Estimating body composition from skinfold thicknesses and bioelectrical impedance analysis in cystic fibrosis patients

Gianfranco Alicandro, Alberto Battezzati, Maria Luisa Bianchi, Silvana Loi, Chiara Speziali, Arianna Bisogno, Carla Colombo

Research output: Contribution to journalArticle

Abstract

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 (Height2/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.

Original languageEnglish
Pages (from-to)784-791
Number of pages8
JournalJournal of Cystic Fibrosis
Volume14
Issue number6
DOIs
Publication statusPublished - Nov 1 2015

Keywords

  • Bioelectrical impedance analysis
  • Body composition
  • Cystic fibrosis
  • DXA
  • Skinfold thicknesses

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Pediatrics, Perinatology, and Child Health

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