4.4 Article

The Ratio Serum Creatinine/Serum Cystatin C (a Surrogate Marker of Muscle Mass) as a Predictor of Hospitalization in Chronic Obstructive Pulmonary Disease Outpatients

Journal

RESPIRATION
Volume 97, Issue 4, Pages 302-309

Publisher

KARGER
DOI: 10.1159/000494296

Keywords

Body composition; Chronic obstructive pulmonary disease; Sarcopenia; Muscle mass; Hospitalization; Creatinine/cystatin C

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Background: In chronic obstructive pulmonary disease (COPD), low muscle mass has been associated with several clinical outcomes such as low exercise capacity, hospital admission, and mortality. The Sarcopenia Index (SI) is a novel way to estimate muscle mass based on the ratio of serum creatinine (produced exclusively by muscle)/cystatin C (produced by all nucleated body cells). Objectives: This study aims to assess the SI in stable COPD outpatients, as compared with a healthy control group, to quantify its relationship with several important clinical features in COPD, and to study its potential usefulness to predict COPD exacerbations and hospital admissions. Methods: The SI was calculated in 18 healthy control subjects and 65 stable COPD outpatients were included in the study. Patients were prospectively followed for 1 year after being enrolled in the study. Results: COPD patients had a lower SI than controls, that is lower muscle mass. Furthermore, patients with a modified Medical Research Council dyspnea score >= 2, patients with a COPD Assessment Test score >= 10, and patients with a high risk of exacerbation had lower levels of SI compared with patients without these characteristics. SI correlated with FEV1 (r = 0.491, p < 0.001), the 6-min walking test (r = 0.560, p = 0.001), and the Fat-Free Mass Index (r = 0.431, p = 0.017). Univariate and multivariate Cox proportional risk analysis showed that a low SI is an independent predictor of hospital admission in COPD outpatients followed for 1 year (HR 5.16, p = 0.025). Conclusions: The ratio serum creatinine/serum cystatin C correlates with several COPD characteristics, and it can be used to predict COPD hospitalization. (C) 2018 S. Karger AG, Basel

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