Comparison between dual-energy x-ray absorptiometry and bioelectrical impedance for body composition measurements in adults with chronic kidney disease: A cross-sectional, longitudinal, multi-treatment analysis

Bellafronte, Natália Tomborelli; Diani, Luisa Maria; Vega-Piris, Lorena; Cuadrado, Guillermina Barril; Chiarello, Paula Garcia

doi:10.1016/j.nut.2020.111059

Cited by 20 publications

(20 citation statements)

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“…Phase angle, body cell mass, body cell mass index, fat free mass body composition monitor, fat free mass index body composition monitor, predicted fat free mass, predicted fat free mass index, appendicular fat free mass, fat mass body composition monitor, fat mass index body composition monitor, predicted fat mass and predicted fat mass index, by bioelectrical impedance. Appendicular fat free mass by Sergi equation [ 20 ]; predicted fat free mass and predicted fat mass by Bellafronte equation [ 22 ]; Fat free mass body composition monitor and fat mass body composition monitor by bioelectrical impedance from body composition monitor (Fresenius Medical Care).…”

Section: Resultsmentioning

confidence: 99%

“…PhA, BCM, BCMI, FFMBCM, FFMIBCM, pFFM, pFFMI and AFFM measures by bioelectrical impedance. AFFM by Sergi equation [ 20 ] and pFFM by Bellafronte equation [ 22 ]. FMBCM by bioelectrical impedance from body composition monitor (Fresenius Medical Care).…”

Section: Resultsmentioning

confidence: 99%

“…The other BIS measures with good performances were pFFM and pFMI, calculated with equations developed by our group [ 22 ] in CKD patients with DXA FFM and FM data as reference. Our pFFM and pFM showed lower limits of agreement than FFMBCM and FMBCM, and a better performance with an increase from 30 to 55% for FFM, and from 39 to 63% for FM in the percentage of residuals lower than 2 kg (DXA–BIS data).…”

Section: Discussionmentioning

confidence: 99%

“…Appendicular fat free mass (AFFM) was calculated by applying Sergi equation [ 20 ], as advised by the revised consensus on sarcopenia of the European Working Group on Sarcopenia in Older People [ 21 ]. Two new equations recently develop by our research group [ 22 ] were applied for FFM (pFFM) and its index (pFFMI), and FM (pFM) and its index (pFMI). Sex was coded 1 for men and 0 for women, age was recorded in years, BMI in kg/m 2 , waist circumference (WC) in cm, resistance (R) and reactance (Xc) of 50 frequency in ohms, extra to intracellular water ratio (ECW/ICW) in L, and FMBCM and FFMBCM in kg using the formulas: …”

Section: Methodsmentioning

confidence: 99%

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Bed-side measures for diagnosis of low muscle mass, sarcopenia, obesity, and sarcopenic obesity in patients with chronic kidney disease under non-dialysis-dependent, dialysis dependent and kidney transplant therapy

et al. 2020

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Muscle depletion and sarcopenic obesity are related to a higher morbimortality risk in chronic kidney disease (CKD). We evaluated bed-side measures/indexes associated with low muscle mass, sarcopenia, obesity, and sarcopenic obesity in CKD and proposed cutoffs for each parameter. Sarcopenia was diagnosed according to the European Working Group on Sarcopenia in Older People revised consensus applying dual energy X-ray absorptiometry (DXA) and hand grip strength (HGS), and obesity according to the International Society for Clinical Densitometry. Anthropometric parameters including calf (CC) and waist (WC) circumferences and WC/height (WC/H); bioelectrical impedance data including appendicular fat free mass (AFFM) and fat mass index (FMI) were assessed. ROC analysis and area under the curve (AUC) were applied for performance analyses. AFFM and CC presented the best performances for low muscle mass diagnosis–AFFM AUC for women was 0.96 and for men, 0.94, and CC AUC for women was 0.89 and for men, 0.85. FMI and WC/H were the best parameters for obesity diagnosis–FMI AUC for women was 0.99 and for men, 0.96, and WC/H AUC for women was 0.94 and for men, 0.95. The cutoffs (sensibility and specificity, respectively) for women were AFFM≤15.87 (90%; 96%), CC≤35.5 (76%; 94%), FMI>12.58 (100%; 93%), and WC/H>0.66 (91%; 84%); and for men, AFFM≤21.43 (98%; 84%), CC≤37 (88%; 69%), FMI>8.82 (93%; 88%), and WC/H>0.60 (95%; 80%). Sensibility and specificity for sarcopenia diagnosis were for AFFM+HGS in women 85% and 99% and in men, 100% and 99%; for CC+HGS in women 85% and 99% and in men, 100% and 100%; and for sarcopenic obesity were for FMI+AFFM in women 75% and 97% and in men, 75% and 95%. The tested bed-side measures/indexes presented excellent performance.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Bed-side measures for diagnosis of low muscle mass, sarcopenia, obesity, and sarcopenic obesity in patients with chronic kidney disease under non-dialysis-dependent, dialysis dependent and kidney transplant therapy

