Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society · 2015
Estimating body composition from skinfold thicknesses and bioelectrical impedance analysis in cystic fibrosis patients.
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Alicandro Gianfranco, Battezzati Alberto, Bianchi Maria Luisa, Loi Silvana, Speziali Chiara, Bisogno Arianna, Colombo Carla
Аннотация
BACKGROUND: The accuracy of body composition estimates based on skinfold thickness measurements and bioelectrical impedance analysis (BIA) is not yet adequately explored in cystic fibrosis (CF). Using DXA as reference method we verified the accuracy of these techniques and identified predictors of body composition specific for CF.
METHODS: One hundred forty-two CF patients (age range: 8-31 years) underwent a DXA scan. Body fat percentage (BF%) was estimated from skinfolds, while fat free mass (FFM) from single-frequency 50 kHz BIA.
RESULTS: Bland-Altman analysis showed poor intra-individual agreement between body composition data provided by DXA and BF% estimated from skinfolds or FFM estimated from BIA. The skinfolds of the upper arm were better predictors of BF% than BMI, while compared to other BIA measurements the best predictor of FFM was the R-index (Height(2)/Resistance).
CONCLUSIONS: Due to poor accuracy at individual level, the estimates of body composition obtained from these techniques cannot be part of the standard nutritional assessment of CF patients until reliable CF-specific equations will become available. BMI has limited value in predicting body fatness in CF patients and should be used in combination with other predictors. Skinfolds of the upper arm and R-index are strongly related to BF% and FFM and should be tested in a large CF population to develop specific predictive equations.
METHODS: One hundred forty-two CF patients (age range: 8-31 years) underwent a DXA scan. Body fat percentage (BF%) was estimated from skinfolds, while fat free mass (FFM) from single-frequency 50 kHz BIA.
RESULTS: Bland-Altman analysis showed poor intra-individual agreement between body composition data provided by DXA and BF% estimated from skinfolds or FFM estimated from BIA. The skinfolds of the upper arm were better predictors of BF% than BMI, while compared to other BIA measurements the best predictor of FFM was the R-index (Height(2)/Resistance).
CONCLUSIONS: Due to poor accuracy at individual level, the estimates of body composition obtained from these techniques cannot be part of the standard nutritional assessment of CF patients until reliable CF-specific equations will become available. BMI has limited value in predicting body fatness in CF patients and should be used in combination with other predictors. Skinfolds of the upper arm and R-index are strongly related to BF% and FFM and should be tested in a large CF population to develop specific predictive equations.