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Bioelectrical impedance analysis (BIA) is the most widely used technique in body composition analysis. When we focus the use of phase sensitive BIA on its raw parameters Resistance (R), Reactance (Xc) and Phase Angle (PhA), we eliminate the bias of using predictive equations based on reference models. In particular PhA, have demonstrated their prognostic utility in multiple aspects of health and disease. In recent years, as a strong association between prognostic and diagnostic factors has been observed, scientific interest in the utility of PhA has increased. In the different fields of knowledge in biomedical research, there are different ways of assessing the impact of a scientific-technical aspect such as PhA. Single frequency with phase detection bioimpedance analysis (SF-BIA) using a 50 kHz single frequency device and tetrapolar wrist-ankle electrode placement is the most widely used bioimpedance approach for characterization of whole-body composition. However, the incorporation of vector representation of raw bioelectrical parameters and direct mathematical calculations without the need for regression equations for the analysis of body compartments has been one of the most important aspects for the development of research in this area. These results provide new evidence for the validity of phase-sensitive bioelectrical measurements as biomarkers of fluid and nutritional status. To enable the development of clinical research that provides consistent results, it is essential to establish appropriate standardization of PhA measurement techniques. Standardization of test protocols will facilitate the diagnosis and assessment of the risk associated with reduced PhA and the evaluation of changes in response to therapeutic interventions. In this paper, we describe and overview the value of PhA in biomedical research, technical and instrumental aspects of PhA research, analysis of Areas of clinical research (cancer patients, digestive and liver diseases, critical and surgical patients, Respiratory, infectious, and COVID-19, obesity and metabolic diseases, Heart and kidney failure, Malnutrition and sarcopenia), characterisation of the different research outcomes, Morphofunctional assessment in disease-related malnutrition and other metabolic disorders: validation of PhA with reference clinical practice techniques, strengths and limitations. Based on the detailed study of the measurement technique, some of the key issues to be considered in future PhA research. On the other hand, it is important to assess the clinical conditions and the phenotype of the patients, as well as to establish a disease-specific clinical profile. The appropriate selection of the most critical outcomes is another fundamental aspect of research.

Though bioelectrical impedance analysis (BIA) is a favorable tool for assessing body composition to estimate nutritional status and physical fitness, such as sarcopenia, there are accuracy issues. Hence, high-frequency (HF) BIA equipment uses an additional frequency of 2 and 3 MHz and has been developed as a commercial model. However, there are no studies validating the accuracy and safety of HF-BIA. Therefore, this study aims to assess the validity of HF-BIA in analyzing body composition relative to dual-energy X-ray absorptiometry (DEXA). Appendicular lean mass (ALM), fat-free mass (FFM), and percentage of body fat (PBF) were assessed by HF-BIA and DEXA in 109 individuals; 50.5% (n = 55) were males. The average age and body mass index (BMI) were 43.4 ± 14.7 years and 25.5 ± 6.7 in males and 44.9 ± 14.1 years and 24.0 ± 6.4 in females, respectively. The HF-BIA results showed a high correlation with the DEXA results for assessing ALM (standard coefficient beta (β) ≥ 0.95), FFM (β ≥ 0.98, coefficient of determinations (R2) ≥ 0.95), and PBF (β ≥ 0.94, R2 ≥ 0.89). Body composition measured by HF-BIA demonstrated good agreement with DEXA in Korean adults.

