Sergey Rudnev1, Jane S Burns2, Paige L Williams3, Mary M Lee4, Susan A Korrick5, Tatiana Denisova6, Yuri Dikov6, Gennady Kozupitsa7, Russ Hauser8, Oleg Sergeyev9. 1. Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkin Str. 8, 119333, Moscow, Russia; Department of Health Statistics Analysis, Federal Research Institute for Health Organization and Informatics, Dobrolyubov Str. 11, 127254, Moscow, Russia. Electronic address: sergey.rudnev@gmail.com. 2. Department of Environmental Health, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, 02115, USA. 3. Department of Biostatistics, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, 02115, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, 02115, USA. 4. Department of Pediatrics, Sidney Kimmel Medical College and Nemours A.I. duPont Children's Hospital, 1600 Rockland Road, Wilmington, DE, 19803, USA. 5. Department of Environmental Health, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, 02115, USA; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA, 02115, USA. 6. Chapaevsk Medical Association, Meditsinskaya Str. 3a, 446100, Chapaevsk, Samara Region, Russia; Group of Epigenetic Epidemiology, A.N. Belozersky Research Institute of Physico-Chemical Biology, Moscow State University, Leninskie Gory 1/40, 119234, Moscow, Russia. 7. Medical University 'Reaviz', Chapaevskaya Str. 227, 443001, Samara, Russia. 8. Department of Environmental Health, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, 02115, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, 02115, USA. 9. Chapaevsk Medical Association, Meditsinskaya Str. 3a, 446100, Chapaevsk, Samara Region, Russia; Group of Epigenetic Epidemiology, A.N. Belozersky Research Institute of Physico-Chemical Biology, Moscow State University, Leninskie Gory 1/40, 119234, Moscow, Russia. Electronic address: olegsergeyev1@yandex.ru.
Abstract
BACKGROUND & AIMS: Body mass index is a simple anthropometric measure (kg/m2) used as an indirect estimate of body fat in individuals, and in assessments of population health and comparisons between populations. Bioelectrical impedance analysis (BIA) is often used to provide additional information on body fat and fat-free mass, and has been used to generate body composition reference data in national health surveys. However, BIA measurements are known to be device-specific and there are few published studies comparing results from different BIA instruments. Therefore, we compared the performance of two BIA instruments in the Russian Children's Study (RCS) of male growth, pubertal development and maturation. METHODS: Paired BIA measurements were obtained using the Tanita BC-418MA (Tanita Corp., Tokyo, Japan) and ABC-01 'Medas' (Medas Ltd, Moscow, Russia) BIA instruments. Cross-sectional data on 236 RCS subjects aged 18-22 years were used for the BIA comparison and the development of a conversion formula between measured resistances; follow-up data (n = 96) were used for validation of the conversion formula. RESULTS: Whole-body resistances were highly correlated (Spearman rho = 0.95), but fat mass (FM) estimates were significantly higher with the Medas than the Tanita device (median difference 3.3 kg, 95% CI: 2.9, 3.6 kg) with large limits of agreement (LoA) for the FM difference (-2.0, 8.6 kg). A conversion formula between the resistances (Res) was obtained: Medas Res = 0.882 × Tanita Res+26.2 (r2 = 0.91, SEE = 17.6 Ohm). After applying the conversion formula to Tanita data and application of the Medas assessment algorithm, the 'converted' Tanita FM estimates closely matched the Medas original estimates (median difference -0.1 kg, 95% CI: -0.3, 0.2 kg), with relatively small LoA for the FM difference (-2.3 to 2.1 kg), suggesting potential interchangeability of the ABC-01 'Medas' and Tanita BC-418MA data at the group level. CONCLUSIONS: Our results support the importance of cross-calibration of BIA instruments for population comparisons and proper data interpretation in clinical and epidemiological studies.
BACKGROUND & AIMS: Body mass index is a simple anthropometric measure (kg/m2) used as an indirect estimate of body fat in individuals, and in assessments of population health and comparisons between populations. Bioelectrical impedance analysis (BIA) is often used to provide additional information on body fat and fat-free mass, and has been used to generate body composition reference data in national health surveys. However, BIA measurements are known to be device-specific and there are few published studies comparing results from different BIA instruments. Therefore, we compared the performance of two BIA instruments in the Russian Children's Study (RCS) of male growth, pubertal development and maturation. METHODS: Paired BIA measurements were obtained using the Tanita BC-418MA (Tanita Corp., Tokyo, Japan) and ABC-01 'Medas' (Medas Ltd, Moscow, Russia) BIA instruments. Cross-sectional data on 236 RCS subjects aged 18-22 years were used for the BIA comparison and the development of a conversion formula between measured resistances; follow-up data (n = 96) were used for validation of the conversion formula. RESULTS: Whole-body resistances were highly correlated (Spearman rho = 0.95), but fat mass (FM) estimates were significantly higher with the Medas than the Tanita device (median difference 3.3 kg, 95% CI: 2.9, 3.6 kg) with large limits of agreement (LoA) for the FM difference (-2.0, 8.6 kg). A conversion formula between the resistances (Res) was obtained: Medas Res = 0.882 × Tanita Res+26.2 (r2 = 0.91, SEE = 17.6 Ohm). After applying the conversion formula to Tanita data and application of the Medas assessment algorithm, the 'converted' Tanita FM estimates closely matched the Medas original estimates (median difference -0.1 kg, 95% CI: -0.3, 0.2 kg), with relatively small LoA for the FM difference (-2.3 to 2.1 kg), suggesting potential interchangeability of the ABC-01 'Medas' and Tanita BC-418MA data at the group level. CONCLUSIONS: Our results support the importance of cross-calibration of BIA instruments for population comparisons and proper data interpretation in clinical and epidemiological studies.
Authors: Juan D Pedrera-Zamorano; Raul Roncero-Martin; Jesus M Lavado-Garcia; Julian F Calderon-Garcia; Purificacion Rey-Sanchez; Vicente Vera; Mariana Martinez; Jose M Moran Journal: Am J Hum Biol Date: 2014-11-29 Impact factor: 1.937
Authors: A Javed; M Jumean; M H Murad; D Okorodudu; S Kumar; V K Somers; O Sochor; F Lopez-Jimenez Journal: Pediatr Obes Date: 2014-06-25 Impact factor: 4.000
Authors: Jane S Burns; Paige L Williams; Oleg Sergeyev; Susan A Korrick; Mary M Lee; Boris Revich; Larisa Altshul; Julie T Del Prato; Olivier Humblet; Donald G Patterson; Wayman E Turner; Mikhail Starovoytov; Russ Hauser Journal: Environ Health Perspect Date: 2011-10-07 Impact factor: 9.031