Advancing body composition assessment in patients with cancer: First comparisons of traditional versus multicompartment models

Development and clinical application of bioelectrical impedance analysis method for body composition assessment

Obesity, which is characterized by excessive body fat, increases the risk of chronic diseases, such as type 2 diabetes, cardiovascular diseases, and certain cancers. Sarcopenia, a decline in muscle mass, is also associated with many chronic disorders and is therefore a major concern in aging populations. Body composition analysis is important in the evaluation of obesity and sarcopenia because it provides information about the distribution of body fat and muscle mass. It is also useful for monitoring nutritional status, disease severity, and the effectiveness of interventions, such as exercise, diet, and drugs, and thus helps assess overall health and longevity. Computed tomography, magnetic resonance imaging, and dual-energy X-ray absorptiometry are commonly used for this purpose. However, they have limitations, such as high cost, long measurement time, and radiation exposure. Instead, bioelectrical impedance analysis (BIA), which was introduced several decades ago and has undergone significant technological advancements, can be used. It is easily accessible, affordable, and importantly, poses no radiation risk, making it suitable for use in hospitals, fitness centers, and even at home. Herein, we review the recent technological developments and clinical applications of BIA to provide an updated understanding of BIA technology and its strengths and limitations.

Variations in bioelectrical impedance devices impact raw measures comparisons and subsequent prediction of body composition using recommended estimation equations

BACKGROUND & AIMS

Bioelectrical impedance analysis (BIA) for body composition estimation is increasingly used in clinical and field settings to guide nutrition and training programs. Due to variations among BIA devices and the proprietary prediction equations used, studies have recommended the use of raw measures of resistance (R) and reactance (Xc) within population-specific equations to predict body composition.

OBJECTIVE

We compared raw measures from three BIA devices to assess inter-device variation and the impact of differences on body composition estimations.

METHODS

Raw R, Xc, impedance (Z) parameters were measured on a calibrated phantom and athletes using tetrapolar supine (BIASUP4), octapolar supine (BIASUP8), and octapolar standing (BIASTA8) devices. Measures of R and Xc were compared across devices and graphed using BIA vector analysis (BIVA) and raw parameters were entered into recommended athlete-specific equations for predicting fat-free mass (FFM) and appendicular lean soft tissue (ALST). Whole-body FFM and regional ALST were compared across devices and to a criterion five-compartment (5C) model and dual energy X-ray absorptiometry for ALST.

RESULTS

Data from 73 (23.2 ± 4.8 y) athletes were included in the analyses. Technical differences were observed between Z (range 12.2-50.1Ω) measures on the calibrated phantom. Differences in whole-body impedance were apparent due to posture (technological) and electrode placement (biological) factors. This resulted in raw measures for all three devices showing greater dehydration on BIVA compared to published norms for athletes using a separate BIA device. Compared to the 5C FFM, significant differences (p < 0.05) were observed on all three equations for BIASUP8 and BIASTA8, with constant error (CE) from -2.7 to -4.6 kg; no difference was observed for BIASUP4 or when device-specific algorithms were used. Published equations resulted in differences as large as 8.8 kg FFM among BIA devices. For ALST, even after a correction in the error of the published empirical equation, all three devices showed significant (p < 0.01) CE from -1.6 to -2.9 kg.

CONCLUSIONS

Raw bioimpedance measurements differ among devices due to technical, technological, and biological factors, limiting interchangeability of data across BIA systems. Professionals should be aware of these factors when purchasing systems, comparing data to published reference ranges, or when applying published empirical body composition prediction equations.

Feasibility of two levels of protein intake in patients with colorectal cancer: findings from the Protein Recommendation to Increase Muscle (PRIMe) randomized controlled pilot trial

BACKGROUND

Low muscle mass (MM) predicts unfavorable outcomes in cancer. Protein intake supports muscle health, but oncologic recommendations are not well characterized. The objectives of this study were to evaluate the feasibility of dietary change to attain 1.0 or 2.0 g/kg/day protein diets, and the preliminary potential to halt MM loss and functional decline in patients starting chemotherapy for stage II-IV colorectal cancer.

PATIENTS AND METHODS

Patients were randomized to the diets and provided individualized counseling. Assessments at baseline, 6 weeks, and 12 weeks included weighed 3-day food records, appendicular lean soft tissue index (ALSTI) by dual-energy X-ray absorptiometry to estimate MM, and physical function by the Short Physical Performance Battery (SPPB) test.

RESULTS

Fifty patients (mean ± standard deviation: age, 57 ± 11 years; body mass index, 27.3 ± 5.6 kg/m2; and protein intake, 1.1 ± 0.4 g/kg/day) were included at baseline. At week 12, protein intake reached 1.6 g/kg/day in the 2.0 g/kg/day group and 1.2 g/kg/day in the 1.0 g/kg/day group (P = 0.012), resulting in a group difference of 0.4 g/kg/day rather than 1.0 g/kg/day. Over one-half (59%) of patients in the 2.0 g/kg/day group maintained or gained MM compared with 44% of patients in the 1.0 g/kg/day group (P = 0.523). Percent change in ALSTI did not differ between groups [2.0 g/kg/day group (mean ± standard deviation): 0.5% ± 4.6%; 1.0 g/kg/day group: -0.4% ± 6.1%; P = 0.619]. No differences in physical function were observed between groups. However, actual protein intake and SPPB were positively associated (β = 0.37; 95% confidence interval 0.08-0.67; P = 0.014).

CONCLUSION

Individualized nutrition counselling positively impacted protein intake. However, 2.0 g/kg/day was not attainable using our approach in this population, and group contamination occurred. Increased protein intake suggested positive effects on MM and physical function, highlighting the potential for nutrition to attenuate MM loss in patients with cancer. Nonetheless, muscle anabolism to any degree is clinically significant and beneficial to patients. Larger trials should explore the statistical significance and clinical relevance of protein interventions.