an independent component of body size while keeping fat and lean outcomes separate. The same approach can be applied in children (21). The value of this approach is most clearly seen by using a graphic approach developed by Hattori et al. (22), who plotted FMI on the y-axis against LMI on the x-axis. Figure 1A illustrates the conceptual approach encapsulated by this chart, and Fig. 1B shows a scatterplot of body composition data from children aged 8 y on a Hattori chart. It can be seen that 2 children with the same BMI value can differ markedly in their adiposity, whether this is expressed as %fat or FMI. Equally, 2 children of the same %fat value can differ markedly in their BMI (21,23). These charts highlight substantial variability in lean mass, an issue that has received little attention in pediatric clinical practice or research. The limitations of BMI as an index of body composition are further highlighted if data from different ethnic groups are compared. Many studies have now reported varying amounts of adiposity for a given BMI value across ethnic groups (24–26). The largest contrast appears to be between South Asians and Europeans. Compared with the latter group, South Asians have been described as having a “thin-fat” phenotype, evident at birth (27), with relatively less lean mass and more fat mass at any given BMI value (24–26,28). The limitations of BMI are perhaps starkest when considering data from children with specific diseases associated with alterations in body composition. In a study in young children receiving artificial ventilation, the patients tended to have low LMI relative to healthy controls, but they had high FMI (19,29). Because the children had normal BMI values, each of these clinical problems was concealed. One patient with myofibromatosis, a condition with many small tumors, showed the opposite pattern. He had high LMI due to the tumors, but low FMI. Dietetic management of these patients had focused specifically on maintaining BMI similar to that of healthy children, and the abnormalities in body composition were undetected and hence not able to be addressed. The need for body composition data. The limitations of weight and height as an index of body composition were already recognized when the first growth charts were produced (30). Similar charts for subcutaneous skinfold thicknesses were published for British children in the 1960s (30) and were updated 15 y later to address changes in children’s adiposity attributable to secular trends in nutritional status (31). As data on BMI revealed the emerging childhood obesity epidemic, interest in differentiating adiposity from lean mass grew, but a limitation of skinfold thicknesses is that they do not necessarily reflect the total amount of fat in the body, inasmuch as fat is internal and not indexed by skinfold measurements (32). Given this limitation, it is not possible to predict lean mass with accuracy from data on skinfold thickness (33), even though several such predictive equations have been published (34,35). In clinical practice, the value of assessing body composition is increasingly recognized (36). Whereas obesity and eating disorders currently remain defined by anthropometric criteria (weight relative to height, or BMI) (14,37), these variables have poor sensitivity for monitoring response to treatment, and so body composition measurement could FIGURE 1 (A) The contribution of lean mass and fat mass to BMI illustrated using a “Hattori chart” that plots fat mass/height on the y-axis against lean mass/height on the x-axis (21). Continuous lines represent constant BMI values; dotted lines represent constant %fat values. (B) The distribution of fat mass/ height and lean mass/height in a sample of children aged 8 y. Children with the same BMI value may vary in their %fat (“A” vs. “B”), whereas those with the same %fat value may vary substantially in their BMI (“B” vs. “C”). Reproduced from reference 23 with permission. %fat, percentage of fat.