Body composition and phase angle (PhA) have been used to predict mortality in multiple diseases. However, little has been studied regarding segmental measurements, which could potentially help assess subtle changes in specific tissue segments. This study aimed to identify the total PhA cut-off point associated with mortality risk and changes in body composition within a week of hospitalisation in non-critical hospitalised patients with COVID-19. A cohort study was conducted where patients underwent to a complete nutritional assessment upon admission and after seven days, and followed up until hospital discharge or death. A receiver operating characteristic curve was constructed to determine the PhA cut-off point, and the Kaplan–Meier estimator was used to determine survival analysis. Segmental and complete body compositions on admission and after 7 d were compared. We included 110 patients (60 men) with a mean age of 50·5 ± 15·0 years and a median BMI of 28·5 (IQR, 25·6–33·5) kg/m2. The median length of hospital stay was 6 (IQR, 4–9) d, and the mortality rate was 13·6 %. The PhA cut-off point obtained was 4°, with significant differences in the survival rate (P < 0·001) and mortality (HR = 5·81, 95 % CI: 1·80, 18·67, P = 0·003). Segmental and whole-body compositions were negatively affected within one week of hospitalisation, with changes in the approach by the graphical method in both sexes. Nutritional status deteriorates within a week of hospitalisation. PhA < 4° is strongly associated with increased mortality in non-critical hospitalised patients with COVID-19.

Background. The association between malnutrition and poor outcomes in stroke patients has, to date, been evaluated using composite scores derived from laboratory measurements. However, Bioelectrical Impedance Analysis (BIA) and its advanced application, Bioelectrical Impedance Vector Analysis (BIVA), offer a non-invasive, cost-efficient, and rapid alternative. These methods enable precise assessment of body composition, nutritional status, and hydration levels, making them valuable tools in the clinical evaluation of stroke patients. Objective. This study aimed to compare the ordinal distribution of modified Rankin Scale (mRS) scores at 90 days following an acute ischemic stroke, stratifying patients based on their nutritional status at the time of Stroke Unit admission, as determined by the Bioelectrical Impedance Vector Analysis (BIVA) malnutrition parameter. Methods. We conducted a single-centre prospective observational study on all consecutive patients admitted for acute ischemic stroke to our Stroke Unit between 1 April 2024, and 30 September 2024. We applied the IPW (Inverse Probability Weighting) statistical technique and ordinal logistic regression to compare mRS scores in malnourished and non-malnourished patients. Results. Overall, our study included 195 patients with ischemic stroke assessed using BIVA. Of these, 37 patients (19%) were malnourished. After IPW, we found that malnourished patients had significantly lower rates of favorable 90-day functional outcomes (cOR 3.34, 95% CI 1.74–6.41; p = 0.001). Even after accounting for relevant covariates, malnutrition remained an independent predictor of unfavorable outcomes (acOR 2.79, 95% CI 1.37–5.70; p = 0.005), along with NIHSS score at admission (acOR 1.19, 95% CI 1.11–1.28; p < 0.001), intravenous thrombolysis (acOR 0.28, 95% CI 0.15–0.52; p < 0.001), absolute lymphocyte count (cOR 1.01, 95% CI 1.00–1.02; p = 0.027), and albumin concentration (cOR 0.82, 95% CI 0.75–0.89; p < 0.001). Conclusions. Malnutrition, assessed through Bioelectrical Impedance Vector Analysis (BIVA) at the time of admission to the Stroke Unit, is associated with worse clinical outcomes at 90 days following the ischemic cerebrovascular event.

Purpose: We aimed to analyze the body composition characteristics of gallstone disease (GD) patients with bioelectrical impedance analysis (BIA) and to construct a nomogram to predict GD based on body composition. Methods: Patients with or without symptomatic cholecystolithiasis or choledocholithiasis diagnosed in Inner Mongolia People's Hospital from July 2020 to December 2021 were selected as the case group, and healthy subjects during the same period were selected as the control group. The body composition of the two groups was determined by BIA. The risk predictors for GD were extracted to construct a nomogram based on regression analysis. ROC curves were used to evaluate the predictive power of the nomogram, and calibration curves were drawn to evaluate the consistency of the model. The bootstrap method was used to verify the model and evaluate the generalizability of the model. Results: A total of 1000 individuals were recruited for the study, including 500 GD cases and 500 controls, to evaluate body composition. Multivariate logistic regression analysis showed that sex (OR = 2.292, 95% CI: 1.436-3.660), BMI (OR = 1.828, 95% CI: 1.738-1.929), body fat percentage (BFP) (OR = 1.904, 95% CI: 1.811-2.205) and waist circumference (WC) (OR = 1.934, 95% CI: 1.899-1.972) were risk predictors of GD. The AUC was 0.770 (95% CI: 0.741-0.799). The calibration curve showed that the C-index was 0.767. The prediction model was validated internally with 1000 bootstrap resamples. The accurate value was 0.72, and the kappa value was 0.43. All of the indices indicated that the model was well constructed and could be used to predict the incidence of GD. Conclusion: A nomogram model of gallstone disease based on sex, BMI, BFP and WC was constructed with good discrimination, calibration and generalizability and can be used for the noninvasive and convenient prediction of gallstone disease in the general population. Keywords: gallstone disease, cholelithiasis, bioelectrical impedance analysis, BIA, body composition, nomogram, prediction model

