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Title: Body composition in dialysis patients: a functional assessment of bioimpedance using different prediction models.
Author: Broers NJ, Martens RJ, Cornelis T, Diederen NM, Wabel P, van der Sande FM, Leunissen KM, Kooman JP.
Journal: J Ren Nutr; 2015 Mar; 25(2):121-8. PubMed ID: 25443694.
Abstract:
OBJECTIVES: The assessment of body composition (BC) in dialysis patients is of clinical importance given its role in the diagnosis of malnutrition and sarcopenia. Bioimpedance techniques routinely express BC as a 2-compartment (2-C) model distinguishing fat mass (FM) and fat-free mass (FFM), which may be influenced by the hydration of adipose tissue and fluid overload (OH). Recently, the BC monitor was introduced which applies a 3-compartment (3-C) model, distinguishing OH, adipose tissue mass, and lean tissue mass. The aim of this study was to compare BC between the 2-C and 3-C models and assess their relation with markers of functional performance (handgrip strength [HGS] and 4-m walking test), as well as with biochemical markers of nutrition. METHODS: Forty-seven dialysis patients (30 males and 17 females) (35 hemodialysis, 12 peritoneal dialysis) with a mean age of 64.8 ± 16.5 years were studied. 3-C BC was assessed by BC monitor, whereas the obtained resistivity values were used to calculate FM and FFM according to the Xitron Hydra 4200 formulas, which are based on a 2-C model. RESULTS: FFM (3-C) was 0.99 kg (95% confidence interval [CI], 0.27 to 1.71, P = .008) higher than FFM (2-C). FM (3-C) was 2.43 kg (95% CI, 1.70-3.15, P < .001) lower than FM (2-C). OH was 1.4 ± 1.8 L. OH correlated significantly with ΔFFM (FFM 3-C - FFM 2-C) (r = 0.361; P < .05) and ΔFM (FM 3-C - FM 2-C) (r = 0.387; P = .009). HGS correlated significantly with FFM (2-C) (r = 0.713; P < .001), FFM (3-C) (r = 0.711; P < .001), body cell mass (2-C) (r = 0.733; P < .001), and body cell mass (3-C) (r = 0.767; P < .001). Both physical activity (r = 0.456; P = .004) and HGS (r = 0.488; P = .002), but not BC, were significantly related to walking speed. CONCLUSIONS: Significant differences between 2-C and 3-C models were observed, which are partly explained by the presence of OH. OH, which was related to ΔFFM and ΔFM of the 2-C and 3-C models, is therefore an important parameter for the differences in estimation of BC parameters of the 2-C and 3-C models. Both FFM (3-C) and FFM (2-C) were significantly related to HGS. Bioimpedance, HGS, and the 4-m walking test may all be valuable tools in the multidimensional nutritional assessment of both hemodialysis and peritoneal dialysis patients.

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