A new method for monitoring body fluid variation by bioimpedance analysis: The RXc graph

Piccoli, Antônio; Rossi, Barbara; Pillon, Luana; Bucciante, G

doi:10.1038/ki.1994.305

Cited by 598 publications

(573 citation statements)

References 15 publications

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“…22,23 First, we measured impedance vectors shorter than normal in about 50% of obese and 100% of edematous patients, which were well separable by a boundary line allowing 91% correct allocation, due to the difference in the phase angle of vectors (Figure 2). Therefore, in addition to con®rming the pattern of mean vectors from subject groups with fat or¯uid overload recognized in a previous pilot study, 8 we could mark a border on the lower pole of the genderspeci®c 75% tolerance ellipse discriminating between fat and¯uid overload in the individual subject. Interestingly, the lower pole of the gender-speci®c 75% tolerance ellipse still behaved as threshold for apparent edema with larger groups and different ellipses than previously.…”

Section: Discussionmentioning

confidence: 99%

“…19 The RXc graph method. 8 Using the bivariate normal distribution of RaH and XcaH, we calculated the bivariate 95% con®dence limits 20,21 for mean impedance vectors of the different classi®cation groups that is, the ellipse containing the mean vectors with 95% probability) (Figure 1). Noteworthy, due to equivalence of numerical and graphical criterion (bivariate con®-dence interval) in vector analysis, two mean vectors with non overlapping 95% con®dence ellipses are considered signi®cantly (P`0.05) different also using the Hotelling's T 2 test.…”

Section: Methodsmentioning

confidence: 99%

“…6,7 We recently overcame the need for assumptions with BIA using direct measurements from the analyzer, which we called the RXc graph method. 8 In this study, we evaluated the performance of the method in discriminating between increased body mass due to fat or¯uid, and for interpreting body weight variation in the obese subject.…”

Section: Introductionmentioning

confidence: 99%

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Discriminating between body fat and fluid changes in the obese adult using bioimpedance vector analysis

et al. 1998

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OBJECTIVE: Conventional body composition methods may produce biased quanti®cation of fat and fat-free mass in obese subjects, due to possible violation of the assumption of constant (73%) tissue hydration. We used an assumption-free, graphical method for interpreting body weight variation in obesity using bioelectrical measurements. DESIGN: 540 obese subjects with body mass index (BMI) b 31 kgam 2 without apparent edema were compared to 726 healthy subjects with BMI`31 kgam 2 and to 50 renal patients with apparent edema. A subgroup of 48 obese subjects were evaluated again after weight loss (8.6 kg, 3 BMI units) following one-month energy restriction (5 MJad, 1200 kcalad). 32 obese uremic patients were evaluated before and after a dialysis session (3.2 kg¯uid removed). Direct measurements obtained from standard 50 kHz frequency bioelectrical impedance analyzer were used as impedance vectors in the Resistance-Reactance Graph. RESULTS: 1) Impedance vectors of obese subjects could be discriminated from those of edematous patients with 91% correct allocation; 2) A signi®cant lengthening of vectors was observed after¯uid loss of 3 kg in obese subjects; but 3) A body weight loss of about 9 kg after energy restriction was associated with no vector displacement. CONCLUSION: A different impedance vector pattern was associated with body weight loss in obesity due to¯uid removal (vector lengthening) versus an energy-restricted diet (no vector displacement).

