BACKGROUND/ OBJECTIVES: (1) To cross-validate tetra- (4-BIA) and octopolar (8-BIA) bioelectrical impedance analysis vs dual-energy X-ray absorptiometry (DXA) for the assessment of total and appendicular body composition and (2) to evaluate the accuracy of external 4-BIA algorithms for the prediction of total body composition, in a representative sample of Swiss children. SUBJECTS/ METHODS: A representative sample of 333 Swiss children aged 6-13 years from the Kinder-Sportstudie (KISS) (ISRCTN15360785). Whole-body fat-free mass (FFM) and appendicular lean tissue mass were measured with DXA. Body resistance (R) was measured at 50 kHz with 4-BIA and segmental body resistance at 5, 50, 250 and 500 kHz with 8-BIA. The resistance index (RI) was calculated as height(2)/R. Selection of predictors (gender, age, weight, RI4 and RI8) for BIA algorithms was performed using bootstrapped stepwise linear regression on 1000 samples. We calculated 95% confidence intervals (CI) of regression coefficients and measures of model fit using bootstrap analysis. Limits of agreement were used as measures of interchangeability of BIA with DXA. RESULTS: 8-BIA was more accurate than 4-BIA for the assessment of FFM (root mean square error (RMSE)=0.90 (95% CI 0.82-0.98) vs 1.12 kg (1.01-1.24); limits of agreement 1.80 to -1.80 kg vs 2.24 to -2.24 kg). 8-BIA also gave accurate estimates of appendicular body composition, with RMSE < or = 0.10 kg for arms and < or = 0.24 kg for legs. All external 4-BIA algorithms performed poorly with substantial negative proportional bias (r> or = 0.48, P<0.001). CONCLUSIONS: In a representative sample of young Swiss children (1) 8-BIA was superior to 4-BIA for the prediction of FFM, (2) external 4-BIA algorithms gave biased predictions of FFM and (3) 8-BIA was an accurate predictor of segmental body composition.
BACKGROUND/ OBJECTIVES: (1) To cross-validate tetra- (4-BIA) and octopolar (8-BIA) bioelectrical impedance analysis vs dual-energy X-ray absorptiometry (DXA) for the assessment of total and appendicular body composition and (2) to evaluate the accuracy of external 4-BIA algorithms for the prediction of total body composition, in a representative sample of Swiss children. SUBJECTS/ METHODS: A representative sample of 333 Swiss children aged 6-13 years from the Kinder-Sportstudie (KISS) (ISRCTN15360785). Whole-body fat-free mass (FFM) and appendicular lean tissue mass were measured with DXA. Body resistance (R) was measured at 50 kHz with 4-BIA and segmental body resistance at 5, 50, 250 and 500 kHz with 8-BIA. The resistance index (RI) was calculated as height(2)/R. Selection of predictors (gender, age, weight, RI4 and RI8) for BIA algorithms was performed using bootstrapped stepwise linear regression on 1000 samples. We calculated 95% confidence intervals (CI) of regression coefficients and measures of model fit using bootstrap analysis. Limits of agreement were used as measures of interchangeability of BIA with DXA. RESULTS:8-BIA was more accurate than 4-BIA for the assessment of FFM (root mean square error (RMSE)=0.90 (95% CI 0.82-0.98) vs 1.12 kg (1.01-1.24); limits of agreement 1.80 to -1.80 kg vs 2.24 to -2.24 kg). 8-BIA also gave accurate estimates of appendicular body composition, with RMSE < or = 0.10 kg for arms and < or = 0.24 kg for legs. All external 4-BIA algorithms performed poorly with substantial negative proportional bias (r> or = 0.48, P<0.001). CONCLUSIONS: In a representative sample of young Swiss children (1) 8-BIA was superior to 4-BIA for the prediction of FFM, (2) external 4-BIA algorithms gave biased predictions of FFM and (3) 8-BIA was an accurate predictor of segmental body composition.
Authors: Jerzy Słowik; Elżbieta Grochowska-Niedworok; Izabela Maciejewska-Paszek; Marek Kardas; Ewa Niewiadomska; Magdalena Szostak-Trybuś; Maria Palka-Słowik; Tomasz Irzyniec Journal: Obes Facts Date: 2019-10-22 Impact factor: 3.942
Authors: Marina Wälti; Jeffrey Sallen; Manolis Adamakis; Fabienne Ennigkeit; Erin Gerlach; Christopher Heim; Boris Jidovtseff; Irene Kossyva; Jana Labudová; Dana Masaryková; Remo Mombarg; Liliane De Sousa Morgado; Benjamin Niederkofler; Maike Niehues; Marcos Onofre; Uwe Pühse; Ana Quitério; Claude Scheuer; Harald Seelig; Petr Vlček; Jaroslav Vrbas; Christian Herrmann Journal: Front Psychol Date: 2022-04-25
Authors: Camila E Orsso; Maria Cristina Gonzalez; Michael Johannes Maisch; Andrea M Haqq; Carla M Prado Journal: Eur J Clin Nutr Date: 2021-10-07 Impact factor: 4.884
Authors: Mya-Thway Tint; Leigh C Ward; Shu E Soh; Izzuddin M Aris; Amutha Chinnadurai; Seang Mei Saw; Peter D Gluckman; Keith M Godfrey; Yap-Seng Chong; Michael S Kramer; Fabian Yap; Barbara Lingwood; Yung Seng Lee Journal: Br J Nutr Date: 2016-02-09 Impact factor: 3.718
Authors: A M Pinto; J Puder; F Bürgi; V Ebenegger; A Nydegger; I Niederer; S Kriemler; P Marques-Vidal Journal: Nutr Diabetes Date: 2013-05-13 Impact factor: 5.097
Authors: Iris Niederer; Susi Kriemler; Lukas Zahner; Flavia Bürgi; Vincent Ebenegger; Tim Hartmann; Ursina Meyer; Christian Schindler; Andreas Nydegger; Pedro Marques-Vidal; Jardena J Puder Journal: BMC Public Health Date: 2009-03-31 Impact factor: 3.295