et al. 2020

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“…Multivariate analysis after adjustment for sex, age and weight, revealed HD, KTx and adiposity as risk factors for low muscle mass. AFFM Sergi (8) equation was recommended by the revised European consensus on sarcopenia (4) as a way to standardize BIA estimation since its assessments vary widely depending on the device applied (34). AFFM Kyle (9) was chosen because it was the first widely evaluated predictive equation for AFFM developed with spectroscopy BIA.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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 AFFMSergi (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). AFFMSergi 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 AFFMSergi and AFFMDXA 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 AFFMSergi, 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.

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Malnutrition, Frailty, and Sarcopenia in Patients With Cirrhosis: 2021 Practice Guidance by the American Association for the Study of Liver Diseases

et al. 2021

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This is the first American Association for the Study of Liver Diseases (AASLD) Practice Guidance on the management of malnutrition, frailty, and sarcopenia in patients with cirrhosis. This guidance represents the consensus of a panel of experts after a thorough review and vigorous debate of the literature published to date, incorporating clinical experience and common sense to fill in the gaps when appropriate. Our goal was to offer clinicians pragmatic recommendations that could be implemented immediately in clinical practice to target malnutrition, frailty, and sarcopenia in this population. This AASLD Guidance document differs from AASLD Guidelines, which are supported by systematic reviews of the literature, formal rating of the quality of the evidence and strength of the recommendations, and, if appropriate, meta-analysis of results using the Grading of Recommendations Assessment Development and Evaluation system. In contrast, this Guidance was developed by consensus of an expert panel and provides guidance statements based on formal review and analysis of the literature on the topics, with oversight Accepted ArticleThis article is protected by copyright. All rights reserved provided by the AASLD Practice Guidelines Committee at all stages of Guidance development. The AASLD Practice Guidelines Committee chose to perform a Guidance on this topic because a sufficient number of randomized controlled trials (RCTs) were not available to support the development of a Guideline. Definitions of Malnutrition, Frailty, and Sarcopenia and Their Relationship in Patients With CirrhosisCirrhosis is a major predisposing condition for the development of malnutrition, frailty, and sarcopenia.Multiple, yet complementary, definitions of these conditions exist in the published domain outside of the field of hepatology, but consensus definitions have not yet been established by the AASLD for patients with cirrhosis. Furthermore, there has been ambiguity related to operationalization of these constructs in clinical practice. To address this, we offer definitions of the theoretical constructs of malnutrition, frailty, and sarcopenia as commonly represented in all populations, partnered with operational definitions, developed by consensus, to facilitate pragmatic implementation of these constructs in clinical practice as applied to patients with cirrhosis (Table 1). Malnutrition is a clinical syndrome that results from "an imbalance (deficiency or excess) of nutrients that causes measurable adverse effects on tissue/body form (body shape, size, composition) or function, and/or clinical outcome." 1 Key to this definition is the recognition that malnutrition represents a spectrum of nutritional disorders across the entire range of body mass index (BMI)from underweight to obese. By this definition, malnutrition leads to adverse physical effects, which, in patients with cirrhosis, are commonly manifested phenotypically as frailty or sarcopenia. Frailty has most commonly been defined as a clinical state of decreased physiologic reserve...

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Comparison between dual-energy x-ray absorptiometry and bioelectrical impedance for body composition measurements in adults with chronic kidney disease: A cross-sectional, longitudinal, multi-treatment analysis

Cited by 20 publications

References 34 publications

Bed-side measures for diagnosis of low muscle mass, sarcopenia, obesity, and sarcopenic obesity in patients with chronic kidney disease under non-dialysis-dependent, dialysis dependent and kidney transplant therapy

Bed-side measures for diagnosis of low muscle mass, sarcopenia, obesity, and sarcopenic obesity in patients with chronic kidney disease under non-dialysis-dependent, dialysis dependent and kidney transplant therapy

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

Malnutrition, Frailty, and Sarcopenia in Patients With Cirrhosis: 2021 Practice Guidance by the American Association for the Study of Liver Diseases

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