Abstract

With increasing obesity prevalence in children globally, accurate and practical methods for quantifying body fat are critical for effective monitoring and prevention, particularly in high-risk settings. No population is at higher risk of obesity than Pacific Islanders, including children living in the independent nation of Samoa. We developed and validated sex-specific prediction models for fat mass in Samoan children. Dual X-ray absorptiometry (DXA) assessments of fat mass and weight, height, circumferences, and skinfolds were obtained from 356 children aged 7-9 years old in the Ola Tuputupua'e "Growing Up" study. Sex-specific models were developed from a randomly selected model development sample (n = 118 females, n = 120 males) using generalized linear regressions. In a validation sample (n = 59 females; n = 59 males), Lin's concordance and Bland-Altman limits-of-agreement (LoA) of DXA-derived and predicted fat mass from this study and other published models were examined to assess precision and accuracy. Models to predict fat mass in kilograms were: e^[(-0.0034355 * Age8 - 0.0059041 * Age9 + 1.660441 * ln (Weight (kg))-0.0087281 * Height (cm) + 0.1393258 * ln[Suprailiac (mm)] - 2.661793)] for females and e^[-0.0409724 * Age8 - 0.0549923 * Age9 + 336.8575 * [Weight (kg)]-2 - 22.34261 * ln (Weight (kg)) [Weight (kg)]-1 + 0.0108696 * Abdominal (cm) + 6.811015 * Subscapular (mm)-2 - 8.642559 * ln (Subscapular (mm)) Subscapular (mm)-2 - 1.663095 * Tricep (mm)-1 + 3.849035]for males, where Age8 = Age9 = 0 for children at age 7 years, Age8 = 1 and Age9 = 0 at 8 years, Age8 = 0 and Age9 = 1 at 9 years. Models showed high predictive ability, with substantial concordance (ρC > 0.96), and agreement between DXA-derived and model-predicted fat mass (LoA female = -0.235, 95% CI:-2.924-2.453; male = -0.202, 95% CI:-1.977-1.572). Only one of four existing models, developed in a non-Samoan sample, accurately predicted fat mass among Samoan children. We developed models that predicted fat mass in Samoans aged 7-9 years old with greater precision and accuracy than the majority of existing models that were tested. Monitoring adiposity in children with these models may inform future obesity prevention and interventions.

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