Bioelectrical impedance can be used to predict muscle mass and hence improve estimation of glomerular filtration rate in non-diabetic patients with chronic kidney disease
Abstract
BACKGROUND: In this article (the second of two companion studies), we report whether bioelectrical impedance analysis (BIA) can be used to predict muscle mass in patients with chronic kidney disease (CKD), and whether using this predicted muscle mass can improve the estimation of glomerular filtration rate (GFR). METHODS: Seventy five non-diabetic patients with CKD (mean age +/- SD, 65.1 +/- 12.0 years; mean GFR 45.9 +/- 28.8 ml/min/1.73 m2) underwent body composition analysis by dual energy X-ray absorptiometry to provide a criterion marker of skeletal muscle mass (appendicular lean mass, ALM). Validity of a published BIA equation to predict ALM was evaluated and a new BIA equation was generated (ALM(BIA)) and cross-validated by the leave-one-out procedure. Renal inulin clearance provided a criterion measure of GFR (GFR(inu)). The performance of the equation including ALM(BIA) to estimate GFR(inu) was compared with demographic variables as used in the modification of diet in renal disease (MDRD) equation, by determining bias, limits of agreement and accuracy. RESULTS: The previously published BIA equation to predict ALM was not valid in these patients with CKD. In contrast, our new ALM(BIA) equation cross-validated successfully. Compared with the MDRD demographic variables, using ALM(BIA) to predict GFR(inu) improved estimation performance, showing reduced bias (4.3 vs 15.6 ml/min) and improved limits of agreement (41.1 vs 59.2 ml/min) and accuracy (69.7 vs 39.4% of patients' predicted GFR did not deviate by more than 30% of GFR(inu)). CONCLUSIONS: ALM(BIA) provides a clinically obtainable and valid method to predict muscle mass in patients with CKD, and using ALM(BIA) improves the estimation of GFR(inu). Researchers developing future GFR estimation equations should consider including ALM(BIA).
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Concerns with applicability were unclear in 12 studies.
“Concerns with applicability were high in 19 studies [37, 42–59] and unclear [41, 50, 60–69] in 12 studies due to the restricted sample size.”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [65]
The Discussion (page unavailable) says this study’s sample was relatively small and that a larger dataset is needed before the proposed method is used in clinical practice. It does not assess applicability concerns as “unclear” or report that finding across 12 studies; those are the citing authors’ assessment and count. (LLM, conf=0.82)
Experimental — accuracy not yet measuredNine equations for people with diseases were made with multifrequency devices involving measures obtained at 50 and 100 kHz.
“Seven equations were developed using single frequency devices at 50 kHz [35, 40, 98], while 9 equations were made with multifrequency devices involving measures obtained at 50 [41, 64, 65, 99] and 100 kHz [56].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [65]
The Methods and Results (page unavailable) report an impedance-based equation developed for patients with chronic kidney disease using resistance and reactance measured at 50 kHz. Our copy does not explicitly identify the device as multifrequency; it also does not establish the cited sentence’s aggregate count of nine equations or a 100-kHz measurement. (LLM, conf=0.82)
Experimental — accuracy not yet measuredRegarding foot-to-hand technology for people with diseases, three equations are for appendicular LSM in frail, chronic kidney, or hemodialysis male and female subjects.
“Regarding the foot to hand technology, there is one equation suitable for assessing FFM in overweight and obese postmenopausal females [98], four for appendicular FM and FFM in functionally limited male and female individuals [40], four for assessing FFM in amputee males [41], one for predicting TBW in anorexic females [56], and three for appendicular LSM in frail [64], chronic kidney [65], or hem”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [65]
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- Authors
- Macdonald, J. H., Marcora, S. M., Jibani, M., Roberts, G., Kumwenda, M. J., Glover, R., Barron, J., & Lemmey, A. B.
- Affiliations
- Bangor University (GB)
- Glan Clwyd Hospital (GB)
- Venue
- Nephrology Dialysis Transplantation
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-09-23
- Volume
- 21
- Issue
- 12
- Pages
- 3481-3487
- ISSN
- 0931-0509 (ISSN-L)
- 1460-2385
- OpenAlex
- W2126698100
- Cited by
- 57
- References
- 25
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 2 · 2022: 3 · 2021: 5 · 2020: 6 · 2019: 6 · 2018: 6 · 2017: 1 · 2016: 2 · 2015: 2 · 2014: 2 · 2013: 5 · 2012: 1
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nephrology Dialysis Transplantation (publishedVersion)
- PubMed (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Funders
- Kidney Research UK
- Topics
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Health Sciences › Medicine › Nephrology › Dialysis and Renal Disease Management
- Keywords
- bioelectrical impedance analysis, appendicular lean mass, muscle mass assessment, glomerular filtration rate estimation, chronic kidney disease, non-diabetic patients, inulin clearance, MDRD equation, body composition, skeletal muscle mass
- MeSH terms
- Aged, Chronic Disease, Female, Glomerular Filtration Rate, Humans, Kidney Diseases, Male, Mathematics, Electric Impedance, Muscle, Skeletal
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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