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Identifying biomarkers for chronic obstructive pulmonary disease in the salivary metabolome.

<p><strong><span>Background:</span></strong><span> The lack of specificity in spirometry and accuracy in &lsquo;pre-disease states&rsquo;, and elderly indicates the need for other accurate, inexpensive and non-invasive tests for diagnosis, triage severity and particularly for choosing and monitoring response to treatments in chronic obstructive pulmonary disease (COPD). We compared salivary metabolomic signatures from patients with COPD and controls across a range of severity of airflow obstruction. </span></p> <p><a name="_61k4y1plsv5"></a><strong><span>Methods</span></strong></p> <p><span>45 people with COPD (mean age 66.1 &plusmn; 23 years, forced expiratory volume [FEV<sub>1</sub>] 47<u>+</u>15% predicted) and 48 healthy controls (mean age 56.0 <u>+</u>16.2 years, FEV<sub>1</sub> 90<u>+</u>23 % predicted) provided saliva that was assessed by flow infusion electrospray mass spectrometry (FIE-MS). Spectra were interrogated using an online library package. Data (patient data and metabolomic outputs) are available from this site.&nbsp;</span></p> <p><a name="_j99pcjszp0an"></a><strong><span>Results</span></strong></p> <p><span>Four potential biomarkers identified the presence of COPD with a sensitivity of 73%, specificity of 72%.<span>&nbsp; </span>Six metabolites predicted FEV1 % in the COPD cohort (<em>P </em>&lt; 0.001, R<sup>2</sup> &gt; 0.3, AUC &gt; 0.7) whilst a range of multivariate approaches targeted six metabolites linked to GOLD stage (<em>P </em>&lt; 0.001, AUC &gt; 0.7). Identification of the metabolites suggested changes in pterin biosynthesis, lipid processing, nucleotide metabolism and melatonin in COPD patients. </span></p> <p><a name="_2n4ucvxs4l1n"></a><strong><span>Conclusions</span></strong></p> <p><span>Metabolic fingerprinting of saliva samples could differentiate COPD from age matched controls and inform COPD severity of airflow obstruction. Potential biomarkers are suggested which could inform the diagnosis and monitoring of COPD.</span></p> <p><span>&nbsp;</span></p> <h2><span> </span></h2>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
20
Reuse readiness
8
Engagement
0