Cited by 337Body Composition Measurement TechniquesNutrition and Health in AgingBone health and osteoporosis research

Comparison of body composition assessment by DXA and BIA according to the body mass index: a retrospective study on 3655 measures.

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Achamrah, Najate, Colange, Guillaume, Delay, Julie, Rimbert, Agnès, Folope, Vanessa, Petit, André, Grigioni, Sébastien, Déchelotte, Pierre +1 more·2018·PLoS ONE

Abstract

BACKGROUND AND AIMS: Body composition assessment is often used in clinical practice for nutritional evaluation and monitoring. The standard method, dual-energy X-ray absorptiometry (DXA), is hardly feasible in routine clinical practice contrary to Bioelectrical Impedance Analysis (BIA) method. We thus aimed to compare body composition assessment by DXA and BIA according to the body mass index (BMI) in a large cohort. METHODS: Retrospectively, we analysed DXA and BIA measures in patients followed in a Nutrition Unit from 2010 to 2016. Body composition was assessed under standardized conditions in the morning, after a fasting period of 12 h, by DXA (Lunar Prodigy Advance) and BIA (Bodystat QuadScan 4000, Manufacturer's equation). Bland-Altman test was performed for each class of BMI (kg/m2) and fat mass and fat free mass values were compared using Kruskal-Wallis test. Pearson correlations were also performed and the concordance coefficient of Lin was calculated. RESULTS: Whatever the BMI, BIA and DXA methods reported higher concordance for values of FM than FFM. Body composition values were very closed for patients with BMI between 16 and 18,5 (difference < 1kg). For BMI > 18,5 and BMI < 40, BIA overestimated fat free mass from 3,38 to 8,28 kg, and underestimated fat mass from 2,51 to 5,67 kg compared with DXA method. For BMI ≥ 40, differences vary with BMI. For BMI < 16, BIA underestimated fat free mass by 2,25 kg, and overestimated fat mass by 2,57 kg. However, limits of agreement were very large either for FM and FFM values, irrespective of BMI. CONCLUSION: The small bias, particularly in patients with BMI between 16 and 18, suggests that BIA and DXA methods are interchangeable at a population level. However, concordance between BIA and DXA methods at the individual level is lacking, irrespective of BMI.

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  • Predictive equations must be selected for subjects with similar characteristics regarding gender, age group, pubertal stage, ethnicity, and nutritional status.

    “However, it is essential that predictive equations are selected for subjects with similar characteristics regarding gender, age group, pubertal stage, ethnicity, and nutritional status (20, 21).”

    From Development and Cross-Validation of a Predictive Equation for Fat-Free Mass in Brazilian Adolescents by Bioelectrical Impedance (2022) · cites this paper as [20]

    The Discussion (page unavailable) recommends population-specific BIA equations or equations that account for age, warning that equations developed in young subjects can be biased in older subjects. The Introduction (page unavailable) identifies age, ethnicity, and body weight as possible contributors to differing results, but the paper does not give the claimed selection rule covering gender and pubertal stage. (LLM, conf=0.85)

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  • The use of predictive equations that have been developed and cross-validated in groups with similar characteristics to the subjects to be evaluated can reduce discrepancies among studies.

    “The use of predictive equations that have been developed and cross-validated in groups with similar characteristics to those of the subjects that we intend to evaluate can reduce discrepancies among studies (7, 20, 47).”

    From Development and Cross-Validation of a Predictive Equation for Fat-Free Mass in Brazilian Adolescents by Bioelectrical Impedance (2022) · cites this paper as [20]

    The Discussion (page unavailable) endorses population-specific BIA equations, noting that equations developed in young subjects can produce substantial bias in older subjects. The Strengths and limitations section (page unavailable) calls for equations specific to BMI class. The paper does not establish that cross-validation in similar groups reduces discrepancies among studies. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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References 50 · Cited by 337

Authors
Achamrah, N., Colange, G., Delay, J., Rimbert, A., Folope, V., Petit, A., Grigioni, S., Déchelotte, P., & Coëffier, M.
Affiliations
  • Inserm (FR)
  • Centre Hospitalier Universitaire de Rouen (FR)
Corresponding author
Achamrah, Najate
Venue
PLoS ONE
Published
2018-07-12
Volume
13
Issue
7
Pages
e0200465
ISSN
1932-6203 (ISSN-L)
OpenAlex
W2875160728
Cited by
337
References
50
Citations by year
2026: 46 · 2025: 51 · 2024: 67 · 2023: 45 · 2022: 48 · 2021: 38 · 2020: 25 · 2019: 15 · 2018: 1
FWCI
13.54
Type
journal article
Language
en
Open access
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
  • Health Sciences › Medicine › Physiology › Nutrition and Health in Aging
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Bone health and osteoporosis research
Keywords
body composition assessment, dual-energy X-ray absorptiometry, bioelectrical impedance analysis, body mass index, fat mass, fat-free mass, measurement agreement, nutritional assessment, underweight patients, obesity
MeSH terms
Adult, Body Composition, Female, Humans, Male, Middle Aged, Retrospective Studies, Absorptiometry, Photon, Body Mass Index, Electric Impedance

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