Dataset Information

Prediction and discrimination of skeletal muscle function by bioelectrical impedance vector analysis using a standing impedance analyzer in healthy Taiwanese adults.

ABSTRACT:

Background

Bioelectrical impedance vector analysis (BIVA) has been used for prediction of muscle performance. However, little is known about BIVA in Asian adults, and even less is known about using standing BIVA devices. Standing impedance analyzer allows quicker and more convenient way to gather data than conventional supine analyzer and is more suitable for clinical practice. This study aimed to investigate the relations between muscle function and BIVA parameters measured with a standing impedance analyzer in healthy Taiwanese adults.

Methods

A total of 406 healthy subjects (age 34.5 ± 17.3 years, body mass index 24.1 ± 4.1 kg/m2) were recruited for BIVA and handgrip strength (HGS) measurements. Impedance parameters, including resistance (R) and reactance (Xc), were measured and normalized to body size by dividing by height (H). The resulting phase angle (PhA) was calculated. HGS in the dominant, left, and right hands were referred to as HGSDH, HGSLH, and HGSRH, respectively. All subjects were divided into 5 grades according to HGS.

Results

Muscle strength in the dominant, right, and left arms was correlated with variables in the order of sex, weight, age, height, Xc/H, and R/H (all, p < 0.001). Using all 6 variables, the determination coefficients were 0.792, 0.782, and 0.745, respectively, whereas the standard errors of estimates were 56.89, 58.01, and 56.67 N for HGSDH, HGSLH, and HGSRH, respectively. HGS was positively correlated with PhA, and negatively correlated with Xc/H and R/H.

Conclusions

BIVA parameters measured with a standing impedance analyzer and anthropometric variables can predict and discriminate muscle function with good performance in healthy Asian adults.

SUBMITTER: Lee LW

PROVIDER: S-EPMC7292401 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction and discrimination of skeletal muscle function by bioelectrical impedance vector analysis using a standing impedance analyzer in healthy Taiwanese adults.

Lee Li-Wen LW Lu Hsueh-Kuan HK Chen Yu-Yawn YY Lai Chung-Liang CL Chu Lee-Ping LP Hsieh Meng-Che MC Hsieh Kuen-Chang KC

PloS one 20200612 6

<h4>Background</h4>Bioelectrical impedance vector analysis (BIVA) has been used for prediction of muscle performance. However, little is known about BIVA in Asian adults, and even less is known about using standing BIVA devices. Standing impedance analyzer allows quicker and more convenient way to gather data than conventional supine analyzer and is more suitable for clinical practice. This study aimed to investigate the relations between muscle function and BIVA parameters measured with a stand ...[more]

PMID: 32530912

Similar Datasets

Project description:BackgroundAccurate volume measurement is important in the management of patients with congestive heart failure or renal insufficiency. A bioimpedance analyser can estimate total body water in litres and has been widely used in clinical practice due to its non-invasiveness and ease of results interpretation. To change impedance data to volumetric data, bioimpedance analysers use equations derived from data from healthy subjects, which may not apply to patients with other conditions. Bioelectrical impedance vector analysis (BIVA) was developed to overcome the dependence on those equations by constructing vector plots using raw impedance data. BIVA requires normal reference plots for the proper interpretation of individual vectors. The aim of this study was to construct normal reference vector plots of bioelectrical impedance for Koreans.MethodsBioelectrical impedance measurements were collected from apparently healthy subjects screened according to a comprehensive physical examination and medical history performed by trained physicians. Reference vector contours were plotted on the RXc graph using the probability density function of the bivariate normal distribution. We further compared them with those of other ethnic groups.ResultsA total of 242 healthy subjects aged 22 to 83 were recruited (137 men and 105 women) between December 2015 and November 2016. The centers of the tolerance ellipses were 306.3 ?/m and 34.9 ?/m for men and 425.6 ?/m and 39.7 ?/m for women. The ellipses were wider for women than for men. The confidence ellipses for Koreans were located between those for Americans and Spaniards without overlap for both genders.ConclusionThis study presented gender-specific normal reference BIVA plots and corresponding tolerance and confidence ellipses on the RXc graph, which is important for the interpretation of BIA-reported volume status in patients with congestive heart failure or renal insufficiency. There were noticeable differences in reference ellipses with regard to gender and ethnic groups.

