Re-analysis of HPLC peaks previously assigned as phenazines from original data collected on an older HPLC.

UV–vis chromatograms are shown on the left, and the full UV–vis spectra of the peaks indicated with an arrow are shown on the right. Where indicated on the y-axis, the data have been normalized to bring the data into the same scale for clarity. (A) Analysis of the PCA peak, comparing a pure PCA standard (chromatogram at 364 nm, orange) to a sputum sample (chromatogram at 398 nm, black). (B) Analysis of the pyocyanin peak, comparing pyocyanin in P. aeruginosa-free sputum (blue) to a P. aeruginosa-positive sputum sample (black) (both chromatograms at 387 nm).</p

Correlation between ferriprotoporphyrin IX concentration and disease status as measured by percent predicted FEV1 (Spearman’s ρ = −0.47, <i>p</i> = 3.6 × 10⁻⁵).

Each data point represents a single sputum sample from a unique patient. The dashed line illustrates a linear regression through the data.</p

Refinement of metabolite detection in cystic fibrosis sputum reveals heme correlates with lung function decline - Fig 1

Identification of heme in sputum samples by comparing sputum (black) to a hemin standard (green). (A) UV–vis chromatogram (top) and extracted ion chromatogram (616.1773 ± 0.01 Da) from the positive mass channel (bottom) demonstrating identical retention times. (B) The associated positive ions detected from the peak shown in A, comparing the sputum peak (top) to the hemin standard (bottom). (C) The associated negative ions detected from the peak shown in B, comparing the sputum peak (top) to the hemin standard (bottom). A collision-energy ramp of 10 to 14 eV was applied to generate a fragmentation pattern. (D) Comparison of the UV–vis spectra for the peaks in A (black and green) compared to the same peak identified in our prior study (gray, dashed). For clarity, the spectra are normalized to their maximum value. (E) The chemical structure assigned to the peak in A, ferriprotoporphyrin IX.</p

Patient cohort characteristics.

Patient cohort characteristics.</p

Model inputs for disease progression and ivacaftor treatment efficacy [10, 13, 14, 35, 37–40].

Model inputs for disease progression and ivacaftor treatment efficacy [10, 13, 14, 35, 37–40].</p

Comparison of observed and modeled 5-year mortality benefits of ivacaftor.

CrI, credible interval; IVA, ivacaftor; RR, relative risk; SC, standard care.</p

Demographics and baseline characteristics for validation cohort from the Volkova study^a [18].

Demographics and baseline characteristics for validation cohort from the Volkova studya [18].</p

Model schematic for CF-PSM [25].

CF-PSM, cystic fibrosis patient-level simulation model; IVA, ivacaftor; SC, standard care. Adapted from Rubin JL, et al. Ther Adv Respir Dis. 2019;13:1753466618820186.</p