157 Body composition measurement methods in preterm infants 7 Accuracy of body proportionality measures Studies conducted so far, have mainly assessed the predictive value of body proportionality measures.17,18,20,21 Two studies assessed the agreement between ADP and a predictive equation including weight and clinical parameters.19,22 Liotto and colleagues found poor agreement between ADP and their predictive equation which estimated fat (free) mass adjusted by length (g/cm).19 However, they included both preterm and term infants in their analysis, which makes it difficult to extrapolate their findings to only preterm infants - the target population of this review. Larcade et al.22 investigated a study population of exclusively preterm infants and could not validate the predictive equation for fat free mass (g) made by Simon and colleagues23. A difference in nutritional care and ensuing better growth in Larcade’s population may have been the cause of an underestimation of fat free mass by the previously modeled equation. In our opinion, it is difficult to develop a predictive equation that can be validated externally. Just as Larcade et al. found nutritional practices to influence the predictionmodel, changes in neonatal care over time and across neonatal intensive care units (NICUs) influence the predictive equations and limit their universal application. Moreover, investigators have used mixed study populations which include small, appropriate as well as large for gestational age infants.17 Meanwhile, Koo et al. demonstrated that associations between weight/length indices and body composition differ for those born large for gestational age, which makes the use of a mixed study population inappropriate.17 In addition, it is important to note that predictive equations generally found that a large proportion of the variance in fat (free) mass (g) could be explained by weight or BMI. This logically follows the fact that fat mass (g) and fat free mass (g) together make up total body weight. However, fat mass percentage was poorly explained by weight or length indices. Meanwhile, in our opinion, fat (free) mass percentage, may be a more relevant parameter when it comes to comparing the body composition of an individual or groups because it takes the subject’s weight into account. All-in-all, we conclude that the predictive equations based on weight and length indices currently cannot be implemented in clinical practice, because of the lack of external validation and a poor predictive value for fat (free) mass percentage. Daly-Wolfe and colleagues found that mid-arm circumference had a moderate predictive value for fat mass percentage measured with ADP. 24 Koo et al., on the other hand, found that midarm circumference together with chest, abdomen and midthigh circumference, added less than 5 % to the variance in fat mass percentage already explained by weight and length. In their study, however, fat mass was determined by DXA. Pereira – Da Silva and colleagues compared upper arm anthropometry to regional fat mass measured with MRI and found it to be an
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