Project description:INTRODUCTION:BMI is the most commonly used indicator to evaluate overweight and obesity, but it cannot distinguish changes in body composition. Over recent years, it has been demonstrated that bioelectrical impedance analysis (BIA) is a more accurate method for analyzing body composition. Bioelectrical impedance vector analysis (BIVA) has revealed its effectiveness as an indicator of nutritional status and hydration. OBJECTIVE:To assess the usefulness of bioimpedance analysis on the study of body composition in a group of children with overweight and obesity. MATERIALS AND METHODS:Cross-sectional observational study. The anthropometric parameters of 167 (79 were older than 12 years) overweight and obese children were recorded. Their body composition was analyzed using BIA and BIVA, and was classified based on different criteria. Concordance was analyzed (intraclass correlation coefficient, Bland-Altman analysis and weighted Kappa coefficient). The BIVA of the subgroups was compared using the Mahalanobis distance and Hotelling's T2. Statistical significance was considered for p<0.05. RESULTS:The BMI revealed that the majority of the assessed subjects were obese, although 12% had a normal percentage of fat mass (%FM). The classification by Z-BMI and Z-%FM significantly discriminate between subjects with different levels of adiposity. In children over the age of 12, the classification of fat mass index also discriminates significantly between obesity and non-obesity. As anticipated, in the tolerance ellipses, most of the individual vectors were situated in the left lower quadrant. CONCLUSIONS:BIVA reflects differences in the bioelectric patterns of children who are classified as being overweight or obese (BMI) and who have different levels of %FM and FMI. BIVA permits a fast and easy monitoring of the evolution of the nutritional state and changes associated with body composition, and it identifies those children whose body compartments may be precisely estimated using traditional BIA methods.

Project description:Bioelectrical impedance analysis (BIA) is currently the most commonly used method in clinical practice to measure body composition. However, the bioelectrical impedance analyzer is not designed according to different countries, races, and elderly populations. Because different races may have different body compositions, a prediction model for the elderly population in Taiwan should be developed to avoid population bias, thereby improving the accuracy of community evaluation surveys.Dual energy X-ray absorptiometry (DXA) was used as a standard method for comparison, and impedance analysis was used for the development of a highly accurate predictive model that is suitable for assessing the body composition of elderly people.This study employed a cross-sectional design and recruited 438 elderly people who were undergoing health examinations at the health management center in the Tri-Service General Hospital as study subjects. Basic demographic variables and impedance analysis values were used in four predictive models, namely, linear regression, random forest, support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) models, to predict DXA body composition. The data from 354 study subjects were used to develop the predictive model, while the data from 84 study subjects were used to validate the accuracy of the predictive model.The body composition of elderly people as estimated by InBody 720 was highly correlated with that estimated by DXA. The correlation coefficient between InBody 720 and DXA for muscle mass was 0.969, and that for fat mass was 0.935. Consistency analysis results showed that InBody 720 tends to underestimate muscle mass and fat mass. A comparison of the accuracy of the linear regression, random forest, SVM, and XGBoost models showed that the linear regression has the highest accuracy. The correlation coefficient between the new model and DXA for muscle mass and fat mass were 0.977 and 0.978, respectively.The new predictive model can be used to monitor the nutrition status of elderly people and identify people with sarcopenia in the community.

Project description:BackgroundLoss of skeletal muscle mass (SMM) increases the risk of frailty and, together with excess fat mass (FM), is a risk factor for cardio-metabolic disease. However, use of body composition measurements in nutritional surveillance and routine clinical practice is limited by the lack of reference data. Our aim was to produce age-specific and sex-specific reference values for SMM and FM in the White ethnic adult population in the UK. Secondary objectives were to examine the tracking over time using a subsample of the population with repeated measures of body composition and to assess the validity of these reference values in different ethnic subgroups.MethodsWe used data from segmental bioelectrical impedance analysis (BIA) in 390 565 participants, aged 40-69 years, in the UK Biobank, and data from dual-energy X-ray absorptiometry from n = 905 participants to validate the BIA measurements. SMM was calculated as the sum of the predicted muscle mass from the limbs. The LMS method was used to produce percentile curves for the SMM index (SMMI = SMM/height2 ) and the FM index (FMI = FM/height2 ). We investigated the validity of the White ethnic reference values by plotting z-scores (99.7% confidence interval) from Black and Asian groups to check if their confidence interval included zero. Longitudinal trajectories were predicted based on the baseline z-scores and the correlation between repeated measurements at follow-up.ResultsThe percentile curves show that SMMI declines in men from the age of 40, whereas in women, SMMI is more stable and decreases only slightly among women in the higher percentiles. FMI increases with age in both men and women. Women have higher FMI and lower SMMI than men in all age groups. The validity of the White-based reference values for non-White ethnic groups is poor. Longitudinal trajectories in body composition in the subsample of participants with a follow-up assessment show regression towards the mean in both men and women, with some evidence of declining SMMI only among men. The predicted 90% limits for the expected 5 year trajectories of SMMI and FMI can be used to identify people with unusual trajectories and in clinical practice to identify and track individuals at risk of excessive loss of SMM.ConclusionsThese body composition reference values developed from BIA in a middle/older-aged healthy White ethnic population in the UK could be used as a simple assessment tool for nutritional surveillance and to identify individuals with low SMMI or high FMI who may be at increased risk of disease and/or frailty.

