Estimation of glomerular filtration rate from skeletal muscle mass. A new equation independent from age, weight, gender, and ethnicity.

Autor: Iacone R; Department of Clinical Medicine and Surgery, Federico II University Medical School, Naples, Italy. Electronic address: roberto.iacone@unina.it., Guida B; Department of Clinical Medicine and Surgery, Federico II University Medical School, Naples, Italy., Scanzano C; Department of Clinical Medicine and Surgery, Federico II University Medical School, Naples, Italy., Iaccarino Idelson P; Department of Clinical Medicine and Surgery, Federico II University Medical School, Naples, Italy., D'Elia L; Department of Clinical Medicine and Surgery, Federico II University Medical School, Naples, Italy., Barbato A; Department of Clinical Medicine and Surgery, Federico II University Medical School, Naples, Italy., Strazzullo P; Department of Clinical Medicine and Surgery, Federico II University Medical School, Naples, Italy.
Jazyk: angličtina
Zdroj: Nutrition, metabolism, and cardiovascular diseases : NMCD [Nutr Metab Cardiovasc Dis] 2020 Nov 27; Vol. 30 (12), pp. 2312-2319. Date of Electronic Publication: 2020 Jul 24.
DOI: 10.1016/j.numecd.2020.07.027
Abstrakt: Background and Aims: The most used indicator for the renal function is the glomerular filtration rate (GFR). Current used predictive GFR equations were calibrated on patients with chronic kidney disease. Thus, they are not very precise in healthy individuals. The estimation of skeletal muscle mass (SMM) allows the prediction of the daily urinary creatinine excretion (24hUCrE). This study proposes an equation for the estimation of GFR based on SMM (eGFRMuscle) and serum creatinine (SCr).
Methods and Results: Four hundred sixty-six free-living men underwent a bioelectrical impedance analysis for the evaluation of SMM (kg), a blood withdrawal for the measurement of SCr (mg/dL), and a 24-h urinary collection for the assessment of 24hUCrE (g/24 h). The linear regression analysis between SMM and 24hUCrE and the measurement of SCr allowed developing a predictive equation of eGFRMuscle. The equation predicting eGFRMuscle (ml/min/1.73 m 2 ) was SMM (kg) × 3.06/SCr (mg/dL). eGFRMuscle was statistically different from eGFR predicted by Cockroft-Gault, MDRD Study, and CKD-EPI equations (p = 0.017, p < 0.001, and p < 0.001, respectively). Pairwise comparison of standard error of the area under the ROC curve (AUC) of eGFRMuscle with all the other AUCs of ROC curves highlighted significant differences.
Conclusions: The equation presented in this study results in age, weight, gender, and ethnicity independent because it arises directly from SMM estimation. Therefore, the proposed equation could allow evaluating the GFR also in healthy people with low, average, or high weight, and in older people, regardless of GFR and SCr levels.
Competing Interests: Declaration of competing interest None declared.
(Copyright © 2020 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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