International journal of chronic obstructive pulmonary disease · 2026
Identifying Biomarkers for Chronic Obstructive Pulmonary Disease in the Salivary Metabolome.
СтатьяНазвание и аннотация: Перевод в очереди
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Asandei Denisa, O'Shea Keiron, Paes de Araujo Rachel, Mironas Adrian, Da Costa Ricardo, Asibey-Berko Timothy, Ghosh Subhamay, Lu Chuan, O'Rourke Scott, Mur Luis A J, Lewis Keir E
Аннотация
PURPOSE: We compared salivary metabolomic signatures from patients with stable COPD across a range of severity of airflow obstruction with healthy controls.
PATIENTS AND METHODS: In this exploratory study, 47 people with COPD and 48 age-matched, healthy controls, provided saliva that was assessed by flow infusion electrospray mass spectrometry (FIE-MS). Spectra were interrogated using an open-source library DIMEpy package.
RESULTS: Four potential biomarkers identified the presence of COPD with a sensitivity of 73% and specificity of 72%. Six metabolites predicted the level of airflow obstruction, FEV1% in the COPD cohort (P < 0.001, R2 > 0.3, AUC > 0.7), whilst a range of multivariate approaches targeted six metabolites linked to COPD stage of severity (P < 0.001, AUC > 0.7). Identification of the metabolites suggested changes in pterin biosynthesis, lipid processing, nucleotide metabolism and melatonin in COPD patients.
CONCLUSION: This proof-of-concept study shows metabolic fingerprinting of saliva samples is feasible and can differentiate patients with COPD from people (including smokers) without COPD and correlates with COPD severity as defined by level of airflow obstruction. Metabolic fingerprinting also offers insights into the metabolic pathways involved in COPD aetiology.
PATIENTS AND METHODS: In this exploratory study, 47 people with COPD and 48 age-matched, healthy controls, provided saliva that was assessed by flow infusion electrospray mass spectrometry (FIE-MS). Spectra were interrogated using an open-source library DIMEpy package.
RESULTS: Four potential biomarkers identified the presence of COPD with a sensitivity of 73% and specificity of 72%. Six metabolites predicted the level of airflow obstruction, FEV1% in the COPD cohort (P < 0.001, R2 > 0.3, AUC > 0.7), whilst a range of multivariate approaches targeted six metabolites linked to COPD stage of severity (P < 0.001, AUC > 0.7). Identification of the metabolites suggested changes in pterin biosynthesis, lipid processing, nucleotide metabolism and melatonin in COPD patients.
CONCLUSION: This proof-of-concept study shows metabolic fingerprinting of saliva samples is feasible and can differentiate patients with COPD from people (including smokers) without COPD and correlates with COPD severity as defined by level of airflow obstruction. Metabolic fingerprinting also offers insights into the metabolic pathways involved in COPD aetiology.