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Discriminating between body fat and fluid changes in the obese adult using bioimpedance vector analysis

et al. 1998

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“…In the vectorial approach of BIA, called bioelectrical impedance vector analysis (BIVA), the individual components of the impedance vector, resistance (R) and reactance (Xc), are normalized by the height of the subject (R/H and Xc/H) and represented in the R-Xc graph (abscissa, R/H; ordinate, Xc/H) [19]. R is inversely related to the intra-and extracellular water (ICW and ECW), whereas Xc is directly related to the amount of soft tissue structures (mass).…”

Section: Introductionmentioning

confidence: 99%

Bioimpedance vector analysis and conventional bioimpedance to assess body composition in older adults with dementia

et al. 2015

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a b s t r a c tObjective: Although dementia and nutritional status have been shown to be strongly associated, differences in body composition (BC) among older people with dementia have not yet been firmly established. The aim of this study was to assess BC through conventional and vector bioimpedance analysis (BIA and BIVA, respectively) in a sample of institutionalized older men with and without dementia, in order to detect dementia-related BC changes. Methods: Forty-one institutionalized men ages 65 y (23 without dementia [CG] and 18 with dementia [DG]) were measured with BIA and interpreted with BIVA and predictive equations. Results: Age (74.4 and 75.7 y) and body mass index (22.5 and 23.6 kg/m 2 ) were similar for DG and CG, respectively. Resistance and ratio of resistance to height did not differ significantly between the two groups. Reactance and ratio of reactance to height were 21.2% and 20.4% lower in DG than in CG. Phase angle was significantly lower in DG (mean ¼ 4.0; 95% confidence interval [CI], 3.6 -4.3 ) than in CG (mean ¼ 4.7; 95% CI, 4.3 -5.1 ). Mean fat mass index (6 and 7 kg/m 2 ), and mean fat-free mass index (16.4 and 16.6 kg/m 2 ) were similar in both groups. BIVA showed a significant downward migration of the ellipse in DG with respect to CG (T 2 ¼ 15.1; P < 0.01). Conclusion: Conventional BIA showed no significant differences in BC between DG and CG, although reactance and ratio of reactance to height were about 21% lower in DG. Nevertheless, a body cell mass depletion and an increase in the ratio of extracellular to intracellular water were identified in DG using BIVA. BIVA reflects dementia-related changes in BC better than BIA.

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“…BIVA approach developed by Piccoli et al (1994) uses the plot of the impedance parameters resistance (R) and reactance (Xc) normalized per height as a bivariate vector in the RXc graph. The position and length of the vector provides information about hydration status, body cell mass and cell integrity (Norman et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Body composition of Slovak midlife women with cardiovascular complications

Drozdová

Daňková

Čerňanová

et al. 2016

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The aim of this study was to analyse differences in body composition of women with and without cardiovascular complications. Bioelectrical parameters were measured with bioimpedance monofrequency analyser (BIA 101) and tissue electric properties were analysed by bioelectric impedance vector analysis (BIVA). The clinical sample (with CVD) consisted of 254 women ranging in age between 39 and 65 years. The sample of women without CVD consisted of 318 women in the same age range and was created from database of our previous studies. Statistical analysis adjusted for age showed significant differences in body composition characteristics of the studied samples. The results of vector analysis showed significantly different tissue electric properties of women in studied groups, what was confirmed by the Hotelling T 2 -test (p=0.0000). More women with CVD attained risky mean values of obesity indices of BMI and WHR than their "healthy" counterparts. Among women with CVD 80.2% had higher value of the BMI index than optimal one (>24.9 kg/m 2 ) and 74.4% of women had higher value of the WHR index than optimal (>0.80). From the BIA parameters strong correlation coefficient was found between BMI and FM in both groups (r=0.962 for women with CVD; r=0.968 for relatively healthy women). Our data confirmed that cardiovascular disease complications are strongly linked in body composition changes. The cross-sectional nature of our study makes it difficult to draw conclusions regarding causal pathways, though variables of obesity are in line with unhealthy conditions.

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A new method for monitoring body fluid variation by bioimpedance analysis: The RXc graph

Cited by 598 publications

References 15 publications

Discriminating between body fat and fluid changes in the obese adult using bioimpedance vector analysis

Discriminating between body fat and fluid changes in the obese adult using bioimpedance vector analysis

Bioimpedance vector analysis and conventional bioimpedance to assess body composition in older adults with dementia

Body composition of Slovak midlife women with cardiovascular complications

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