CONSORT diagram of the study.

<p>CONSORT diagram of the study.</p

Mixed linear models modeling eGFR change.

<p>Mixed linear models modeling eGFR change.</p

Distribution of the variables of interest by tertiles of baseline leptin.

<p>Distribution of the variables of interest by tertiles of baseline leptin.</p

Relationship between changes in eGFR at 6 years and change in (log) leptin at 6 years.

<p>Colors indicate baseline eGFR values.</p

Mixed linear models modeling eGFR change, by sex.

<p>Mixed linear models modeling eGFR change, by sex.</p

Reproducibility and Respiratory Function Correlates of Exhaled Breath Fingerprint in Chronic Obstructive Pulmonary Disease

<div><h3>Background</h3><p>The electronic nose (e nose) provides distinctive breath fingerprints for selected respiratory diseases. Both reproducibility and respiratory function correlates of breath fingerprint are poorly known.</p> <h3>Objectives</h3><p>To measure reproducibility of breath fingerprints and to assess their correlates among respiratory function indexes in elderly healthy and COPD subjects.</p> <h3>Method</h3><p>25 subjects (5 COPD patients for each GOLD stage and 5 healthy controls) over 65 years underwent e-nose study through a seven sensor system and respiratory function tests at times 0, 7, and 15 days. Reproducibility of the e nose pattern was computed. The correlation between volatile organic compound (VOC) pattern and respiratory function/clinical parameters was assessed by the Spearman's rho.</p> <h3>Measurements and Main Results</h3><p>VOC patterns were highly reproducible within healthy and GOLD 4 COPD subjects, less among GOLD 1–3 patients.VOC patterns significantly correlated with expiratory flows (Spearman's rho ranging from 0.36 for MEF25% and sensor Co-Buti-TPP, to 0.81 for FEV1% and sensor Cu-Buti-TPP p<0.001)), but not with residual volume and total lung capacity.</p> <h3>Conclusions</h3><p>VOC patterns strictly correlated with expiratory flows. Thus, e nose might conveniently be used to assess COPD severity and, likely, to study phenotypic variability. However, the suboptimal reproducibility within GOLD 1–3 patients should stimulate further research to identify more reproducible breath print patterns.</p> </div

Coefficients of correlation between the sensor patterns and the main respiratory function indexes (only rho with p<0.001).

<p>Coefficients of correlation between the sensor patterns and the main respiratory function indexes (only rho with p<0.001).</p

Root Mean Square Error (in Cross-Validation) (RMSECV) for the Partial Least Square – Discriminant Analysis (PLS-DA) model for the respiratory function parameters based on the e-nose data.

<p>The RMSECV provides a measure of how reliably PLS-DA model predicts respiratory function indexes.</p

Demographic and clinical characteristics of control subjects and COPD patients grouped according to GOLD stage of disease severity.

<p>Comparisons between groups were performed by χ-square test for categorical variables, and one-way ANOVA analyses (followed by Bonferroni post-hoc multiple comparison adjustment) for continuous variables.</p

Boxplots and bar-graphs comparing e-nose data and respiratory function indexes in terms of reproducibility in a GOLD 4 patient.

<p>Boxplot and standard deviation normalized to the mean value respectively. First columns: boxplots and normalized standard deviations for the six e-nose sensor responses. Second column: boxplots and normalized standard deviations for six selected respiratory function indexes (% of FVC, FEV1, FEF25–75, RV, RV/TLC, TLCO, KCO).</p