TY - JOUR
T1 - Adiposity in Adolescents
T2 - Change in Actual BMI Works Better Than Change in BMI z Score for Longitudinal Studies
AU - Berkey, Catherine S.
AU - Colditz, Graham A.
PY - 2007/1
Y1 - 2007/1
N2 - Purpose: Longitudinal epidemiologic studies often relate adiposity changes to suspected causal factors. In growing adolescents, this becomes complicated. Many investigators use within-child change in body mass index (BMI) z scores (Δz) from sex- and age-specific BMI charts developed by the Centers for Disease Control and Prevention (CDC). These charts, derived from cross-sectional data, may not represent BMI growth patterns of real children. Furthermore, because cross-sectional BMIs are not Gaussian, these z scores are from month-specific transformed distributions, with possible unintended consequences when used longitudinally. Alternatively, we can directly analyze BMI change (ΔBMI). We compare these two widely used measures of change in adiposity. Methods and Results: With real adolescent data, we show that annual ΔBMIs have nonlinear peaks that are inconsistent with the CDC curves. We also show that a specified Δz represents a broad range of adiposity changes for children measured at the same two ages. To see how this affects power, we performed simulation studies confirming that analyzing ΔBMIs in models with hypothesized factors is more powerful than analyzing Δzs. Conclusions: In longitudinal studies of adolescent adiposity, investigators should be encouraged to analyze ΔBMI rather than Δz because analyses using BMI are more powerful and findings presented in BMI units are more interpretable.
AB - Purpose: Longitudinal epidemiologic studies often relate adiposity changes to suspected causal factors. In growing adolescents, this becomes complicated. Many investigators use within-child change in body mass index (BMI) z scores (Δz) from sex- and age-specific BMI charts developed by the Centers for Disease Control and Prevention (CDC). These charts, derived from cross-sectional data, may not represent BMI growth patterns of real children. Furthermore, because cross-sectional BMIs are not Gaussian, these z scores are from month-specific transformed distributions, with possible unintended consequences when used longitudinally. Alternatively, we can directly analyze BMI change (ΔBMI). We compare these two widely used measures of change in adiposity. Methods and Results: With real adolescent data, we show that annual ΔBMIs have nonlinear peaks that are inconsistent with the CDC curves. We also show that a specified Δz represents a broad range of adiposity changes for children measured at the same two ages. To see how this affects power, we performed simulation studies confirming that analyzing ΔBMIs in models with hypothesized factors is more powerful than analyzing Δzs. Conclusions: In longitudinal studies of adolescent adiposity, investigators should be encouraged to analyze ΔBMI rather than Δz because analyses using BMI are more powerful and findings presented in BMI units are more interpretable.
KW - Body Mass Index (BMI)
KW - Children
KW - Longitudinal
KW - Overweight
KW - Simulation
KW - Weight Gain
KW - z Scores
UR - http://www.scopus.com/inward/record.url?scp=33845519508&partnerID=8YFLogxK
U2 - 10.1016/j.annepidem.2006.07.014
DO - 10.1016/j.annepidem.2006.07.014
M3 - Article
C2 - 17140812
AN - SCOPUS:33845519508
SN - 1047-2797
VL - 17
SP - 44
EP - 50
JO - Annals of Epidemiology
JF - Annals of Epidemiology
IS - 1
ER -