Project description:BACKGROUND:Bioelectrical impedance vector analysis (BIVA) is a general concept that includes all methodologies used in the analysis of the bioelectrical vector, whereas the "classic" BIVA is a patented methodology included among these methods of analysis. Once this was clarified, the systematic review of the literature provides a deeper insight into the scope and range of application of BIVA in sport and exercise. OBJECTIVE:The main goal of this work was to systematically review the sources on the applications of BIVA in sport and exercise and to examine its usefulness and suitability as a technique for the evaluation of body composition, hydration status, and other physiological and clinical relevant characteristics, ultimately to trace future perspectives in this growing area, including a proposal for a research agenda. METHODS:Systematic literature searches in PubMed, SPORTDiscus and Scopus databases up to July, 2017 were conducted on any empirical investigations using phase-sensitive bioimpedance instruments to perform BIVA within exercise and sport contexts. The search included healthy sedentary individuals, physically active subjects and athletes. RESULT:Nineteen eligible papers were included and classified as sixteen original articles and three scientific conference communications. Three studies analysed short-term variations in the hydration status evoked by exercise/training through whole-body measurements, eleven assessed whole-body body composition changes induced by long-term exercise, four compared athletic groups or populations using the whole-body assessment, and two analysed bioelectrical patterns of athletic injuries or muscle damage through localised bioimpedance measurements. CONCLUSIONS:BIVA is a relatively new technique that has potential in sport and exercise, especially for the assessment of soft-tissue injury. On the other hand, the current tolerance ellipses of "classic" BIVA are not a valid method to identify dehydration in individual athletes and a new approach is needed. "Specific" BIVA, a method which proposes a correction of bioelectrical values for body geometry, emerges as the key to overcome "classic" BIVA limitations regarding the body composition assessment. Further research establishing standardised testing procedures and investigating the relationship between physiology and the bioelectrical signal in sport and exercise is needed.

Project description:PurposeThe assessment of body hydration is a complex process, and no measurement is valid for all situations. Bioelectrical impedance vector analysis (BIVA) has emerged as a relatively novel technique for assessing hydration status in sports. We applied BIVA a) to determine hydration changes evoked by an intense synchronized swimming (SS) training session; b) to characterize the sample of young elite swimmers in relation with a nonathletic reference population; and c) to generate its 50%, 75% and 95% percentiles of the bioelectrical variables.MethodsForty-nine elite SS female swimmers of two age categories, comen (Co: 13.9 ± 0.9 years, n = 34) and junior (Jr: 16.3 ± 0.6 years, n = 15), performed a long, high intensity training session. Body mass (BM) and bioelectrical variables (R, resistance; Xc, reactance; PA, phase angle; and Z, impedance module) were assessed pre- and post-training. BIVA was used to characterize 1) the distribution pattern of the bioelectrical vector (BIA vector) for both age groups, and 2) pre- to post-training BIA vector migration. Bioelectrical variables were also correlated with BM change values.ResultsMost swimmers were mostly located outside the 75% and some beyond the 95% percentile of the bioelectrical tolerance ellipses of the general population. The BIA vector showed statistically significant differences in both Co (T2 = 134.7, p = 0.0001) and Jr (T2 = 126.2, p < 0.001). Both groups were also bioelectrically different (T2 = 17.6, p < 0.001). After the training session, a decrease in BM (p = 0.0001) and an increase in BIA variables (p = 0.01) was observed. BIVA also showed a significant pre-post vector migration both in Co (T2 = 82.1; p < 0.001) and Jr (T2 = 41.8; p < 0.001). No correlations were observed between BM changes and bioelectrical variables.ConclusionsBIVA showed specific bioelectrical characteristics in young elite SS athletes. Considering the decrease in BM and the migration of the BIA vector, we conclude that the homeostatic hydration status of these young elite female swimmers was affected by the execution of intense training sessions. From a methodological perspective, BIVA appears to be sensitive enough to detect subtle hydration changes, but further research is needed to ensure its validity and reliability. Moreover, these findings highlight the importance of ensuring adequate fluid intake during training in young SS athletes.

Dataset Information

Prediction and discrimination of skeletal muscle function by bioelectrical impedance vector analysis using a standing impedance analyzer in healthy Taiwanese adults.

Background

Methods

Results

Conclusions

Publications

Prediction and discrimination of skeletal muscle function by bioelectrical impedance vector analysis using a standing impedance analyzer in healthy Taiwanese adults.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets