Literature DB >> 26868146

Associations of pulmonary function with serum biomarkers and dialysis adequacy in patients undergoing peritoneal dialysis.

Pei Zhang1,2, Hui-Mei Wu1,3, Qi-Ying Shen1,3, Rong-Yu Liu4,5, Xiang-Ming Qi2.   

Abstract

BACKGROUND: As lung impairment is an indicator of increased morbidity and mortality in patients receiving continuous ambulatory peritoneal dialysis (CAPD), the risk factors associated with impaired lung function are of great significance. The aim of this study is to elucidate the effects of inflammatory biomarkers and dialysis adequacy on pulmonary function, in CAPD patients.
METHODS: 101 patients undergoing CAPD, 30 CKD5 patients and 30 healthy subjects were enrolled. Spirometry and serum biomarkers were evaluated in each subject. Pulmonary function was compared among patients and control groups. Pearson analysis was used to analyze the correlation between serum biomarkers, dialysis adequacy and pulmonary function.
RESULTS: Lower vital capacity, maximal voluntary ventilation (MVV), forced vital capacity (FVC), peak expiratory flow (PEF), maximal mid-expiratory flow rate (MMEF), and diffusing capacity of the lung for carbon monoxide (DLCO) were observed in the CAPD group (all P < 0.05) when compared with control subjects. DLCO % was negatively correlated with CRP (r = -0.349, P = 0.007) and positively correlated with albumin (r = 0.401, P = 0.002). Total Kt/V was associated positively with MMEF % (r = 0.316, P = 0.019), and MVV % (r = 0.362, P = 0.007). nPNA was positively correlated with FVC % (r = 0.295, P = 0.049) and MMEF % (r = 0.381, P = 0.010).
CONCLUSION: The results suggest that lung function decline was directly related to higher CRP level, hypoalbuminemia, and dialysis inadequacy. These findings provide the evidence that inflammation and dialysis adequacy play a role in predicting outcomes of CAPD patients with pulmonary impairment.

Entities:  

Keywords:  Continuous ambulatory peritoneal dialysis; Dialysis adequacy; End-stage renal disease; Inflammatory biomarkers; Pulmonary function

Mesh:

Substances:

Year:  2016        PMID: 26868146     DOI: 10.1007/s10157-016-1244-1

Source DB:  PubMed          Journal:  Clin Exp Nephrol        ISSN: 1342-1751            Impact factor:   2.801


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3.  Machine learning for the prediction of severe pneumonia during posttransplant hospitalization in recipients of a deceased-donor kidney transplant.

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