Bioelectrical impedance analysis (BIA) is a simple, inexpensive, quick and non-invasive technique for measuring body composition. The clinical benefit of BIA can be further enhanced by combining it with bioelectrical impedance vector analysis (BIVA). However, there is a substantial lack of information on the practical aspects of BIA/BIVA for those primarily interested in learning how to use and interpret this method in practice. The purpose of this article is to provide some guidance on the use of BIA/BIVA with special attention to practical considerations. This report reflects the authors' practical experience with the use of single-frequency BIA in combination with BIVA, particularly in COPD patients. First, the method and principles of BIA/BIVA are briefly described. Then, a practice-oriented approach to the interpretation and analysis of characteristic examples of altered nutritional and fluid status as seen with BIA/BIVA in COPD patients (e.g. malnutrition in obese and underweight patients with COPD, water retention) is presented. As our examples show BIA/BIVA is an attractive and easy-to-learn tool for quick nutritional assessment and is therefore of great clinical benefit in daily practice.

Chronic conditions including non-communicable diseases have become increasingly prevalent in the past decade. Proinflammatory cytokines are associated with the development of several pathologies, their prognoses, and their associated mortality. Chronic inflammation is also associated with oxidative stress whereby reactive oxygen species (ROS) induce cellular injury and, thus, by doing so, initiate inflammatory signaling. Phase angle (PhA) is a measurable body composition parameter obtained using bioelectrical impedance analysis (BIA). PhA is considered an indicator of cellular health and integrity and is also related to inflammatory markers and inflammation. Given the association among oxidative stress, cell damage, and inflammation that may in turn be associated with low PhA values, it is expected that PhA could mirror oxidative stress. In this hypothesis-generating, narrative review we summarize the current knowledge regarding the potential relationship between PhA and oxidative stress and their interrelationship in chronic conditions.

We evaluate the accuracy of whole body muscle mass (WBMM) and appendicular skeletal muscle mass (ASMM) assessed by bioelectrical impedance analysis (BIA) using an InBody770 machine (InBody, Seoul, Korea) referenced to dual-energy X-ray absorptiometry (DXA) in 507 people (mean age 63.7 ± 10.8 years, body mass index (BMI) 25.2 ± 3.5 kg/m2). Mean WBMMs measured by BIA and DXA were 49.3 ± 6.6 kg and 46.8 ± 6.5 kg in men and 36.1 ± 4.7 kg and 34.0 ± 4.8 kg in women, respectively. The respective effect sizes and 95% confidence intervals (CIs) for the difference were 2.49 (2.22–2.76) for men, and 2.12 (1.91–2.33) for women. Mean ASMMs measured by BIA and DXA were 22.1 ± 3.3 kg and 19.9 ± 3.2 kg in men, and 15.3 ± 2.5 kg and 13.5 ± 2.2 kg in women, respectively. The respective effect sizes and 95% CIs for the difference were 2.26 (2.10–2.41) for men and 1.75 (1.65–1.87) for women. The BIA clearly overestimated WBMM by 2.28 kg and ASMM by 1.97 kg compared with DXA. Using BMI, gender, and fat percentage, we derive equations that improved the residuals to <2 kg between methods from 38.29% to 85.91% for WBMM and 52.78% to 97.02% for ASMM.