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Zastosuj identyfikator do podlinkowania lub zacytowania tej pozycji: http://hdl.handle.net/20.500.12128/114
Tytuł: Fusion of the 1H NMR data of serum, urine and exhaled breath condensate in order to discriminate chronic obstructive pulmonary disease and obstructive sleep apnea syndrome
Autor: Ząbek, Adam
Stanimirova, Ivana
Deja, Stanisław
Barg, Wojciech
Kowal, Aneta
Korzeniewska, Anna
Orczyk-Pawiłowicz, Magdalena
Baranowski, Daniel
Gdaniec, Zofia
Jankowska, Renata
Młynarz, Piotr
Słowa kluczowe: Chemometrics; Chronic obstructive pulmonary disease (COPD); Discriminant models; NMR spectroscopy; Obstructive sleep apnea syndrome (OSAS)
Data wydania: 2015
Źródło: Metabolomics, vol. 11 (2015), s. 1563–1574
Abstrakt: Chronic obstructive pulmonary disease, COPD, affects the condition of the entire human organism and causes multiple comorbidities. Pathological lung changes lead to quantitative changes in the composition of the metabolites in different body fluids. The obstructive sleep apnea syndrome, OSAS, occurs in conjunction with chronic obstructive pulmonary disease in about 10–20 % of individuals who have COPD. Both conditions share the same comorbidities and this makes differentiating them difficult. The aim of this study was to investigate whether it is possible to diagnose a patient with either COPD or the OSA syndrome using a set of selected metabolites and to determine whether the metabolites that are present in one type of biofluid (serum, exhaled breath condensate or urine) or whether a combination of metabolites that are present in two biofluids or whether a set of metabolites that are present in all three biofluids are necessary to correctly diagnose a patient. A quantitative analysis of the metabolites in all three biofluid samples was performed using 1H NMR spectroscopy. A multivariate bootstrap approach that combines partial least squares regression with the variable importance in projection score (VIP-score) and selectivity ratio (SR) was adopted in order to construct discriminant diagnostic models for the groups of individuals with COPD and OSAS. A comparison study of all of the discriminant models that were constructed and validated showed that the discriminant partial least squares model using only ten urine metabolites (selected with the SR approach) has a specificity of 100 % and a sensitivity of 86.67 %. This model (AUCtest = 0.95) presented the best prediction performance. The main conclusion of this study is that urine metabolites, among the others, present the highest probability for correctly identifying patents with COPD and the lowest probability for an incorrect identification of the OSA syndrome as developed COPD. Another important conclusion is that the changes in the metabolite levels of exhaled breath condensates do not appear to be specific enough to differentiate between patients with COPD and OSAS
URI: http://hdl.handle.net/20.500.12128/114
DOI: 10.1007/s11306-015-0808-5
ISSN: 1573-3882
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