Assessment of Body Composition in Health and Disease Using Bioelectrical Impedance Analysis (BIA) and Dual Energy X-Ray Absorptiometry (DXA): A Critical Overview.
Maurizio MarraRosa SammarcoAntonino De LorenzoFerdinando IellamoMario SiervoAngelo PietrobelliLorenzo Maria DoniniLidia SantarpiaMauro CataldiFabrizio PasanisiFranco ContaldoPublished in: Contrast media & molecular imaging (2019)
The measurement of body composition (BC) represents a valuable tool to assess nutritional status in health and disease. The most used methods to evaluate BC in the clinical practice are based on bicompartment models and measure, directly or indirectly, fat mass (FM) and fat-free mass (FFM). Bioelectrical impedance analysis (BIA) and dual energy X-ray absorptiometry (DXA) (nowadays considered as the reference technique in clinical practice) are extensively used in epidemiological (mainly BIA) and clinical (mainly DXA) settings to evaluate BC. DXA is primarily used for the measurements of bone mineral content (BMC) and density to assess bone health and diagnose osteoporosis in defined anatomical regions (femur and spine). However, total body DXA scans are used to derive a three-compartment BC model, including BMC, FM, and FFM. Both these methods feature some limitations: the accuracy of BIA measurements is reduced when specific predictive equations and standardized measurement protocols are not utilized whereas the limitations of DXA are the safety of repeated measurements (no more than two body scans per year are currently advised), cost, and technical expertise. This review aims to provide useful insights mostly into the use of BC methods in prevention and clinical practice (ambulatory or bedridden patients). We believe that it will stimulate a discussion on the topic and reinvigorate the crucial role of BC evaluation in diagnostic and clinical investigation protocols.
Keyphrases
- dual energy
- body composition
- bone mineral density
- clinical practice
- computed tomography
- resistance training
- image quality
- public health
- postmenopausal women
- healthcare
- mental health
- contrast enhanced
- health information
- end stage renal disease
- ejection fraction
- newly diagnosed
- machine learning
- magnetic resonance imaging
- blood pressure
- human health
- multidrug resistant
- mass spectrometry
- fatty acid
- prognostic factors
- climate change
- patient reported outcomes