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Performance of Bioelectrical Impedance and Anthropometric Predictive Equations for Estimation of Muscle Mass in Chronic Kidney Disease Patients.

ABSTRACT: Background: Patients with chronic kidney disease (CKD) are vulnerable to loss of muscle mass due to several metabolic alterations derived from the uremic syndrome. Reference methods for body composition evaluation are usually unfeasible in clinical settings. Aims: To evaluate the accuracy of predictive equations based on bioelectrical impedance analyses (BIA) and anthropometry parameters for estimating fat free mass (FFM) and appendicular FFM (AFFM), compared to dual energy X-ray absorptiometry (DXA), in CKD patients. Methods: We performed a longitudinal study with patients in non-dialysis-dependent, hemodialysis, peritoneal dialysis and kidney transplant treatment. FFM and AFFM were evaluated by DXA, BIA (Sergi, Kyle, Janssen and MacDonald equations) and anthropometry (Hume, Lee, Tian, and Noori equations). Low muscle mass was diagnosed by DXA analysis. Intra-class correlation coefficient (ICC), Bland-Altman graphic and multiple regression analysis were used to evaluate equation accuracy, linear regression analysis to evaluate bias, and ROC curve analysis and kappa for reproducibility. Results: In total sample and in each CKD group, the predictive equation with the best accuracy was AFFM_Sergi (men, n = 137: ICC = 0.91, 95% CI = 0.79-0.96, bias = 1.11 kg; women, n = 129: ICC = 0.94, 95% CI = 0.92-0.96, bias = -0.28 kg). AFFM_Sergi also presented the best performance for low muscle mass diagnosis (men, kappa = 0.68, AUC = 0.83; women, kappa = 0.65, AUC = 0.85). Bias between AFFM_Sergi and AFFM_DXA was mainly affected by total body water and fat mass. None of the predictive equations was able to accurately predict changes in AFFM and FFM, with all ICC lower than 0.5. Conclusion: The predictive equation with the best performance to asses muscle mass in CKD patients was AFFM_Sergi, including evaluation of low muscle mass diagnosis. However, assessment of changes in body composition was biased, mainly due to variations in fluid status together with adiposity, limiting its applicability for longitudinal evaluations.

SUBMITTER: Bellafronte NT

PROVIDER: S-EPMC8177428 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe present study refers to an anthropometric model, Dahlmann-Body-Analysis (DBA), based on formerly developed weight-height-frame tables. Including the anthropometric variable abdomen circumference (AC), a further differentiation into muscularity and fat mass could be realized. This enables to calculate the individual percentage fat mass (%FM), providing a cost-effective method for epidemiological studies. The present work sets out to investigate, whether %FM computed by the DBA model compares to BIA measurements, notably under conditions of heavy obesity.Subjects/methodsIn 103 adults (37 males, 66 females, age 42.5 ± 12.5 years, BMI 38.2 ± 4.8 kg/m²) %FM was estimated by a tetrapolar BIA device and compared to results derived by the DBA system. Bland-Altman and simple linear regression analyses were used to determine agreement between methods.ResultsThe mean %FM estimates of men (women) ± standard deviation were 36.1 ± 4.8 (49.7 ± 4.5) for BIA and 36.7 ± 4.2 (49.1 ± 4.9) for DBA measurements. Pearson correlation coefficients (r) and Lin's concordance correlation coefficient (CCC) were r = 0.86 and CCC = 0.84 for men and r = 0.85 and CCC = 0.83 for women, respectively. Bland-Altman plot showed limits of agreement between DBA and BIA %FM that ranged from 5.5% to -4.3% for men and 4.6% to- 5.7% for women, respectively. Correlations between values measured by both methods were high and the observed confidence interval (SD of the difference between DBA and BIA result multiplied by 2) was low. No systematic error was found. The DBA system overestimates FM by 0.63 ± 4.98 (2SD) % for men and underestimates FM by -0.56 ± 5.28 (2SD) % for women, respectively, compared to the BIA results. The result for both genders is -0.066 ± 5.17 (2SD) %FM. Over all, there is a strong concordance and reproducibility between the DBA and BIA data sets.ConclusionsThe implementation of the abdomen circumference (AC) into the DBA model as a proxy for body fat (%FM) resulted in a strong concordance with BIA measurements. These findings indicate that the DBA model may reflect the body shape of severely obese white European patients with regard to body composition.

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:BackgroundSkeletal muscle atrophy commonly occurs in critically ill patients, and decreased muscle mass is associated with worse clinical outcomes. Muscle mass can be assessed using various tools, including ultrasound and bioelectrical impedance analysis (BIA). However, the effectiveness of muscle mass monitoring is unclear in critically ill patients. This study was conducted to compare ultrasound and BIA for the monitoring of muscle mass in critically ill patients.MethodsWe recruited adult patients who were expected to undergo mechanical ventilation for > 48 h and to remain in the intensive care unit (ICU) for > 5 days. On days 1, 3, 5, 7, and 10, muscle mass was evaluated using an ultrasound and two BIA devices (Bioscan: Malton International, England; Physion: Nippon Shooter, Japan). The influence of fluid balance was also evaluated between each measurement day.ResultsWe analyzed 93 images in 21 patients. The age of the patients was 69 (interquartile range, IQR, 59-74) years, with 16 men and 5 women. The length of ICU stay was 11 days (IQR, 9-25 days). The muscle mass, monitored by ultrasound, decreased progressively by 9.2% (95% confidence interval (CI), 5.9-12.5%), 12.7% (95% CI, 9.3-16.1%), 18.2% (95% CI, 14.7-21.6%), and 21.8% (95% CI, 17.9-25.7%) on days 3, 5, 7, and 10 (p < 0.01), respectively, with no influence of fluid balance (r = 0.04, p = 0.74). The muscle mass did not decrease significantly in both the BIA devices (Bioscan, p = 0.14; Physion, p = 0.60), and an influence of fluid balance was observed (Bioscan, r = 0.37, p < 0.01; Physion, r = 0.51, p < 0.01). The muscle mass assessment at one point between ultrasound and BIA was moderately correlated (Bioscan, r = 0.51, p < 0.01; Physion, r = 0.37, p < 0.01), but the change of muscle mass in the same patient did not correlate between these two devices (Bioscan, r = - 0.05, p = 0.69; Physion, r = 0.23, p = 0.07).ConclusionsUltrasound is suitable for sequential monitoring of muscle atrophy in critically ill patients. Monitoring by BIA should be carefully interpreted owing to the influence of fluid change.Trial registrationUMIN000031316. Retrospectively registered on 15 February 2018.

Project description:IntroductionMethods of body composition estimation such as dual-energy X-ray absorptiometry (DXA), anthropometry, and bioimpedance (BIA) are used for the estimation of skeletal muscle mass (SMM) and lean body mass (LBM). No previous studies have examined whether these methods generate comparable results, or whether they are valid by using DXA as the reference. The aims of the present investigation were: (a) to assess the differences between DXA, anthropometry, and BIA in the estimation of SMM and LBM, taking into consideration the impact of sex and hydration status; and (b) to examine the agreement of anthropometry and BIA as compared to DXA for the estimation of SMM and LBM.MethodsA descriptive cross-sectional design was followed with 262 healthy young adults (159 males and 103 females). LBM and SMM were assessed by anthropometry with the formulas from Lee et al. and Kulkarni et al. for LBM; and Kerr (opt a), Kerr (opt b), Lee et al., Poortmans, Matiegka, Martin et al., Drinkwater and Ross, and Heymsfield et al. for SMM; by BIA with the formula reported by the TANITA MC-780-MA software for LBM and SMM; and DXA with the formula reported by the Hologic Horizon software for LBM, and the conversion by Kim et al. for SMM.ResultsSignificant differences were found for both SMM and LBM in kg, and percentages between most methods and formulas for the overall sample (p < 0.001-0.003) and divided by sex (p < 0.001-0.035). Hydration status did not have a significant effect on the differences between methods and formulas (p = 0.058-0.870). Lin's coefficient revealed limited agreement among the majority of formulas and methods (CCC = 0.007-0.880). The Bland-Altman analysis showed significant differences in most methods and formulas, both in the overall sample and divided by sex, when using SMM and LBM with DXA as the reference (p < 0.001-0.030).ConclusionThere is a lack of agreement between methods and formulas for assessing SMM and LBM. Sex was found to be a significant factor in this analysis. Furthermore, significant differences were observed between most formulas and methods as compared to DXA, except for the equations to estimate SMM with anthropometry by Poortmans.

Project description:Background: Appendicular skeletal muscle (or lean) mass (ALM) estimated using dual-energy X-ray absorptiometry (DXA) is considered to be a preferred method for sarcopenia studies. However, DXA is expensive, has limited portability, and requires radiation exposure. Bioelectrical impedance analysis (BIA) is inexpensive, easy to use, and portable; thus BIA might be useful in sarcopenia investigations. However, a large variety of models have been commercially supplied by different companies, and for most consumer products, the equations estimating ALM are not disclosed. It is therefore difficult to use these equations for research purposes. In particular, the BIA equation is often age-dependent, which leads to fundamental difficulty in examining age-related ALM loss. The aims of the current study were as follows: (1) to develop and validate an equation to estimate ALM using multi-frequency BIA (MF-BIA) based on theoretical models, and (2) to establish sarcopenia cutoff values using the equation for the Japanese population. Methods: We measured height (Ht), weight, and ALM obtained using DXA and a standing-posture 8-electrode MF-BIA (5, 50, 250 kHz) in 756 Japanese individuals aged 18 to 86-years-old (222 men and 301 women as developing equation group and 97 men and 136 women as a cross validation group). The traditional impedance index (Ht²/Z50) and impedance ratio of high and low frequency (Z250/Z₅) of hand to foot values were calculated. Multiple regression analyses were conducted with ALM as dependent variable in men and women separately. Results: We created the following equations: ALM = (0.6947 × (Ht²/Z50)) + (-55.24 × (Z250/Z₅)) + (-10,940 × (1/Z50)) + 51.33 for men, and ALM = (0.6144 × (Ht²/Z50)) + (-36.61 × (Z250/Z₅)) + (-9332 × (1/Z50)) + 37.91 for women. Additionally, we conducted measurements in 1624 men and 1368 women aged 18 to 40 years to establish sarcopenia cutoff values in the Japanese population. The mean values minus 2 standard deviations of the skeletal muscle mass index (ALM/Ht²) in these participants were 6.8 and 5.7 kg/m² in men and women, respectively. Conclusion: The current study established and validated a theoretical and age-independent equation using MF-BIA to estimate ALM and provided reasonable sarcopenia cutoff values.

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Performance of Bioelectrical Impedance and Anthropometric Predictive Equations for Estimation of Muscle Mass in Chronic Kidney Disease Patients.

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